research paper topics on renewable energy

116 Renewable Energy Essay Topics

🏆 best essay topics on renewable energy, 🌶️ hot renewable energy essay topics, 👍 good renewable energy research topics & essay examples, 💡 simple renewable energy essay ideas, ❓ renewable energy research questions.

• Siemens Energy: Renewable Energy System
• How Wind Turbines Convert Wind Energy into Electrical Energy?
• Solving the Climate Change Crisis by Using Renewable Energy Sources
• Renewable Energy Technology in Egypt
• Renewable Energy Usage: Advantages and Disadvantages
• Discussion of Renewable Energy Resources
• The Use of Renewable Energy: Advantages and Disadvantages
• Solar Energy and Its Impact on Environment The purpose of this paper is to determine the impact of solar energy on the environment. The major positive impact is the minimal emission of greenhouse gases.
• Renewable Energy Sources: Popularity and Benefits Renewable fuels are not as pollutive as fossil fuels; they can be reproduced quickly from domestic resources. They became popular because of the decreasing amount of fossil fuels.
• Wind Energy as an Alternative Source While energy is a must for our survival, wind energy as a seemingly perpetual source of energy is the potential answer to the energy security of our generations to come.
• Discussion of Realization of Solar Energy Company ABC is interested in creating a “solar” project which will fully install and staff solar panels to ensure the safe transformation of solar energy into electricity.
• Renewable Energy Sources: Definition, Types and Stocks This research report analyzes the growing interest of the use renewable energy as an alternative to the non-renewable energy.
• Renewable Energy in Japan: Clean Energy Transition Renewable energy in Japan became significantly important after the Fukushima Daiichi tsunami that struck Japan in 2011.
• Solar Energy: Advantages and Disadvantages Renewable energy sources are being supported and invested in by governments to instigate a new environment-friendly technology.
• Renewable Energy Sources for Saudi Arabia This paper will provide background information on the Kingdom of Saudi Arabia, its energy resources, and how it may become more modern and efficient.
• Environmental Degradation and Renewable Energy The global community relies on the surrounding environment for food production, transport, and economic development.
• The Concept of Sustainability in Energy Plan for 2030-2040 The paper discusses the concept of sustainability takes a central role in the global discussion and presents of environment safety plan.
• The G20 Countries’ Competitiveness in Renewable Energy Resources “Assessing national renewable energy competitiveness of the G20” by Fang et al. presents an assessment of competitiveness in renewable energy resources among G20 countries.
• Future of 100% Renewable Energy This article explores the future of renewable green energy and a review the topical studies related to 100% renewable energy.
• Full Renewable Energy Plan Feasibility for 2030-2040 This paper argues that green energy in its current state will struggle to meet humanity’s demand and the development of better hybrid, integrated grids is required.
• Profitability of Onshore and Offshore Wind Energy in Australia Undoubtedly, the recent increase in popularity of campaigns to decarbonize the globe proves renewable energy to be a current and future trend globally.
• Renewable Energy: The Use of Fossil Fuel The paper states that having a combination of renewable energy sources is becoming critical in the global effort to reduce the use of fossil fuels.
• Is Nuclear Power Renewable Energy? Renewable energy is obtained from the naturally-occurring elements, implying that it can be easily accessed, cheaply generated, and conveniently supplied to consumers.
• Solar Energy in China and Its Influence on Climate Change The influence of solar energy on climate change has impacted production, the advancement of solar energy has impacted climate change in the geography of China.
• Full Renewable Energy Plan Feasibility: 2030-2040 The paper argues that green energy in its current state will struggle to meet the humanity’s demand and the development of better hybrid, integrated grids is required.
• Energy Efficiency and Renewable Energy Utilization This paper aims at expounding the effectiveness of renewable energy and the utilization of energy efficiency in regard to climate change.
• Utilization of Solar Energy for Thermal Desalination The following research is set to outline the prospects of utilization of solar energy for thermal desalination technologies.
• A World With 100% Renewable Energy Large corporations, countries, and separate states have already transferred or put a plan into action to transfer to 100% renewable energy in a couple of decades.
• Renewable Energy: Why Do We Need It? Renewable sources of energy such as solar, wind, or hydropower can bring multiple environmental benefits and tackle the problems of climate change and pollution in several ways.
• Renewable Energy Programs in Five Countries Energy production is vital for the drive of the economy. The world at large should diversify the sources to reduce the over-usage of fossil energy that is a threat of depletion.
• Wind Works Ltd.: Wind Energy Development Methodology Wind Works Ltd, as the company, which provides the alternative energy sources, and makes them available for the wide range of the population needs to resort to a particular assessment strategies.
• Solar Power as the Best Source of Energy The concepts of environmental conservation and sustainability have forced many countries and organizations to consider the best strategies or processes for generating electricity.
• Installing Solar Panels to Reduce Energy Costs The purpose of the proposal is to request permission for research to install solar panels to reduce energy costs, which represent a huge part of the company’s expenses.
• Sunburst Renewable Energy Corporation: Business Structuring The proposed Sunburst Renewable Energy Corporation will function on a captivating value statement in product strategy and customer relationships as the core instruments of sustainable operations.
• Renewable Energy: Economic and Health Benefits The US should consider the adoption of renewable sources of energy, because of the high cost of using fossil fuels and expenses related to health problems due to pollution.
• Renewable Energy Systems Group and Toyota Company The application of the Lean Six Sigma to the key company processes, creates prerequisites for stellar success, as the examples of Toyota and the Renewable Energy Systems Group have shown.
• Renewable Energy Systems: Australia’s Electricity
• Accelerating Renewable Energy Electrification and Rural Economic Development With an Innovative Business Model
• Renewable Energy Systems: Role of Grid Connection
• Breaking Barriers Towards Investment in Renewable Energy
• California Dreaming: The Economics of Renewable Energy
• Marine Renewable Energy Clustering in the Mediterranean Sea: The Case of the PELAGOS Project
• Differences Between Fossil Fuel and Renewable Energy
• Addressing the Renewable Energy Financing Gap in Africa to Promote Universal Energy Access: Integrated Renewable Energy Financing in Malawi
• Causality Between Public Policies and Exports of Renewable Energy Technologies
• Achieving the Renewable Energy Target for Jamaica
• Economic Growth and the Transition From Non-renewable to Renewable Energy
• Between Innovation and Industrial Policy: How Washington Succeeds and Fails at Renewable Energy
• Increasing Financial Incentive for Renewable Energy in the Third World
• Does Financial Development Matter for Innovation in Renewable Energy?
• Financing Rural Renewable Energy: A Comparison Between China and India
• Alternative Energy for Renewable Energy Sources
• Low-Carbon Transition: Private Sector Investment in Renewable Energy Projects in Developing Countries
• Effective Renewable Energy Activities in Bangladesh
• China’s Renewable Energy Policy: Commitments and Challenges
• Analyzing the Dynamic Impact of Electricity Futures on Revenue and Risk of Renewable Energy in China
• Driving Energy: The Enactment and Ambitiousness of State Renewable Energy Policy
• Carbon Lock-Out: Advancing Renewable Energy Policy in Europe
• Big Oil vs. Renewable Energy: A Detrimental Conflict With Global Consequences
• Efficient Feed-In-Tariff Policies for Renewable Energy Technologies
• Balancing Cost and Risk: The Treatment of Renewable Energy in Western Utility Resource Plans
• Active and Reactive Power Control for Renewable Energy Generation Engineering
• Mainstreaming New Renewable Energy Technologies
• Carbon Pricing and Innovation of Renewable Energy
• Economic Growth, Carbon Dioxide Emissions, Renewable Energy and Globalization
• Figuring What’s Fair: The Cost of Equity Capital for Renewable Energy in Emerging Markets
• Distributed Generation: The Definitive Boost for Renewable Energy in Spain
• Biodiesel From Green Rope and Brown Algae: Future Renewable Energy
• Electricity Supply Security and the Future Role of Renewable Energy Sources in Brazil
• Contracting for Biomass: Supply Chain Strategies for Renewable Energy
• Advanced Education and Training Programs to Support Renewable Energy Investment in Africa
• Domestic Incentive Measures for Renewable Energy With Possible Trade Implications
• Affordable and Clean Renewable Energy
• Catalyzing Investment for Renewable Energy in Developing Countries
• Better Health, Environment, and Economy With Renewable Energy Sources
• Afghanistan Renewable Energy Development Issues and Options
• How Economics Can Change the World With Renewable Energy?
• Are Green Hopes Too Rosy? Employment and Welfare Impacts of Renewable Energy Promotion
• Marketing Strategy for Renewable Energy Development in Indonesia Context Today
• Biomass Residue From Palm Oil Industries is Used as Renewable Energy Fuel in Southeast Asia
• Assessing Renewable Energy Policies in Palestine
• Chinese Renewable Energy Technology Exports: The Role of Policy, Innovation, and Markets
• Business Models for Model Businesses: Lessons From Renewable Energy Entrepreneurs in Developing Countries
• Economic Impacts From the Promotion of Renewable Energy Technologies: The German Experience
• Key Factors and Recommendations for Adopting Renewable Energy Systems by Families and Firms
• Improving the Investment Climate for Renewable Energy
• How Will Renewable Energy Play a Role in Future Economies?
• What Are the Advantages of Renewable Energy?
• What Is the Term for a Renewable Energy Source That Taps Into Heat Produced Deep Below Ground?
• What Are the Basic Problems of Renewable Energy?
• Why Is Solar Energy the Best Resource of Renewable Energy?
• How Can You Make a Potentially Renewable Energy Resource Sustainable?
• What Is a Possible Cost of Using Renewable Energy Resources?
• What Is the Contribution of Renewable Energy Sources to Global Energy Consumption?
• How Do Renewable Energy Resources Work?
• What Is the Most Viable Renewable Energy Source for the US to Invest In?
• Why Isn’t Renewable Energy More Widely Used Than It Is?
• Is Coal Still a Viable Resource Versus Windpower Being Renewable Energy?
• What Is the Difference Between Non-renewable and Renewable Energy?
• Why Is It Necessary to Emphasize Renewable Energy Sources in Order to Achieve a Sustainable Society?
• Is Aluminum an Example of a Renewable Energy Resource?
• What Fraction of Our Energy Currently Comes From Renewable Energy Sources?
• What Are the Disadvantages of Renewable Energy?
• What Would Have to Happen to Completely Abandon Non-renewable Energy Sources?
• Why Are Renewable Energy Better Than Fossil Fuels?
• How Could a Renewable Energy Resource Become Non-renewable?
• How Have Renewable Energy Resources Replaced a Percentage of Fossil Fuels in Different Countries?
• How Can Water Be Used as a Renewable Energy Resource?
• What Is the Most Practical Renewable Energy Source?
• What Steps Are Necessary to Further the Use of Renewable Energy Resources in THE US?
• Why Is Renewable Energy Use Growing?
• What Type of Renewable Energy Should Businesses in Your Region Invest In?
• How Does Renewable Energy Reduce Climate Change?
• Can the Development of Renewable Energy Sources Lead To Increased International Tensions?
• How Do Renewable Energy Resources Affect the Environment?
• Why Have So Many Governments Decided to Subsidize Renewable Energy Initiatives?

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Towards Sustainable Energy: A Systematic Review of Renewable Energy Sources, Technologies, and Public Opinions

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• v.5(1); 2019 Jan

Breaking barriers in deployment of renewable energy

Seetharaman.

a S P Jain School of Global Management, Singapore

Krishna Moorthy

b Faculty of Business and Finance, Universiti Tunku Abdul Rahman, Kampar Campus, Perak, Malaysia

Nitin Patwa

c S P Jain School of Global Management, Dubai, United Arab Emirates

d Taylors University, Malaysia

Associated Data

Several economic, institutional, technical and socio-cultural barriers hinder countries from moving from the high to the low emission pathway. The objective of this research is to find out the impacts of social, economic, technological and regulatory barriers in the deployment of renewable energy. Data were collected through an online questionnaire responded to by 223 professionals working in the energy sector all over the globe. This research shows that social, technological and regulatory barriers have a strong influence on the deployment of renewable energy, while economic barriers significantly influence it indirectly. By breaking research and development-related barriers, organizations will be able to invest greatly in developing advanced technologies that can optimize usage of renewable energy and make renewable energy appear more lucrative. With less polluting and lower tariff energy solutions being made available to local people, and higher profits for manufacturers, this will create an atmosphere where all stakeholders are satisfied.

1. Introduction

The world's population is growing at an unprecedented rate and that has necessitated a dramatic increase in energy demand globally. Matching supply with this surging demand is a principal and critical challenge for countries around the world. Currently, this demand is being met through the increased use of fossil fuels. The majority of the world's power is generated from the use of coal, oil and gas. These so-called fossil fuels, when burned, release heat energy which is then converted into electricity releasing into the atmosphere a lot of carbon dioxide (CO2), a greenhouse gas that contributes to the issue of global warming. A renewable energy supply offers a solution to both challenges. For economic growth and human advancement, energy has always been universally considered one of the most crucial measures ( Rawat and Sauni, 2015 ). There is a three-dimensional relationship alongside a bi-directional causal relationship between economy, the environment and energy ( Azad et al., 2014 ).

Globally, the population is growing at fast rate; however, the world's energy demand is likely to grow even more rapidly than the increase in the population. According to International Energy Outlook (2013), global energy demand will be increased by 56 per cent between 2010 and 2040 ( Azad et al., 2014 ). Currently, the majority of the world's energy consumption is satisfied by consuming energy created using fossil fuels. To satisfy the ever-increasing energy demand and to protect the climate, breakthrough advancements have been made in the past to design technologies that can control and harness power from alternative energy sources. As controlling carbon emissions is critical in dealing with climate change, renewable energy is an appropriate way to satisfy energy demand without degrading the ecosystem ( Jing, 2016 ). Apart from bringing environmental sustainability, renewable energy offers another advantage—the ability to provide power to even the most underprivileged people living in the remotest areas where traditional power is not yet available ( Rawat and Sauni, 2015 ).

Awareness of the need to encourage deployment of renewable energy has increased drastically in recent years. More countries, whether developed or developing, are promoting and changing policies to promote the deployment of renewable energy. In 2005, only 55 countries had taken steps to make renewable targets and create policies supporting renewable energy. This number had increased to 144 countries by 2013, with almost all the world understanding the need to reduce carbon emissions.

2. Background

Despite remarkable promotion and commitment from various nations, only a small percentage of energy is generated from renewable energy, especially in developing countries. This scenario is because of the numerous barriers that control the diffusion of renewable energy. These barriers prevent renewable energy from effectively competing with traditional energy and hamper achievement of the necessary large-scale deployment ( Nasirov et al., 2015 ). Penetration and scale-up of renewable require a strong political and regulatory framework which supports and promotes a continued focus on fossil fuels ( Karatayev et al., 2016 ).

A review of the literature shows that many studies have been conducted to identify barriers to the use of renewable energy. However, very few studies have grouped these barriers and discussed the impact of these barriers in the deployment of renewable energy. The variables which were identified from the literature review for use in future research were social barriers , economic barriers, technological barriers and regulatory barriers.

The objective of this research is to discover the impacts of breaking barriers in the deployment of renewable energy. This research tries to resolve the following questions to reach a solution which is in line with the objective of this research:

• a. What are the factors affecting the deployment of renewable energy and are they significant or not?
• b. What impact will breaking barriers have on the deployment of renewable energy?
• c. In the wake of breaking barriers, is Rogers' (2003) theory of diffusion (political and social) valid for renewable energy?

Theory of diffusion (technical, political & social) in the wake of breaking barriers.

Diffusion of innovation theory is one of the most important concepts in theorizing the changing format of energy provision, being concerned with the process of adoption of innovations by society ( Lacerda et al., 2014 ). Rogers (1983 : 11) defined diffusion as ‘the process by which innovation is communicated through certain channels over time among members of a social system’ and innovation as ‘an idea, practice or object that is perceived as new by an individual or other unit of adoption’ ( Sahin, 2006 ). Other types of diffusion include social diffusion and theories of change, going back to Lewin's description of the need to alter group standards to promote lasting individual change ( Lewin, 1951 ). The focus has since shifted towards external conditions that are likely to be more influential than group decisions ( Darnton, 2008 ). Political diffusion deals with the spread of policies and governance approaches across jurisdictional boundaries which come about because of external pressures and/or internal pressures relating to quests for legitimacy ( Weyland, 2005 ). More fundamentally, diffusion defines the often random movement of a characteristic. The theory of diffusion is used to understand the attitude and perception of people with regard to government policies.

4. Hypotheses

This literature review looks at the outcomes of penetration and deployment of renewable energy, which are affected by four major factors: social barriers, economic barriers, technological barriers and regulatory barriers.

4.1. Social barriers

The transition from conventional resources to renewable energy has encountered public resistance and opposition. This is due to a lack of awareness of the benefits of renewable energy, disruption of seascape, and acquisition of land which could have been used for agriculture, tourism, etc. ( Goldsmiths, 2015 ).

Public awareness and information barriers: Sustainable development stems from the satisfaction of human desires, through socially recognized technological systems and suitable policies and regulatory tools ( Paravantis et al., 2014 ). The main concerns with respect to public understanding are: 1) insufficient information regarding ecological and financial benefits; 2) inadequate awareness of renewable energy technologies (RET); and 3) uncertainties about the financial feasibility of RE installation projects ( Nasirov et al., 2015 ).

Not in my backyard’ (NIMBY) syndrome: According to NIMBY syndrome, people do support renewable energy generally, but not in their own neighbourhood. Renewable power project proposals often face opposition from individual citizens, political leaders, grassroots organizations, national interest groups and, in some cases, even environmental groups ( Jianjun and Chen, 2014 ). Public opposition occurs for a number of reasons, including landscape impact, environmental degradation and lack of consultation concerns among local communities ( Nasirov et al., 2015 ).

