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Articles on Renewable energy

Displaying 1 - 20 of 1326 articles.

Wondering what to make of warnings about our electricity system? The outlook is improving – but we’re not out of the woods

Alison Reeve , Grattan Institute

NZ energy crisis: electricity demand will jump as NZ decarbonises – can renewable generation keep up?

Alan Brent , Te Herenga Waka — Victoria University of Wellington

Yes, it’s difficult for governments to pick green industry winners – but it’s essential Australia tries

Llewelyn Hughes , Crawford School of Public Policy, Australian National University

NZ’s electricity market is a mess. Rolling out rooftop solar would change the game

Stephen Poletti , University of Auckland, Waipapa Taumata Rau ; Bruce Mountain , Victoria University , and Geoff Bertram , Te Herenga Waka — Victoria University of Wellington

Solar above, batteries below: here’s how warehouses and shopping centres could produce 25% of Australia’s power

Bruce Mountain , Victoria University

Could we use volcanoes to make electricity?

David Kitchen , University of Richmond

Books That Shook the Business World: Exponential by Azeem Azhar

Sreevas Sahasranamam , University of Glasgow

If we want more solar and wind farms, we need to get locals on board by ensuring they all benefit too

Simon Wright , Charles Sturt University

From net zero to Indigenous knowledge, Australia has finally set new science priorities. How can we meet them?

Kylie Walker , Australian National University

Quantum computers can accelerate the transition to net zero power grids

Thomas Morstyn , University of Oxford

Why relying on technology to keep ASEAN’s coal plants running is risky

Lay Monica , Center of Economic and Law Studies (CELIOS) and Panji Kusumo , Center of Economic and Law Studies (CELIOS)

Offshore wind farms connected by an underwater power grid for transmission could revolutionize how the East Coast gets its electricity

Tyler Hansen , Dartmouth College ; Abraham Silverman , Johns Hopkins University ; Elizabeth J. Wilson , Dartmouth College , and Erin Baker , UMass Amherst

China: still the world’s biggest emitter, but also an emerging force in climate diplomacy

Xu Yi-chong , Griffith University

Paris Olympics promote sustainability for good reason: Climate change is putting athletes and their sports at risk

Brian P. McCullough , University of Michigan

Sodium-ion batteries are set to spark a renewable energy revolution – and Australia must be ready

Peter Newman , Curtin University

Is Britain on track for a zero-carbon power sector in six years?

Andrew Crossland , Durham University and Jon Gluyas , Durham University

No room for nuclear power, unless the Coalition switches off your solar

Bill Grace , The University of Western Australia

Can humanity address climate change without believing it? Medical history suggests it is possible

Ron Barrett , Macalester College

Fusion power could transform how we get our energy – and worsen problems it’s intended to solve

Sophie Cogan , University of York

Here’s how ‘microgrids’ are empowering regional and remote communities across Australia

Related topics.

• Climate change
• Electricity
• Energy transition
• Fossil fuels
• Solar energy
• Solar power
• Wind energy

Top contributors

Professorial Fellow, University of Canberra

Professor of Engineering, Australian National University

Senior Research Associate, Renewable Energy & Energy Systems Analyst, UNSW Sydney

Program Director, Energy, Grattan Institute

Professor Emeritus, Macquarie Business School, Macquarie University

Professor, Crawford School of Public Policy and Head of Energy, Institute for Climate Energy and Disaster Solutions, Australian National University

Professor of Physics, University of Johannesburg

Honorary Associate Professor, UNSW Sydney

Senior Industry Fellow, RMIT University

Director Monash Energy Institute, Monash University

Professor, School of Economics, The University of Queensland

Honorary Associate Professor, Centre for Climate Economics and Policy, Australian National University

Professor and Director, Victoria Energy Policy Centre, Victoria University

Associate Professor of Economics, Griffith University

Research Fellow, ANU College of Engineering and Computer Science, Australian National University

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• Alternative Energy Paper Topics Topics: 92
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118 Renewable Energy Essay Topics

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

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

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These essay examples and topics on Renewable Energy were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on June 24, 2024 .

