wind energy essay introduction

ENCYCLOPEDIC ENTRY

Wind energy.

Scientists and engineers are using energy from the wind to generate electricity. Wind energy, or wind power, is created using a wind turbine.

Earth Science, Climatology

As renewable energy technology continues to advance and grow in popularity, wind farms like this one have become an increasingly common sight along hills, fields, or even offshore in the ocean.

Photograph by inga spence / Alamy Stock Photo

Anything that moves has kinetic energy , and scientists and engineers are using the wind’s kinetic energy to generate electricity. Wind energy , or wind power , is created using a wind turbine , a device that channels the power of the wind to generate electricity.

The wind blows the blades of the turbine , which are attached to a rotor. The rotor then spins a generator to create electricity. There are two types of wind turbines : the horizontal - axis wind turbines (HAWTs) and vertical - axis wind turbines (VAWTs). HAWTs are the most common type of wind turbine . They usually have two or three long, thin blades that look like an airplane propeller. The blades are positioned so that they face directly into the wind. VAWTs have shorter, wider curved blades that resemble the beaters used in an electric mixer.

Small, individual wind turbines can produce 100 kilowatts of power, enough to power a home. Small wind turbines are also used for places like water pumping stations. Slightly larger wind turbines sit on towers that are as tall as 80 meters (260 feet) and have rotor blades that extend approximately 40 meters (130 feet) long. These turbines can generate 1.8 megawatts of power. Even larger wind turbines can be found perched on towers that stand 240 meters (787 feet) tall have rotor blades more than 162 meters (531 feet) long. These large turbines can generate anywhere from 4.8 to 9.5 megawatts of power.

Once the electricity is generated, it can be used, connected to the electrical grid, or stored for future use. The United States Department of Energy is working with the National Laboratories to develop and improve technologies, such as batteries and pumped-storage hydropower so that they can be used to store excess wind energy. Companies like General Electric install batteries along with their wind turbines so that as the electricity is generated from wind energy, it can be stored right away.

According to the U.S. Geological Survey, there are 57,000 wind turbines in the United States, both on land and offshore. Wind turbines can be standalone structures, or they can be clustered together in what is known as a wind farm . While one turbine can generate enough electricity to support the energy needs of a single home, a wind farm can generate far more electricity, enough to power thousands of homes. Wind farms are usually located on top of a mountain or in an otherwise windy place in order to take advantage of natural winds.

The largest offshore wind farm in the world is called the Walney Extension. This wind farm is located in the Irish Sea approximately 19 kilometers (11 miles) west of the northwest coast of England. The Walney Extension covers a massive area of 149 square kilometers (56 square miles), which makes the wind farm bigger than the city of San Francisco, California, or the island of Manhattan in New York. The grid of 87 wind turbines stands 195 meters (640 feet) tall, making these offshore wind turbines some of the largest wind turbines in the world. The Walney Extension has the potential to generate 659 megawatts of power, which is enough to supply 600,000 homes in the United Kingdom with electricity.

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Related Resources

Essay on Wind Energy

Students are often asked to write an essay on Wind Energy in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Wind Energy

Introduction to wind energy.

Wind energy is a form of renewable energy produced by wind turbines. These are large structures that capture the wind’s power and convert it into electricity.

How Wind Energy Works

Wind turbines use blades to collect the wind’s kinetic energy. The wind turns the blades, which spin a shaft connected to a generator, creating electricity.

Advantages of Wind Energy

Wind energy is sustainable and doesn’t release harmful emissions. It’s a great way to reduce our reliance on fossil fuels, helping to combat climate change.

In conclusion, wind energy is a valuable, renewable source of power with many benefits for our planet.

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250 Words Essay on Wind Energy

Wind energy, a renewable source of power, has been harnessed by humans for centuries. Today, it plays a pivotal role in the global energy landscape, offering a sustainable alternative to fossil fuels.

The Science Behind Wind Energy

Wind energy is derived from the natural movement of air across the Earth’s surface. When heated by the sun, air rises and cooler air rushes in to replace it, creating wind. Wind turbines capture this kinetic energy and convert it into electricity. The larger the turbine and the faster the wind speed, the more electricity is produced.

Environmental Impact and Sustainability

Wind energy is a clean, renewable source of power that produces no greenhouse gas emissions during operation. Moreover, wind turbines take up less space than the average power station, making them less detrimental to the environment. The sustainability of wind energy makes it a key player in the fight against climate change.

Economic Implications

The initial investment for wind energy infrastructure can be high. However, the long-term benefits include low operational costs and a stable power source not subject to fuel market fluctuations. As technology advances, the cost of wind energy continues to decrease, making it an increasingly viable economic choice.

Conclusion: The Future of Wind Energy

Wind energy is poised to play a significant role in the future of global energy production. As we strive for a more sustainable future, harnessing the power of the wind is a practical and necessary step. With advancements in technology and increased investment, the potential of wind energy is limitless.

500 Words Essay on Wind Energy

Wind energy, a form of renewable energy, harnesses the power of the wind to generate electricity. It is an increasingly significant part of the global renewable energy landscape and plays a fundamental role in reducing greenhouse gas emissions.

The science behind wind energy is simple yet powerful. Wind turbines capture the wind’s kinetic energy and convert it into electrical power. The blades of a wind turbine rotate when hit by the wind, which then drives an electric generator to produce electricity. The stronger the wind, the more electricity is generated.

Wind energy offers a multitude of benefits. Firstly, it is a renewable resource, meaning it is inexhaustible and can be replenished naturally. This contrasts with fossil fuels, which are finite and harmful to the environment.

Secondly, wind energy is clean and does not emit any greenhouse gases during operation, contributing to the fight against climate change. It also requires no water for operation, thus conserving water resources.

Lastly, wind energy can be a significant job creator. The design, manufacturing, installation, and maintenance of wind turbines require a diverse range of skills, thus creating employment opportunities.

Challenges and Solutions

Despite its advantages, wind energy also faces challenges. Wind is an intermittent source of energy, and wind turbines produce electricity only when the wind blows. This intermittency can be mitigated by pairing wind farms with energy storage systems or other forms of renewable energy like solar power.

Another challenge is the environmental impact of wind turbines, including noise pollution and the potential harm to wildlife, particularly birds. However, advances in technology are mitigating these issues. For example, newer turbines are quieter and designed to minimize harm to birds.

The Future of Wind Energy

The future of wind energy is promising. With advancements in technology and increasing investment, wind energy’s efficiency and affordability continue to improve. Offshore wind farms, which can harness stronger and more consistent winds, are expected to play a significant role in the future energy mix.

Furthermore, the integration of wind energy with other renewable energy sources and storage technologies will enhance grid reliability and resilience. This will allow for a higher penetration of wind energy into the energy system, contributing to a sustainable and carbon-neutral future.

In conclusion, wind energy is a crucial component of the global renewable energy portfolio, offering a clean, renewable, and increasingly cost-effective solution to our energy needs. While there are challenges to overcome, the future of wind energy is bright, promising a sustainable and carbon-neutral energy future.

That’s it! I hope the essay helped you.

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Wind Energy - Introduction

► Back to Wind Portal

• 2 The Technology
• 3.1 Wind Electric
• 3.2 Wind Pumps
• 4 Wind Energy - Overview
• 5.1 Potentials
• 5.2 Challenges
• 6 Wind Capacities
• 7 Further Information
• 8 References

In recent years, wind energy has become one of the most economical renewable energy technology. Today, electricity generating wind turbines employ proven and tested technology, and provide a secure and sustainable energy supply. At good, windy sites, wind energy can already successfully compete with conventional energy production [1] . Many countries have considerable wind resources, which are still untapped.

A technology which offers remarkable advantages is not used to its full potential:

• Wind power plants can make a significant contribution to the regional electricity supply and to power supply diversification.
• A very short lead time for planning and construction is required as compared to conventional power projects.
• Wind energy projects are flexible with regard to an increasing energy demand - single turbines can easily be added to an existing park.
• Finally, wind energy projects can make use of local resources in terms of labour, capital and materials.

The technological development of recent years, bringing more efficient and more reliable wind turbines, is making wind power more cost-effective. In general, the specific energy costs per annual kWh decrease with the size of the turbine notwithstanding existing supply difficulties.

Many African countries expect to see electricity demand expand rapidly in coming decades. At the same time, finite natural resources are becoming depleted, and the environmental impact of energy use and energy conversion have been generally accepted as a threat to our natural habitat. Indeed these have become major issues for international policy. [2]

Many developing countries and emerging economies have substantial unexploited wind energy potential. In many locations, generating electricity from wind energy offers a cost-effective alternative to thermal power stations. It has a lower impact on the environment and climate, reduces dependence on fossil fuel imports and increases security of energy supply [3] .

For many years now, developing countries and emerging economies have been faced with the challenge of meeting additional energy needs for their social and economic development with obsolete energy supply structures. Overcoming supply bottlenecks through the use of fossil fuels in the form of coal, oil and gas increases dependency on volatile markets and eats into valuable foreign currency reserves. At the same time there is growing pressure on emerging newly industrialised countries in particular to make a contribution to combating climate change and limit their pollutant emissions.

In the scenario of alternatives, more and more developing countries and emerging economies are placing their faith in greater use of renewable energy and are formulating specific expansion targets for a ‘green energy mix’. Wind power, after having been tested for years in industrialised countries and achieving market maturity, has a prominent role to play here. In many locations excellent wind conditions promise inexpensive power generation when compared with costly imported energy sources such as diesel. Despite political will and considerable potential, however, market development in these countries has been relatively slow to take off. There is a shortage of qualified personnel to establish the foundations for the exploitation of wind energy and to develop projects on their own initiative. The absence of reliable data on wind potential combined with unattractive energy policy framework conditions deters experienced international investors, who instead focus their attention on the expanding markets in Western countries.

It is only in recent years that appreciable development of the market potential in developing countries and emerging economies has taken place. The share of global wind generating capacity accounted for by Africa, Asia and Latin America reached about 20% at the end of 2008, with an installed capacity of 26 GW. This is attributable above all to breathtaking growth in India and China: these two countries alone are responsible for 22 GW. This proves that economic use of wind energy in developing countries and emerging economies is possible, and also indicates that there is immense potential that is still unexploited [4] .

The Technology

Wind power is the conversion of wind energy into electricity or mechanical energy using wind turbines . The power in the wind is extracted by allowing it to blow past moving blades that exert torque on a rotor. The amount of power transferred is dependent on the rotor size and the wind speed. Wind turbines range from small four hundred watt generators for residential use to several megawatt machines for wind farms and offshore. The small ones have direct drive generators, direct current output, aeroelastic blades, lifetime bearings and use a vane to point into the wind; while the larger ones generally have geared power trains, alternating current output, flaps and are actively pointed into the wind.

Direct drive generators and aeroelastic blades for large wind turbines are being researched and direct current generators are sometimes used. Since wind speed is not constant, the annual energy production of a wind converter is dependent on the capacity factor. A well sited wind generator will have a capacity factor of about 35%. This compares to typical capacity factors of 90% for nuclear plants, 70% for coal plants, and 30% for thermal plants. As a general rule, wind generators are practical where the average wind speed is 4.5 m/s or greater. Usually sites are pre-selected on the basis of a wind atlas, and validated with on site wind measurements. Wind energy is plentiful, renewable, widely distributed, clean, and reduces greenhouse gas emissions if used to replace fossil-fuel-derived electricity. The intermittency of wind does not create problems when using wind power at low to moderate penetration levels [5] .

Applications and Efficiency

Most modern wind power is generated in the form of electricity by converting the rotation of turbine blades into electrical current by means of an electrical generator. In windmills (a much older technology), wind energy is used to turn mechanical machinery to do physical work, such as crushing grain or pumping water [5] .