Loss of other/alternative income: A major issue with renewable plants (especially solar and wind farms) is the vast area of land required to produce an amount of energy equivalent to that which can be produced from a small coal fire power plant ( Chauhan and Saini, 2015 ).

To make a significant contribution to global energy consumption, there is a need to develop large scale renewable energy plants, but this requires vast areas of countryside. Enormous parts of the countryside, which includes farmland, need to be converted into buildings or roads or any other infrastructure to support a renewable energy power plant. In achieving this, often agriculture, tourism, fishing, etc. can be affected ( Nesamalar et al., 2017 ).

Lack of experienced professionals: Universal transition from fossil fuels to renewable energy sources requires the solid foundation of a skilled labour force. There is huge demand for skilled professionals to design, build, operate and maintain a renewable energy plant.

Incompetent technical professionals and lack of training institutes prevent renewable energy technologies from scaling new heights ( Ansari et al., 2016 ). There is a need to teach renewable energy courses and for proper training to be conducted to develop the skills required to install and operate renewable energy projects. The shortage of trained workforce to design, finance, build, operate and maintain renewable energy projects is considered a major obstacle to the wide penetration of renewable energy ( Karakaya and Sriwannawit, 2015 ).

Social barriers have a significant influence on the deployment of renewable energy.

Social barriers have a significant influence on economic barriers.

4.2. Economic barriers

Factors influencing economic and financial barriers are high initial capital, lack of financial institutes, lack of investors, competition from fossil fuels, and fewer subsidies compared to traditional fuel ( Raza et al., 2015 ). These factors have prevented renewable energy from becoming widespread.

Tough competition from fossil fuel: Fossil fuels will remain a dominant player in supplying energy in the future. A report by EIA's International Energy Outlook (2016) suggests that fossil fuels (oil, natural gas and coal) are expected to supply 78 per cent of the global energy used in 2040. Investment in fossil fuels (including supply and power generation) still accounts for 55 per cent of 2016 global energy investment, compared with 16 per cent for renewable energy. Coal is still a dominant fuel source in most counties because of its abundance, which makes it cheap and accessible ( Dulal et al., 2013 ). There have been huge changes in energy since summer 2014. Oil, as measured by the Brent crude contract, which was priced at $115.71/barrel in June 2014, fell to $27.10 on 20 January 2016, a huge drop of 76 per cent. Similarly, the ARA coal contract dropped from $84/tonne in April 2014 to $36.30 in February 2016. There was a huge decline in the price of natural gas, which slid from around $4.50/MMBtu in June 2014 to $1.91 in mid-February 2016. Due to falling prices and fossil fuel still emerging as a cheaper alternative to renewable energy, it is able to offer tough competition to renewable energy projects.

Government grants and subsidies: The amount of government subsidies provided to conventional energy is much higher than the subsidies awarded to renewable energy. This keeps renewable energy at a disadvantage. The subsidies provided by governments to generate electricity from fossil fuel sources is overshadowing the wide use of low emission technologies. For example, coal companies in Australia and Indonesia still receive government subsidies for mining and exploration ( Dulal et al., 2013 ).

Fewer financing institutions: Renewable energy developers and producers face severe difficulties in securing financing for projects at rates which are as low as are made available for fossil fuel energy projects ( Ansari et al., 2016 ). There are limited financial instruments and organizations for renewable project financing. This reflects that the investments are considered somewhat risky, thus demotivating investors ( Ohunakin et al., 2014 ).

High initial capital cost: Renewable energy projects require high initial capital cost and, because of the lower efficiency of renewable technology, the net pay back period is high, which in turn pushes investors on to the back foot ( Ansari et al., 2016 ). Both the users and the manufacturers may have very low capital and to install a plant they require capital financing. This problem is further highlighted by the strict lending measures that restrict access to financing even when funding is available for traditional energy projects ( Suzuki, 2013 ). High cost of capital, often lack of capital and most important with high payback period projects often becomes un-viable ( Painuly. J, 2001 ).

Intangible costs: Currently, in almost all countries, the total cost of fuel includes the cost of exploration, production, distribution and usage, but it does not include the cost of the damage it does to the environment and society. Despite severe effects on health and on the atmosphere, the unseen costs (externalities) which are connected with traditional fuels are not included in their price ( Arnold, 2015 ). Understanding these impacts is essential for evaluating the actual cost of utilizing fossil fuels for energy generation.

Economic barriers have a significant influence on the deployment of renewable energy.

4.3. Technological barriers

There are a number of legitimate technological barriers to the widespread deployment of renewable energy, including limited availability of infrastructure, inefficient knowledge of operations and maintenance, insufficient research and development initiatives, and technical complexities like energy storage and unavailability of standards ( Zhao et al., 2016 ).

Limited availability of infrastructure and facilities: There is limited availability of advanced technologies required for renewable energy, especially in developing countries, which acts as a factor preventing penetration of renewable energy. Even if this technology is available, the cost of procuring it is very high ( Dulal et al., 2013 ). Since renewable energy power plants are mostly placed in remote locations, they require additional transmission lines to connect to the main grid. Since most of the existing grids are not designed to integrate with renewable energy, these existing grids need to be upgraded or modified ( Izadbakhsh et al., 2015 ). Grid integration is amongst the biggest problems affecting the development of renewable energy projects.

Lack of operation and maintenance culture: Since renewable energy technology is comparatively new and not optimally developed, there is a lack of knowledge about operation and maintenance. Efficiency cannot be achieved if a plant is not optimally operated and if maintenance is not carried out regularly ( Sen and Bhattacharyya, 2014 ). Lack of availability of equipment, components and spare parts will require a substantial increase in the production costs, as these items need to be imported from other countries, therefore being procured at high prices and so increasing the overall cost ( Bhandari et al., 2015 ).

Lack of research and development (R&D) capabilities: Investment in research and development (R&D) is insufficient to make renewable energies commercially competitive with fossil fuel. Both governments and energy firms shy away from spending on R&D as renewable energy is in its development stage and risks related to this technology are high ( Cho et al., 2013 ).

Technology complexities: There are not enough standards, procedures and guidelines in renewable energy technologies in terms of durability, reliability, performance, etc. This prevents renewable energy from achieving large scale commercialization ( Nasirov et al., 2015 ). A major technical issue which renewable energy is facing today is the storage of energy. The supply of sun or wind is not continuous despite their infinite abundance and electricity grids cannot operate unless they are able to balance supply and demand. To resolve these issue, large batteries need to be developed which can compensate for the times when a renewable resource is not available ( Weitemeyer et al., 2014 ).

Technological barriers have a significant influence on the deployment of renewable energy.

Technological barriers have a significant influence on economic barriers.

4.4. Regulatory barriers

Factors like lack of national policies, bureaucratic and administrative hurdles, inadequate incentives, impractical government targets, and lack of standards and certifications have prevented renewable energy from expanding dramatically ( Stokes, 2013 ).

Ineffective policies by government: Strong regulatory policies within the energy industry are not only required for a nation's sustainable development, but also resolve the inconsistency between renewable and non-renewable energy. Lack of effective policies creates confusion among various departments over the implementation of the subsidies. Major issues such as unstable energy policy, insufficient confidence in RET, absence of policies to integrate RET with the global market and inadequately equipped governmental agencies act as barriers to the deployment of renewable energy projects ( Zhang et al., 2014 ).

Inadequate fiscal incentives: There have not been enough measures by governments to remove tax on imports of the equipment and parts required for renewable energy plants. Feed-in tariffs are the measures by which governments aim to subsidize renewable energy sources to make them cost-competitive with fossil fuel-based technologies, but the absence of these adequate financial incentives results in high costs that hinder the industry's development, operation and maintenance, and stagnate the future ( Sun and Nie, 2015 ).

Administrative and bureaucratic complexities: Obstacles arising in the deployment of renewable energy projects are manifold, including (and not limited to) administrative hurdles such as planning delays and restrictions. Lack of coordination between different authorities and long lead times in obtaining authorization unnecessarily increase the timeline for the development phase of the project. Higher costs are also associated with obtaining permission due to lobbying. All these factors prolong the project start-up period and reduce the motivation required to invest in renewable energy ( Ahlborg & Hammar, 2014 ).

Impractical government commitments: There is a gap between the policy targets set by governments and the actual results executed by implementation ( Goldsmiths, 2015 ). There is a lack of understanding of a realistic target and loopholes in the implementation process itself. The responsibility for overcoming these commitment issues lies with governments. Policies should be devised that can offer clear insight into important legislation and regulatory issues so that the industry can be promoted as stable and offering growth. Governments can fix this mismatch by becoming more responsive and reactive.

Lack of standards and certifications: Standards and certificates are required to ensure that the equipment and parts manufactured or procured from overseas are in alignment with the standards of the importing company. These certifications make sure that companies are operating the plant in compliance with local law. Absence of such standards creates confusion and energy producers have to face unnecessary difficulties ( Emodi et al., 2014 ).

Regulatory barriers have a significant influence on the deployment of renewable energy.

Regulatory barriers have a significant influence on economic barriers.

4.5. Breaking barriers in deployment of renewable energy

Deployment of renewable energy is crucial not only to meet energy demands but also to address concerns about climate change ( Byrnes et al., 2013 ). However, the barriers (social, economic, technological and regulatory) existing in this sector prevents the development and penetration of renewable energy globally.

User-friendly procedures: Bureaucratic procedures in the deployment of renewable energy are considered the biggest hindrance, and this demotivates investors and entrepreneurs from entering and investing in renewable energy. Government policies are not aligned at national and state level, thus failing to attract energy sector investment ( Nesamalar et al., 2017 ). Countries with excessively complicated administrative procedures have less penetration of renewable energy compared to countries with simple and straightforward procedures ( Huang et al., 2013 ).

Higher stakeholder satisfaction: Energy is the backbone of the socioeconomic development of any country ( Raza et al., 2015 ). By utilizing more renewable energy resources, nations can help fulfil energy deficiencies without damaging nature. The repercussions of this change would be the creation of more jobs in the designing, building, operation and maintenance of renewable energy project infrastructures. Higher levels of diffusion will help to achieve economies of scale, and that will bring down the costs and thus the price for the end user. This will improve investors' confidence and will trigger increased investments in renewable energy projects. Higher benefits can be reaped from the availability of green energy as there will not be severe environmental implications, and that can help in maintaining the earth's ecosystem.

Successful research and development (R&D) ventures: In a study conducted by Halabi et al. (2015) , it was pointed out that technological advancement to effectively generate, store and distribute renewable energy at lower costs is crucial. However, compared to conventional energy, insufficient R&D initiatives are undertaken. This is due to fact that organizations are unable to earn beneficial returns from R&D, and that makes the future of these initiatives look dull.

Cost savings: The biggest challenge that renewable energy faces is the competition from low cost fossil fuels ( El-katiri, 2014 ). Renewable energy projects require huge land areas to produce the amount of energy which a conventional plant can produce in a small area. Prohibitive costs are involved in establishing and running renewable energy projects, mainly due to the huge financial capital required to acquire a suitable piece of land, the costs associated with lobbying, and power losses due to inefficient energy storage capabilities.

5. Methodology

The research framework of this study is given in Fig. 1 below:

Research framework.

5.1. Data collection

The survey questionnaire (please see the questionnaire) was framed based on independent variables and their sub-variables. The questionnaire, a pretesting of the questionnaire was conducted to ensure that all the questions were relevant and understandable to respondents. Initially, the survey questionnaire was sent out to 33 energy industry experts and their feedbacks were collected. The insights generated from this pilot testing led to further refinement of the questionnaire and a final questionnaire was developed. The final survey form consisted of 26 main questions for both dependent and independent variables and another three questions to understand the demographics of the respondent. Each question consisted of five options (Likert scale) from which the respondent had to select the one which he/she thought suited the best, with ‘1’ as strongly disagree and ‘5’ as strongly agree.

5.2. Profile of respondents

The survey respondents were professionals in the energy industry (manufacturing of rigs, power generation, power distribution, oil and gas, mining and renewable energy). The participants were selected based on their familiarity with and knowledge of renewable energy sources and technology across America, Europe, Asia Pacific, Africa and Australia. The survey questionnaire was sent out to 645 potential respondents, of which only 223 practical survey responses were received. The response rate is calculated to be 34.5 per cent. The demographics of the respondents are provided in Table 1 .

Demographics of the respondents (n = 223) with respect to job level, region and industry sector across energy sector.

5.3. Data analysis

The data collected from the survey questionnaire were analysed using ADANCO 2.0.1 software. ADANCO software is used for this purpose as it is specialised for variance based structural equation modelling. It implements several limited information estimators such as partial least squares path modelling or ordinary least squares regression based on sum scores for testing the hypothesis and analysing research models ( Henseler et al., 2014 ). To verify the correlation and confidence in the hypotheses, ADANCO software works well as it does not enforce normality on the data. Data analysis was conducted by first gauging the modelling of the structural model and then measuring the reliability and validity of the model by estimating model parameters.

5.4. Reliability

Cronbach's alpha value was considered to determine the reliability of the model fit. Alpha values above 0.7 show a satisfactory level of reliability. Jöreskog's Rho value also confirms that the model is consistent and uniform: i.e. composite reliability is within the appropriate range ( Marshall, 2014 ). The figures for each construct are listed in Table 3 .

Discriminant validity: Fornell-Larcker criteria.

5.5. Convergent validity

Convergent validity can be defined as the degree to which two measures of constructs that theoretically should be related are in fact related ( Campbell and Fiske, 1959 ). The value of average variance extracted is required to be above 0.5 in order to be accepted. The convergent validity is shown in Table 2 below. The minimum AVE value obtained is 0.5042, which proves that the validity of this model is acceptable.

Overall Reliability of the construct and Convergent validity.

5.6. Discriminant validity

Discriminant validity is used to test if the models or concepts that are not in relation are unrelated. According to Fornell and Larcker's theory ( Cable et al., 2014 ), if the root of the average variance extracted (AVE) of one path is less than the average variance extracted (AVE) of the other path, then it is considered accepted. In Table 3 below, Fornell and Larcker's theory is successfully matched; thus the discriminant validity of this model is satisfactory.

5.7. Structural equation model (SEM)

Structural modelling through bootstrapping is provided in Fig. 2 . Path analysis is a special case of structural equation modelling and employs a causal modelling approach to explore the correlations within a defined network. This correlation is equated by calculating the sum of the contributions of the paths that connect all the variables. To evaluate the strength of each path, products of the path coefficients along the path are calculated ( Schreiber et al., 2015 ). The R-squared value of our research model is 0.545, which supports the research model.

Structural modelling through bootstrapping.

5.8. Hypothesis testing

ADANCO 2.0.1 is used to conduct hypothesis testing because it uses variance to model structural equations. The bootstrapping option can be selected in the ADANCO software to model unknown population data ( Sarstedt et al., 2011 ). The level of significance is measured by establishing the t-statistic. The outcomes of the hypothesis testing is given in Table 4 below:

Outcomes of the hypothesis testing.

Note: SB = social barriers; EB = economic barriers; TB = technological barriers; RB = regulatory barriers; RE = deployment of renewable energy.

In total, seven hypotheses were identified. Out of the seven hypotheses, six hypotheses are accepted as their path coefficient is either positively or significantly related. A detailed explanation of each hypothesis is given below.

Hypotheses H1 highlights the influence of social barriers on the deployment of renewable energy. The effect of social barriers is moderately significant with (t- value = 1.8749) and (β=0.1063, p < 0.01) thus hypothesis H1 got accepted. This shows that social barriers have a moderate influence on the deployment of renewable energy. Earlier studies ( Paravantis et al., 2014 ) have advised that future studies be conducted to determine whether renewable energy is socially accepted. In our study, the positively related t-value testifies to a positive level of significance, implying that social barriers are still a hindrance to the deployment of renewable energy. Fig. 3 below shows the Social barriers with associated path coefficients.

Social barriers with associated path coefficients.

Hypothesis H2 highlights the impact of social barriers on economic barriers. The effect of social barriers is highly significant with (t- value = 4.505) and (β=0.317, p < 0.01) was accepted. This indicates that the parameters, such as opportunity cost and opposition by residents, strongly influence economic parameters. Earlier studies ( Jianjun and Chen, 2014 ) have supported that social barriers impact economic parameters. However, the earlier studies did not conduct research to understand the strength of the impact. Through our survey, we have determined that social barriers do have a strong correlation with the economic barriers associated with the implementation of renewable energy.

Hypothesis H3 tested the influence of economic barriers on the deployment of renewable energy. The statistical results with (t- value = 0.4968) and (p > 0.01) as not supported. This indicates that the parameters of economic barriers do not influence the deployment of renewable energy directly. Previous studies ( Boie et al., 2014 ) have pointed out that financial and economic parameters act as hurdles in the wide usage of renewable energy. However, this research contradicts the earlier findings. Fig. 4 below depicts the Economic barriers with associated path coefficients.

Economic barriers with associated path coefficients.

Hypothesis H4 tested the effect of technological barriers on the deployment of renewable energy. The effect of technological barriers is moderately related (t- value = 1.6491) and (β=0.1317, p < 0.01) thus H4 is accepted. This indicates that technological barriers are moderately significant in the deployment of renewable energy. Earlier research ( Gullberg et al., 2014 ) has pointed out that lack of technology advancement has created obstacles for implementing renewable energy. This research paper corroborates the findings of previous studies. Fig. 5 shows the Technological barriers with associated path coefficients.

Technological barriers with associated path coefficients.

Hypothesis H5 examined the impact of technological barriers on economic barriers. The effect of technological barriers on economic barriers is highly significant, with a (t- value = 3.0797) and (β=0.2367, p < 0.01) thus hypothesis H5 is accepted. This indicates that the technological barriers have a highly significant impact on economic barriers. Earlier research ( Zyadin et al., 2014 ) pointed out that lack of research and development has kept the costs of renewable energy higher compared to energy produced from fossil fuels. This study validates the findings of earlier studies.