• ENVIRONMENT

Renewable energy, explained

Solar, wind, hydroelectric, biomass, and geothermal power can provide energy without the planet-warming effects of fossil fuels.

In any discussion about climate change , renewable energy usually tops the list of changes the world can implement to stave off the worst effects of rising temperatures. That's because renewable energy sources such as solar and wind don't emit carbon dioxide and other greenhouse gases that contribute to global warming .

Clean energy has far more to recommend it than just being "green." The growing sector creates jobs , makes electric grids more resilient, expands energy access in developing countries, and helps lower energy bills. All of those factors have contributed to a renewable energy renaissance in recent years, with wind and solar setting new records for electricity generation .

For the past 150 years or so, humans have relied heavily on coal, oil, and other fossil fuels to power everything from light bulbs to cars to factories. Fossil fuels are embedded in nearly everything we do, and as a result, the greenhouse gases released from the burning of those fuels have reached historically high levels .

As greenhouse gases trap heat in the atmosphere that would otherwise escape into space, average temperatures on the surface are rising . Global warming is one symptom of climate change, the term scientists now prefer to describe the complex shifts affecting our planet’s weather and climate systems. Climate change encompasses not only rising average temperatures but also extreme weather events, shifting wildlife populations and habitats, rising seas , and a range of other impacts .

Of course, renewables—like any source of energy—have their own trade-offs and associated debates. One of them centers on the definition of renewable energy. Strictly speaking, renewable energy is just what you might think: perpetually available, or as the U.S. Energy Information Administration puts it, " virtually inexhaustible ." But "renewable" doesn't necessarily mean sustainable, as opponents of corn-based ethanol or large hydropower dams often argue. It also doesn't encompass other low- or zero-emissions resources that have their own advocates, including energy efficiency and nuclear power.

Types of renewable energy sources

Hydropower: For centuries, people have harnessed the energy of river currents, using dams to control water flow. Hydropower is the world's biggest source of renewable energy by far, with China, Brazil, Canada, the U.S., and Russia the leading hydropower producers . While hydropower is theoretically a clean energy source replenished by rain and snow, it also has several drawbacks.

Large dams can disrupt river ecosystems and surrounding communities , harming wildlife and displacing residents. Hydropower generation is vulnerable to silt buildup, which can compromise capacity and harm equipment. Drought can also cause problems. In the western U.S., carbon dioxide emissions over a 15-year period were 100 megatons higher than they normally would have been, according to a 2018 study , as utilities turned to coal and gas to replace hydropower lost to drought. Even hydropower at full capacity bears its own emissions problems, as decaying organic material in reservoirs releases methane.

Dams aren't the only way to use water for power: Tidal and wave energy projects around the world aim to capture the ocean's natural rhythms. Marine energy projects currently generate an estimated 500 megawatts of power —less than one percent of all renewables—but the potential is far greater. Programs like Scotland’s Saltire Prize have encouraged innovation in this area.

Wind: Harnessing the wind as a source of energy started more than 7,000 years ago . Now, electricity-generating wind turbines are proliferating around the globe, and China, the U.S., and Germany are the leading wind energy producers. From 2001 to 2017 , cumulative wind capacity around the world increased to more than 539,000 megawatts from 23,900 mw—more than 22 fold.

Some people may object to how wind turbines look on the horizon and to how they sound, but wind energy, whose prices are declining , is proving too valuable a resource to deny. While most wind power comes from onshore turbines, offshore projects are appearing too, with the most in the U.K. and Germany. The first U.S. offshore wind farm opened in 2016 in Rhode Island, and other offshore projects are gaining momentum . Another problem with wind turbines is that they’re a danger for birds and bats, killing hundreds of thousands annually , not as many as from glass collisions and other threats like habitat loss and invasive species, but enough that engineers are working on solutions to make them safer for flying wildlife.

You May Also Like

Can energy harnessed from Earth’s interior help power the world?

How the historic climate bill will dramatically reduce U.S. emissions

We took the Great American Road Trip—in electric cars

Solar: From home rooftops to utility-scale farms, solar power is reshaping energy markets around the world. In the decade from 2007 and 2017 the world's total installed energy capacity from photovoltaic panels increased a whopping 4,300 percent .