Recently, wind energy has also been used to desalinate water. For further information on use of wind power for water desalination, see Wind Energy - Water Desalination .

Wind Electric

In wind electric systems, the rotor is coupled via a gearing or speed control system to a generator, which produces electricity. Wind power is used in large scale wind farms for national electrical grids as well as in small individual turbines for providing electricity to rural residences or grid-isolated locations. For small turbines the electricity generated can be used to charge batteries or used directly. Larger, more sophisticated wind energy converters are used to feed power into the grid.

Small turbines intended for battery charging have a turbine diameter of between 0.5 –5 m and a power out put of 0.5 – 2 kW. Installed costs vary between US$ 4 – 10 per watt. Medium sized turbines are used in small independent grids in hybrid with a diesel or PV generator. These turbines have diameters of between 5-30 m and a power output of 10- 250 kW. Large wind turbines are normally grid connected. This category includes diameters of 30-90 m and power outputs 0.5 – 3 MW. Total installed global capacity is 58,982 MW of which Europe accounts for 69% (2005). In the Eastern Africa region experience with wind generators has been isolated and largely driven by donors and missionaries. In Europe wind energy cost was estimated at $55.80/MWh, coal at $53.10/MWh and natural gas at $52.50/MWh.

• For further information on costs see section Financing Aspects - Wind Energy [5] .
• See also Economic Analyses of Wind Energy Projects

Wind has been harnessed to lift water for more than 2000 years, first in China and the Middle East, and spreading to Europe. In Africa, settlers historically made use of wind pumps in Namibia and South Africa and to a lesser extent Zimbabwe and Kenya.With wind pumps, moving air turns a "rotor", and the rotational motion of the blades is transferred to harmonic motion of the shaft, which is used to pump water or drive other mechanical devices such as grain mills. Water from wells as deep as 200m can be pumped to the surface by wind pumps.

In off-grid areas where there is sufficient wind (3-5 m/s) and ground water supply, wind pumps often offer a cost-effective method for domestic and community water supply, small-scale irrigation and livestock watering.To select a suitable wind pump, the following information is needed: mean wind speed, total pumping head, daily water requirement, well draw down, water quality and storage requirements. In the Eastern Africa region, there are at least 3 manufacturers with a production of less than 100 units per year. Donors and missionaries have been the main purchasers of both imported and locally manufactured wind pumps [5] .

Wind Energy - Overview

The first commercial wind energy converters entered service back in the 1980s, although the wind energy boom as such did not begin until the mid 1990s, when the total installed wind generation capacity in the world was only 5,000 MW. Since then the installed capacity has increased at double-digit rates of annual growth. By the end of 2006 global installed capacity had reached 74,233 MW. Currently the industry is enjoying a boom with 239,000 MW installed globally as at 2011. Almost without exception, the installed systems are used to generate electricity. The largest market at present is still Europe, where some 48,545 MW (65%) is installed; of this, 22,000 MW is located in Germany (figures from end of 2006). Germany is also a leader among the system manufacturers. Four German companies are counted among the world’s major manufacturers, and the German component industry supplies gearboxes, clutches and other assemblies to numerous producers in other countries.

Even if it remains a matter of dispute whether wind energy would still be competitive without promotional support, it is beyond doubt that the wind industry has made considerable progress. While in the early 1990s the cost of systems still averaged almost 1,300 EUR/kW, in the meantime specific investment costs have fallen to around 900 EUR/kW. The advantages of mass production have been further boosted by considerable increases in the efficiency of turbines (greater hub height, larger rotor diameter etc.), which have improved the economics of wind energy. There are now turbines on the market with a rated output of up to 6 MW, for example. This trend further illustrates that the growth market in the wind industry is mainly seen in electricity generation and grid feed-in. [6]

Wind Energy for Development

"The wind energy potential in many developing and emerging countries is substantial. In many locations, generating electricity from wind energy presents an economically viable alternative to the use of conventional fossil energy sources such as coal or diesel. In developing and emerging countries, wind turbines are an alternative to conventional power stations. In comparison to fossil-fueled power stations, wind energy can now be cost-effective in many places, as well as being non-polluting and reducing dependence on imports of fossil fuels."

Advantages of wind can be:

• Use of an indigenous resource without producing greenhouse gases or other pollution;
• Wind energy contributes to the power supply diversification,
• Wind energy projects can develop local resources in terms of labour, capital and materials,
• Wind projects reinforce the cooperation with different donors including Germany, enhacing local capacities and technological know-how,
• Wind projects attract new capital and can be included in the new approach of Independent Power Production (IPP). [7]

Despite the economic and ecological advantages, so far even good wind resources in developing and emerging countries have not been used to the desirable extent.

The essential reasons for this are based in the lack of knowledge in the developing and emerging countries.

From the view of international wind energy companies, beside the difficulties of raising of capital and risk covering, the barriers for private investment are especially:

• Lack of information on foreign markets
• Lack of knowledge of the energy-sector framework conditions and support mechanisms
• Insufficient wind energy legal framework (technical and economical conditions for feeding wind-generated electricity into power grids, permit procedure, ...)
• Lack of qualified staff, especially in the field of service/maintenance [8] . Technicians and buyers are often unfamiliar with wind technology, and in remote locations installements often break down because of a lack of servicing, spare parts, or trained manpower to administer them. In reality, wind pumps are less maintenance intensive than diesel pumps. However, the wind pump technology is "strange" to many people and there is a need to train maintenance staff where pumps are installed.
• Infrastructure to support the installation, commissioning and maintenance of wind generators is not developed. Users and technicians are generally unaccustomed to the technology.
• Investment Cost. Although the lifetime cost of wind is often less than diesel or petrol-powered pumps, the investment cost of purchasing a wind pump is usually higher than that of diesel pumps. Groups purchasing water supplies often have limited funds and cannot take a long-term view toward the technology.
• Wind energy does not have as consistent an output as fuel-fired power plants. Small-scale wind generators require battery storage to allow usage in periods of low or no wind. For grid connected systems, a stable grid is required to act as the storage. Wind pumps require water storage.
• Wind generators are designed to work over a given range of wind speeds, usually 4– 12m/s. This means that the technology can only be used in areas with sufficient winds [5] .

Wind Capacities

Global Installed Wind Capacity:

Further Information

• Wind Portal on energypedia
• World Wind Energy Association
• European Wind Energy Association
• Windpower Monthly
• Wikipedia: wind power
• Practical Action - Energy From The Wind
• ↑ GTZ (2000) Wind Energy Projects in Morocco and Namibia. Eschborn, retrieved 08.01.2013 https://www.docstoc.com/pass/22042181 ]
• ↑ GTZ (2000) Wind Energy Projects in Morocco and Namibia. Eschborn, retrieved 08.01.2013 [1]
• ↑ Kaldellis J.K. (2010) Overview of stand-alone and hybrid wind energy systems, in: Kaldellis J.K. (2010) Stand-alone and hybrid wind energy systems, Woodhead publishing
• ↑ GTZ (2009) TERNA Wind Energy Programme 1997-2009 – Impact Report –. Eschborn, Germany, retrieved 25.7.2011 [ [2] ]
• ↑ 5.0 5.1 5.2 5.3 5.4 GTZ (2007): Eastern Africa Resource Base: GTZ Online Regional Energy Resource Base: Regional and Country Specific Energy Resource Database: I - Energy Technology. Cite error: Invalid <ref> tag; name "Energy Technology" defined multiple times with different content Cite error: Invalid <ref> tag; name "Energy Technology" defined multiple times with different content Cite error: Invalid <ref> tag; name "Energy Technology" defined multiple times with different content Cite error: Invalid <ref> tag; name "Energy Technology" defined multiple times with different content
• ↑ GTZ 2009: Technical and Economic Potential of Wind-Powered Water Desalination. TERNA Wind Energy Programme. Eschborn, retrieved 25.7.2011 [ [3] ]
• ↑ GTZ (2000) Wind Energy Projects in Morocco and Namibia. Eschborn, retrieved 08.01.2013 [4]
• ↑ World Wind Energy Association / GTZ: Wind Energy in Developing and Emerging Countries, retrieved 25.7.2011 [ [5] ]
• Pages with reference errors

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New to Climate Change?

Wind energy.

Wind energy is a form of renewable energy , typically powered by the movement of wind across enormous fan-shaped structures called wind turbines. Once built, these turbines create no climate-warming greenhouse gas emissions , making this a “carbon-free” energy source that can provide electricity without making climate change worse. Wind energy is the third-largest source of carbon-free electricity in the world (after hydropower and nuclear ) 1 and the second-fastest-growing (after solar ). 2

Cheap, clean energy

The major reason for wind energy’s success is that it’s cheap. In fact, the International Energy Agency estimates that an onshore wind farm built today will make electricity at a lower average cost than any other form of new-built energy. 3   We can thank recent advances in wind turbine technology, and economies of scale from its rapid growth, for this ultra-cheap energy. 4 Wind turbines aren’t “pushed” like sails catching the wind: they actually work more like airplane wings, with blades shaped so that wind flows unequally fast above and below them. This creates an area of high pressure on one side and low pressure on the other, which “lifts” the blades toward the low-pressure area and makes them turn, powering a generator that makes electricity.   Over the past 40 years, turbine blades have become longer and lighter, letting them turn faster with less wind. Modern turbines also pivot automatically to catch the wind at the best angle. These and other advances have led the price of wind energy to fall almost 95% since 1980. 5   Wind energy is also remarkably clean, even compared to other types of carbon-free energy like solar and hydropower. Building new wind turbines does create some greenhouse gas emissions—from making the steel for their towers and fiberglass for their blades, and mining the rarer minerals sometimes used in their generators. But even factoring that in, a wind turbine creates only around a quarter of the greenhouse gas emissions of a solar panel for every kilowatt of electricity, and only a little over 1% the greenhouse gas emissions of a coal-fired power plant. 6   And future innovations could make wind energy even cheaper and cleaner. Researchers are experimenting with new materials and construction techniques, as well as designs very different from the familiar “horizontal axis turbine” with its three blades rotating like a pinwheel. “Vertical axis turbines” spin instead like a carousel, while “airborne wind energy” looks more like a kite or plane tethered to a generator on the ground­.

Wind power in the larger energy system

Wind energy is “variable”: how much electricity it produces depends on how much wind is blowing. In any energy system that relies partly on wind, other energy sources have to be ramped up when winds are low. Energy storage (saving some energy for later when wind turbines are over-producing) and long-distance transmission (moving electricity from places with lots of wind to places with lots of demand) can help the energy system rely more heavily on wind power around the clock.   Wind energy also needs wide stretches of open space. The average wind turbine in the U.S. is around 300 feet tall, and its blades span a circle over 400 feet wide—longer than a football field. 7 These turbines are spaced far apart, sometimes by half a mile or more, so they won’t compete for wind. If you include the entire area of a wind farm in its land footprint, wind farms can take up tens of thousands of acres and make less electricity per acre than any other energy source except bioenergy . 8   However, if you only include the land directly affected by the footprint of each turbine, wind power consumes much less land. Wind energy is unique in how easily it can share land with other uses. In the U.S., around 90% of wind turbines are built on cropland or rangeland for grazing animals, most of it actively used. 9 In this sense, wind energy “takes up” hardly any land at all.   Wind turbines can also be built offshore, sharing space with fishing and shipping. Offshore wind is more expensive than onshore wind, but it takes advantage of stronger, more consistent wind to provide reliable electricity, and is less visible to people living nearby. 10 For built-up coastal regions like the northeastern U.S., where energy demand is high and open land is scarce, offshore wind may be the best way to make clean, renewable energy at a large scale.