Hypothesis H6 examined the effects of regulatory barriers on the deployment of renewable energy. Once again, the effect of regulatory barriers on the deployment of renewable energy is highly significant, as the (t- value = 7.7281) and (β=0.5705, p < 0.01). This indicates that regulatory barriers have a significant impact on the implementation of renewable energy. Earlier studies ( Jing, 2016 ) discuss how government policies and administration affect the usage of renewable energy. However, the earlier studies were specific to a country. This study fills the gap by conducting research globally and taking all major countries into consideration. Fig. 6 shows the Regulatory barriers with associated path coefficients.

Regulatory barriers with associated path coefficients.

Hypothesis H7 argued for the effects of regulatory barriers on economic barrier parameters. The effect of regulatory barriers on economic barriers is once more highly significant with (t- value = 5.0687 ) and (β= 0.3249 , p < 0.01) thus supported strongly. This indicates that regulatory barriers have a highly significant impact on economic barriers regarding the deployment of renewable energy. Conversely, the earlier literature ( Harrison, 2015 ) discusses how regulatory and government policies affect the implementation of renewable energy. This research fills the gap by establishing a strong association between regulatory and economic barriers.

7. Discussion & conclusion

Research was conducted to understand the barriers associated with the deployment of renewable energy and the benefits of overcoming these barriers. This research answers all the questions identified as part of the research objective.

Firstly, the factors affecting the deployment of renewable energy were identified and grouped into social, economic, technological and regulatory barriers. This research shows that social, technological and regulatory barriers have a strong influence on the deployment of renewable energy, while economic barriers, though not directly influencing it, and significantly influence it indirectly. Fig. 7 indicates the Deployment of renewable energy and its path coefficients.

Deployment of renewable energy and its path coefficients.

Secondly, in the structural equation model above, the path coefficient of user-friendly procedures is 0.808, that of stakeholder satisfaction is 0.81, successful R&D ventures is 0.86 and cost savings is 0.80. Since the path coefficient for the entire four constructs is equal or greater than 0.80, this implies that breaking barriers in the deployment of renewable energy has a strong impact on all four constructs (user-friendly procedures, stakeholder satisfaction, successful R&D ventures and cost savings).

Finally, the research confirms that political implications have a big impact on the deployment of renewable energy. Technological barriers are preventing renewable energy from being efficient and preventing it from being cost effective. Social awareness and opposition also have a positive impact on the deployment of energy. These results are in line with the theory of diffusion and answer the third question of the research objective.

7.1. Implications for renewable energy industry

In our research, we have studied the impact of various barriers on the deployment of renewable energy. By breaking research and development-related barriers, organizations will be able to invest greatly in developing advanced technologies that can optimize usage of renewable energy and make renewable energy appear more lucrative. With less polluting and lower tariff energy solutions being made available to local people, and higher profits for manufacturers, this will create an atmosphere where all stakeholders are satisfied. Breaking red tape in government procedures will lead to generating interest among investors in renewable energy projects and, by breaking the barriers to the deployment of renewable energy, a greater number of projects will start up. This will help to achieve economies of scale and will bring down operation and maintenance costs. By supporting further innovative technological advancements, more efficient plants will be developed which may require smaller portions of land. Modern technologies will also make offshore wind/solar farms economically feasible.

Though renewable energy would prevent degradation of the environment, however, a small fraction of the ecosystem will still be affected: for example, in the case of offshore wind farms, underwater marine life might be disturbed.

7.2. Limitations and future research

In this research, we have considered the presence of four barriers as factors preventing the successful deployment of renewable energy globally; however, it is reasonable to expect that not all the barriers will be present in each country and there could be some new barriers that have not yet been conceptualized. Though this research has been conducted to understand the global perception, the data collected constituted only 9.8 per cent from Europe, 6.3 per cent from America, 5.9 per cent from the Middle East and Africa, and five per cent from Australia. The research conducted was mainly based on data collected from the Asia Pacific region. Cultural characteristics of Asians can be considered to be different from those of other countries; hence it is advised to practise caution when generalizing the findings in the context of renewable energy.

Finally, regarding future research, further study is required to understand and compare the impact of barriers to renewable energy in developing and developed countries.

7.3. Conclusion

In the long run, due to increasing awareness of environmental damage, conventional power generation based on exhaustible fuels (oil, coal and gas) is generally considered unsustainable. Alternative energies that have minimal impact on the environment and are inexhaustible, such as renewable energy, can be a solution to the long-fought sustainability problem. However, despite on-going awareness of the manifold advantages of renewable energy, the diffusion of renewable energy is limited globally. This restriction has been attributed to social, economic, technological and regulatory barriers.

This research presents the impact of social, economic, technological and regulatory barriers on the deployment of renewable energy and how these barriers are interrelated. Focusing on factors influencing barriers and the deployment of renewable energy, a research model was developed and tested by analysing the data collected from 223 respondents. Respondents were experienced professionals from the energy industry. The findings show that social barriers have a positive impact while technological and regulatory barriers have a very significant impact on the deployment of renewable energy. However, this research shows that economic barriers do not directly impact the deployment of renewable energy, but are interrelated with social, technological and regulatory barriers, thus indirectly affecting the deployment of renewable energy. The simultaneous increase in energy demand and the negative impact of fossil fuels on the environment underscores the need for energy production from renewable energy sources. Renewable energy sources strike a perfect balance between economic, technical and environmental considerations, and contribute to a more sustainable development that will favour future generations.

Declarations

Author contribution statement.

Seetharaman Conceived and designed the experiments.

Krishna Moorthy: Performed the experiments, Analyzed and interpreted the data, Wrote the paper.

Nitin Patwa: Performed the experiments.

Saravanan: Analyzed and interpreted the data.

Yash Gupta: Contributed reagents, materials, analysis tools or data.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Competing interest statement

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

Appendix A. Supplementary data

The following is the supplementary data related to this article:

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EERE SETO Postdoctoral Research Award 2018

The Energy Efficiency and Renewable Energy (EERE) Postdoctoral Research Awards are intended to be an avenue for significant energy efficiency and renewable energy innovation. The EERE Postdoctoral Research Awards are designed to engage early career postdoctoral recipients in research that will provide them opportunities to understand the mission and research the needs of EERE and make advances in research topics of importance to EERE programs. Research Awards will be provided to exceptional applicants interested in pursuing applied research to address topics listed by the EERE programs sponsoring the Research Awards.

Applicants may select one research proposal on one research topic. Proposals must be approved by the research mentor listed in the application. 

Solar Energy

S-501 Applying Data Science to Solar Soft Cost Reduction

Possible disciplines: Economics, computer science, business management

The emergence of new big data tools can revolutionize how solar technologies are researched, developed, demonstrated, and deployed. From computational chemistry and inverse material design to adoption, reliability, and correlation of insolation forecasts with load use patterns, data scientists have opportunities to dramatically impact the future scaling of solar energy.

EERE's Solar Energy Technologies Office (SETO) is seeking to support postdoctoral researchers to apply and advance cutting-edge data science to drive toward the national solar cost reduction goals.

Areas of interest include:

• Novel analysis of Green Button (smart meter) and PV performance data with the Durable Module Materials (DuraMAT) Consortium.
• Power system planning and operation modeling to better understand the performance of solar generation assets on both the transmission and distribution grid.
• Quantification of direct and total system cost and benefits of distributed energy generation and storage, especially as related to reliability and resiliency.
• Data analytics for prediction of solar generation and PV system performance.
• Computational methods for revealing insights about diffusion of solar technologies at the residential, commercial, and utility scales that integrate large administrative, geospatial, economic, and financial datasets.
• Data tools for advancing photovoltaic (PV) and concentrating solar power (CSP) to reduce the non-hardware-related costs for solar energy. Specifically this could include work related to transactive energy value, such as analysis of the potential for PV and CSP to act autonomously in response to different grid and market signals and/or creating software that can perform these activities, as well as other novel topics not included here.
• Studies of the impact of federal government funding of solar technologies and programs (e.g. connecting scientific articles, patents, and commercial press releases to understand how federal R&D dollars in clean energy are communicated to and understood by the marketplace).

S-502 Solar Systems Integration

Possible disciplines: Power systems engineering, electrical engineering, computer science, mechanical engineering, atmospheric sciences

The Systems Integration program of SETO aims to address the technical and operational challenges associated with connecting solar energy to the electricity grid. We seek postdoctoral research projects that will help address significant challenges in the following areas:

• Planning and operation models and software tools are essential to the safe, reliable and resilient operation of solar PV on the interconnected transmission and distribution grid, especially for understanding how power flows fluctuate due to clouds or other fast-changing conditions, as well as interacting with multiple inverter-based technologies.
• Sensors and cybersecurity communication infrastructures and big data analytics enable visibility and situational awareness of solar resources for grid operators to better manage generation, transmission and distribution, and consumption of energy, especially in the face of man-made or natural threats.
• Higher solar PV penetration will require more advanced protection systems in distribution grids given that normal power flow (and fault current) are no longer unidirectional. Directional and distance relays may no longer operate as expected with inverter-based distributed energy resources.
• Cybersecurity for PV systems integration into utility operations, such as isolated layers of trust and mutual authentication. Advanced PV cybersecurity may be needed to ensure access control, authorization, authentication, confidentiality, integrity, and availability for the future smart grid.
• Power electronic devices, such as PV inverters and relevant materials, are critical links between solar panels and the electric grid, ensuring reliable and efficient power flows from solar generation.
• Integrating solar PV with energy storage would help to enable more flexible generation and grid and provide operators more control options to balance electricity generation and demand, while increasing resiliency. When combined with the capability to island from the area power grid, solar -- plus energy storage microgrids -- support facility resiliency. Resiliency is particularly needed for strengthening the security and resilience of the nation's critical infrastructure (e.g. for safety, public health and national security.)
• The ability to better predict solar generation levels can help utilities and grid operators meet consumer demand for power and reliability.

S-503 Concentrating Solar Thermal for Electricity, Chemicals, and Fuels

Possible disciplines: Mechanical engineering, chemical engineering, materials science

Concentrating solar power (CSP) technologies use mirrors or other light collecting elements to concentrate and direct sunlight onto receivers.[1]  These receivers absorb the solar flux and convert it to heat. The heat energy may be stored until desired for dispatch to generate electricity, synthesize chemicals, desalinate water or produce fuels, among other applications. The dispatchable nature of solar thermal energy derives from the relative ease and cost-effectiveness of storing heat for later use, for example, when the sun does not shine or when customer demand increases or time value premiums warrant. Heat and/or extreme UV intensities from sunlight may also be used to synthesize chemicals or produce fuels. The ability to produce heat for chemical processes without the added cost of fuel and to shift electricity production to alternative energy forms can provide benefits. To realize these benefits operations must be efficient and cost-effective.

SETO seeks to develop processes that can occur at a competitive cost compared to traditional synthetic routes. Careful analysis of integrated solar thermochemical systems will be required due to the complexity of most chemical processes and the typically thin profit margins in commodity chemical markets.

Topics of interest include, but are not limited to:

• Novel thermochemical materials or cycles for high volumetric energy density storage systems (with accessible thermal energy storage densities > 3000 MJ/m3 of storage media). Of particular interest are designs that are capable of cost-effective, simple, periodic recovery from performance degradation.
• Novel concepts for using solar thermal sources to produce value-added chemicals, such as ammonia, methanol, dimethyl ether or other chemicals for which there is a sizeable market.
• Innovative catalysts, materials, and reactor designs to enhance the thermochemical conversion processes.
• Development of thermal transport systems and components. Generally, proposed innovations should support a 50% efficient power cycle (or other highly efficient end use), a 90% efficient receiver module, and multiple hours of thermal energy storage with 99% energetic efficiency and 95% exergetic efficiency, while minimizing parasitic losses. Novel concepts should also be compatible with 30 years of reliable operation at the targeted temperature conditions.

This is a broad call and postdoctoral applicants interested in using heat from solar installations to create value-added products at a national scale are encouraged to apply.

Stekli, J.; Irwin, L.; Pitchumani, R.  “Technical Challenges and Opportunities for Concentrating Solar Power With Thermal Energy Storage,” ASME Journal of Thermal Science Engineering and Applications; Vol. 5, No. 2; Article 021011; 2013; http://dx.doi.org/10.1115/1.4024143.

S-504 Photovoltaic Materials, Devices, Modules, and Systems

Possible disciplines: Materials science and engineering, electrical engineering, chemical engineering, applied physics, physics, chemistry

In photovoltaic hardware, substantial materials and system challenges remain in many current and near-commercial technologies.  Research projects are sought in applied and interdisciplinary science and engineering to improve the performance and reliability of photovoltaic materials, devices, modules, and systems in order to drive down energy costs.  Areas of interest include:

• New module architectures, module components, and innovative cell designs that enable modules to produce more electricity at lower cost and improved reliability; modules that are compatible with higher system voltage and/or have improved shading tolerance especially in monolithically integrated thin-film modules.
• Development or adaptation of new characterization techniques to evaluate defects and increase collection efficiency of absorber materials or interfaces. Projects should expand understanding of effective methods to control material quality in order to improve PV device efficiency and stability.
• Scalable, high-speed measurement and characterization methods and tools for cells, modules, panels and systems.
• Fundamental understanding of degradation mechanisms in PV devices, modules and systems. Development of models based on fundamental physics and material properties to predict PV device or module degradation and lifetime in order to enable shorter testing time and high-confidence performance prediction.
• Cost-effective methods to recycle PV modules and related components that can be implemented into the current recycling infrastructure or module architectures designed for improved recyclability.
• Stable, high-performance photovoltaic absorber materials and cell architectures to enable module efficiencies above 25% while reducing manufacturing costs.
• Transparent electrodes and carrier selective contacts to enable low-cost cell and module architectures amenable to mass production.
• Low-cost materials and high throughput, low cost processes for current collection and transport.

Renewable Energy Dissertation Topics

Renewable energy is a topic which is at the forefront of energy development. The global drive to manage, mitigate and prevent climate change has seen the contribution of renewable energy, as an alternative to traditional fossil fuels, to global energy generation increase significantly over the past decade. The growing importance of renewable energy as a solution to the global climate crisis has seen extensive research undertaken and necessitates substantial future research to be conducted. This has made renewable energy a highly popular choice for dissertations, both with undergraduates and for postgraduate studies.

When selecting a dissertation topic that is focused on renewable energy it is important to choose a topic which presents a novel and engaging approach. There is an extensive body of published literature which the dissertation topic should enable critical engagement with. However, it is important to ensure that a selected dissertation topic does not simply rehash previous research, the development of renewable energy is constant and presents opportunities for numerous dissertations which examine key issues and debates including those related to sustainability, energy security, justice, equality and development.

Governing the Renewable Energy Transition

Renewable energy and energy security, emerging renewable energy technologies, renewable energy in developing countries, renewable energy within the circular economy.

Governance is and will be a highly important component of the regime shift to renewable energy. Government policies have the potential to support, guide and increase the rate of the energy transition, equally, there is the potential for ineffective policies to hamper the transition to renewables-based energy sectors. A successful transition will require a transformative governance which encourages the integration of knowledge across all aspects of the energy sector and enables the development of a sustainable and just renewable energy-based society. Under this purview falls some dissertation topics which are highly relevant to current events, namely the on-going global Covid-19 pandemic and how it and similar disruptive events may have a negative impact on renewable energy deployment if not appropriately managed. The role of governance remains an on-topic aspect of renewable energy which provides for a variety of dissertation examinations. Some examples of dissertation topics which examine renewable energy and governance are:

• Is the urgency of energy sector reform reflected in government policies or is there a need for new economic incentives to facilitate the transition to a renewables-based energy sector?
• How do disruptive events impact the transition to renewable energy generation?
• Will renewable energy generation enable new forms of alternative governance structures?
• Are governments effectively engaging citizens in the process of renewable energy generation and energy conservation?
• Do grassroots innovations positively contribute to the renewable energy transition and what influence does government policy have on the success or failure of grassroots renewable energy systems?

Increasing the capacity of renewable energy provision within a nation has the potential to contribute significantly towards enhancing energy security through the development of national energy provision which does not rely on foreign energy imports. Renewables-based energy sectors have complex interactions with energy security due to the variation in energy generation potential which is observed for many renewables. Reconciling renewable energy generation with energy security is a highly important component of future energy sectors, if renewables-based energy sectors cannot provide energy security then they will not be successful. There are multiple perspectives which can be taken in dissertations investigating this aspect of renewable energy, ranging from the development of diverse renewable resources, through to energy storage and distribution. Here are a few topic suggestions which investigate this aspect of renewable energy:

• Can we store enough: The future of batteries and energy storage.
• Can renewable energy resources present a viable future: Are renewables sufficient?
• Securing the future: Are Renewables the solution?
• The justice of renewable energy in developing countries; All for one and one for all.
• Energy storage: breaking the barriers to the future of energy solutions.
• Batteries: Which is the most desirable option?
• The future of energy supply, can we meet demand?

The status of development of renewable energy technologies differs between renewable resources. Some, such as solar PV and wind turbines are well-established and current research focuses on the refinement and improvement of these technologies and their associated infrastructure. However, the energy demands of society are diverse and there is a need to ensure that renewable energy generation can meet this diversity of needs. The replacement of traditional fossil fuels poses a greater challenge in some areas compared to others, for example, the replacement of aviation fuel with a renewable and low-carbon alternative. Dissertation topics examining emerging renewable energy technologies present an interesting option which looks to the future of renewable energy and identifies gaps in our current knowledge pool. Some examples of dissertation topics based on emerging renewable energy technologies are given below:

• How ‘green’ is green hydrogen? Examining the potential for green hydrogen utilisation in a sustainable society.
• Guilt free jet setting: Can biofuels make aviation fuels carbon neutral and sustainable?
• Reconciling biofuels and food security can we achieve both?
• Why is Geothermal renewable energy underutilised?
• Are all biofuels the same: Quantifying the environmental impact of biofuel production.