In addition to solar panels, which convert the sun's light to electricity, concentrating solar power (CSP) plants use mirrors to concentrate the sun's heat, deriving thermal energy instead. China, Japan, and the U.S. are leading the solar transformation, but solar still has a long way to go, accounting for around two percent of the total electricity generated in the U.S. in 2017. Solar thermal energy is also being used worldwide for hot water, heating, and cooling.

Biomass: Biomass energy includes biofuels such as ethanol and biodiesel , wood and wood waste, biogas from landfills, and municipal solid waste. Like solar power, biomass is a flexible energy source, able to fuel vehicles, heat buildings, and produce electricity. But biomass can raise thorny issues.

Critics of corn-based ethanol , for example, say it competes with the food market for corn and supports the same harmful agricultural practices that have led to toxic algae blooms and other environmental hazards. Similarly, debates have erupted over whether it's a good idea to ship wood pellets from U.S. forests over to Europe so that it can be burned for electricity. Meanwhile, scientists and companies are working on ways to more efficiently convert corn stover , wastewater sludge , and other biomass sources into energy, aiming to extract value from material that would otherwise go to waste.

Geothermal: Used for thousands of years in some countries for cooking and heating, geothermal energy is derived from the Earth’s internal heat . On a large scale, underground reservoirs of steam and hot water can be tapped through wells that can go a mile deep or more to generate electricity. On a smaller scale, some buildings have geothermal heat pumps that use temperature differences several feet below ground for heating and cooling. Unlike solar and wind energy, geothermal energy is always available, but it has side effects that need to be managed, such as the rotten egg smell that can accompany released hydrogen sulfide.

Ways to boost renewable energy

Cities, states, and federal governments around the world are instituting policies aimed at increasing renewable energy. At least 29 U.S. states have set renewable portfolio standards —policies that mandate a certain percentage of energy from renewable sources, More than 100 cities worldwide now boast at least 70 percent renewable energy, and still others are making commitments to reach 100 percent . Other policies that could encourage renewable energy growth include carbon pricing, fuel economy standards, and building efficiency standards. Corporations are making a difference too, purchasing record amounts of renewable power in 2018.

Wonder whether your state could ever be powered by 100 percent renewables? No matter where you live, scientist Mark Jacobson believes it's possible. That vision is laid out here , and while his analysis is not without critics , it punctuates a reality with which the world must now reckon. Even without climate change, fossil fuels are a finite resource, and if we want our lease on the planet to be renewed, our energy will have to be renewable.

Related Topics

• SUSTAINABILITY
• RENEWABLE ENERGY
• GEOTHERMAL ENERGY
• SOLAR POWER
• HYDROELECTRIC POWER
• CLIMATE CHANGE

5 environmental victories from 2021 that offer hope

Activists fear a new threat to biodiversity—renewable energy

How the Ukraine war is accelerating Germany's renewable energy transition

3 ways COVID-19 is making us rethink energy and emissions

Let’s not waste this crucial moment: We need to stop abusing the planet

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Renewable Energy Dissertation Topics

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

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

Governing the Renewable Energy Transition

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

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

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

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

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

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

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

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

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

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

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

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EERE SETO Postdoctoral Research Award 2018

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

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

Solar Energy

S-501 Applying Data Science to Solar Soft Cost Reduction

Possible disciplines: Economics, computer science, business management

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

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

Areas of interest include:

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

S-502 Solar Systems Integration

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

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

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

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

Possible disciplines: Mechanical engineering, chemical engineering, materials science

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

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

Topics of interest include, but are not limited to:

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

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

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

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

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

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

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

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Renewable Energy Dissertation Topics

• Updated on  
• Jan 12, 2023

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

This Blog Includes:

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

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

Renewable Energy’s Untapped Potential

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

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

Topic 1: Renewable Energy: Prospects and Challenges Today

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

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

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

Topic 5: Wind Energy’s Future

Topic 6: How valuable is home wind energy?

Topic 7: Renewable Energy’s Economic and Environmental Benefits

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

Topic 9: Is it expensive to finance renewable energy?

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

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

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

Topic 13: Is solar energy the way to go?