Published May 22, 2023.

1 International Energy Agency: Electricity . (Updated February 16, 2023.)

2 International Energy Agency: Renewables 2021: Executive Summary .

3 International Energy Agency: Projected Costs of Generating Electricity 2020 .

4 Wiser, Ryan, et al . " Expert elicitation survey predicts 37% to 49% declines in wind energy costs by 2050 ." Nature Energy 6, 2021, doi:10.1038/s41560-021-00810-z.

5 U.S. Department of Energy Office of Energy Efficiency and Renewable Energy: " U.S. Department of Energy's Wind Energy Technologies Office—Lasting Impressions ." January 2021.

6 Intergovernmental Panel on Climate Change: "Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change ." Annex III: Technology-Specific Cost and Performance Parameters . 2014.

7 U.S. Department of Energy Office of Energy Efficiency and Renewable Energy: " Wind Turbines: The Bigger, the Better ." August 16, 2022.

8 Lovering, Jessica, et al. " Land-use intensity of electricity production and tomorrow's energy landscape ." PLoS One 17(7), July 2022, doi:10.1371/journal.pone.0270155.

9 U.S. Department of Agriculture: " Wind Energy Land Distribution in the United States of America ." July 2017.

10 U.S. Department of the Interior Bureau of Ocean Energy Management: Renewable Energy on the Outer Continental Shelf . Accessed May 22, 2023.

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Home — Essay Samples — Environment — Wind Energy — Essay On Wind Energy

Essay on Wind Energy

• Categories: Climate Change Renewable Energy Wind Energy

About this sample

Words: 1582 |

Published: Mar 19, 2024

Words: 1582 | Pages: 3 | 8 min read

Table of contents

I. introduction, a. definition and importance of wind energy, b. thesis statement, ii. history and development of wind energy, a. origins of wind energy usage, b. technological advancements in wind turbines, c. global adoption and growth of wind energy, iii. environmental benefits of wind energy, a. reduced greenhouse gas emissions, b. conservation of natural resources, c. impact on biodiversity, iv. economic benefits of wind energy, a. job creation in the wind energy sector, b. cost-effectiveness compared to fossil fuels, c. economic growth in regions with wind farms, v. challenges and limitations of wind energy, a. intermittency and variability of wind, b. land use and visual impact, c. impact on wildlife, vi. future prospects of wind energy, a. research and development in wind energy technology, b. integration of wind energy with other renewable sources, c. policy and government support for wind energy, vii. case studies of successful wind energy projects, a. offshore wind farms in europe, b. wind energy in developing countries, c. community-owned wind energy projects, viii. conclusion.

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What Is Wind Energy? Definition and How It Works

• Columbia University
• Syracuse University

• University of Tennessee

Treehugger / Hilary Allison

• Renewable Energy
• Fossil Fuels

Wind Energy Basics

How does wind energy work, what is a wind farm.

• Pros and Cons

Wind energy is electricity created from the naturally flowing air in the Earth's atmosphere. As a renewable resource that won't get depleted through use, its impact on the environment and climate crisis is significantly smaller than burning fossil fuels.

Wind energy can be created by something as simple as a set of 8-foot sails positioned to capture prevailing winds that then turn a stone and grind grain (a gristmill). Or it can be as complex as a 150-foot vane turning a generator that produces electricity to be stored in a battery or deployed over a power distribution system. There are even bladeless wind turbines .

As of 2021, there are over 67,000 wind turbines running in the United States, found in 44 states, Guam, and Puerto Rico. Wind generated about 8.4% of the electricity in the U.S. in 2020. Worldwide, it provides about 6% of the world's electricity needs. Wind energy is growing year-over-year by about 10% and is a key part of most climate change reduction and sustainable growth plans in a variety of countries, including China, India, Germany, and the United States.

Wind Energy Definition

Human beings use wind energy in a variety of ways, from the simple (it's still used to pump water for livestock in more remote locations) to the increasingly complex—think of the thousands of turbines that dominate the hills that cut through highway 580 in California (pictured above).

The basic components of any wind energy system are fairly similar. There are blades of some size and shape that are connected to a drive shaft, and then a pump or generator that either uses or collects the wind energy. If the wind energy is used directly as a mechanical force, like milling grain or pumping water, it's a called a windmill; if it converts wind energy to electricity, it's known as a wind turbine. A turbine system requires additional components, such as a battery for electricity storage, or it may be connected to a power distribution system like power lines.

Nobody really knows when the wind was first harnessed by a human being, but wind was definitely being utilized as a way to move boats on Egypt's Nile River around the year 5,000 BC. By 200 BC people in China were using the wind to power simple water pumps, and windmills with hand-woven blades were used to grind grain in the Middle East. Over time, wind pumps and mills were used in all kinds of food production there, and the concept then spread to Europe, where the Dutch built large wind pumps to drain wetlands—and from there the idea traveled to the Americas.

Wind is produced naturally when the sun heats the atmosphere, from variations in the surface of the Earth, and from the planet's rotation. Wind can then increase or decrease as a result of the influence of bodies of water, forests, meadows and other vegetation, and elevation changes. Wind patterns and speeds vary significantly across terrain, as well as seasonally, but some of those patterns are predictable enough to plan around.

Site Selection

The best locations to place a wind turbine are the tops of rounded hills, on open plains (or open water for offshore wind), and mountain passes where wind is naturally funneled through (producing regular high wind speeds). Generally, the higher the elevation the better, since higher elevations usually have more wind.

Wind energy forecasting is an important tool for siting a wind turbine. There are a variety of wind speed maps and data from the National Oceanic and Atmospheric Administration (NOAA) or the National Renewable Energy Laboratory (NREL) in the U.S. that provide these details.

Then, a site-specific survey should be done to assess the local wind conditions and to determine the best direction to place the wind turbines for maximum efficiency. For at least a year, projects on land track wind speed, turbulence, and direction, as well as air temperatures and humidity. Once that information is determined, turbines that will deliver predictable results can be built.

Wind isn't the only factor for siting turbines. Developers for a wind farm must consider how close the farm is to transmission lines (and cities that can utilize the power); possible interference to local airports and plane traffic; underlying rock and faults; flight patterns of birds and bats ; and local community impact (noise and other possible effects).

Most larger wind projects are designed to last at least 20 years, if not more, so these factors must be considered over the long term.

Types of Wind Energy

Utility scale wind energy.

These are large-scale wind projects designed to be used as a source of energy for a utility company. They are similar in scope to a coal-fired or natural gas power plant, which they sometimes replace or supplement. Turbines exceed 100 kilowatts of power in size and are usually installed in groups to provide significant power—currently these types of systems provide about 8.4% of all energy in the United States.

Offshore Wind Energy

These are generally utility-scale wind energy projects that are planned in the waters off coastal areas. They can generate tremendous power near larger cities (which tend to cluster closer to shore in much of the United States). Wind blows more consistently and strongly in offshore areas than in land, according to the U.S. Department of Energy. Based on the organization's data and calculations, the potential for offshore wind energy in the U.S. is more than 2,000 gigawatts of power, which is two times the generating capacity of all U.S. electric power plants. Worldwide, wind energy could provide more than 18 times what the world currently uses, according to the International Energy Agency.

Small Scale or Distributed Wind Energy

This type of wind energy is the opposite of the examples above. These are wind turbines that are smaller in physical size and are used to meet the energy demands of a specific site or local area. Sometimes, these turbines are connected to the larger energy distribution grid, and sometimes they are off-grid. You'll see these smaller installations (5 kilowatt size) in residential settings, where they might provide some or most of a home's needs, depending on weather, and medium-sized versions (20 kilowatts or so) at industrial or community sites, where they might be part of a renewable energy system that also includes solar power, geothermal, or other energy sources.

The function of a wind turbine is to use blades of some shape (which can vary) to catch the wind's kinetic energy. As the wind flows over the blades, it lifts them, just like it lifts a sail to push a boat. That push from the wind makes the blades turn, moving the drive shaft that they're connected to. That shaft then turns a pump of some kind—whether directly moving a piece of stone over grain (windmill), or pushing that energy into a generator that creates electricity that can be used right away or stored in a battery.

The process for an electricity-generating system (wind turbine) includes the following steps:

Wind Pushes Blades

Ideally, a windmill or wind turbine is located in a place with regular and consistent winds. That air movement pushes specially designed blades that allow the wind to push them as easily as possible. Blades can be designed so they are pushed upwind or downwind of their location.

Kinetic Energy Is Transformed

Kinetic energy is the free energy that comes from the wind. For us to be able to use or store that energy, it needs to be changed into a usable form of power. Kinetic energy is transformed into mechanical energy when the wind meets the windmill blades and pushes them. The movement of the blades then turns a drive shaft.

Electricity Is Generated

In a wind turbine, a spinning drive shaft is connected to a gearbox that increases the speed of the rotation by a factor of 100—which in turn spins a generator. Therefore, the gears end up spinning much faster than the blades being pushed by the wind. Once these gears reach a fast enough speed, they can power a generator that produces electricity.

The gearbox is the most expensive and heavy part of the turbine, and engineers are working on direct drive generators that can operate at lower speeds (so they don't need a gear box).

Transformer Converts Electricity

The electricity produced by the generator is 60-cycle AC (alternating current) electricity. A transformer may be needed to convert that to another type of electricity, depending on local needs.

Electricity Is Used or Stored

Electricity produced by a wind turbine might be used on site (more likely to be true in small or medium-sized wind projects), it could be delivered to transmission lines for use right away, or it could be stored in a battery.

More efficient battery storage is key for advancements in wind energy in the future. Increased storage capacity means that on days when the wind blows less, stored electricity from windier days could supplement it. Wind variability would then become less of an obstacle for reliable electricity from wind.

A wind farm is a collection of wind turbines that form a type of power plant, producing electricity from wind. There's no official number requirement for an installation to be considered a wind farm, so it could include a few or hundreds of wind turbines working in the same area, whether on land or offshore.

• Wind Energy Pros and Cons
• When properly placed, wind energy can produce low-cost and nonpolluting electricity about 90% of the time.
• There is minimal waste generated by a wind farm—nothing needs to be carted away and dumped, no water supply is needed to cool machinery, and there's no effluent to scrub or clean.
• Once installed, wind turbines have a low operating cost, as wind is free.
• It's space flexible: You can use a small turbine to power a home or farm building, a large turbine for industrial energy needs, or a field of giant turbines to create a power plant-level source of energy for a city.
• Wind reliability can vary. In addition, weak or strong winds will shut down a turbine and electricity won't be produced at all.
• Turbines can be noisy depending on where they are placed, and some people don't like the way they look. Home wind turbines might offend neighbors.
• Wind turbines have been found to harm wildlife, especially birds and bats.
• They have a high initial cost, though they pay for themselves relatively quickly.

" How Many Turbines Are Contained in the U.S. Wind Turbine Database? " United States Geological Survey .

“ Electricity Explained: Electricity in the United States .” U.S. Energy Information Agency, 2021.

“ International Energy Outlook 2019 With Projections to 2050 .” U.S. Energy Information Agency, 2019, pp. 90-91.

“ Global Wind Report 2019 .” Global Wind Energy Council.

“ Wind Explained History of Wind Power .” U.S. Energy Information Agency.

“ Offshore Wind Research and Development .” U.S. Department of Energy Office of Energy Efficiency & Renewable Energy.

" Offshore Wind Outlook 2019 ." International Energy Agency .

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What Is the Future of Wind Energy?

This article was reviewed by a member of Caltech's Faculty .

Humans have used windmills to capture the force of the wind as mechanical energy for more than 1,300 years . Unlike early windmills, however, modern wind turbines use generators and other components to convert energy from the spinning blades into a smooth flow of AC electricity.