The case of developing countries is highly relevant to the subject of renewable energy systems. This is due to the potential for developing countries to avoid the negative impacts of increasing energy demand with economic development if renewable energy resources are selected rather than traditional fossil fuels. This way the mistakes of developed nations and the resulting environmental degradation could potential be avoided. However, there comes into play issues regarding justice and equity, whereby it can be argued that developing countries should be afforded the same development opportunities as already developed countries and that to impose conditions on the energy sector development would be unjust. Dissertation topics in this area can be varied and the following titles are just some examples of areas you could potential explore:

• How will an energy transition to a renewables-based energy sector impact energy poverty in developing countries?
• Are decentralised, small-scale renewable energy generation systems the answer to supporting the development of rural communities?
• What are the barriers to renewable energy based economic development pathways for developing countries?
• Empowering rural communities: Renewable energy for the future.
• Can renewable-based energy transitions be just?
• Economic development and renewable futures can the two be reconciled?

The development of a sustainable future will be influenced by our approach to the use and consumption of resources. The nature of renewable energy is such that it will play a vital role in reducing the consumption of natural resources and limiting environmental degradation. The circular economy is being increasingly touted as the way forward for resource use and renewable energy resources are likely to be an integral aspect of the circular economy. However, the role of renewable energy within the circular economy is one which needs to be explored and developed, yes, the use of renewable energy has a lesser environmental impact that fossil fuels, but this does not mean that renewable energy does not have a degradative environmental impact. The sustainability of renewable energy, resource consumption and their role within the circular economy is an important area of research which is likely to receive considerable attention in the coming years and thus is a highly on-trend topic for a dissertation. Some example of dissertation titles which would fall within this area are:

• Can the sustainability of renewable energy systems be increased through the development of end-of-life component recycling?
• The place of renewable energy resources within the circular economy: Will it be possible to produce energy without consuming natural resources?
• Which renewable resource presents the most sustainable option: A life-cycle approach to calculating the environmental impact of renewable energy.
• Does the use of limited or rare natural resources in renewable energy systems mean that there is a finite lifespan of renewable energy systems?
• Powering the circular economy, what role will renewable energy systems play?
• The future of solar energy: Will it be possible to reduce resource consumption in solar energy systems?
• Do we perceive renewable energy systems as ‘greener’ than they are: A case study of the environmental impact of solar photovoltaic panel production.

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Renewable Energy Dissertation Topics

• Updated on  
• Jan 12, 2023

Renewable energy is one of the most popular research topics. Thousands of students used these topics for their MTech and PhD theses, but a few of them struggled to find the right topic and a good paper for their graduation. Now, all thesis on renewable energy resources problems can be solved with a single phone call, which means that our Leverage Edu experts can help MTech and PhD students who are having problems with their thesis on renewable energy resources. As a master’s student, you may choose renewable energy as your thesis topic . If you decide to write a thesis on renewable energy, you may be unsure of how to begin or even what you are required to do. Don’t worry, we have you covered. In this blog, you’ll find renewable energy dissertation topics to help you write your thesis.

This Blog Includes:

Why is renewable energy important, best renewable energy research topics 2023, topic 1 .

Renewable energy is one of the fastest-growing systems in developing countries. It is widely used for “self-service” purposes. It is quite popular due to some unique advantages in its application. PhD research topics in Renewable Energy provide a distinguished platform for PhD/ MS scholars. We assist our serving hands in developing the best profile for their career.

Renewable Energy’s Untapped Potential

• Ecofriendly
• Reasonable Price
• Lower Maintenance
• Health Advantages
• Unending and also Reliable Resource

It is the “core portion of the modern power system” all at once. It aids in the regulation of low, high, and variable power generation. As a result, we are also current in all of these recent areas. As a result, we guide you in every nook and cranny of your area with the help of our expert advice.

Topic 1: Renewable Energy: Prospects and Challenges Today

Topic 2: Renewable energy for Africa ‘s long-term development

Topic 3: The Impact of COVID – 19 on the Biofuel Market

Topic 4: Geothermal energy is an untapped abundant energy resource.

Topic 5: Wind Energy’s Future

Topic 6: How valuable is home wind energy?

Topic 7: Renewable Energy’s Economic and Environmental Benefits

Topic 8: Why is it more important than ever to prioritise renewable energy?

Topic 9: Is it expensive to finance renewable energy?

Topic 10: Climate change mitigation; can renewable energy help?

Topic 11: Living Green: How many people have access to renewable energy?

Topic 12: Understanding the distinctions between renewable and alternative energy technology 

Topic 13: Is solar energy the way to go?

Topic 14: 2030 Approach to Renewable Energy

Topic 15: The cost of solar energy versus other renewable energy sources

Renewable Energy Dissertation Examples

Here are some dissertation topics for you to cover under the renewable energy topic. The examples are personalised for the UK, but you can mend them according to the country that you choose to write about.

Topic Name: Investigating the economic benefits of increasing biomass conversion – a case study of the renewable energy industry in the United Kingdom .

Aim of the Study: The current study aims to investigate the economic benefits of increasing biomass conversion using the UK renewable energy industry as a case study.

Objectives:

• To present an initial concept of biomass conversion and its benefits.
• In the context of the UK renewable energy industry, describe the economic benefits of increasing biomass conversion.
• Identifying challenges in biomass conversion and devising strategies to overcome these challenges.

Topic Name: Examining the benefits of using solar energy and its role in addressing the global threat of climate change .

Aim of the study: The current study aims to investigate the benefits of using solar energy and how it is addressing the issue of climate change.

• To explain the advantages of using solar energy and its increasing use in various sectors.
• To demonstrate how solar energy can be used to address a global threat such as climate change.
• To provide a stringent set of recommendations for the most effective use of solar energy in combating climate change.

Topic Name: Investigating UK retail organisations’ use of renewable energy to meet environmental sustainability goals.

Aim of the Study: The purpose of this research is to assess the strategy of using renewable energy in the UK retail sector to achieve environmental sustainability goals.

• To express the importance of renewable energy sources in the UK retail industry.
• To investigate how retail organisations in the United Kingdom use renewable energy to achieve environmental sustainability goals.
• To share effective ideas on how UK retail organisations can use renewable energy sources effectively to achieve environmental sustainability goals.

Topic Name: A critical assessment of the growing concern for sustainability in the UK construction industry, which is driving the use of renewable energy.

Aim of the Study: The purpose of this research is to evaluate the growing concern for sustainability in the UK construction industry, which drives overall renewable energy consumption.

• To explain why the UK construction industry is becoming increasingly concerned about sustainability.
• To investigate how renewable energy consumption in the UK construction industry is increasing in tandem with the growing concern for sustainability.
• To encourage organisations in the UK construction industry to increase their use of renewable energy sources in order to meet sustainability goals.

Topic Name: Assessing the impact of solar energy on agricultural sustainability practices in the United Kingdom.

Aim of the Study: The current study aims to assess the effects of using solar energy in sustainability practises in the UK agriculture industry.

• To demonstrate the concept of solar energy consumption and its implications for environmental practices.
• To place the use of solar energy in the UK agriculture industry within the context of sustainability practices.
• To make recommendations for improving the use of solar energy and reaping its benefits in the UK agriculture industry.

How renewable energy affects the future of our planet. Use of biomass as a renewable energy source. The limitations of fossil fuels: the significance of renewable energy and its economic benefits. Methods for extracting power from flow-structure interactions.

A thesis statement example: Solar power is an excellent alternative energy source because it is renewable, cost-effective, and does not pollute the environment.

Three obstacles to renewable energy are: Putting energy storage in place. Traditional fossil-fuel plants operate at a reduced level, producing a consistent and predictable amount of electricity Bringing together distributed systems Renewable energy reporting

This was all about Renewable energy Dissertation Topics. For more information, subscribe to Leverage Edu and if you wish to study abroad, connect with our counsellors at 1800 57 2000 and book a 30-minute free session.

Vidisha Dewan

Graduated with English as a major, I’m a writing enthusiast. Writing helps me blend my passion and profession to achieve creative satisfaction. I am an opinionated person, but always open to change. Try to keep my work surroundings creative and fun, with space for constructive feedback.

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Renewable Energy

Start learning about your topic, create research questions to focus your topic, using and finding books, recommended books, find articles in library databases, find videos on renewable energy, find web resources, cite your sources, key search words.

Use the words below to search for useful information in books and articles .

• biomass / biofuel
• geo-thermal energy
• green energy
• hydropower / hydroelectricity
• solar power / solar energy
• sustainable energy

Background Reading:

It's important to begin your research learning something about your subject; in fact, you won't be able to create a focused, manageable thesis unless you already know something about your topic.

This step is important so that you will:

• Begin building your core knowledge about your topic
• Be able to put your topic in context
• Create research questions that drive your search for information
• Create a list of search terms that will help you find relevant information
• Know if the information you’re finding is relevant and useful

If you're working from off campus , you'll need to sign in. Once you click on the name of a database, simply enter your student ID (without the W) and your six-digit birth date.

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• CQ Researcher Online This link opens in a new window This is the resource for finding original, comprehensive reporting and analysis to get background information on issues in the news. It provides overviews of topics related to health, social trends, criminal justice, international affairs, education, the environment, technology, and the economy in America.
• Issues & Controversies This link opens in a new window This is a great database to use when you want to explore different viewpoints on controversial or hot-button issues. It includes reports on more than 800 hot topics in business, politics, government, education, and popular culture. Use the search or browse topics by subject or A to Z.
• Gale eBooks This link opens in a new window Use this database for preliminary reading as you start your research. You'll learn about your topic by reading authoritative topic overviews on a wide variety of subjects.
• Gale In Context: Global Issues This link opens in a new window Use this database when you want to explore your topic from a global perspective or to analyze and understand the most important issues of the modern world with a global awareness. You'll find news, global viewpoints, reference materials, country information, primary source documents, videos, statistics, and more.
• Alternative Energy This 3-volume encyclopedia is available as an eBook. Use the search box to find articles on specific alternative energy topics
• What is renewable energy?
• What are the different types of renewable energy?
• What is the difference between renewable energy and clean energy?
• What is the history of renewable energy in the United States?
• What are the advantages of renewable energy?
• What are the disadvantages of renewable energy?
• What are the economic arguments for and against renewable energy?
• What are the political arguments for and against renewable energy?
• How should research into renewable energy be funded?
• Should the U.S. government provide subsidies or tax breaks to renewable energy companies?
• Based on what I have learned from my research, what do I think about the issue of renewable energy?

Why Use Books:

Use books to read broad overviews and detailed discussions of your topic. You can also use books to find  primary sources , which are often published together in collections.  

Where Do I Find Books?

You'll use the library catalog to search for books, ebooks, articles, and more.  

What if MJC Doesn't Have What I Need?

If you need materials (books, articles, recordings, videos, etc.) that you cannot find in the library catalog , use our  interlibrary loan service .

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• GreenFILE This link opens in a new window Well-researched information covering all aspects of human impact to the environment. This collection of scholarly, government and general-interest titles includes content on global warming, green building, pollution, sustainable agriculture, renewable energy, recycling, and more.
• Today's Science This link opens in a new window Covers a full range of current scientific developments.
• Gale Databases This link opens in a new window Search over 35 databases simultaneously that cover almost any topic you need to research at MJC. Gale databases include articles previously published in journals, magazines, newspapers, books, and other media outlets.
• Access World News This link opens in a new window Search the full-text of editions of record for local, regional, and national U.S. newspapers as well as full-text content of key international sources. This is your source for The Modesto Bee from January 1989 to the present. Also includes in-depth special reports and hot topics from around the country. To access The Modesto Bee , limit your search to that publication. more... less... Watch this short video to learn how to find The Modesto Bee .

Find videos and documentaries about renewable energy in Films on Demand.  These film resources are free for MJC students, faculty, & staff.  If you're working from off campus, you'll need to sign in , using your student ID (without the W) and your six-digit birth date.

Type renewable energy  in the search box to access videos on this topic.

• Films on Demand This link opens in a new window Use Films on Demand when you want educational video content. This streaming video collection contains unlimited, 24/7 access to thousands of videos. Teachers can embed videos in Canvas. In addition, there are mobile options for iPad and Android. more... less... Instructions for embedding Films on Demand into Canvas .
• Kanopy This link opens in a new window Kanopy is a video streaming database with a broad selection of over 26,000 documentaries, feature films and training videos from thousands of producers. Instructions for embedding Kanopy into Canvas .

Use Google Scholar to find scholarly literature on the Web:

Browse Featured Web sites:

• Modesto Irrigation District Electrical power and water utility
• MIT Energy Initiative Use the search box or click on the Research and Studies tab to find information on energy from the Massachusetts Institute of Technology.
• United States Department of Energy The U.S. Department of Energy's site has information on all aspects of energy use and production. Use the search box at the top of the page to access specific information.

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• Last Updated: Feb 28, 2024 3:06 PM
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113 Renewable Energy Essay Topic Ideas & Examples

🏆 best renewable energy topic ideas & essay examples, 👍 good essay topics on renewable energy, 💡 interesting topics to write about renewable energy, ❓ questions about renewable energy.