Topic 14: 2030 Approach to Renewable Energy

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

Renewable Energy Dissertation Examples

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

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

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

Objectives:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Vidisha Dewan

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

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renewable energy , usable energy derived from replenishable sources such as the Sun ( solar energy ), wind ( wind power ), rivers ( hydroelectric power ), hot springs ( geothermal energy ), tides ( tidal power ), and biomass ( biofuels ).

At the beginning of the 21st century, about 80 percent of the world’s energy supply was derived from fossil fuels such as coal , petroleum , and natural gas . Fossil fuels are finite resources; most estimates suggest that the proven reserves of oil are large enough to meet global demand at least until the middle of the 21st century. Fossil fuel combustion has a number of negative environmental consequences. Fossil-fueled power plants emit air pollutants such as sulfur dioxide , particulate matter , nitrogen oxides, and toxic chemicals (heavy metals: mercury , chromium , and arsenic ), and mobile sources, such as fossil-fueled vehicles, emit nitrogen oxides, carbon monoxide , and particulate matter. Exposure to these pollutants can cause heart disease , asthma , and other human health problems. In addition, emissions from fossil fuel combustion are responsible for acid rain , which has led to the acidification of many lakes and consequent damage to aquatic life, leaf damage in many forests, and the production of smog in or near many urban areas. Furthermore, the burning of fossil fuels releases carbon dioxide (CO 2 ), one of the main greenhouse gases that cause global warming .

In contrast, renewable energy sources accounted for nearly 20 percent of global energy consumption at the beginning of the 21st century, largely from traditional uses of biomass such as wood for heating and cooking . By 2015 about 16 percent of the world’s total electricity came from large hydroelectric power plants, whereas other types of renewable energy (such as solar, wind, and geothermal) accounted for 6 percent of total electricity generation. Some energy analysts consider nuclear power to be a form of renewable energy because of its low carbon emissions; nuclear power generated 10.6 percent of the world’s electricity in 2015.

Growth in wind power exceeded 20 percent and photovoltaics grew at 30 percent annually in the 1990s, and renewable energy technologies continued to expand throughout the early 21st century. Between 2001 and 2017 world total installed wind power capacity increased by a factor of 22, growing from 23,900 to 539,581 megawatts. Photovoltaic capacity also expanded, increasing by 50 percent in 2016 alone. The European Union (EU), which produced an estimated 6.38 percent of its energy from renewable sources in 2005, adopted a goal in 2007 to raise that figure to 20 percent by 2020. By 2016 some 17 percent of the EU’s energy came from renewable sources. The goal also included plans to cut emissions of carbon dioxide by 20 percent and to ensure that 10 percent of all fuel consumption comes from biofuels . The EU was well on its way to achieving those targets by 2017. Between 1990 and 2016 the countries of the EU reduced carbon emissions by 23 percent and increased biofuel production to 5.5 percent of all fuels consumed in the region. In the United States numerous states have responded to concerns over climate change and reliance on imported fossil fuels by setting goals to increase renewable energy over time. For example, California required its major utility companies to produce 20 percent of their electricity from renewable sources by 2010, and by the end of that year California utilities were within 1 percent of the goal. In 2008 California increased this requirement to 33 percent by 2020, and in 2017 the state further increased its renewable-use target to 50 percent by 2030.

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• Perspective
• Published: 18 October 2022

Machine learning for a sustainable energy future

• Zhenpeng Yao   ORCID: orcid.org/0000-0001-8286-8257 1 , 2 , 3 , 4   na1 ,
• Yanwei Lum   ORCID: orcid.org/0000-0001-7261-2098 5 , 6   na1 ,
• Andrew Johnston 6   na1 ,
• Luis Martin Mejia-Mendoza 2 ,
• Xin Zhou 7 ,
• Yonggang Wen 7 ,
• Alán Aspuru-Guzik   ORCID: orcid.org/0000-0002-8277-4434 2 , 8 ,
• Edward H. Sargent   ORCID: orcid.org/0000-0003-0396-6495 6 &
• Zhi Wei Seh   ORCID: orcid.org/0000-0003-0953-567X 5  

Nature Reviews Materials volume  8 ,  pages 202–215 ( 2023 ) Cite this article

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• Computer science

Electrocatalysis

• Energy grids and networks
• Solar cells

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

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Introduction.