In the video below, Resnick Sustainability Institute researcher John Dabiri discusses the future of wind energy technology.

How much of global electricity demand is met by wind energy?

Wind energy is a small but fast-growing fraction of electricity production. It accounts for 5 percent of global electricity production and 8 percent of the U.S. electricity supply.

Globally, wind energy capacity surpasses 743 gigawatts , which is more than is available from grid-connected solar energy and about half as much as hydropower can provide. Nearly three-quarters of that 651 gigawatts comes from wind farms in five countries: China, the U.S., Germany, India, and Spain. Wind energy capacity in the Americas has tripled over the past decade.

In the U.S., wind is now a dominant renewable energy source , with enough wind turbines to generate more than 100 million watts, or megawatts, of electricity, equivalent to the consumption of about 29 million average homes.

The cost of wind energy has plummeted over the past decade. In the U.S., it is cost-competitive with natural gas and solar power.

Wind energy and solar energy complement each other, because wind is often strongest after the sun has heated the ground for a time. Warm air rises from the most heated areas, leaving a void where other air can rush in, which produces horizontal wind currents . We can draw on solar energy during the earlier parts of the day and turn to wind energy in the evening and night. Wind energy has added value in areas that are too cloudy or dark for strong solar energy production, especially at higher latitudes.

How big are wind turbines and how much electricity can they generate?

Typical utility-scale land-based wind turbines are about 250 feet tall and have an average capacity of 2.55 megawatts, each producing enough electricity for hundreds of homes. While land-based wind farms may be remote, most are easy to access and connect to existing power grids.

Smaller turbines, often used in distributed systems that generate power for local use rather than for sale, average about 100 feet tall and produce between 5 and 100 kilowatts.

One type of offshore wind turbine currently in development stands 853 feet tall, four-fifths the height of the Eiffel Tower, and can produce 13 megawatts of power. Adjusted for variations in wind, that is enough to consistently power thousands of homes. While tall offshore turbines lack some of the advantages of land-based wind farms, use of them is burgeoning because they can capture the energy of powerful, reliable winds high in the air near coastlines, where most of the largest cities in the world are located.

What are some potential future wind technologies other than turbines?

Engineers are in the early stages of creating airborne wind turbines , in which the components are either floated by a gas like helium or use their own aerodynamics to stay high in the air, where wind is stronger. These systems are being considered for offshore use, where it is expensive and difficult to install conventional wind turbines on tall towers.

Trees, which can withstand gale forces and yet move in response to breezes from any direction, also are inspiring new ideas for wind energy technology. Engineers speculate about making artificial wind-harvesting trees . That would require new materials and devices that could convert energy from a tree's complex movements into the steady rotation that traditional generators need. The prize is wind energy harvested closer to the ground with smaller, less obtrusive technologies and in places with complex airflows, such as cities.

What are the challenges of using wind energy?

Extreme winds challenge turbine designers. Engineers have to create systems that will start generating energy at relatively low wind speeds and also can survive extremely strong winds. A strong gale contains 1,000 times more power than a light breeze, and engineers don't yet know how to design electrical generators or turbine blades that can efficiently capture such a broad range of input wind power. To be safe, turbines may be overbuilt to withstand winds they will not experience at many sites, driving up costs and material use. One potential solution is the use of long-term weather forecasting and AI to better predict the wind resources at individual locations and inform designs for turbines that suit those sites.

Climate change will bring more incidents of unusual weather, including potential changes in wind patterns . Wind farms may help mitigate some of the harmful effects of climate change. For example, turbines in cold regions are routinely winterized to keep working in icy weather when other systems may fail, and studies have demonstrated that offshore wind farms may reduce the damage caused by hurricanes . A more challenging situation will arise if wind patterns shift significantly. The financing for wind energy projects depends critically on the ability to predict wind resources at specific sites decades into the future. One potential way to mitigate unexpected, climate-change-related losses or gains of wind is to flexibly add and remove groups of smaller turbines, such as vertical-axis wind turbines , within existing large-scale wind farms.

Wind farms do have environmental impacts . The most well-known is harm to wildlife, including birds and bats . Studies are informing wind farm siting and management practices that minimize harm to wildlife , and Audubon, a bird conservation group, now supports well-planned wind farms. The construction and maintenance of wind farms involves energy-intensive activities such as trucking, road-building, concrete production, and steel construction. Also, while towers can be recycled, turbine blades are not easily recyclable. In hopes of developing low-to-zero-waste wind farms, scientists aim to design new reuse and disposal strategies , and recyclable plastic turbine blades. Studies show that wind energy's carbon footprint is quickly offset by the electricity it generates and is among the lowest of any energy source .

Dive Deeper

Wind Vision: A New Era for Wind Power in the United States

Caltech Energy 10 to Develop the Roadmap for 50% Reduction in Emissions by 2030

Tweaking Turbine Angles Squeezes More Power Out of Wind Farms

• Wind Energy Technologies Office
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Wind energy offers many advantages, which explains why it's one of the fastest-growing energy sources in the world. To further expand wind energy’s capabilities and community benefits, researchers are working to address technical and socio-economic challenges in support of a decarbonized electricity future.

Learn more about ongoing research to take advantage of these benefits and tackle wind energy challenges.

Advantages of Wind Power

• Wind power creates good-paying jobs.  There are over 125,000 people working in the U.S. wind industry across all 50 states, and that number continues to grow. According to the U.S. Bureau of Labor Statistics , wind turbine service technicians are the fastest growing U.S. job of the decade. Offering career opportunities ranging from blade fabricator to asset manager, the wind industry has the potential to support hundreds of thousands of more jobs by 2050.
• Wind power is a domestic resource that enables U.S. economic growth. In 2022, wind turbines operating in all 50 states generated more than 10% of the net total of the country’s energy . That same year, investments in new wind projects added $20 billion to the U.S. economy.
• Wind power is a clean and renewable energy source. Wind turbines harness energy from the wind using mechanical power to spin a generator and create electricity. Not only is wind an abundant and inexhaustible resource, but it also provides electricity without burning any fuel or polluting the air. Wind energy in the United States helps avoid 336 million metric tons of carbon dioxide emissions annually —equivalent to the emissions from 73 million cars.
• Wind power benefits local communities. Wind projects deliver an estimated $2 billion in state and local tax payments and land-lease payments each year. Communities that develop wind energy can use the extra revenue to put towards school budgets, reduce the tax burden on homeowners, and address local infrastructure projects.
• Wind power is cost-effective. Land-based, utility-scale wind turbines provide one of the lowest-priced energy sources available today. Furthermore, wind energy’s cost competitiveness continues to improve with advances in the science and technology of wind energy.
• Wind turbines work in different settings. Wind energy generation fits well in agricultural and multi-use working landscapes. Wind energy is easily integrated in rural or remote areas, such as farms and ranches or coastal and island communities, where high-quality wind resources are often found.

Challenges of Wind Power

• Wind power must compete with other low-cost energy sources. When comparing the cost of energy associated with new power plants , wind and solar projects are now more economically competitive than gas, geothermal, coal, or nuclear facilities. However, wind projects may not be cost-competitive in some locations that are not windy enough. Next-generation technology , manufacturing improvements , and a better understanding of wind plant physics can help bring costs down even more.
• Ideal wind sites are often in remote locations. Installation challenges must be overcome to bring electricity from wind farms to urban areas, where it is needed to meet demand. Upgrading the nation’s transmission network to connect areas with abundant wind resources to population centers could significantly reduce the costs of expanding land-based wind energy. In addition, offshore wind energy transmission and grid interconnection capabilities are improving.
• Turbines produce noise and alter visual aesthetics. Wind farms have different impacts on the environment compared to conventional power plants, but similar concerns exist over both the noise produced by the turbine blades and the  visual impacts on the landscape .
• Wind plants can impact local wildlife. Although wind projects rank lower than other energy developments in terms of wildlife impacts, research is still needed to minimize wind-wildlife interactions . Advancements in technologies,  properly siting wind plants, and ongoing environmental research are working to reduce the impact of wind turbines on wildlife.

Renewable Energy Systems pp 1780–1784 Cite as

Wind Power, Introduction

• Lennart Söder 7  
• Reference work entry
• First Online: 01 January 2016

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Windmills have existed for at least 3,000 years, mainly for grinding grain or pumping water. The use of windmills to generate electricity is a twentieth-century development and in the early 1970s started the development toward modern wind turbine technology. Wind power has increased significantly during the last decade and there are currently many power systems, or subsystems, with comparatively large amounts of wind power. In 2009, Spain covered 14% [ 1 ] of their electric energy demand with wind power. The corresponding figures were for Portugal 15% [ 2 ], Ireland 11% [ 3 ], and Western Denmark 25% [ 4 ].

This means that wind power has developed from a marginal source to a source with important impact on national power system balances. But in many countries, wind power still only contributes to a small part of the electric supply. The total installed capacity at the end of 2009 reached 158,000 MW including 37,000 MW installed during 2009. A third of these additions were made in China, which...

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Bibliography

Spain: http://www.ree.es/ingles/sistema_electrico/pdf/infosis/sintesis_REE_2009_eng.pdf

Portugal: http://www.centrodeinformacao.ren.pt/EN/InformacaoTecnica/TechnicalData/2009.pdf

Ireland: http://www.eirgrid.com/media/Annual%20Report%202009.pdf and http://www.ieawind.org/AnnualReports_PDF/2009/Ireland.pdf

Denmark: http://www.danskenergi.dk/~/media/Energi_i_tal/Dansk_Elforsyning_Statistik_2009.pdf.ashx

Global Wind Energy Council (2008/2009) Global installed wind power capacity (MW). http://www.ewea.org

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German Biomass Research Centre, Leipzig, Germany

Martin Kaltschmitt

Institute of Environmental Technology and Energy Economics, Hamburg University of Technology, Hamburg, Germany

Earth Engineering Center, Columbia University, New York, NY, USA

Nickolas J. Themelis

Ormat Technologies, Inc., Reno, NV, USA

Lucien Y. Bronicki

Royal Institute of Technology, Electric Power Systems, Stockholm, Sweden

Hawaii Natural Energy Institute, School of Ocean And Earth Science And Technology, University of Hawaii at Manoa, Honolulu, HI, USA

Luis A. Vega

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Söder, L. (2013). Wind Power, Introduction. In: Kaltschmitt, M., Themelis, N.J., Bronicki, L.Y., Söder, L., Vega, L.A. (eds) Renewable Energy Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5820-3_75

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• What is renewable energy?
• Wind power introduction

Man has been capturing wind energy for millennia and it is now seen as an increasingly mainstream source of power.

In this guide:

• Wind power in the UK
• Off-shore wind farms
• Future for wind farms
• Case study: CIS Tower Manchester
• Solar power introduction
• Future of solar
• Marine energy introduction
•   Tidal power introduction
•   Case study: Tidal power in the UK – SeaGen
•   Future of tidal power
•   Archimedes Waveswing
•   Future of wave power

The expanding market for wind power

It is forecast that, worldwide, the market for wind energy will continue its recent trend of growing massively (by 30-40% per annum). While much attention has been paid to the fact that China brings a new coal-fired power station online every ten days, less well known is the country’s interest in wind power: China has doubled its capacity every year since 2004.

How Wind Energy Works

Wind energy can be harvested in many different ways: with large scale wind farms featuring giant turbines, on or off shore; smaller embedded systems built in to office blocks and modern houses and domestic scale turbines (also known as ‘microwind’), often mounted to chimneys.

Regardless of the size of the turbine, the operating principles are the same: blades are angled into the prevailing wind at different degrees according to wind strength. When it reaches around 4 metres per second (mps), 30% less than the UK local average, the turbines start generating electricity.