• Solar Energy as an Alternative Source of Energy It is of essence to note that, with the depletion of fossil fuels, more emphasis is now being put on the use of solar energy as an alternate energy source.
• Solar Energy Installation Project Management 0 Pilot solar energy project Managers will run a pilot project to determine the feasibility of the project. A number of resources will be required to complete the project. We will write a custom essay specifically for you by our professional experts 808 writers online Learn More
• Renewable Energy Sources: Existence, Impacts and Trends It is important to note that about 20% of the world energy sources come from renewable sources. The management and maintenance of renewable energy production may be in the short run or long run.
• Biofuel: Renewable Energy Type The purpose of this essay is to discuss this statement and evaluate its accuracy in accordance to the latest studies, as well as the pros and cons of biofuel in general.
• The Benefits of Renewable and Non-Renewable Energy This research paper seeks to describe renewable and non renewable energy sources, their effects on the environment and economic benefits.”Fossils fuels are one of the most widely used sources of energy”.
• Using Solar (PV) Energy to Generate Hydrogen Gas for Fuel Cells With the current technologies, an electrolyzer working at 100% efficiency needs 39 kWh of electricity to liberate 1 kg of hydrogen.
• Solar Energy in the United Arab Emirates The success of the solar power initiatives in the UAE is largely attributed to the wide range of financial incentives that the UAE government has offered to the companies that are prepared to advance the […]
• Renewable Energy: Geothermal Energy Of all these forms of renewables, geothermal energy is perceived as one of the renowned forms of renewable energy which is generated from the crust of the earth.
• Renewable Energy: Comparison Between Biogas and Solar Energies Again, the research finds that the cost of installation is higher compared to solar energy sources. However, the paper is going to compare solar and biogas energy sources.
• Adopting Renewable Energies Proponents of fossil fuels assert that while alternative energy sources purport to be the solution to the problems that fossil fuels have caused, alternative energy sources can simply not cater for the huge energy needs […]
• Wind Energy as Forms of Sustainable Energy Sources T he only costs to be met in producing wind energy is the cost of equipment for harnessing wind, wind turbines for converting the energy and photovoltaic panels for storing energy.
• Wind Power as an Alternative Energy Source Wind energy is a renewable source of energy that is an alternative to fossil fuel use, which is necessary for the conservation of the environment.
• Wind and Solar Energy as a Sources of Alternative Energy Fthenakis, Mason and Zweibel also examined the economical, geographical and technical viability of solar power to supplement the energy requirements of the U.S.and concluded that it was possible to substitute the current fossil fuel energy […]
• Technology and Wind Energy Efforts by the elite members of the society enlightened the global countries about the benefits of renewable energy sources in conserving the environment prompting the need to consider wind energy.
• How Solar Energy Can Save the Environment? Over the past few decades, the level of greenhouse gasses in the environment has been on the rise. The only cost in the production of solar energy is making the solar panels.
• Advantages and Disadvantages of Wind Energy Another advantage is the fact that most of the turbines that are used in the generation of wind power are located in ranches, and on farms.
• Social Background of Renewable Energy Development According to Craddock, although some people believe that the development of renewable sources of energy is driven by the economic needs, the social force also plays an important role in increasing popularity of this form […]
• Climate Change: Renewable Energy Sources Climate change is the biggest threat to humanity, and deforestation and “oil dependency” only exacerbate the situation and rapidly kill people. Therefore it is important to invest in the development of renewable energy sources.
• The Role of Renewable Energy in Addressing Electricity Demand in Zambia In this regard, ZESCO Limited, the Zambian power utility company, has an obligation to generate and supply the electricity in the country.
• Barriers to Deploying Renewable Energy in Hotels The main benefit of renewable energy is environmental protection, improving the environmental and social performance of the industry, and reducing utility costs.
• Renewable Energy: An International Profile To illustrate the severity of some of the outlined consequences and challenges presented to the national environment, the following graph is presented, illustrating the growth rate of the US fracking industry.
• “The American Recovery and Reinvestment Act”: Developing Renewable Energy The focus of this bill on the technological aspect of environmental protection is seen in the allocation of funds on loan guarantees, grants for researchers, and the manufacturing of advanced systems.
• Efficient Solar Refrigeration: A Technology Platform for Clean Energy and Water Refrigeration cycle capable to be driven by low grade energy, substituting gas-phase ejector used in conventional mechanical compressor.
• Non-Renewable Energy and Gross Domestic Product of China The use of non-renewable energy in China has the negative impact on the GDP, as indicated by the negative values of DOLS and CCR coefficients. The generation of renewable energy has a negligible negative impact […]
• Making Solar Energy Affordable Solar energy is a type of energy that is obtained through tapping the sun’s rays radiant and converting it into other energy forms such as heat and electricity.
• Government Subsidies for Solar Energy This approach has enabled solar companies and developers to penetrate the energy market despite the high costs involved in developing solar power.
• Electrical Engineering Building Uses Wind Energy The purpose of this fact-finding mission was to determine an appropriate type and rating of the wind turbine based on three factors: the average wind data at UNSW; the peak power demand for the EE […]
• The Sun’s Light and Heat: Solar Energy Issue The figure below provides an overview of the major parts of the solar system, which include the solar core, the radiative zone, the convective zone, the photosphere, the chromosphere, and the corona among others.
• Solar Energy: Review and Analysis Available literature shows that most commercial CSP plants in Spain and the United States using synthetic oil as the transfer fluid and molten salt as the thermal energy storage technology are able to achieve a […]
• Solar and Wind Energy in the Empty Quarter Desert However, the main bulk of the report focuses on the proposal to build a stand alone renewable energy source, a combination of a solar power wind turbine system that will provide a stable energy source […]
• Wind Energy for the Citizens of Shikalabuna, Sri Lanka The citizens of Shikalabuna are shot of the possibility to implement the required wind turbines and get a chance to pay less using the natural source available.
• Renewable Energy and Transport Fuel Use Patterns The base data is as follows: Table 1 The first segment of this analysis tests for differences between consumption of natural gas and ethanol.
• Renewable Energy Technologies As for the construction decision and the way of harnessing the wave power, a variety of solutions has been proposed. Cheap and reliable desalinization technology such as one described in the Economist article could be […]
• Solar Energy Selling Framework The list of actions to complete the required activity goes in the following sequence: planning actions, sales pitch itself, and reflection. The actions, aimed at doing are the four stages of a sales pitch, that […]
• Renewable Energy Ethical Question Despite the fact that the power of wind, sun, and water can be transformed into energy the great majority of people argue the importance of the renewable energy system implementation proving that the disadvantages should […]
• Renewable Energy Resources in Qatar The topicality of the problem is evidenced by current trends favoring the use of renewable energy resources and the necessity to avoid further deterioration of the situation in this sphere.
• The Solar Energy and Photovoltaic Effect The key difference factor of the solar cells is the material and technology that is used. Photon behavior in a solar cell is defined by the materials used and the construction of the cell itself.
• Solar Energy Project: Stakeholder and Governance Analysis The stakeholders on this issue are the central governments of the three countries, the local government bodies the industrial and business groups and the civil society groups interested on the issue.
• Canadian Renewable Energy Industry The profile of the renewable energy resource industry in Canada is likely to witness a big boost in the coming years.
• Solar Energy: Commercial and Industrial Power Source This is made further possible by the inspirational circulars related to the application of more solar energy in the state. This is one of the major participations that came in to the notice.
• Conceptual Chemistry. Wind Turbine vs. Coal Energy The current paper is aimed at considering and comparing traditional coal energy and the newly popular wind turbine energy in the context of their costs and environmental benefits.
• Solar Energy and Its Impact on Society He believed that the wheel was the extension of our feet, the hammer was an extension of our hands, and technology is the extension of our mind and mentality.
• Bismuth Vanadate Photocatalyst for Solar Energy 20 In the scheelite BiVO4, it is possible to find out a hybridized band structure with Bi 6s and O 2p orbitals.
• Renewable Energy and Politics Relationships The global regimes concerned with energy use and distribution call on states to stick to commerce when dealing with energy matters instead of mixing it with politics, but it seems the pleas are ignored given […]
• Solar Energy Power Plant & Utility Supply Contract The first assumption from the case above is that the advisement by SEPP to the US not to provide EEC certificates was made orally and was came after the contract had been signed.
• Renewable Energy in Saudi Arabia, Qatar and the UAE Adoption of solar power technologies would be extremely beneficial to Gulf countries, and it will help to address most of the issues related to resource shortages.
• Solar Energy Industry in the UAE The UAE International Investors Council insists that the sustainable use of the available financial resources, particularly, FDI, should be viewed as the foundation for enhancing the development of the state industries, especially as far as […]
• Wind Energy Feasibility in Russia In Russia, feasibility studies have been conducted to establish the viability of wind turbine projects. In conjunction with the problem statement above, the following aims have been formulated: To use the available and relevant data […]
• Innergex Renewable Energy Inc. at Canada’s Market The company’s mission is to expand the renewable energy production through the development and operation of their high-quality facilities, considering the need to preserve the environment and the necessity to balance the interests of the […]
• Google’s Driverless Cars and Renewable Energy Some of the firms are testing the technology with the application of low-powered green energy. The violations of the road regulations are not the only issue with the driverless car technology.
• Solar Energy: Definition and Ways of Usage Observers believe that the energy from the sun has the potential to satisfy the world’s energy requirements. Energy from the solar is free, and we can never deplete solar energy.
• Solar Energy Panels in UAE This report will examine the future of solar energy and the incentive schemes that can be put in place to develop the United Arab Emirates solar energy industry.
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Turning Rocks Into Renewable Energy With Hydrogen Breakthrough

By University of Texas at Austin March 31, 2024

Researchers at The University of Texas at Austin are pioneering a method to produce hydrogen from iron-rich rocks without CO2 emissions, potentially revolutionizing the hydrogen industry. Credit: SciTechDaily.com

Innovative research on hydrogen production from geological sources could significantly impact the sustainable energy landscape, offering a low-carbon alternative to current methods.

In a project that could be a game changer for the energy transition, researchers at The University of Texas at Austin are exploring a suite of natural catalysts to help produce hydrogen gas from iron-rich rocks without emitting carbon dioxide.

If the scientists are successful, the project could jump-start a brand-new type of hydrogen industry: geologic hydrogen.

A Leap for the Hydrogen Industry

“We’re producing hydrogen from rocks,” said Toti Larson, a research associate professor at the UT Jackson School of Geosciences Bureau of Economic Geology and the lead researcher on the project. “It’s a type of non-fossil fuel production of hydrogen from iron-rich rocks that has never been attempted at an industrial scale.”

The research team recently received a $1.7 million grant from the Department of Energy and is collaborating with scientists at the University of Wyoming’s School of Energy Resources to explore the feasibility of this process on different rock types across the United States.

Researchers are studying chemical catalysts that can produce hydrogen gas from iron-rich rocks. Credit: Toti Larson / UT Austin

Hydrogen is an important player in the energy transition because it does not produce CO 2 gas emissions when it’s burned for fuel. Its only byproduct is water. However, most hydrogen gas today is produced from natural gas in a process that also produces CO 2 .

Producing geologic hydrogen from iron-rich rocks would offer a major shift in the energy transition because of its low-carbon emission footprint, said Larson.

“If we could replace hydrogen that is sourced from fossil fuels with hydrogen sourced from iron-rich rocks, it will be a huge win,” Larson said

Innovations in Geologic Hydrogen Production

The catalysts the team is exploring will stimulate a natural geologic process called “serpentinization.” During serpentinization, iron-rich rocks release hydrogen as a byproduct of chemical reactions.

Serpentinization usually occurs at high temperatures. With natural catalysts that include nickel and other platinum group elements, the team is working to stimulate hydrogen production at lower temperatures and at depths easily accessible by today’s technology where iron-rich rocks are found throughout the world. That means catalyst-enhanced production of hydrogen from iron-rich rocks has the potential to significantly increase hydrogen production globally.

Esti Ukar (left) and Toti Larson are leading a project to produce geologic hydrogen from rocks. They are both researchers at the Bureau of Economic Geology, a research unit of the UT Jackson School of Geosciences. Credit: Toti Larson / UT Austin

“Natural accumulations of geologic hydrogen are being found all over the world, but in most cases they are small and not economical, although exploration continues,” said Esti Ukar, a research associate professor at the Jackson School and a collaborator on the project. “If we could help generate larger volumes of hydrogen from these rocks by driving reactions that would take several million years to happen in nature, I think geologic hydrogen could really be a game changer.”

Ukar is also leading work on another energy transition project to develop carbon-free mining techniques that store CO 2 as part of the mineral extraction process.

Researchers have already conducted successful tests at the laboratory scale. The grant, from the Department of Energy Advanced Research Projects Agency-Energy (ARPA-E), will be used to scale up the experiments and test the process on a broad range of iron-rich rock types found across North America. The team will investigate using the catalysts on basalts from the Midcontinent Rift in Iowa, banded iron formations in Wyoming and ultramafic rocks in the Midwest.

This project is one of several research initiatives at the Bureau of Economic Geology investigating the role of the subsurface in the generation and storage of hydrogen as part of the energy transition.

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7 comments on "turning rocks into renewable energy with hydrogen breakthrough".

This is only part of the problem. We still haven’t solved the problem of embrittlement of steel storage tanks, supply lines, and valves. If used as fuel in internal combustion engines, then the cylinders, rings, and cylinder walls will have reduced lifetimes with possible catastrophic failures resulting from embrittlement.

While rarely acknowledged by alarmists, water vapor is a more powerful ‘greenhouse gas’ than CO2. It is rationalized that water vapor isn’t important because it precipitates out in a few days and is part of the natural cycles. If the water vapor is released into the atmosphere then it can result in warming, just like CO2, and will be replenished continuously 24/7/365. However, if it is purposely created by humans, then it has to be considered anthropogenic. The water vapor could be condensed and carried on board vehicles, but that would result in reduced efficiency from the added weight. Also, it would require additional attention from owners to release the water periodically. There may be issues resulting from the fact that water is a ‘universal solvent’ whose ability to dissolve things is enhanced when it is pure. There may be unintended consequences from releasing large quantities of distilled water into drains continuously.

Then there is the economics. Whether the rocks are blasted, comminuted, and hauled to a chemical factory, or a potential source rock is fractured with explosives and the hydrogen is produced in situ, CO2 will be released.

The problem has to be examined from a holistic viewpoint, not just from the viewpoint of whether the chemistry allows it.

Clearly not a “renewable” resource even if it doesn’t emit CO2.

Gotta say it feels weird to be using solvents on rock, catalyst or not, but that -releasing water- take, extracting rock without wanting the aggregate (v. just running ops downbore) and missing DARPA’s high-entropy materials ventures are bad takes (though the lifetime of the metals is still 35 Y without coating specially, or more if you run electrochemistry and just skip structural metals in that. No high pressure steam in fissile core, no problem?) But…this is ARPA-E.

Thank you for mentioning Hydrogen embrittlement. The clowns seem to ignore the disaster that is. They are delusional twits.

All this expensive and heavily subsidized bizarre technology because of the consensus belief that there is some sort of catastrophic crisis and global climate emergency in our future. This technology, like most others designed to avoid CO2 emissions will need conventional vehicles for the transportation involved. That means more oil. And there is no way around it using EVs.

Here is the problem with this fiel source, it will deplete atmospheric oxygen levels if we use H2 and don’t use H2O as the fuel source. Obviously, less Oxygen is not good and starting a downward trend in O2 in the atmosphere is not a good long term solution.

Perhaps we should focus on geological molecular H2 in carbon shale deposits catalyzed for low energy nuclear power systems, engineering dense hydrogen energy-based systems by Subtle Atomics, look it up.

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Managing water and climate risk with renewable energy

Dwindling supplies of fresh water pose a material business risk: one estimate shows that the lack of clean fresh water threatens some $425 billion of value across more than 500 companies. 1 Cleaning up their act: Are companies responding to the risks and opportunities posed by water pollution? , CDP Global Water Report 2019, cdp.net. Companies with water-intensive operations are apt to be attuned to water risk. But all companies can be indirectly exposed to water risk through their purchases of electricity, for water is widely used to generate electricity from steam-powered turbines. By contrast, electricity from renewable sources is generally less water intensive than electricity from fossil fuels. 2 While electricity generated from renewables is often less water intensive, other factors might also influence choices about renewables deployment. Since these important factors, which include land-use requirements and environmental impacts on wildlife, are evaluated in permitting processes for renewables deployment across jurisdictions, we have not addressed them in this article. A promising way for businesses to lessen their risk exposure while helping relieve local water stress, therefore, is to make greater use of renewable power, whether by sourcing a larger share of grid power from renewable sources or by installing their own renewable-generation capacity. 3 Power grids, too, can be less or more water intensive. Individual companies and facilities will seldom be able to select an alternative-power grid; however, they can sometimes opt for virtual power-plant agreements that allow them to source all of their purchased electricity from renewable-power sources.

About the authors

It’s also well known that switching to renewables can help reduce carbon emissions —something that companies are increasingly seeking to do, given the need to limit the buildup of physical climate risks by achieving net-zero emissions. These dual water and climate benefits of renewable power can be significant and should be considered in tandem. The idea that energy management affects water stewardship and climate stewardship is not new: the so-called energy-water-carbon nexus has long been a focus of academic research related to a wide variety of topics, such as seawater desalination. But it is increasingly relevant to multinational companies’ decisions about how to reduce their water footprints in water-scarce regions and lower their carbon emissions. 4 Power management is only one of several methods that companies can use to manage their water footprints, down to the site and basin level. Other methods include improving operational efficiency to reduce the amount of water used (for example, to cool machinery or to wash textiles). Companies can apply those methods simultaneously with power management.

Assessing the potential water and carbon savings from using more renewable energy requires a granular analysis of site-level factors, ideally guided by a company-level strategy. To ascertain how these factors play out at the industry level, we analyzed data from more than 1,500 companies on the water consumption and carbon emissions associated with their electricity purchases in 2019, and then looked closely at two industries: chemicals, and food-and-beverage processing. 5 Disclosures on water consumption are documented by the CDP (formerly the Carbon Disclosure Project), a global organization focused on promoting corporate disclosure of environmental risks and impacts. (We selected these two industries because both had large data samples with extensive location footprints.)

Two site-level factors stood out in our analysis for both industries. The first factor is the water and carbon intensity of electricity purchased from the power grid; this varies considerably among regions. The second factor is the degree of water stress in the locations where a business operates, which also differs from region to region. For the chemical companies in our data set, 40 percent of energy purchases take place in regions with medium-high or higher levels of water stress, compared with 25 percent for food-and-beverage-processing companies. 6 We use the definitions of water stress developed by the World Resources Institute. Countries are designated medium stress to high stress if their ratio of water withdrawals to water supply is in the range of 20 to 40 percent, high stress if the ratio is 40 to 80 percent, and extremely high if the ratio is 80 percent or greater. For more, see Francis Gassert, Tianyi Luo, Andrew Maddocks, and Paul Reig, “Water stress by country,” World Resources Institute, December 12, 2013, wri.org. In this article, we show how considering these factors together can help executives maximize the water and carbon benefits of switching to renewable energy where feasible.

Locating opportunities to reduce water consumption and carbon emissions

Companies’ purchases of electricity from the grid affect local water quality and availability because the fossil-fuel and nuclear power plants that generate most of the world’s electricity withdraw considerable fresh water to support their operations. Some power plants discharge some or all of that water back into the local basin, which lessens their impact on water availability. The water that is not discharged is said to be consumed, and water consumption reduces the quantity available locally. Our analysis focuses on water consumption because it tends to increase water stress. By contrast, wind farms and solar arrays consume little to no fresh water; at most, water is used to clean solar panels. 7 In this analysis, rates of water withdrawal and consumption for wind and solar account only for water withdrawn or consumed during power generation, not for water usage over the life cycle of renewable-generation facilities (including manufacturing of renewable-power equipment).

In general, countries that generate less grid power from renewable sources consume water at higher rates per unit of purchased electricity (Exhibit 1). Looking at the sources of grid power for the 119 countries covered by the data set, we found that 47 percent generate less than 1 percent of their grid power using wind or solar. Only 9 percent of countries generate more than 5 percent of their power from wind or solar. 8 Hydropower, which provides a large fraction of grid power in many countries, is a renewable source of energy that results in no carbon emissions and, in many cases, little water consumption. However, we have chosen to model only the increased use of solar power and wind power because companies are unable to increase their use of hydropower everywhere they operate. The limitation exists for two reasons. First, not all countries can deploy hydropower; they can do so only if they possess certain natural endowments, such as major rivers. Second, the large scale of hydropower installations makes them impractical for companies to deploy at their own facilities, whereas companies can readily deploy small-scale solar and wind installations. To find promising opportunities to reduce water consumption and carbon emissions by switching to renewables—through power-purchase agreements or self-operated renewable installations—companies might prioritize operations in countries with electricity grids that rely less on solar and wind power.

The other factor that bears consideration is water stress. Using information on the water-stress levels of countries, we assessed exposures to water stress for the companies in two sectors within the data set: 111 companies in the chemicals industry and 86 companies in the food-and-beverage-processing industry. In total, the 111 chemical companies reported 209 terawatt-hours (TWh) of purchased energy; our estimates indicate that this energy use resulted in 89 megatons of carbon emissions and 16 billion gallons of water consumed. The 86 food-and-beverage-processing companies reported purchasing 102 TWh of purchased energy, resulting in 39 megatons of carbon emissions and eight billion gallons of water consumed, according to our estimates.

When it comes to managing water impact, companies should know how much of their energy consumption takes place in regions and countries that experience greater water stress. The food-and-beverage-processing companies that we analyzed purchased 20 percent of their grid power in countries with medium to high or higher levels of water stress. The resulting water and carbon impacts were disproportionately large, accounting for 56 percent of the companies’ water consumed, and 32 percent of their carbon emissions. Companies in the chemicals sector recorded a higher fraction of their energy purchases in water-stressed countries, 40 percent, which accounted for 44 percent of the sector’s water consumption and 49 percent of carbon emissions from purchased energy (Exhibit 2). Across both sectors, energy purchases in water-stressed countries accounted for outsize shares of water consumption and carbon emissions, suggesting an opportunity to reduce both by switching to renewables in those countries.