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

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

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

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

Performance indicators

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

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

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

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

Acceleration factor of new materials, XPI-1

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

Number of new materials with threshold performance, XPI-2

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

Performance of best material over time, XPI-3

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

Repeatability and reproducibility of new materials, XPI-4

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

Human cost of the accelerated platform, XPI-5

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

Use of the XPIs

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

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

Closed-loop ML for materials discovery

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

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

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

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

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

ML for energy

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

Box 1 Essential concepts in ML

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

Property prediction

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

Generative materials design

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

Self-driving laboratories

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

Aiding characterization

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

Accelerating theoretical computations

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

Optimizing system management

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

Box 2 Grand challenges in energy materials research

Photovoltaics.

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

Identify and minimize materials defects in light-absorbing materials

Design effective recombination-layer materials for tandem solar cells

Develop materials design strategies for long-term operational stability 125

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

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

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

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

Design electrolytes with wider electrochemical windows and high conductivity 4

Identify electrolyte systems to boost battery performance and lifetime 4

Discover new molecules for redox flow batteries with suitable voltage 4

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

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

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

Identify and study active sites on catalytic materials 58

Engineer catalytic materials for extended durability 58 , 60 , 61

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

Engineer multiple catalytic functionalities into the same material 60 , 61

Design multiscale electrode structures for optimized catalytic activity

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

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

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

Photovoltaics materials discovery

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

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

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

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

Solar device structure and fabrication

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

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

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

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

Electrochemical energy storage

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

Electrode and electrolyte materials design

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

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

Battery device and stack management

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

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

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

Electrocatalysts

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

Electrocatalyst materials discovery

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

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

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

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

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

Fuel cell and electrolyser device management

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

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

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

Smart power grids

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

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

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

Opportunities for ML in renewable energy

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

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

Materials with novel geometries

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

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

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

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

Robust predictive models

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

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

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

Stable and synthesizable new materials

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

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

Optimized smart power grids

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

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

Policy optimization

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

Conclusions

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

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Acknowledgements

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

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

Authors and Affiliations

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

Zhenpeng Yao

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

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

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

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

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

Yanwei Lum & Zhi Wei Seh

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

Yanwei Lum, Andrew Johnston & Edward H. Sargent

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

Xin Zhou & Yonggang Wen

Vector Institute for Artificial Intelligence, Toronto, Ontario, Canada

Alán Aspuru-Guzik

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

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

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

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

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

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

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

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

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

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

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

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A computerized system composed of multiple interacting intelligent agents.

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

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A neural network is composed of parameterized and optimizable transformations.

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

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

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

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

Technique for solving problems in the planning of chemical synthesis.

A robotic equipment automated chemical synthesis plan.

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

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Solar, wind, hydroelectric, biomass, and geothermal power can provide energy without the planet-warming effects of fossil fuels.

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Braes of Doune Wind Farm

As of 2017, wind turbines, like the Braes of Doune wind farm near Stirling, Scotland, are now producing 539,000 megawatts of power around the world—22 times more than 16 years before. Unfortunately, this renewable, clean energy generator isn't perfect.