They operate most efficiently in winds of around 15mps and have a variety of features built in, such as variable blade pitch or designed-in ‘passive stall’ which ensure the system is not overloaded by gusts or storms where speeds may be far higher.

Criticisms of wind power

Perhaps surprisingly for an inexhaustible, clean energy source, wind power has been subject to intense criticism, principally for its relatively low overall availability (turbines only work to maximum effect when the wind speed is neither too low nor too high) and therefore cannot be relied on as a sole energy source.

Increasing the efficiency of wind power

Turbine efficiency is increasing year on year, in line with new investment and research. While there is some threat to bird life, especially if turbines are located in migration paths (as has been the case in the early days of the industry), this is no greater and probably less than that posed by any tall building and power cables – both of which are far more prevalent.

Case study: Building wind farms

Vestas has installed over 35,000 wind turbines and were responsible for the turbines at the UK’s first and largest offshore wind power plant at North Hoyle in Wales.  Read more about the technical advancements being made in new wind farms .

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• GRID INTEGRATION
• INDUSTRY & MARKETS
• ENVIRONMENT
• SCENARIOS & TARGETS
• Executive Summary

MAIN PUBLICATION :

• Introduction
• Regional wind resources
• Wind atlases
• The importance of the wind resource
• Best practice for accurate wind speed measurements
• The annual variability of wind speed
• Analytical methods for the prediction of the long-term wind regime at a site
• Information required for an analysis
• Wind farm energy loss factors
• Detailed loss factors
• Definition of uncertainty in the predicted energy production
• Overview of the method
• Example time series power prediction results
• Example statistical accuracy of forecasts
• Portfolio effects
• Conclusions
• Future developments
• The technical challenge of a unique technology
• The development of commercial technology
• Design styles
• Design drivers for modern technology
• Architecture of a modern wind turbine
• Growth of wind turbine size
• Large commercial wind turbines
• Larger diameters
• Tip speed trends
• Pitch versus stall
• Speed variation
• Drive train trends
• Rotor and nacelle mass
• Transport and installation
• Rotor blade development
• Alternative drive train configurations
• Controller capabilities
• Network operator requirements
• Testing, standardisation and certification
• Future innovations
• Airborne turbines
• Wind Turbine Technology
• Optimisation of energy production
• Visual influence
• Turbine loads
• Civil works
• Electrical works
• SCADA and instruments
• Construction issues
• Commissioning, operation and maintenance
• Fundamentals
• Measurement offshore
• Wind analysis offshore
• Energy prediction
• Availability, reliability and access
• Lightning risk offshore
• Maintenance strategy - reliability vs maintenance provision
• Site selection
• Wind turbine selection
• Offshore support structures
• Future trends for offshore wind
• Electrical system
• Installation
• Markets and applications for small wind turbines
• Evolution of commercial small wind turbine technology
• Market development
• Technology trends and recent developments
• Technology status
• Future trends
• Concluding remConcluding remarks and future R&D needsarks and future R&D needs
• Added value of R&D
• Priority R&D areas in Wind Energy
• Market deployment strategy
• Support at EC level
• Support for wind R&D at MemberStatelevel
• Current effort from the private sector
• ABREVIATIONS
• How to order?

CHAPTER 2: WIND RESOURCE ESTIMATION

The wind is the fuel for the wind power station.  Small changes in wind speed produce greater changes in the commercial value of a wind farm.  For example, a 1 per cent increase in the wind speed might be expected to yield a 2 per cent increase in energy production. This chapter explains why knowledge of the wind is important for each and every stage of the development of a wind farm, from initial site selection right through to operation.    Europe has an enormous wind resource.  It can be considered on various levels.  At the top level, the potential resource can be examined from a strategic standpoint: Where is it? How does it compare to the EU and national electricity demands? What regions and areas offer good potential? At the next level it is necessary to understand the actual wind resource on a site in great detail: How is it measured? How will it change with time? How does it vary over the site? How is it harnessed? It is at this stage that commercial evaluation of a wind farm is required and robust estimates must be provided to support investment and financing decisions. Once the wind speed on the site has been estimated, it is then vital to make an accurate and reliable estimate of the resulting energy production from a wind farm that might be built there. This requires wind farm modelling and detailed investigation of the environmental and ownership constraints.   As its contribution to electricity consumption increases, in the context of liberalised energy markets, new questions are beginning to emerge, which are critically linked to the nature of the wind: How can wind energy be consolidated, traded and generally integrated into our conventional electricity systems? Will an ability to forecast wind farm output help this integration? These questions, and more, are addressed in this chapter.  The first section looks at the strategic “raw” resource issues, and the following sections provide a detailed step-by-step evaluation of the assessment process.  A worked example of a real wind farm is provided and, finally, recommendations are made about the important matters that need to be tackled in the near future to help wind energy play its full part.

Wind and Solar Energy as a Sources of Alternative Energy Research Paper

Introduction, wind turbine energy technology, solar energy technology, cost, efficiency and energy produced via wind and solar technology, resources required for wind and solar systems.

There is an urgent need for dependable, efficient and low-cost energy to alleviate problems of energy insecurity as well as environmental pollution. For example, Jacobson and Masters (2001) proposed that the U.S. could meet its Kyoto Protocol obligations for decreasing carbon dioxide discharges by substituting 60% of its coal production plants with wind energy turbines to supplement the country’s energy requirements (p.1438).

Fthenakis, Mason and Zweibel (2009) 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 infrastructure with solar energy in order to decrease carbon emissions to internationally accepted levels (p.397).

There is no doubt that efforts to adopt renewable, effective and low-cost energy options have attracted global attention. Consequently, this paper will compare two forms renewable energy (wind and solar energy) in terms of cost, efficiency, energy produced, resources needed, environmental impact and maintenance.

Wind turbines usually convert wind energy into electricity. Generally, a gearbox rotates the turbine rotor into fast-rotating gears that eventually transform mechanical energy into electricity in a generator. Although a number of current turbines are gearless and less proficient, they are nonetheless useful when installed in buildings or residential homes (Jacobson & Delucchi, 2011, p.1157).

Solar photo-voltaics (PVs) refers to groups of cells with silicon materials that transform solar radiation into electricity. As of now, solar PVs are utilized in several different applications, ranging from residential home power generation to medium-scale use. On the other hand, concentrated solar power (CSP) systems utilize reflective lenses or mirrors to focus sunbeams on a liquid to warm it to a high temperature.

The heated liquid runs from the collector to a heat engine in which a part of the heat is transformed into electricity. There are various forms of CSP systems that permit the heat to be stocked up for several hours to facilitate production of electricity at night (Jacobson & Delucchi, 2011, p.1157).

Figure 1(see appendix) provides the projected amount of power available globally from renewable energy with respect to raw resources available in high-energy regions. It is worth mentioning that these resources can plausibly be mined in the near future given the location as well as the low extraction costs involved.

Figure 1 demonstrates that only wind and solar energy can provide adequate power to meet global energy demands. For example, wind in developable regions can satisfy global energy demands up to about 4 times over while areas with solar energy potential can meet global demands by over 18 times over (Jacobson & Delucchi, 2011, p.1159). Figure 2 illustrates a model of wind resources at 100m in the hub height range of wind turbines.

The global wind energy potential (available over the world’s ocean surface and land at 100m assuming that all wind at speeds is utilized to power wind turbines) has been estimated at 1700 TW. About half of this wind energy (1700 TW) is found in areas that can be extracted feasibly and efficiently (Jacobson & Delucchi, 2011, p.1159).

Jacobson and Delucchi (2011) estimate that both solar and wind make up 90% of the future energy supply on the basis of their relative availability (p.1160). Solar PV is split into 70% power-plant and 30% rooftop on the basis of an assessment of the expected available rooftop area.

Rooftop PV has three main benefits: it does not need new land surface; it can be incorporated into a hybrid solar infrastructure that generate electricity, light and heat for onsite use; and it neither requires an electricity transmission nor distribution infrastructure. The authors suggests that approximately 90,000 solar power plants and about 4 million wind turbines are required to satisfy global energy demands (Jacobson & Delucchi, 2011, p.1160).

The material required for wind turbine energy include: carbon-filament reinforced plastic (for rotor blades); glass-fiber reinforced plastic (for rotor blades); wood epoxy (rotor blades); aluminum (for nacelles); magnetic materials (for gearboxes); pre-stressed concrete (for towers); and steel materials (for rotors, nacelles, towers, etc).

It is worth mentioning that most of these resources are available in abundance supply. For instance, the main components of concrete (i.e. limestone, sand, and gravel) are extensively available at lower costs and can be re-used (Jacobson & Delucchi, 2011, p.1161). On the other hand, the required resources for solar PVs include: copper indium sulfide/selenide; cadmium telluride; micro-crystalline silicon; polycrystalline silicon; and amorphous silicon.

Nonetheless, it is important to note that the power generated via silicon PV technologies is constrained by the limited availability of silver materials which are utilized as electrodes (Jacobson & Delucchi, 2011, p.1162). Nevertheless, given that most of resources required for the installation of renewable energy plants are in abundance supply, both wind and solar energy technologies provide low-cost, environmental-friendly and efficient energy options to meet global demand.

Fthenakis, V., Mason, J., & Zweibel, K. (2009). The technical, geographical, and economic feasibility of solar energy to supply the energy needs of the US. Energy Policy, 37, 387–399.

Jacobson, M., & Delucchi, M. (2011). Providing all global energy with wind, water, and solar, Part I: Technologies, energy resources, quantities and areas of infrastructures, and materials. Energy Policy, 39, 1154-1169.

Jacobson, M., & Masters, G. (2001). Exploiting wind versus coal. Science, 293, 1438.

Figure 1: Power available in energy resource worldwide if the energy is used in conversion devises, in locations where the energy resource is high, in likely-developable locations, and in delivered electricity (for wind and solar energy)

Source: Jacobson & Delucchi (2011).

a Comprises of all wind speeds at 100m over ocean and land

b Locations over land or near the coast where the mean wind speed ≥7m/s at 80m and at 100m.

c Eliminating remote locations

d Assuming 160 W panels are used over latitudes, land, and ocean.

e Same as (d) but locations over land between 50S and 50N.

• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2023, November 30). Wind and Solar Energy as a Sources of Alternative Energy. https://ivypanda.com/essays/wind-and-solar-energy/

"Wind and Solar Energy as a Sources of Alternative Energy." IvyPanda , 30 Nov. 2023, ivypanda.com/essays/wind-and-solar-energy/.

IvyPanda . (2023) 'Wind and Solar Energy as a Sources of Alternative Energy'. 30 November.

IvyPanda . 2023. "Wind and Solar Energy as a Sources of Alternative Energy." November 30, 2023. https://ivypanda.com/essays/wind-and-solar-energy/.

1. IvyPanda . "Wind and Solar Energy as a Sources of Alternative Energy." November 30, 2023. https://ivypanda.com/essays/wind-and-solar-energy/.

Bibliography

IvyPanda . "Wind and Solar Energy as a Sources of Alternative Energy." November 30, 2023. https://ivypanda.com/essays/wind-and-solar-energy/.

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• Published: 01 April 2024

Optimal configuration method of demand-side flexible resources for enhancing renewable energy integration

• Hao Bai 2 ,
• Yongxiang Cai 1 ,
• Weichen Yang 2 &

Scientific Reports volume  14 , Article number:  7658 ( 2024 ) Cite this article

Metrics details

• Electrical and electronic engineering
• Energy grids and networks

Demand-side flexible load resources, such as Electric Vehicles (EVs) and Air Conditioners (ACs), offer significant potential for enhancing flexibility in the power system, thereby promoting the full integration of renewable energy. To this end, this paper proposes an optimal allocation method for demand-side flexible resources to enhance renewable energy consumption. Firstly, the adjustable flexibility of these resources is modeled based on the generalized energy storage model. Secondly, we generate random scenarios for wind, solar, and load, considering variable correlations based on non-parametric probability predictions of random variables combined with Copula function sampling. Next, we establish the optimal allocation model for demand-side flexible resources, considering the simulated operation of these random scenarios. Finally, we optimize the demand-side resource transformation plan year by year based on the growth trend forecast results of renewable energy installed capacity in Jiangsu Province from 2025 to 2031.