Estimating the effects of switching to renewables

Next, we estimated the potential water and carbon reductions that would result as companies replaced nonrenewable sources of purchased energy (starting with coal power, then oil power, then gas power) with renewables. Adjustments were applied at the country level, to account for variations in the shares of nonrenewable power generated by using different fossil fuels. These variations can make for large differences in the water intensity of nonrenewable electricity: for example, nonrenewable-power generation in Mexico consumes nearly twice as much water, per kilowatt-hour, than in Egypt.

Substituting renewables for the most carbon-intensive energy sources had a profound impact on emissions, even when the increases in renewables were modest. In the chemicals sector, we estimate that lowering the share of nonrenewable energy by five percentage points and increasing the share of renewable energy by five percentage points would reduce carbon emissions from purchased energy by approximately 40 percent. The same five-percentage-point change in purchased energy had an even greater effect in the food-and-beverage-processing sector: a 58 percent reduction in carbon emissions. Upping the share of renewables by 50 percentage points would prevent 78 percent of carbon emissions for chemical companies and 84 percent of carbon emissions for food-and-beverage processors (Exhibit 3).

The water savings from switching to renewables were also significant. A 50-percentage-point increase in purchases of renewables results in a nearly 60 percent reduction in water consumption for both the chemical companies and the food-and-beverage-processing companies (Exhibit 4).

Switching to renewables may not be a practical near-term option in every country where a company operates. Utilities might lack the renewable-generation capacity to supply a company with all the renewable energy that it needs. And adding capacity takes time, whether the utility does so or the company sets up its own renewable installations. Companies might therefore take a more gradual approach to increasing their use of renewable energy. Some companies have also made renewable-power purchasing agreements with local utilities. These enable the utilities to accelerate investment in renewable installations by ensuring long-term demand for the electricity that the installations produce.

To illustrate the effect of a more gradual and targeted ramp-up in renewable-energy purchasing, we modeled the reductions in water consumption and carbon emissions that the two sets of companies would achieve if they increased their use of renewable energy only in countries with medium to high or higher levels of water stress. A five-percentage-point increase in renewable-energy use in water-stressed countries would reduce water consumption by around 6 percent for both groups of companies; with a 50-percentage-point increase in renewables, they would lower water consumption by about 60 percent for both groups. In other words, increasing the use of renewables in water-stressed countries results in an appreciable decrease in water consumption—the sort of result that can help guard against water risk.

What’s more, switching to renewables in water-stressed countries alone produces significant reductions in carbon emissions. With a five-percentage-point increase in renewables in water-stressed countries alone, we estimate that the chemical companies would lower their global carbon emissions by 13 percent; for food-and-beverage-processing companies, the reduction would be 7 percent. A 50-percentage-point increase in renewables in water-stressed countries would lower chemical companies’ global carbon emissions by 36 percent, and food-and-beverage-processing companies’ emissions by 23 percent overall (Exhibit 5).

Making the switch to renewables: How to begin

Business leaders in all industries face questions from investors, regulators, and other stakeholders about their companies’ impact on the climate and on local water basins and about the actions being taken to manage both types of impact. Increasing the use of renewable energy represents one potential action that companies might take as part of a balanced, comprehensive approach to improving both water efficiency and carbon efficiency, mitigating related risks, and supporting sustainable, inclusive growth for the communities where they operate. Here are five actions that executives can take to support such an approach:

• Evaluate the company’s energy purchases and the resulting water consumption and carbon emissions in the aggregate as well as at the level of individual sites and for both direct operations as well as purchased electricity. For water, in particular, location-specific assessments matter because levels of water stress differ from place to place.
• Set integrated targets rather than separate ones for lessening water consumption and carbon emissions. In doing so, management might benchmark the company’s activities against those of its peers.
• Think cross-functionally about how water and carbon programs can support each other. This article has focused on how companies can manage electricity sourcing for both water and carbon impact. But many business operations result in both water consumption and carbon emissions. Carbon-management efforts related to other areas, such as manufacturing processes, could be expanded to address water consumption, and vice versa.
• Collaborate with others in and beyond the direct value chain. When it comes to managing water and carbon impact by changing the types and sources of energy they use, companies that do business in a given locale may wish to explore joint sourcing of renewables and collaborative stewardship of water resources. Especially in areas with high levels of water stress, companies might consider coordinating their activities and consulting local stakeholders to devise water-management plans that don’t put undue strain on shared local resources.
• Engage local utilities and regional or municipal authorities to understand their plans for phasing out fossil fuels and for increasing renewable capacity, then seek ways of working together to hasten the transition. If businesses voice interest in or commit to purchasing more renewable energy, they can encourage utilities to make needed capital investments.

Water and carbon priorities don’t need to be at odds. An integrated renewable-energy strategy can address these two sets of priorities at once, enhancing the company’s performance and improving its standing with stakeholders.

Alyssa Bryan is a consultant in McKinsey’s Charlotte office; Thomas Hundertmark is a senior partner in the Houston office, where Kun Lueck is a partner; Wilson Roen is a consultant in the Chicago office; Giulia Siccardo is an associate partner in the San Francisco office; Humayun Tai is a senior partner in the New York office; and Jason Morrison is the president of the Pacific Institute and head of the UN Global Compact’s CEO Water Mandate.

The authors wish to thank Daniel Aminetzah, Anjan Asthana, Taylor Bacon, Gualtiero Jaeger, Joshua Katz, Adam Kendall, Kee Wen Ng, and Dickon Pinner from McKinsey; Peter Schulte from the Pacific Institute; and the member companies of the UN Global Compact’s Water Resilience Coalition for their contributions to this article.

This article was edited by Josh Rosenfield, an executive editor in the New York office.

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• Energy grids and networks
• Solar cells

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it demands advances — at the materials, devices and systems levels — for the efficient harvesting, storage, conversion and management of renewable energy. Energy researchers have begun to incorporate machine learning (ML) techniques to accelerate these advances. In this Perspective, we highlight recent advances in ML-driven energy research, outline current and future challenges, and describe what is required to make the best use of ML techniques. We introduce a set of key performance indicators with which to compare the benefits of different ML-accelerated workflows for energy research. We discuss and evaluate the latest advances in applying ML to the development of energy harvesting (photovoltaics), storage (batteries), conversion (electrocatalysis) and management (smart grids). Finally, we offer an overview of potential research areas in the energy field that stand to benefit further from the application of ML.

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Introduction.

The combustion of fossil fuels, used to fulfill approximately 80% of the world’s energy needs, is the largest single source of rising greenhouse gas emissions and global temperature 1 . The increased use of renewable sources of energy, notably solar and wind power, is an economically viable path towards meeting the climate goals of the Paris Agreement 2 . However, the rate at which renewable energy has grown has been outpaced by ever-growing energy demand, and as a result the fraction of total energy produced by renewable sources has remained constant since 2000 (ref. 3 ). It is thus essential to accelerate the transition towards sustainable sources of energy 4 . Achieving this transition requires energy technologies, infrastructure and policies that enable and promote the harvest, storage, conversion and management of renewable energy.

In sustainable energy research, suitable material candidates (such as photovoltaic materials) must first be chosen from the combinatorial space of possible materials, then synthesized at a high enough yield and quality for use in devices (such as solar panels). The time frame of a representative materials discovery process is 15–20 years 5 , 6 , leaving considerable room for improvement. Furthermore, the devices have to be optimized for robustness and reproducibility to be incorporated into energy systems (such as in solar farms) 7 , where management of energy usage and generation patterns is needed to further guarantee commercial success.

Here we explore the extent to which machine learning (ML) techniques can help to address many of these challenges 8 , 9 , 10 . ML models can be used to predict specific properties of new materials without the need for costly characterization; they can generate new material structures with desired properties; they can understand patterns in renewable energy usage and generation; and they can help to inform energy policy by optimizing energy management at both device and grid levels.

In this Perspective, we introduce Acc(X)eleration Performance Indicators (XPIs), which can be used to measure the effectiveness of platforms developed for accelerated energy materials discovery. Next, we discuss closed-loop ML frameworks and evaluate the latest advances in applying ML to the development of energy harvesting, storage and conversion technologies, as well as the integration of ML into a smart power grid. Finally, we offer an overview of energy research areas that stand to benefit further from ML.

Performance indicators

Because many reports discuss ML-accelerated approaches for materials discovery and energy systems management, we posit that there should be a consistent baseline from which these reports can be compared. For energy systems management, performance indicators at the device, plant and grid levels have been reported 11 , 12 , yet there are no equivalent counterparts for accelerated materials discovery.

The primary goal in materials discovery is to develop efficient materials that are ready for commercialization. The commercialization of a new material requires intensive research efforts that can span up to two decades: the goal of every accelerated approach should be to accomplish commercialization an order-of-magnitude faster. The materials science field can benefit from studying the case of vaccine development. Historically, new vaccines take 10 years from conception to market 13 . However, after the start of the COVID-19 pandemic, several companies were able to develop and begin releasing vaccines in less than a year. This achievement was in part due to an unprecedented global research intensity, but also to a shift in the technology: after a technological breakthrough in 2008, the cost of sequencing DNA began decreasing exponentially 14 , 15 , enabling researchers to screen orders-of-magnitude more vaccines than was previously possible.

ML for energy technologies has much in common with ML for other fields like biomedicine, sharing the same methodology and principles. However, in practice, ML models for different technologies are exposed to additional unique requirements. For example, ML models for medical applications usually have complex structures that take into account regulatory oversight and ensure the safe development, use and monitoring of systems, which usually does not happen in the energy field 16 . Moreover, data availability varies substantially from field to field; biomedical researchers can work with a relatively large amount of data that energy researchers usually lack. This limited data accessibility can constrain the usage of sophisticated ML models (such as deep learning models) in the energy field. However, adaptation has been quick in all energy subfields, with a rapidly increased number of groups recognizing the importance of statistical methods and starting to use them for various problems. We posit that the use of high-throughput experimentation and ML in materials discovery workflows can result in breakthroughs in accelerating development, but the field first needs a set of metrics with which ML models can be evaluated and compared.

Accelerated materials discovery methods should be judged based on the time it takes for a new material to be commercialized. We recognize that this is not a useful metric for new platforms, nor is it one that can be used to decide quickly which platform is best suited for a particular scenario. We therefore propose here XPIs that new materials discovery platforms should report.

Acceleration factor of new materials, XPI-1

This XPI is evaluated by dividing the number of new materials that are synthesized and characterized per unit time with the accelerated platform by the number of materials that are synthesized and characterized with traditional methods. For example, an acceleration factor of ten means that for a given time period, the accelerated platform can evaluate ten times more materials than a traditional platform. For materials with multiple target properties, researchers should report the rate-limiting acceleration factor.

Number of new materials with threshold performance, XPI-2

This XPI tracks the number of new materials discovered with an accelerated platform that have a performance greater than the baseline value. The selection of this baseline value is critical: it should be something that fairly captures the standard to which new materials need to be compared. As an example, an accelerated platform that seeks to discover new perovskite solar cell materials should track the number of devices made with new materials that have a better performance than the best existing solar cell 17 .

Performance of best material over time, XPI-3

This XPI tracks the absolute performance — whether it is Faradaic efficiency, power conversion efficiency or other — of the best material as a function of time. For the accelerated framework, the evolution of the performance should increase faster than the performance obtained by traditional methods 18 .

Repeatability and reproducibility of new materials, XPI-4

This XPI seeks to ensure that the new materials discovered are consistent and repeatable: this is a key consideration to screen out materials that would fail at the commercialization stage. The performance of a new material should not vary by more than x % of its mean value (where x is the standard error): if it does, this material should not be included in either XPI-2 (number of new materials with threshold performance) or XPI-3 (performance of best material over time).

Human cost of the accelerated platform, XPI-5

This XPI reports the total costs of the accelerated platform. This should include the total number of researcher hours needed to design and order the components for the accelerated system, develop the programming and robotic infrastructure, develop and maintain databases used in the system and maintain and run the accelerated platform. This metric would provide researchers with a realistic estimate of the resources required to adapt an accelerated platform for their own research.

Use of the XPIs

Each of these XPIs can be measured for computational, experimental or integrated accelerated systems. Consistently reporting each of these XPIs as new accelerated platforms are developed will allow researchers to evaluate the growth of these platforms and will provide a consistent metric by which different platforms can be compared. As a demonstration, we applied the XPIs to evaluate the acceleration performance of several typical platforms: Edisonian-like trial-test, robotic photocatalysis development 19 and design of a DNA-encoded-library-based kinase inhibitor 20 (Table  1 ). To obtain a comprehensive performance estimate, we define one overall acceleration score S adhering to the following rules. The dependent acceleration factors (XPI-1 and XPI-2), which function in a synergetic way, are added together to reflect their contribution as a whole. The independent acceleration factors (XPI-3, XPI-4 and XPI-5), which may function in a reduplicated way, are multiplied together to value their contributions respectively. As a result, the overall acceleration score can be calculated as S  = (XPI-1 + XPI-2) × XPI-3 × XPI-4 ÷ XPI-5. As the reference, the Edisonian-like approach has a calculated overall XPIs score of around 1, whereas the most advanced method, the DNA-encoded-library-based drug design, exhibits an overall XPIs score of 10 7 . For the sustainability field, the robotic photocatalysis platform has an overall XPIs score of 10 5 .

For energy systems, the most frequently reported XPI is the acceleration factor, in part because it is deterministic, but also because it is easy to calculate at the end of the development of a workflow. In most cases, we expect that authors report the acceleration factor only after completing the development of the platform. Reporting the other suggested XPIs will provide researchers with a better sense of both the time and human resources required to develop the platform until it is ready for publication. Moving forward, we hope that other researchers adopt the XPIs — or other similar metrics — to allow for fair and consistent comparison between the different methods and algorithms that are used to accelerate materials discovery.

Closed-loop ML for materials discovery

The traditional approach to materials discovery is often Edisonian-like, relying on trial and error to develop materials with specific properties. First, a target application is identified, and a starting pool of possible candidates is selected (Fig.  1a ). The materials are then synthesized and incorporated into a device or system to measure their properties. These results are then used to establish empirical structure–property relationships, which guide the next round of synthesis and testing. This slow process goes through as many iterations as required and each cycle can take several years to complete.

a | Traditional Edisonian-like approach, which involves experimental trial and error. b | High-throughput screening approach involving a combination of theory and experiment. c | Machine learning (ML)-driven approach whereby theoretical and experimental results are used to train a ML model for predicting structure–property relationships. d | ML-driven approach for property-directed and automatic exploration of the chemical space using optimization ML (such as genetic algorithms or generative models) that solve the ‘inverse’ design problem.

A computation-driven, high-throughput screening strategy (Fig.  1b ) offers a faster turnaround. To explore the overall vast chemical space (~10 60 possibilities), human intuition and expertise can be used to create a library with a substantial number of materials of interest (~10 4 ). Theoretical calculations are carried out on these candidates and the top performers (~10 2 candidates) are then experimentally verified. With luck, the material with the desired functionality is ‘discovered’. Otherwise, this process is repeated in another region of the chemical space. This approach can still be very time-consuming and computationally expensive and can only sample a small region of the chemical space.

ML can substantially increase the chemical space sampled, without costing extra time and effort. ML is data-driven, screening datasets to detect patterns, which are the physical laws that govern the system. In this case, these laws correspond to materials structure–property relationships. This workflow involves high-throughput virtual screening (Fig.  1c ) and begins by selecting a larger region (~10 6 ) of the chemical space of possibilities using human intuition and expertise. Theoretical calculations are carried out on a representative subset (~10 4 candidates) and the results are used for training a discriminative ML model. The model can then be used to make predictions on the other candidates in the overall selected chemical space 9 . The top ~10 2 candidates are experimentally verified, and the results are used to improve the predictive capabilities of the model in an iterative loop. If the desired material is not ‘discovered’, the process is repeated on another region of the chemical space.

An improvement on the previous approaches is a framework that requires limited human intuition or expertise to direct the chemical space search: the automated virtual screening approach (Fig.  1d ). To begin with, a region of the chemical space is picked at random to initiate the process. Thereafter, this process is similar to the previous approach, except that the computational and experimental data is also used to train a generative learning model. This generative model solves the ‘inverse’ problem: given a required property, the goal is to predict an ideal structure and composition in the chemical space. This enables a directed, automated search of the chemical space, towards the goal of ‘discovering’ the ideal material 8 .

ML for energy

ML has so far been used to accelerate the development of materials and devices for energy harvesting (photovoltaics), storage (batteries) and conversion (electrocatalysis), as well as to optimize power grids. Besides all the examples discussed here, we summarize the essential concepts in ML (Box  1 ), the grand challenges in sustainable materials research (Box  2 ) and the details of key studies (Table  2 ).

Box 1 Essential concepts in ML

With the availability of large datasets 122 , 125 and increased computing power, various machine learning (ML) algorithms have been developed to solve diverse problems in energy. Below, we provide a brief overview of the types of problem that ML can solve in energy technology, and we then summarize the status of ML-driven energy research. More detailed information about the nuts and bolts of ML techniques can be found in previous reviews 173 , 174 , 175 .

Property prediction

Supervised learning models are predictive (or discriminative) models that are given a datapoint x , and seek to predict a property y (for example, the bandgap 27 ) after being trained on a labelled dataset. The property y can be either continuous or discrete. These models have been used to aid or even replace physical simulations or measurements under certain circumstances 176 , 177 .

Generative materials design

Unsupervised learning models are generative models that can generate or output new examples x ′ (such as new molecules 104 ) after being trained on an unlabelled dataset. This generation of new examples can be further enhanced with additional information (physical properties) to condition or bias the generative process, allowing the models to generate examples with improved properties and leading to the property-to-structure approach called inverse design 52 , 178 .

Self-driving laboratories

Self-driving or autonomous laboratories 19 use ML models to plan and perform experiments, including the automation of retrosynthesis analysis (such as in reinforcement-learning-aided synthesis planning 124 , 179 ), prediction of reaction products (such as in convolutional neural networks (CNNs) for reaction prediction 137 , 138 ) and reaction condition optimization (such as in robotic workflows optimized by active learning 19 , 160 , 180 , 181 , 182 , 183 ). Self-driving laboratories, which use active learning for iterating through rounds of synthesis and measurements, are a key component in the closed-loop inverse design 52 .

Aiding characterization

ML models have been used to aid the quantitative or qualitative analysis of experimental observations and measurements, including assisting in the determination of crystal structure from transmission electron microscopy images 184 , identifying coordination environment 81 and structural transition 83 from X-ray absorption spectroscopy and inferring crystal symmetry from electron diffraction 176 .

Accelerating theoretical computations

ML models can enable otherwise intractable simulations by reducing the computational cost (processor core amount and time) for systems with increased length and timescales 69 , 70 and providing potentials and functionals for complex interactions 68 .

Optimizing system management

ML models can aid the management of energy systems at the device or grid power level by predicting lifetimes (such as battery life 43 , 44 ), adapting to new loads (such as in long short-term memory for building load prediction 95 ) and optimizing performance (such as in reinforcement learning for smart grid control 94 ).

Box 2 Grand challenges in energy materials research

Photovoltaics.

Discover non-toxic (Pd- and Cd-free) materials with good optoelectronic properties

Identify and minimize materials defects in light-absorbing materials

Design effective recombination-layer materials for tandem solar cells

Develop materials design strategies for long-term operational stability 125

Develop (hole/electron) transport materials with high carrier mobility 125

Optimize cell structure for maximum light absorption and minimum use of active materials

Tune materials bandgaps for optimal solar-harvesting performance under complex operation conditions 21 , 22

Develop Earth-abundant cathode materials (Co-free) with high reversibility and charge capacity 4

Design electrolytes with wider electrochemical windows and high conductivity 4

Identify electrolyte systems to boost battery performance and lifetime 4

Discover new molecules for redox flow batteries with suitable voltage 4

Understand correlation between defect growth in battery materials and overall degradation process of battery components

Tune operando (dis)charging protocol for minimized capacity loss, (dis)charging rate and optimal battery life under diversified conditions 7 , 53

Design materials with optimal adsorption energy for maximized catalytic activity 60 , 61

Identify and study active sites on catalytic materials 58

Engineer catalytic materials for extended durability 58 , 60 , 61

Identify a fuller set of materials descriptors that relate to catalytic activity 60 , 61

Engineer multiple catalytic functionalities into the same material 60 , 61

Design multiscale electrode structures for optimized catalytic activity

Correlate atomistic contamination and growth of catalyst particles with electrode degradation process

Tune operando (dis)charging protocol for minimized capacity loss and optimal cell life

ML is accelerating the discovery of new optoelectronic materials and devices for photovoltaics, but major challenges are still associated with each step.

Photovoltaics materials discovery

One materials class for which ML has proved particularly effective is perovskites, because these materials have a vast chemical space from which the constituents may be chosen. Early representations of perovskite materials for ML were atomic-feature representations, in which each structure is encoded as a fixed-length vector comprised of an average of certain atomic properties of the atoms in the crystal structure 21 , 22 . A similar technique was used to predict new lead-free perovskite materials with the proper bandgap for solar cells 23 (Fig.  2a ). These representations allowed for high accuracy but did not account for any spatial relation between atoms 24 , 25 . Materials systems can also be represented as images 26 or as graphs 27 , enabling the treatment of systems with diverse number of atoms. The latter representation is particularly compelling, as perovskites, particularly organic–inorganic perovskites, have crystal structures that incorporate a varying number of atoms, and the organic molecules can vary in size.

a | Energy harvesting 23 . b | Energy storage 38 . c | Energy conversion 76 . d | Energy management 93 . ICSD, Inorganic Crystal Structure Database; ML, machine learning.

Although bandgap prediction is an important first step, this parameter alone is not sufficient to indicate a useful optoelectronic material; other parameters, including electronic defect density and stability, are equally important. Defect energies are addressable with computational methods, but the calculation of defects in structures is extremely computationally expensive, which inhibits the generation of a dataset of defect energies from which an ML model can be trained. To expedite the high-throughput calculation of defect energies, a Python toolkit has been developed 28 that will be pivotal in building a database of defect energies in semiconductors. Researchers can then use ML to predict both the formation energy of defects and the energy levels of these defects. This knowledge will ensure that the materials selected from high-throughput screening will not only have the correct bandgap but will also either be defect-tolerant or defect-resistant, finding use in commercial optoelectronic devices.

Even without access to a large dataset of experimental results, ML can accelerate the discovery of optoelectronic materials. Using a self-driving laboratory approach, the number of experiments required to optimize an organic solar cell can be reduced from 500 to just 60 (ref. 29 ). This robotic synthesis method accelerates the learning rate of the ML models and drastically reduces the cost of the chemicals needed to run the optimization.

Solar device structure and fabrication

Photovoltaic devices require optimization of layers other than the active layer to maximize performance. One component is the top transparent conductive layer, which needs to have both high optical transparency and high electronic conductivity 30 , 31 . A genetic algorithm that optimized the topology of a light-trapping structure enabled a broadband absorption efficiency of 48.1%, which represents a more than threefold increase over the Yablonovitch limit, the 4 n 2 factor (where n is the refractive index of the material) theoretical limit for light trapping in photovoltaics 32 .

A universal standard irradiance spectrum is usually used by researchers to determine optimal bandgaps for solar cell operation 33 . However, actual solar irradiance fluctuates based on factors such as the position of the Sun, atmospheric phenomena and the season. ML can reduce yearly spectral sets into a few characteristic spectra 33 , allowing for the calculation of optimal bandgaps for real-world conditions.

To optimize device fabrication, a CNN was used to predict the current–voltage characteristics of as-cut Si wafers based on their photoluminescence images 34 . Additionally, an artificial neural network was used to predict the contact resistance of metallic front contacts for Si solar cells, which is critical for the manufacturing process 35 .

Although successful, these studies appear to be limited to optimizing structures and processes that are already well established. We suggest that, in future work, ML could be used to augment simulations, such as the multiphysics models for solar cells. Design of device architecture could begin from such simulation models, coupled with ML in an iterative process to quickly optimize design and reduce computational time and cost. In addition, optimal conditions for the scaling-up of device area and fabrication processes are likely to be very different from those for laboratory-scale demonstrations. However, determining these optimal conditions could be expensive in terms of materials cost and time, owing to the need to construct much larger devices. In this regard, ML, together with the strategic design of experiments, could greatly accelerate the optimization of process conditions (such as the annealing temperatures and solvent choice).

Electrochemical energy storage

Electrochemical energy storage is an essential component in applications such as electric vehicles, consumer electronics and stationary power stations. State-of-the-art electrochemical energy storage solutions have varying efficacy in different applications: for example, lithium-ion batteries exhibit excellent energy density and are widely used in electronics and electric vehicles, whereas redox flow batteries have drawn substantial attention for use in stationary power storage. ML approaches have been widely employed in the field of batteries, including for the discovery of new materials such as solid-state ion conductors 36 , 37 , 38 (Fig.  2b ) and redox active electrolytes for redox flow batteries 39 . ML has also aided battery management, for example, through state-of-charge determination 40 , state-of-health evaluation 41 , 42 and remaining-life prediction 43 , 44 .

Electrode and electrolyte materials design

Layered oxide materials, such as LiCoO 2 or LiNi x Mn y Co 1- x - y O 2 , have been used extensively as cathode materials for alkali metal-ion (Li/Na/K) batteries. However, developing new Li-ion battery materials with higher operating voltages, enhanced energy densities and longer lifetimes is of paramount interest. So far, universal design principles for new battery materials remain undefined, and hence different approaches have been explored. Data from the Materials Project have been used to model the electrode voltage profile diagrams for different materials in alkali metal-ion batteries (Na and K) 45 , leading to the proposition of 5,000 different electrode materials with appropriate moderate voltages. ML was also employed to screen 12,000 candidates for solid Li-ion batteries, resulting in the discovery of ten new Li-ion conducting materials 46 , 47 .

Flow batteries consist of active materials dissolved in electrolytes that flow into a cell with electrodes that facilitate redox reactions. Organic flow batteries are of particular interest. In flow batteries, the solubility of the active material in the electrolyte and the charge/discharge stability dictate performance. ML methods have explored the chemical space to find suitable electrolytes for organic redox flow batteries 48 , 49 . Furthermore, a multi-kernel-ridge regression method accelerated the discovery of active organic molecules using multiple feature training 48 . This method also helped in predicting the solubility dependence of anthraquinone molecules with different numbers and combinations of sulfonic and hydroxyl groups on pH. Future opportunities lie in the exploration of large combinatorial spaces for the inverse design of high-entropy electrodes 50 and high-voltage electrolytes 51 . To this end, deep generative models can assist the discovery of new materials based on the simplified molecular input line entry system (SMILES) representation of molecules 52 .

Battery device and stack management

A combination of mechanistic and semi-empirical models is currently used to estimate capacity and power loss in lithium-ion batteries. However, the models are applicable only to specific failure mechanisms or situations and cannot predict the lifetimes of batteries at the early stages of usage. By contrast, mechanism-agnostic models based on ML can accurately predict battery cycle life, even at an early stage of a battery’s life 43 . A combined early-prediction and Bayesian optimization model has been used to rapidly identify the optimal charging protocol with the longest cycle life 44 . ML can be used to accelerate the optimization of lithium-ion batteries for longer lifetimes 53 , but it remains to be seen whether these models can be generalized to different battery chemistries 54 .

ML methods can also predict important properties of battery storage facilities. A neural network was used to predict the charge/discharge profiles in two types of stationary battery systems, lithium iron phosphate and vanadium redox flow batteries 55 . Battery power management techniques must also consider the uncertainty and variability that arise from both the environment and the application. An iterative Q -learning ( reinforcement learning ) method was also designed for battery management and control in smart residential environments 56 . Given the residential load and the real-time electricity rate, the method is effective at optimizing battery charging/discharging/idle cycles. Discriminative neural network-based models can also optimize battery usage in electric vehicles 57 .

Although ML is able to predict the lifetime of batteries, the underlying degradation mechanisms are difficult to identify and correlate to the state of health and lifetime. To this end, incorporation of domain knowledge into a hybrid physics-based ML model can provide insight and reduce overfitting 53 . However, incorporating the physics of battery degradation processes into a hybrid model remains challenging; representation of electrode materials that encode both compositional and structural information is far from trivial. Validation of these models also requires the development of operando characterization techniques, such as liquid-phase transmission electron microscopy and ambient-pressure X-ray absorption spectroscopy (XAS), that reflect true operating conditions as closely as possible 54 . Ideally, these characterization techniques should be carried out in a high-throughput manner, using automated sample changers, for example, in order to generate large datasets for ML.

Electrocatalysts

Electrocatalysis enables the conversion of simple feedstocks (such as water, carbon dioxide and nitrogen) into valuable chemicals and/or fuels (such as hydrogen, hydrocarbons and ammonia), using renewable energy as an input 58 . The reverse reactions are also possible in a fuel cell, and hydrogen can be consumed to produce electricity 59 . Active and selective electrocatalysts must be developed to improve the efficiency of these reactions 60 , 61 . ML has been used to accelerate electrocatalyst development and device optimization.

Electrocatalyst materials discovery

The most common descriptor of catalytic activity is the adsorption energy of intermediates on a catalyst 61 , 62 . Although these adsorption energies can be calculated using density functional theory (DFT), catalysts possess multiple surface binding sites, each with different adsorption energies 63 . The number of possible sites increases dramatically if alloys are considered, and thus becomes intractable with conventional means 64 .

DFT calculations are critical for the search of electrocatalytic materials 65 and efforts have been made to accelerate the calculations and to reduce their computational cost by using surrogate ML models 66 , 67 , 68 , 69 . Complex reaction mechanisms involving hundreds of possible species and intermediates can also be simplified using ML, with a surrogate model predicting the most important reaction steps and deducing the most likely reaction pathways 70 . ML can also be used to screen for active sites across a random, disordered nanoparticle surface 71 , 72 . DFT calculations are performed on only a few representative sites, which are then used to train a neural network to predict the adsorption energies of all active sites.

Catalyst development can benefit from high-throughput systems for catalyst synthesis and performance evaluation 73 , 74 . An automatic ML-driven framework was developed to screen a large intermetallic chemical space for CO 2 reduction and H 2 evolution 75 . The model predicted the adsorption energy of new intermetallic systems and DFT was automatically performed on the most promising candidates to verify the predictions. This process went on iteratively in a closed feedback loop. 131 intermetallic surfaces across 54 alloys were ultimately identified as promising candidates for CO 2 reduction. Experimental validation 76 with Cu–Al catalysts yielded an unprecedented Faradaic efficiency of 80% towards ethylene at a high current density of 400 mA cm – 2 (Fig.  2c ).

Because of the large number of properties that electrocatalysts may possess (such as shape, size and composition), it is difficult to do data mining on the literature 77 . Electrocatalyst structures are complex and difficult to characterize completely; as a result, many properties may not be fully characterized by research groups in their publications. To avoid situations in which potentially promising compositions perform poorly as a result of non-ideal synthesis or testing conditions, other factors (such as current density, particle size and pH value) that affect the electrocatalyst performance must be kept consistent. New approaches such as carbothermal shock synthesis 78 , 79 may be a promising avenue, owing to its propensity to generate uniformly sized and shaped alloy nanoparticles, regardless of composition.

XAS is a powerful technique, especially for in situ measurements, and has been widely employed to gain crucial insight into the nature of active sites and changes in the electrocatalyst over time 80 . Because the data analysis relies heavily on human experience and expertise, there has been interest in developing ML tools for interpreting XAS data 81 . Improved random forest models can predict the Bader charge (a good approximation of the total electronic charge of an atom) and nearest-neighbour distances, crucial factors that influence the catalytic properties of the material 82 . The extended X-ray absorption fine structure (EXAFS) region of XAS spectra is known to contain information on bonding environments and coordination numbers. Neural networks can be used to automatically interpret EXAFS data 83 , permitting the identification of the structure of bimetallic nanoparticles using experimental XAS data, for example 84 . Raman and infrared spectroscopy are also important tools for the mechanistic understanding of electrocatalysis. Together with explainable artificial intelligence (AI), which can relate the results to underlying physics, these analyses could be used to discover descriptors hidden in spectra that could lead to new breakthroughs in electrocatalyst discovery and optimization.

Fuel cell and electrolyser device management

A fuel cell is an electrochemical device that can be used to convert the chemical energy of a fuel (such as hydrogen) into electrical energy. An electrolyser transforms electrical energy into chemical energy (such as in water splitting to generate hydrogen). ML has been used to optimize and manage their performance, predict degradation and device lifetime as well as detect and diagnose faults. Using a hybrid method consisting of an extreme learning machine, genetic algorithms and wavelet analysis, the degradation in proton-exchange membrane fuel cells has been predicted 85 , 86 . Electrochemical impedance measurements used as input for an artificial neural network have enabled fault detection and isolation in a high-temperature stack of proton-exchange membrane fuel cells 87 , 88 .

ML approaches can also be employed to diagnose faults, such as fuel and air leakage issues, in solid oxide fuel cell stacks. Artificial neural networks can predict the performance of solid oxide fuel cells under different operating conditions 89 . In addition, ML has been applied to optimize the performance of solid oxide electrolysers, for CO 2 /H 2 O reduction 90 , and chloralkali electrolysers 91 .

In the future, the use of ML for fuel cells could be combined with multiscale modelling to improve their design, for example to minimize Ohmic losses and optimize catalyst loading. For practical applications, fuel cells may be subject to fluctuations in energy output requirements (for example, when used in vehicles). ML models could be used to determine the effects of such fluctuations on the long-term durability and performance of fuel cells, similar to what has been done for predicting the state of health and lifetime for batteries. Furthermore, it remains to be seen whether the ML techniques for fuel cells can be easily generalized to electrolysers and vice versa, using transfer learning for example, given that they are essentially reactions in reverse.

Smart power grids

A power grid is responsible for delivering electrical energy from producers (such as power plants and solar farms) to consumers (such as homes and offices). However, energy fluctuations from intermittent renewable energy generators can render the grid vulnerable 92 . ML algorithms can be used to optimize the automatic generation control of power grids, which controls the power output of multiple generators in an energy system. For example, when a relaxed deep learning model was used as a unified timescale controller for the automatic generation control unit, the total operational cost was reduced by up to 80% compared with traditional heuristic control strategies 93 (Fig.  2d ). A smart generation control strategy based on multi-agent reinforcement learning was found to improve the control performance by around 10% compared with other ML algorithms 94 .

Accurate demand and load prediction can support decision-making operations in energy systems for proper load scheduling and power allocation. Multiple ML methods have been proposed to precisely predict the demand load: for example, long short-term memory was used to successfully and accurately predict hourly building load 95 . Short-term load forecasting of diverse customers (such as retail businesses) using a deep neural network and cross-building energy demand forecasting using a deep belief network have also been demonstrated effectively 96 , 97 .