Introduction

In the context of an energy crisis and environmental pollution, China has proposed constructing a new power system with renewable energy as the main source. Wind power, photovoltaic, and other renewable energy sources are rapidly advancing. However, due to the increased integration of a high proportion of renewable energy, its volatility and uncertainty have led to a significant rise in the demand for power system flexibility. This poses substantial risks to the safe and stable operation of the system. Therefore, it is urgent to fully tap into the flexible adjustment potential of each aspect, including generation, transmission, consumption, and storage, establishing a balance between demand and supply of flexibility 1 . This is crucial to support the consumption of a high proportion of renewable energy and ensure the economic and safe operation of the system.

Flexibility aggregation, seen as a promising solution to invoke system flexibility resources, has been extensively applied in various cluster studies such as electric vehicles (EVs), temperature-controlled loads, and micro energy networks 2 , 3 , 4 , 5 . In reference 6 , a generalized battery model is formulated, and the aggregated flexibility of controllable temperature loads is accurately calculated using power and capacity bounds derived from the continuous model. References 7 , 8 apply a geometric approach to represent individual cells as polyhedral feasible sets, and then aggregate the operating power of controllable temperature loads and distributed generation based on Minkowski sums. In reference 9 , a real-time aggregation method based on reinforcement learning is proposed for fast aggregation of electric vehicles' flexibility feedback design. Reference 10 simplifies the constraints of the aggregated feasible domain using k-order approximation and multi-timescale approximation models, aiming to reduce the complexity of large-scale multi-energy flexibility load aggregation, thereby enhancing computational speed while maintaining result accuracy.

Based on the aggregation of flexibility resources, references 11 , 12 , 13 have conducted studies on the participation of flexibility resources in grid optimization and dispatch. On the other hand, reference 14 fits the conditional probability distribution of wind power prediction error through nonparametric kernel density estimation. It applies numerical methods to obtain the upper and lower bounds of wind power prediction error under a certain level of confidence, quantifying it as flexibility demand. Reference 15 , treats the forecast error of renewable energy and load-shedding due to system failures as flexibility demands. Reference 16 develops a robust optimization model to address prediction errors in wind power by allocating reserves and energy storage (ES) for conventional units. Reference 17 uses the fluctuating power of net load, obtained based on the power of wind turbines, PV, and loads on a typical day, as a flexibility demand. Additionally, reference 18 incorporates the fluctuation of the net load forecast value in the neighboring moment and the net load output interval of the next moment as the flexibility demand. Reference 19 deems this approach not comprehensive enough and expresses the flexibility demand as the fluctuation of the net load forecast value in the neighboring moment plus the net load output interval of the current and next moments. Moreover, reference 20 derives the flexibility demand envelope based on the probability distribution of the fluctuating power of wind power at different time scales. However, none of these studies considered the conditional probability distribution of wind power fluctuation power. In contrast, references 21 , 22 determine the variation interval of wind power fluctuation power as flexibility demand based on the conditional probability distribution of the actual fluctuation power of wind power. However, the failure to analyze relevant quantities of wind power fluctuation might decrease the accuracy of the conditional probability distribution model, leading to inaccuracies in the obtained wind power fluctuation power interval. In summary, the current stage of studies on the quantification of flexibility demand primarily focuses on either uncertainty or volatility individually, without comprehensive consideration of both. Moreover, there is limited research on the conditional probability of wind power wave momentum.

Compared with direct control for demand-side flexible resources, indirect control method based on dynamic pricing reserves more autonomy for end users. The utility company or aggregator sets the electricity prices, and the consumers respond to the prices by adjusting the amount of energy they use. Various retail pricing models have been investigated in the existing literature. The dynamic pricing mechanisms, which denote the time varying pricing schemes 23 , 24 , 25 , 26 , are well adopted due to the high efficiency. In 23 , the authors investigate the required information and communications systems that are needed to realize the control-by-price concept for such units. The dynamic pricing algorithm is also used in demand response programs to maximize the retailer’s profit 24 , 25 , 26 .

In the context of a high proportion of wind power integration, the demand for flexibility is growing. Consequently, it is necessary to conduct reasonable capacity planning for flexibility resources and ensure adequate resource configuration to maximize their regulatory role through optimal scheduling. This will effectively enhance system flexibility, reduce the likelihood of wind and solar energy curtailment, and promote the utilization of renewable energy.

The unit's output and rotational reserve are the most conventional flexibility resources. Currently, research on unit scheduling has delved deeper. Reference 27 , 28 , 29 proposed a unit combination method for grid-connected wind power, considering the prediction error of wind power generation and the uncertainty of system operation in the optimization model. Reference 30 established a robust unit combination model incorporating transmission constraints and introduced a cost evaluation method to mitigate model conservatism. Reference 31 adopted the objective of maximizing social benefit to achieve optimal unit combination, considering demand-side response. Additionally, reference 32 integrated the operation risk model with the unit combination model for synergistic optimization of operation cost and risk.

In the context of optimal scheduling with storage, some studies focus on the joint operation optimization of storage and other resources. Reference 33 optimized the scheduling of a coupled PV-storage system under various scenarios. Reference 34 comprehensively considered multiple resources, including distributed generation, energy storage, and controllable load, utilizing two models for flexibility regulation. Capacity-rich resources were directly involved, while capacity-scarce resources responded based on tariff incentives. A joint optimal scheduling model was established by combining these two models. Reference 35 proposed a two-phase optimization model for energy storage to maximize the integrator's profit while considering the uncertainty of customer demand.

Several studies have applied energy storage in a narrowly defined flexibility demand scenario, particularly in peaking scenarios. References 36 , 37 investigated the economic potential of utilizing energy storage to provide peaking capacity in shorter time scales. Reference 38 argued that configuring energy storage on the thermal power plant side is akin to increasing the depth of thermal power unit peaking. They established an optimized scheduling model for energy storage, thermal power units, and demand-side response, comprehensively considering the deep peaking initiative of thermal power units configured with energy storage and the moderating role of demand-side response. However, optimizing the dispatch of thermal power plants and energy storage to maximize power sent to the electricity market for higher revenue, ensuring sufficient reserve capacity, and fully developing the reserve potential of energy storage to optimize unit capacity allocation remains a pressing issue to be addressed.

In addition to the two types of conventional flexibility resources, namely unit output and energy storage, some literature has also explored the optimal scheduling of other flexibility resources such as interruptible loads and electric vehicles. Reference 39 proposes three flexibility evaluation indices that can characterize the flexibility of distribution networks and establishes a two-stage flexibility enhancement optimization model for distribution networks, integrating EV charging with energy storage and interruptible load scheduling. Reference 40 considers heat pumps as a flexibility resource and establishes a day-ahead optimal dispatch model based on cooperative game theory for distribution networks. Reference 41 uses a virtual battery model to represent uncertain clusters of EVs and considers their flexibility, optimizing the day-ahead power generation and standby capacity of EVs to assist in regulating the operational flexibility of the system. Reference 42 explores the flexibility of soft switches in the distribution network and establishes an optimal scheduling model including soft switches, taking into account the operational constraints of soft switches and aiming to minimize operational costs while improving the system's flexibility. An optimal scheduling model including soft switches is established.

The studies mentioned above have focused on aggregating flexibility resources and optimizing dispatching. However, few studies have addressed the optimal configuration of flexibility resources.

In this paper, a demand-side flexible resource optimal allocation method for renewable energy consumption enhancement is proposed. The main contributions of this work are three-fold.

To reduce the complexity of optimization model when dealing with numerous demand-side flexible devices, generalized energy storage model is adopted to characterize the aggregate flexibility of demand-side resource cluster;

To consider the correlations among different random factors such as wind, photovoltaic and load, when generating stochastic scenarios for robust optimization, a non-parametric probability prediction method based on Copula function is developed;

For obtaining an optimal demand-side flexible resources configuration scheme with high robustness against uncertainties, a robust optimization method based on stochastic scenario traversal is proposed. Besides, the effectiveness of the proposed method is evaluated based on a practical case in Jiangsu, China.

The rest of the paper is organized as follows. Section " Aggregate flexibility modeling for demand-side resources " presents the aggregate flexibility modeling method for demand-side resources. Section " Scenario generation considering wind, solar, and load correlations " introduces the scenario generation method considering wind, solar, and load correlations. The proposed optimal configuration method of demand-side flexible resources are explained in detail in Section " Robust optimal configuration method of demand-side flexible resources ". In Section " Example analysis ", the effectiveness of the proposed method is validated on real-world cases. Finally, Section " Conclusion " concludes the paper.

Aggregate flexibility modeling for demand-side resources

The proportion of Flexible Load (FL) with regulation capability on the demand side is steadily increasing. Loads that can alter electricity consumption patterns are referred to as demand-side resources (DSRs). Numerous scholars and experts worldwide have conducted relevant research on assessing the potential response of demand-side resources. When the grid experiences failures or encounters power supply inadequacies during peak periods, demand response utilizing controllable resources can achieve short-term load reduction. This helps maintain a balance between supply and demand on the grid and enhances the grid's operational quality. Temperature-controlled loads and electric vehicles possess significant response capacities, rapid response speeds, and good adjustability, making them valuable fast-response resources.

For electric vehicles and air conditioners, categorized as demand-side flexible resources with time-coupled characteristics, they demonstrate charging, discharging, and storage traits akin to energy storage. Instead of fixed boundary parameters as seen in traditional energy storage models, we employ time-varying power and energy boundaries. This accounts for the fact that loads like electric vehicles and air conditioners need to adhere to user comfort constraints during operation. Building upon the generalized energy storage model that encompasses individual devices, we derive the aggregated flexibility model of demand-side flexible resources by calculating the geometric centers of all device parameters.

For facilitating the management of these flexible DSRs, DSR aggregators are introduced to aggregates these massive DSRs and represent them in interactions with the electricity market and power system operators. In this paper, the DSR owners (i.e., users) enter into long-term contracts with aggregators. The contracts stipulate that users can access electricity at a rate lower than the general level, while the aggregators, through direct control means, conduct energy arbitrage by utilizing flexible resources, ensuring user comfort, travel needs, and other constraints are met.

Generalized battery model

The generalized battery model (GBM) includes the energy storage state change equation, energy constraint and power constraint:

where: \(e_{i,t}\) denotes the energy of device \(i\) at moment \(t\) ; \(\rho_{i}\) denotes the energy decay coefficient of device \(i\) ; \(\Delta e_{i,t}\) denotes the energy change of device \(i\) at moment \(t\) ; \(p_{i,t}\) denotes the power of \(i\) at moment \(t\) ; \(e_{i,t}^{ \wedge }\) and \(e_{i,t}^{V}\) denote the energy boundary of device \(i\) at moment \(t\) ; \(p_{i,t}^{ \wedge }\) and \(p_{i,t}^{V}\) denote the power boundary of device \(i\) at moment \(t\) ; \(\eta_{i}^{{\text{ in }}}\) in and \(\eta_{i}^{{\text{ out }}}\) denote the charging and discharging efficiency of device \(i\) at moment \(t\) , respectively.

Electric vehicle

The large-scale adoption of electric vehicles is of paramount importance in reducing fossil fuel consumption and safeguarding the environment. The widespread proliferation of electric vehicles will result in a significant increase in electricity demand. Electric vehicles, known for their energy efficiency and environmental friendliness, can serve as unconventional energy storage devices, actively participating in demand response and offering auxiliary services to the power grid. Strategically planning the charging and discharging cycles of these vehicles can help smooth out peak demand, minimize troughs, stabilize the system, and effectively ease the burden on the power grid. Investigating the charging patterns of electric vehicles enables a comprehensive understanding of their charging habits. With the advancement of Vehicle-to-Grid (V2G) technology, electric vehicles can interact with microgrids to exchange energy. Leveraging the energy storage capabilities of electric vehicles can not only relieve stress on the power grid but also provide advantages to vehicle owners. Thus, to optimize the utilization of electric vehicle energy storage capabilities, accurate prediction of charging loads and an in-depth study of charging behavior are imperative.

Before calculating the GBM parameter set \(\{{p}_{t}^{\bigwedge },{p}_{t}^{\bigvee },{e}_{t}^{\bigwedge },{e}_{t}^{\bigvee },\Delta {e}_{t}\}\) for each individual EV, it is essential to introduce a fundamental knowledge: for vehicle owners, the most preferred charging trajectory is when the electric vehicle charges at the maximum power until the battery is fully charged. In this paper, we refer to it as the optimal charging trajectory. On the other hand, the least acceptable charging trajectory for vehicle owners is when the electric vehicle charges at the slowest speed until departing, ensuring the battery is fully charged before leaving. In this paper, we refer to it as the worst charging trajectory. Any charging trajectories within the region bounded by the optimal charging trajectory and the worst charging trajectory is acceptable for users. Therefore, the energy state corresponds to the best/worst charging trajectory is regarded as the upper/lower boundary of the GBM, which is calculated by Eq. (2). The power boundary is jointly determined by the GBM energy boundaries and the battery rated power, which is calculated by Eq. (3). The energy change of the GBM is defined as the remaining battery energy when plugged into the charging pile or the battery energy when leaving the charging pile, which is calculated by Eq. ( 4 ).

Energy boundaries

For each electric vehicle, in order to meet the charging needs of users, the upper and lower energy limits at each moment can be calculated using Eq. (2):

where Eq. ( 2a ) represents the upper limit of the electric vehicle's power at each moment; Eq. ( 2b ) represents the power curve corresponding to the time when the electric vehicle starts charging from the lowest power and charges to the target power when it leaves the station, Eq. ( 2c ) represents the power curve corresponding to the time when the electric vehicle arrives at the station and starts discharging until it reaches the lowest power, and Eq. ( 2d ) represents the electric vehicle's lower limit of the electric vehicle's power at each moment, and it can be calculated to The lower limit of the electric vehicle's power at each moment is obtained. Where: \(e_{i,t}^{ev, \wedge / \vee }\) denote the upper/lower energy boundaries of EV; \(ta_{i}\) , \(tl_{i}\) denotes the driving-in/driving-out time of EV; \(\overline{e}_{i}^{ev}\) / \(\underline {e}_{i}^{ev}\) denotes the upper and lower limits of battery capacity of EV; and \(\overline{p}_{i}^{ev}\) denotes the rated power of EV.

Power boundaries

For each electric vehicle, the upper and lower limits of charging and discharging power are limited by both the energy limit and the power rating:

where Eq. ( 3a ) denotes the upper power limit of EV and Eq. ( 3b ) denotes the lower power limit of EV. Where: \(p_{i,t}^{ev, \wedge / \vee }\) denote the upper/lower power boundaries of the electric vehicle; \(ta_{i}\) , \(tl_{i}\) denotes the electric vehicle drive-in/drive-out time; and \(\overline{p}_{i}^{ev}\) denotes the rated power of the electric vehicle.

Energy change

The energy change due to EVs leaving or entering the station can be calculated based on the state of the EV at the station, the initial energy at the time of entering the station, and the final energy at the time of leaving the station:

where: \(\Delta e_{t}^{ev }\) denotes the amount of transferred electricity due to EVs leaving or entering the station; \(x_{i,t}\) denotes the state of EVs at the station.

Air conditioner

Air conditioners, being the most prevalent flexible loads, have the ability to convert electrical energy into heat or refrigeration for short-term storage. The output of air conditioning loads is influenced by seasonal and weather conditions, resulting in significant loads during winter and summer, and lighter loads during spring and fall. Loads are higher during extreme temperatures and lower during moderate ones, closely related to human body temperature. The energy storage features of air conditioning can be effectively utilized for regulation and control, thus serving the microgrid as an energy storage device.

To determine the GBM parameter set \(\{{p}_{t}^{\bigwedge },{p}_{t}^{\bigvee },{e}_{t}^{\bigwedge },{e}_{t}^{\bigvee }\}\) for each individual AC, several optimization problems are formulated. In detail, the power boundaries, \({p}_{t}^{\bigwedge }\) and \({p}_{t}^{\bigvee }\) , correspond to the minimum and maximum power consumption of the AC while ensuring the user's thermal comfort constraints, which is calculated by Eqs. (5) and (6). Additionally, the energy boundaries, \({e}_{t}^{\bigwedge }\) and \({e}_{t}^{\bigvee }\) , represent the energy levels at which the indoor temperature is maintained at the maximum acceptable and minimum acceptable levels, respectively. They are calculated by solving the optimization problem (7).

Reference power

To obtain the baseline power of a single air conditioner, an optimization model is established with the objective of minimizing the temperature deviation, considering the user comfort constraints and the dynamic equations of building room temperature:

where ( 5a ) denotes the optimization objective, i.e., the indoor temperature deviation from the user's temperature setpoint; ( 5b )–( 5e ) denote the constraints of the optimization model, where ( 5b ) denotes the discrete form of the temperature variation equation, ( 5c ) denotes the indoor temperature constraints, ( 5d ) denotes the maximum and minimum temperature acceptable to the user and ( 5e ) denotes the power constraints. Where: \(T = \left\{ {1, \cdots 24} \right\}\) denotes a discrete set of time points; \(P_{i,t}^{{\text{ hvac,base }}}\) denotes the base power of the air conditioner to maintain the indoor temperature at the user set temperature; \(\overline{P}_{i,t}^{{\text{ hvac }}}\) denotes the rated power of the air conditioner; \(\theta_{i}^{{\text{ set }}}\) , \(\theta_{i,t}\) , \(\theta_{i,t}^{{\text{ out }}}\) , \(\Delta \theta_{i}\) denote the user set temperature, the indoor temperature, the outdoor temperature, and the maximum deviation, respectively; and a, b denote the coefficients of the equation of variation of the indoor temperature, which are related to the equivalent heat capacity (C), the equivalent thermal resistance (R), and the operating efficiency (η) as:

In order to obtain the maximum/minimum power limits for a single air conditioner, an optimization model is built with the objective of minimum or maximum power consumption at each moment, respectively:

After obtaining the minimum or maximum value of the power consumption at each moment, the adjustable power upper and lower bounds of the aggregation model can be obtained by subtracting the baseline power:

where: \(P_{i,t}^{{{\text{ hvac,}} \vee {/} \wedge }}\) denotes the lower and upper adjustable power limits of the air conditioner.

When the aggregation model state is at the upper and lower energy boundaries, the indoor temperature is considered to be at the boundary of the user's acceptable range. Therefore, when calculating the energy boundaries of the aggregation model, the optimization model is built with the objective of minimizing the deviation value between the indoor temperature and the maximum/minimum acceptable temperature, respectively:

After obtaining the power values \(P_{i,t}^{{\text{ hvac, - }}}\) and \(P_{i,t}^{{\text{ hvac, + }}}\) corresponding to the maximum/minimum temperatures, the upper and lower bounds of the energy of the aggregation model can be obtained by substituting the following equations:

where: \(e_{i,t}^{{{\text{ hvac,}} \vee {/} \wedge }}\) indicates the min/max power of the air conditioner.

Aggregate flexibility modeling

When performing aggregation flexibility modeling, we obtain the parameters of the aggregates by directly summing or computing a weighted average of the parameters of the generalized energy storage model corresponding to the individual devices.

where: \(\Phi_{agg}\) denotes the set of all devices contained in the aggregator; \(\omega_{i}\) denotes the weighting factor of device i within the aggregator.

Scenario generation considering wind, solar, and load correlations

Wind power and photovoltaic power depend directly on natural meteorological conditions, resulting in natural uncertainty. Additionally, load is influenced by meteorological conditions, production, and lifestyle factors, further contributing to uncertainty. The combination of these factors amplifies the uncertainty in the distribution network trends, particularly in geographically similar regions. Within the same wind zone, wind turbines and photovoltaic equipment exhibit a correlation in power output. Similarly, in the same radiation zone, there is a strong correlation between wind speed, solar intensity, and load. These uncertainties and correlations significantly impact the operation of the distribution network. Therefore, it is crucial to consider randomized scenario sampling that accounts for the correlations among wind, solar, and load for both wind and solar power.

Generation and load uncertainty modeling based on nonparametric probabilistic prediction

The existing forecasting methods for renewable energy and load are mainly categorized into point forecasting and probabilistic forecasting. Point prediction provides the single-point expected value of the forecasted object at a specific time in the future, yet it inherently incurs a prediction error due to its deterministic nature. On the other hand, probabilistic forecasting allows for the determination of the probability distribution associated with the forecasted object, enabling effective quantification of power system uncertainty. Within probabilistic prediction methods, nonparametric probabilistic prediction does not rely on assumptions about parametric probability distributions. This characteristic significantly enhances the accuracy of probabilistic prediction and forms a foundation for optimal decision-making in grid operations, taking uncertainty into account.

First, the historical data are normalized, and the direct quantile regression method is used to obtain the sequence of predicted quantile regression values, i.e., the discrete approximation form \(\hat{F}_{t}\) of the cumulative probability distribution function, which can be expressed as:

where: \(\hat{q}_{{\alpha_{k} ,t}}\) , is the regression value of quantile \({q}_{{\alpha }_{k},t}\) at time \(t\) ; \(\alpha_{k}\) is the kth quantile; \(m\) is the number of quantile points. The adjacent quantile points are approximated as random variables obeying a uniform distribution, and then an approximately complete probability distribution function \(F_{{\text{t}}} \left( {x_{t} } \right)\) at each moment is obtained by the cubic interpolation method, where \(x_{t}\) is the power of the predicted object at time \(t\) .

Typical scenario generation considering source and load correlation

At the current stage, there are numerous reports on methods for generating random scenarios for a single variable, and these methods are relatively easy to implement. However, constructing and simulating joint distribution functions for multiple variables is challenging. The construction theory for most joint distribution functions is a simple extension of univariate distribution functions, often requiring all marginal distributions to follow the same distribution. In practical scenarios, it's difficult to satisfy such a strict requirement because different types of random factors, such as wind, solar, and load, typically follow different probability distributions. Addressing this issue, this paper uses Copula functions (link functions) to describe the correlation between wind, solar, and load, proposing a method based on Copula functions to generate random scenarios for wind, solar, and load. This method imposes no restrictions on the marginal distributions and can capture nonlinearity, asymmetry, and tail correlation relationships between variables.