Demand-side management consists of a set of mechanisms that shape consumer electricity consumption by dynamically adjusting the price of electricity. These include reducing (peak shaving), increasing (load growth) and rescheduling (load shifting) the energy demand, which allows for flexible balancing of renewable electricity generation and load 98 . A reinforcement-learning-based algorithm resulted in substantial cost reduction for both the service provider and customer 99 . A decentralized learning-based residential demand scheduling technique successfully shifted up to 35% of the energy demand to periods of high wind availability, substantially saving power costs compared with the unscheduled energy demand scenario 100 . Load forecasting using a multi-agent approach integrates load prediction with reinforcement learning algorithms to shift energy usage (for example, to different electrical devices in a household) for its optimization 101 . This approach reduced peak usage by more than 30% and increased off-peak usage by 50%, reducing the cost and energy losses associated with energy storage.

Opportunities for ML in renewable energy

ML provides the opportunity to enable substantial further advances in different areas of the energy materials field, which share similar materials-related challenges (Fig.  3 ). There are also grand challenges for ML application in smart grid and policy optimization.

a | Energy materials present additional modelling challenges. Machine learning (ML) could help in the representation of structurally complex structures, which can include disordering, dislocations and amorphous phases. b | Flexible models that scale efficiently with varied dataset sizes are in demand, and ML could help to develop robust predictive models. The yellow dots stand for the addition of unreliable datasets that could harm the prediction accuracy of the ML model. c | Synthesis route prediction remains to be solved for the design of a novel material. In the ternary phase diagram, the dots stand for the stable compounds in that corresponding phase space and the red dot for the targeted compound. Two possible synthesis pathways are compared for a single compound. The score obtained would reflect the complexity, cost and so on of one synthesis pathway. d | ML-aided phase degradation prediction could boost the development of materials with enhanced cyclability. The shaded region represents the rocksalt phase, which grows inside the layered phase. The arrow marks the growth direction. e | The use of ML models could help in optimizing energy generation and energy consumption. Automating the decision-making processes associated with dynamic power supplies using ML will make the power distribution more efficient. f | Energy policy is the manner in which an entity (for example, a government) addresses its energy issues, including conversion, distribution and utilization, where ML could be used to optimize the corresponding economy.

Materials with novel geometries

A ML representation is effective when it captures the inherent properties of the system (such as its physical symmetries) and can be utilized in downstream ancillary tasks, such as transfer learning to new predictive tasks, building new knowledge using visualization or attribution and generating similar data distributions with generative models 102 .

For materials, the inputs are molecules or crystal structures whose physical properties are modelled by the Schrödinger equation. Designing a general representation of materials that reflects these properties is an ongoing research problem. For molecular systems, several representations have been used successfully, including fingerprints 103 , SMILES 104 , self-referencing embedded strings (SELFIES) 105 and graphs 106 , 107 , 108 . Representing crystalline materials has the added complexity of needing to incorporate periodicity in the representation. Methods like the smooth overlap of atomic positions 109 , Voronoi tessellation 110 , 111 , diffraction images 112 , multi-perspective fingerprints 113 and graph-based algorithms 27 , 114 have been suggested, but typically lack the capability for structure reconstruction.

Complex structural systems found in energy materials present additional modelling challenges (Fig.  3a ): a large number of atoms (such as in reticular frameworks or polymers), specific symmetries (such as in molecules with a particular space group and for reticular frameworks belonging to a certain topology), atomic disordering, partial occupancy, or amorphous phases (leading to an enormous combinatorial space), defects and dislocations (such as interfaces and grain boundaries) and low-dimensionality materials (as in nanoparticles). Reduction approximations alleviate the first issue (using, for example, RFcode for reticular framework representation) 8 , but the remaining several problems warrant intensive future research efforts.

Self- supervised learning , which seeks to lever large amounts of synthetic labels and tasks to continue learning without experimental labels 115 , multi-task learning 116 , in which multiple material properties can be modelled jointly to exploit correlation structure between properties, and meta-learning 117 , which looks at strategies that allow models to perform better in new datasets or in out-of-distribution data, all offer avenues to build better representations. On the modelling front, new advances in attention mechanisms 118 , 119 , graph neural networks 120 and equivariant neural networks 121 expand our range of tools with which to model interactions and expected symmetries.

Robust predictive models

Predictive models are the first step when building a pipeline that seeks materials with desired properties. A key component for building these models is training data; more data will often translate into better-performing models, which in turn will translate into better accuracy in the prediction of new materials. Deep learning models tend to scale more favourably with dataset size than traditional ML approaches (such as random forests). Dataset quality is also essential. However, experiments are usually conducted under diverse conditions with large variation in untracked variables (Fig.  3b ). Additionally, public datasets are more likely to suffer from publication bias, because negative results are less likely to be published even though they are just as important as positive results when training statistical models 122 .

Addressing these issues require transparency and standardization of the experimental data reported in the literature. Text and natural language processing strategies could then be employed to extract data from the literature 77 . Data should be reported with the belief that it will eventually be consolidated in a database, such as the MatD3 database 123 . Autonomous laboratory techniques will help to address this issue 19 , 124 . Structured property databases such as the Materials Project 122 and the Harvard Clean Energy Project 125 can also provide a large amount of data. Additionally, different energy fields — energy storage, harvesting and conversion — should converge upon a standard and uniform way to report data. This standard should be continuously updated; as researchers continue to learn about the systems they are studying, conditions that were previously thought to be unimportant will become relevant.

New modelling approaches that work in low-data regimes, such as data-efficient models, dataset-building strategies (active sampling) 126 and data-augmentation techniques, are also important 127 . Uncertainty quantification , data efficiency, interpretability and regularization are important considerations that improve the robustness of ML models. These considerations relate to the notion of generalizability: predictions should generalize to a new class of materials that is out of the distribution of the original dataset. Researchers can attempt to model how far away new data points are from the training set 128 or the variability in predicted labels with uncertainty quantification 129 . Neural networks are a flexible model class, and often models can be underspecified 130 . Incorporating regularization, inductive biases or priors can boost the credibility of a model. Another way to create trustable models could be to enhance the interpretability of ML algorithms by deriving feature relevance and scoring their importance 131 . This strategy could help to identify potential chemically meaningful features and form a starting point for understanding latent factors that dominate material properties. These techniques can also identify the presence of model bias and overfitting, as well as improving generalization and performance 132 , 133 , 134 .

Stable and synthesizable new materials

The formation energy of a compound is used to estimate its stability and synthesizability 135 , 136 . Although negative values usually correspond to stable or synthesizable compounds, slightly positive formation energies below a limit lead to metastable phases with unclear synthesizability 137 , 138 . This is more apparent when investigating unexplored chemical spaces with undetermined equilibrium ground states; yet often the metastable phases exhibit superior properties, as seen in photovoltaics 136 , 139 and ion conductors 140 , for example. It is thus of interest to develop a method to evaluate the synthesizability of metastable phases (Fig.  3c ). Instead of estimating the probability that a particular phase can be synthesized, one can instead evaluate its synthetic complexity using ML. In organic chemistry, synthesis complexity is evaluated according to the accessibility of the phases’ synthesis route 141 or precedent reaction knowledge 142 . Similar methodologies can be applied to the inorganic field with the ongoing design of automated synthesis-planning algorithms for inorganic materials 143 , 144 .

Synthesis and evaluation of a new material alone does not ensure that material will make it to market; material stability is a crucial property that takes a long time to evaluate. Degradation is a generally complex process that occurs through the loss of active matter or growth of inactive phases (such as the rocksalt phases formed in layered Li-ion battery electrodes 145 (Fig.  3d ) or the Pt particle agglomeration in fuel cells 146 ) and/or propagation of defects (such as cracks in cycled battery electrode 147 ). Microscopies such as electron microscopy 148 and simulations such as continuum mechanics modelling 149 are often used to investigate growth and propagation dynamics (that is, phase boundary and defect surface movements versus time). However, these techniques are usually expensive and do not allow rapid degradation prediction. Deep learning techniques such as convolutional neural networks and recurrent neural networks may be able to predict the phase boundary and/or defect pattern evolution under certain conditions after proper training 150 . Similar models can then be built to understand multiple degradation phenomena and aid the design of materials with improved cycle life.

Optimized smart power grids

A promising prospect of ML in smart grids is automating the decision-making processes that are associated with dynamic power supplies to distribute power most efficiently (Fig.  3e ). Practical deployment of ML technologies into physical systems remains difficult because of data scarcity and the risk-averse mindset of policymakers. The collection of and access to large amounts of diverse data is challenging owing to high cost, long delays and concerns over compliance and security 151 . For instance, to capture the variation of renewable resources owing to peak or off-peak and seasonal attributes, long-term data collections are implemented for periods of 24 hours to several years 152 . Furthermore, although ML algorithms are ideally supposed to account for all uncertainties and unpredictable situations in energy systems, the risk-adverse mindset in the energy management industry means that implementation still relies on human decision-making 153 .

An ML-based framework that involves a digital twin of the physical system can address these problems 154 , 155 . The digital twin represents the digitalized cyber models of the physical system and can be constructed from physical laws and/or ML models trained using data sampled from the physical system. This approach aims to accurately simulate the dynamics of the physical system, enabling relatively fast generation of large amounts of high-quality synthetic data at low cost. Notably, because ML model training and validation is performed on the digital twin, there is no risk to the actual physical system. Based on the prediction results, suitable actions can be suggested and then implemented in the physical system to ensure stability and/or improve system operation.

Policy optimization

Finally, research is generally focused on one narrow aspect of a larger problem; we argue that energy research needs a more integrated approach 156 (Fig.  3f ). Energy policy is the manner in which an entity, such as the government, addresses its energy issues, including conversion, distribution and utilization. ML has been used in the fields of energy economics finance for performance diagnostics (such as for oil wells), energy generation (such as wind power) and consumption (such as power load) forecasts and system lifespan (such as battery cell life) and failure (such as grid outage) prediction 157 . They have also been used for energy policy analysis and evaluation (for example, for estimating energy savings). A natural extension of ML models is to use them for policy optimization 158 , 159 , a concept that has not yet seen widespread use. We posit that the best energy policies — including the deployment of the newly discovered materials — can be improved and augmented with ML and should be discussed in research reporting accelerated energy technology platforms.

Conclusions

To summarize, ML has the potential to enable breakthroughs in the development and deployment of sustainable energy techniques. There have been remarkable achievements in many areas of energy technology, from materials design and device management to system deployment. ML is particularly well suited to discovering new materials, and researchers in the field are expecting ML to bring up new materials that may revolutionize the energy industry. The field is still nascent, but there is conclusive evidence that ML is at least able to expose the same trends that human researchers have noticed over decades of research. The ML field itself is still seeing rapid development, with new methodologies being reported daily. It will take time to develop and adopt these methodologies to solve specific problems in materials science. We believe that for ML to truly accelerate the deployment of sustainable energy, it should be deployed as a tool, similar to a synthesis procedure, characterization equipment or control apparatus. Researchers using ML to accelerate energy technology discovery should judge the success of the method primarily on the advances it enables. To this end, we have proposed the XPIs and some areas in which we hope to see ML deployed.

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Acknowledgements

Z.Y. and A.A.-G. were supported as part of the Nanoporous Materials Genome Center by the US Department of Energy, Office of Science, Office of Basic Energy Sciences under award number DE-FG02-17ER16362 and the US Department of Energy, Office of Science — Chicago under award number DE-SC0019300. A.J. was financially supported by Huawei Technologies Canada and the Natural Sciences and Engineering Research Council (NSERC). L.M.M.-M. thanks the support of the Defense Advanced Research Projects Agency under the Accelerated Molecular Discovery Program under cooperative agreement number HR00111920027 dated 1 August 2019. Y.W. acknowledges funding support from the Singapore National Research Foundation under its Green Buildings Innovation Cluster (GBIC award number NRF2015ENC-GBICRD001-012) administered by the Building and Construction Authority, its Green Data Centre Research (GDCR award number NRF2015ENC-GDCR01001-003) administered by the Info-communications Media Development Authority, and its Energy Programme (EP award number NRF2017EWT-EP003-023) administered by the Energy Market Authority of Singapore. A.A.-G. is a Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow. E.H.S. acknowledges funding by the Ontario Ministry of Colleges and Universities (grant ORF-RE08-034), the Natural Sciences and Engineering Research Council (NSERC) of Canada (grant RGPIN-2017-06477), the Canadian Institute for Advanced Research (CIFAR) (grant FS20-154 APPT.2378) and the University of Toronto Connaught Fund (grant GC 2012-13). Z.W.S. acknowledges funding by the Singapore National Research Foundation (NRF-NRFF2017-04).

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These authors contributed equally: Zhenpeng Yao, Yanwei Lum, Andrew Johnston.

Authors and Affiliations

Shanghai Key Laboratory of Hydrogen Science & Center of Hydrogen Science, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Zhenpeng Yao

Chemical Physics Theory Group, Department of Chemistry and Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

Zhenpeng Yao, Luis Martin Mejia-Mendoza & Alán Aspuru-Guzik

Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China

State Key Laboratory of Metal Matrix Composites, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

Institute of Materials Research and Engineering, Agency for Science, Technology and Research (A*STAR), Innovis, Singapore, Singapore

Yanwei Lum & Zhi Wei Seh

Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada

Yanwei Lum, Andrew Johnston & Edward H. Sargent

School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

Xin Zhou & Yonggang Wen

Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada

• Alán Aspuru-Guzik

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Z.Y., Y.L. and A.J. contributed equally to this work. All authors contributed to the writing and editing of the manuscript.

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Machine learning techniques that can query a user interactively to modify its current strategy (that is, label an input).

(AI). Theory and development of computer systems that exhibit intelligence.

A system for adjusting the power output of multiple generators at different power plants, in response to changes in the load.

A technology development pipeline that incorporates automation to go from idea to realization of technology. ‘Closed’ refers to the concept that the system improves with experience and iterations.

Process of increasing the amount of data through adding slightly modified copies or newly created synthetic data from existing data.

A generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables, with connections between the layers but not between units within each layer.

(DL). Machine learning subfield that is based on neural networks with representation learning.

The ability to adapt to new, unseen data, drawn from the same distribution as the one used to create the model.

Machine learning techniques that learn to model the data distribution of a dataset and sample new data points.

Degree to which a human can understand a model’s decision. Interpretability can be used to build trust and credibility.

A design method where new materials and compounds are ‘reverse-engineered’ simply by inputting a set of desired properties and characteristics and then using an optimization algorithm to generate a predicted solution.

A special kind of recurrent neural networks that are capable of selectively remembering patterns for a long duration of time.

(ML). Field within artificial intelligence that deals with learning algorithms, which improve automatically through experience (data).

A computerized system composed of multiple interacting intelligent agents.

The combination of ridge regression (a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated) with multiple kernel techniques.

Models that involve the analysis of multiple, simultaneous physical phenomena, which can include heat transfer, fluid flow, deformation, electromagnetics, acoustics and mass transport.

The field of solving problems that have important features at multiple scales of time and/or space.

A neural network is composed of parameterized and optimizable transformations.

A class of artificial neural networks where connections between nodes form a directed or undirected graph along a temporal sequence.

Process of incorporating additional information into the model to constrain its solution space.

Machine learning techniques that make a sequence of decisions to maximize a reward.

Features used in a representation learning model, which transforms inputs into new features for a task.

Technique for solving problems in the planning of chemical synthesis.

A robotic equipment automated chemical synthesis plan.

Design process composed of several stages where materials are iteratively filtered and ranked to arrive to a few top candidates.

Machine learning techniques that involve the usage of labelled data.

Machine learning techniques that adapt a learned representation or strategy from one dataset to another.

Process of evaluating the statistical confidence of model.

Machine learning techniques that learn patterns from unlabelled data.

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Yao, Z., Lum, Y., Johnston, A. et al. Machine learning for a sustainable energy future. Nat Rev Mater 8 , 202–215 (2023). https://doi.org/10.1038/s41578-022-00490-5

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Issue Date : March 2023

DOI : https://doi.org/10.1038/s41578-022-00490-5

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METHODS article

This article is part of the research topic.

Grid Optimization Under Flexible, Uncertain and Dynamic Power Grids

Bidding Strategies for Multi-Microgrid Markets Taking into Account Risk Indicators Provisionally Accepted

• 1 Changchun Institute of Technology, China
• 2 National Local Joint Engineering Research Center for Smart Distribution Grid Measurement and Control with Safety Operation Technology, China
• 3 Jilin Electric Power Research Institute Co., Ltd, China

The final, formatted version of the article will be published soon.

A large proportion of new energy generation is integrated into the power grid, making it difficult for the power grid system to maintain reliable, stable, and efficient operation. In order to address these challenges, this paper proposes a multiple microgrid hierarchical optimization structure based on energy routers as the core equipment for energy regulation within microgrids. Considering the uncertainty of renewable energy generation within microgrids, a two-layer energy management bidding strategy based on risk indicators is further proposed. In the process of trading, with the goal of maximizing comprehensive economy, the energy trading model of the distribution network center and energy router is divided into two sub objectives for solving. In the first stage, based on the interests and energy supply and demand relationships of each microgrid, a risk assessment model considering wind and solar uncertainty is established to determine the risk preferences and expected returns of each microgrid. In the second stage, the original problem is decomposed into two subproblems: the minimum cost sub-problem and the maximum transaction volume sub-problem. And an asymmetric bargaining mechanism is adopted to determine the production and sales payment of the microgrid containing energy routers based on the contribution values of energy routers in each microgrid. Finally, the rationality and effectiveness of energy routers as an intelligent decision-maker in energy optimization are verified in a 3-node system.

Keywords: electricity market, Multi-microgrids, conditional value at risk, Scenery absorption, Energy routers

Received: 11 Dec 2023; Accepted: 25 Mar 2024.

Copyright: © 2024 Xiu, Chenglong, Xiangyv, Yv and Huanhuan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Qi Chenglong, National Local Joint Engineering Research Center for Smart Distribution Grid Measurement and Control with Safety Operation Technology, Changchun, 130012, Hebei Province, China

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