Based on the probabilistic prediction results, we generate stochastic scenarios of renewable energy output, which can significantly reduce the difficulty of solving the problem by transforming the complex problem containing random variables into a deterministic optimization problem under each scenario when formulating the scheduling plan. Based on the Sklar theorem of multivariate distribution, the Copula function \(C\left( \cdot \right)\) is used to construct the multivariate probability distribution function \(F(x_{1} ,x_{2} , \ldots ,x_{T} )\) that takes into account the time dependence of PV output due to the correlation of renewable energy output, i.e.

where: \(T\) is the duration of a scheduling cycle. By performing N times Monte Carlo sampling on the multivariate Copula function, we can obtain N renewable energy output scenarios with correlation. To avoid the huge renewable energy computational burden caused by the excessive number of scenarios, this paper clusters the generated scenarios to achieve scenario size reduction and obtain several typical PV output scenarios. In this paper, the K-Medoids clustering algorithm is used to cluster the scenes, which can avoid the clustering bias caused by the presence of anomalies, where the optimal number of clusters is determined by the combination of the contour coefficient method and the elbow method.

Robust optimal configuration method of demand-side flexible resources

Since wind power, photovoltaic, and other renewable energy sources are significantly influenced by weather and environmental factors, the complexity of system planning increases upon their integration with the grid. Many scholars, both domestically and internationally, have extensively researched the uncertainty issue surrounding renewable energy. Ultimately, the solutions obtained from these efforts can be categorized into three groups: stochastic optimization, robust optimization, and fuzzy optimization. The stochastic optimization method possesses inherent limitations. Firstly, the probability density function, fundamental to stochastic optimization, is derived from fitting a vast amount of historical data. Secondly, as historical and sample data accumulates, the complexity of analysis escalates, and biased data can introduce errors in the analysis results. The fuzzy optimization method employs fuzzy numbers to represent uncertain variables and address the uncertainty issue. However, determining the affiliation function of fuzzy variables is challenging, resulting in a highly subjective and arbitrary affiliation function. The proposed robust optimization method overcomes the subjective and arbitrary nature of fuzzy optimization methods by defining the problem through the uncertainty set. It identifies the optimal solution that satisfies all specified conditions based on proposed constraints. In contrast to the previous two optimization methods, the robust optimization method does not necessitate fitting the distribution function of uncertain parameters using extensive historical data or constructing the affiliation function. It simply delineates the range of variation for each parameter to determine the optimal solution for the problem. Moreover, most decision schemes obtained through this method are robust against a range of disturbances.

In summary, the robust optimization method not only rectifies the shortcomings of the aforementioned two methods but also exhibits advantages that the other two methods lack. Simultaneously, the proposed robust optimization method contributes to enhanced operational efficiency.

Optimization model

In the demand-side flexible resource optimal allocation model, the demand-side resource flexibility is modeled using a generalized energy storage model with the objective of minimizing the investment cost, while considering the power balance constraint of the system and the thermal generating unit output constraint. In addition to this, the constraints are simulated using N wind, solar and load day typical scenarios for the annual operation of the grid, with the aim of ensuring the robustness of the proposed configuration scheme, i.e., to be able to guarantee 100% grid consumption of renewable energy throughout the year. The specific optimization model is as follows:

where: \(N_{HVAC}\) , \(N_{EV}\) denote the number of air conditioners and electric vehicles involved in the retrofit, respectively, and \(N_{ESS}\) denote the quantity of renewable energy storage; \(c_{HVAC}\) , \(c_{EV}\) denote the unit investment cost of air conditioners and electric vehicles involved in the retrofit, respectively, and \(c_{ESS}\) denote the unit investment cost of energy storage; \(P_{s,t}^{RES}\) denotes the power generation of renewable energy at moment \(t\) under scenario \(s\) ; \(P_{s,t}^{GEN}\) denotes the generation capacity of thermal power plant at moment \(t\) under scenario \(s\) . \(P_{t}^{agg,v}\) and \(P_{t}^{agg, \wedge }\) denote the minimum and maximum generation capacity constraints, respectively; \(P_{s,t}^{agg}\) Represents the electricity consumption of flexible resources on the demand side at moment \(t\) in scenario \(s\) . In the above optimization model, the objective function contains the retrofitting cost of installing intelligent control terminals for air conditioners and electric vehicles and the investment cost of adding renewable energy storage, and the constraints ( 11b ) denote the power balance constraint; ( 11c )–( 11e ) denote the operation constraint of generalized energy storage, and ( 11g ) denotes the generation capacity constraint of thermal power plants.

Overall flow of the proposed method

The overall flow of the algorithm is shown in Fig.  1 . Generalized energy storage modeling for DSR aggregators and Copula-based stochastic scenario generation is conducted firstly. With the obtained DSR aggregator models and the uncertainty representations, the robust configuration optimization for DSRs is carried out. The step-by-step process is detailed as:

The predicted information (outdoor temperature, users’ thermal comfort limits, charging demands of EVs) and device parameters (parameters of ACs, EV batteries and ESs) for DSRs are collected and utilized as inputs for GBM parameter extraction (Section " Aggregate flexibility modeling for demand-side resources ");

Based on the historical data of wind power, solar power and load consumption, Copula function is adopted to model the multivariable distribution with consideration of the dependence structure of wind power, solar power and load consumption. Stochastic scenarios sampling considering multivariable correlations is then achieved using Monte Carlo sampling from the predicted multivariable distribution (Section " Scenario generation considering wind, solar, and load correlations ");

After getting the DSR aggregator models based on GBM and the uncertainty representations based on scenario set, the robust optimization model is built as presented in Section " Robust optimal configuration method of demand-side flexible resources ";

Using commercial solution software to solve the optimization problem, the optimal configuration results are obtained, which is consist of the number of ACs, EV charging stations for retrofit, and additional ES capacity.

Over flow of the proposed method.

The above optimization models are linear programming models, which can be solved efficiently with the help of commercial solution software (e.g. Gurobi, Cplex).

Example analysis

To verify the effectiveness of demand-side flexible resources in delaying grid transformation, reducing investment costs, and improving grid consumption, we applied the proposed method to optimize the configuration of demand-side flexible resources in Jiangsu province as an example. The installed capacities of wind, PV, and coal power planned for the province from 2025 to 2031 are shown in Table 1 , and the investment/remodeling costs and maximum allocated capacities of each type of demand-side resources are presented in Table 2 . According to Table 1 , the installed capacity of coal-fired units shows a slight upward trend. The reason for the continuous increase in the installed capacity of coal-fired units is because, in the foreseeable future, coal-fired units remain irreplaceable. Despite the abundance of demand-side resources, the flexibility provided by individual resources is very limited, and they exhibit a high degree of randomness. It is unreliable and impractical to rely entirely on demand-side resources for achieving 100% renewable energy integration.

Stochastic scenario generation results

Using a method for generating random scenarios that takes into account the correlation of random variables, we can derive N typical scenarios that depict the fluctuations in renewable energy and inflexible load. To streamline the optimization process, we've set the number of typical scenarios to 12, representing the 12 months in a year. The outcomes of the scenario generation are illustrated in Fig.  2 .

Typical daily fluctuation curves of wind, solar and load during the year.

Figure  2 displays the sampling results of wind, solar, and load random scenarios. These results reveal that among the three types of random factors, wind power output exhibits the strongest uncertainty due to various environmental factors like wind speed, temperature, and humidity. This uncertainty is prominently manifested in the significant fluctuations shown in the figure, displaying the most pronounced differences among different random scenarios. Influence by user electricity consumption behavior, the power curve randomness of the load comes next in intensity, presenting certain regularities. Specifically, it reaches peaks in electricity demand around 10:00 in the morning and approximately 20:00 in the evening due to user consumption patterns. In the case of photovoltaic power generation, influenced by factors such as sunlight intensity, temperature, and humidity, the power curve demonstrates relatively strong regularity. It consistently exhibits a characteristic shape, peaking around noon.

Overall, the random scenarios sampled based on the proposed methodology accurately and comprehensively cover the uncertainty distribution of supply and demand. These can effectively enhance the robustness and risk resistance of decision results in subsequent robust optimization.

Demand-side flexible resource configuration results

Based on the forecasted planned installed capacities of wind, PV, and coal power in Jiangsu Province from 2025 to 2031, we conducted year-by-year optimization for demand-side flexible resource allocation. The optimized configuration results are presented in Tables 3 and 4 . To demonstrate the influence of various demand-side flexible resources on investment economics, different maximum retrofit table AC quantities are considered in Tables 3 and 4 , respectively. Figure  3 depicts the cumulative investment cost curve and the cumulative investment cost of each component for the period 2025–2031.

Cumulative investment cost curve 2025–2031. ( a ) 15 million units of air conditioners ( b ) 25 million units of air conditioners.

As indicated in Table 3 , the period from 2025 to 2030 demonstrates a steady and balanced increase in the number of air conditioners and electric vehicles in the absence of energy storage. However, as the scale of electric vehicle and air conditioner upgrades approaches its maximum limit in 2031, the grid is compelled to address the mounting pressure of renewable energy consumption by integrating energy storage. In 2031, a critical juncture is reached where the grid grapples with the necessity of incorporating energy storage due to the saturation of electric vehicle and air conditioner upgrades. However, it's important to note that this strategic shift comes at a cost. The investment required for energy storage significantly outweighs the transformation expenses associated with other distributed energy resources at this stage. Consequently, there is a remarkable upswing in the investment costs for power grid renovation in 2031, clearly depicted in Fig.  3 a where the cumulative investment cost curve for the grid displays a sharp incline during this pivotal year. In summary, the flexibility and relatively modest retrofitting costs of air conditioning and electric vehicles play a pivotal role in extending the need for substantial grid retrofitting investments. This delay in major investments underscores the efficacy of leveraging the adaptability and cost-effectiveness of air conditioning and electric vehicles to manage the burgeoning demands on the power grid.

To further illustrate the significant role of demand-side flexible resources in postponing investments for grid upgrades, we increased the upper limit of air conditioner retrofitting scale from 15 to 25 million, and compared the investment costs between these two scenarios. A comparison between Tables 3 and 4 reveals that with the increase in the upper limit of air conditioner retrofitting scale, the cumulative investment cost by 2031 decreased from 1194.32 billion to 706.13 billion, reducing by approximately 42.2%. This clearly underscores the pivotal role of demand-side flexible resources in deferring investments for grid upgrades. It emphasizes the importance of fully exploiting the flexibility potential of demand-side resources and utilizing policies, markets, and other means to encourage active participation from demand-side users. This approach facilitates a win–win situation for the supply, demand, and grid stakeholders.

In this paper, we propose an optimal configuration method for demand-side flexible resources to enhance renewable energy consumption. Firstly, we model the adjustable flexibility of demand-side resources based on the generalized battery model. Secondly, we generate random scenarios of wind, solar, and load with variable correlations using non-parametric probability prediction results of random variables combined with Copula function sampling. Next, we establish an optimal configuration model for demand-side flexible resources based on an improved robust optimization method. Finally, we optimize the demand-side resource renovation plan on an annual basis, considering the growth trend of installed renewable energy capacity in Jiangsu Province from 2025. The simulation results verify that the utilization of demand-side flexible resources can efficiently mitigate the costly investment in energy storage equipment.

Under price-based indirect control strategies, user responsiveness to prices is a significant factor influencing the adjustability of flexible resources. For our future work, users’ demand elastic to dynamic pricing strategy will be investigated by considering interactions between the aggregator and users.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

Supported by the Guizhou Provincial Science and technology support plan ([2022] 012); Key science and technology projects of China Southern Power Grid Corporation (GZKJXM20210481).

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Y.F., H.B., Y.C. and W.Y. wrote the main manuscript text and Y.L. prepared figures and tables. All authors reviewed the manuscript.

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Fu, Y., Bai, H., Cai, Y. et al. Optimal configuration method of demand-side flexible resources for enhancing renewable energy integration. Sci Rep 14 , 7658 (2024). https://doi.org/10.1038/s41598-024-58266-6

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Received : 25 July 2023

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DOI : https://doi.org/10.1038/s41598-024-58266-6

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