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In any discussion about climate change , renewable energy usually tops the list of changes the world can implement to stave off the worst effects of rising temperatures. That's because renewable energy sources, such as solar and wind, don't emit carbon dioxide and other greenhouse gases that contribute to global warming. Clean energy has far more to recommend it than just being "green." The growing sector creates jobs, makes electric grids more resilient, expands energy access in developing countries, and helps lower energy bills. All of those factors have contributed to a renewable energy renaissance in recent years, with wind and solar setting new records for electricity generation. For the past 150 years or so, humans have relied heavily on coal, oil, and other fossil fuels to power everything from light bulbs to cars to factories. Fossil fuels are embedded in nearly everything we do, and as a result, the greenhouse gases released from the burning of those fuels have reached historically high levels. As greenhouse gases trap heat in the atmosphere that would otherwise escape into space, average temperatures on the surface are rising. Global warming is one symptom of climate change, the term scientists now prefer to describe the complex shifts affecting our planet’s weather and climate systems. Climate change encompasses not only rising average temperatures but also extreme weather events, shifting wildlife populations and habitats, rising seas, and a range of other impacts. Of course, renewables—like any source of energy—have their own trade-offs and associated debates. One of them centers on the definition of renewable energy. Strictly speaking, renewable energy is just what you might think: perpetually available, or as the United States Energy Information Administration puts it, "virtually inexhaustible." But "renewable" doesn't necessarily mean sustainable, as opponents of corn-based ethanol or large hydropower dams often argue. It also doesn't encompass other low- or zero-emissions resources that have their own advocates, including energy efficiency and nuclear power. Types of Renewable Energy Sources Hydropower: For centuries, people have harnessed the energy of river currents, using dams to control water flow. Hydropower is the world's biggest source of renewable energy by far, with China, Brazil, Canada, the U.S., and Russia being the leading hydropower producers. While hydropower is theoretically a clean energy source replenished by rain and snow, it also has several drawbacks. Large dams can disrupt river ecosystems and surrounding communities, harming wildlife, and displacing residents. Hydropower generation is vulnerable to silt buildup, which can compromise capacity and harm equipment. Drought can also cause problems. In the western U.S., carbon dioxide emissions over a 15-year period were 100 megatons higher than they would have been with normal precipitation levels, according to a 2018 study, as utilities turned to coal and gas to replace hydropower lost to drought. Even hydropower at full capacity bears its own emissions problems, as decaying organic material in reservoirs releases methane. Dams aren't the only way to use water for power: Tidal and wave energy projects around the world aim to capture the ocean's natural rhythms. Marine energy projects currently generate an estimated 500 megawatts of power—less than one percent of all renewables—but the potential is far greater. Programs like Scotland’s Saltire Prize have encouraged innovation in this area. Wind: Harnessing the wind as a source of energy started more than 7,000 years ago. Now, electricity-generating wind turbines are proliferating around the globe, and China, the U.S., and Germany are the world's leading wind-energy producers. From 2001 to 2017, cumulative wind capacity around the world increased to more than 539,000 megawatts from 23,900 megawatts—more than 22 fold. Some people may object to how wind turbines look on the horizon and to how they sound, but wind energy, whose prices are declining, is proving too valuable a resource to deny. While most wind power comes from onshore turbines, offshore projects are appearing too, with the most in the United Kingdom and Germany. The first U.S. offshore wind farm opened in 2016 in Rhode Island, and other offshore projects are gaining momentum. Another problem with wind turbines is that they’re a danger for birds and bats, killing hundreds of thousands annually, not as many as from glass collisions and other threats like habitat loss and invasive species, but enough that engineers are working on solutions to make them safer for flying wildlife. Solar: From home rooftops to utility-scale farms, solar power is reshaping energy markets around the world. In the decade from 2007 and 2017 the world's total installed energy capacity from photovoltaic panels increased a whopping 4,300 percent. In addition to solar panels, which convert the sun's light to electricity, concentrating solar power (CSP) plants use mirrors to concentrate the sun's heat, deriving thermal energy instead. China, Japan, and the U.S. are leading the solar transformation, but solar still has a long way to go, accounting for around just two percent of the total electricity generated in the U.S. in 2017. Solar thermal energy is also being used worldwide for hot water, heating, and cooling. Biomass: Biomass energy includes biofuels, such as ethanol and biodiesel, wood, wood waste, biogas from landfills, and municipal solid waste. Like solar power, biomass is a flexible energy source, able to fuel vehicles, heat buildings, and produce electricity. But biomass can raise thorny issues. Critics of corn-based ethanol, for example, say it competes with the food market for corn and supports the same harmful agricultural practices that have led to toxic algae blooms and other environmental hazards. Similarly, debates have erupted over whether it's a good idea to ship wood pellets from U.S. forests over to Europe so that it can be burned for electricity. Meanwhile, scientists and companies are working on ways to more efficiently convert corn stover, wastewater sludge, and other biomass sources into energy, aiming to extract value from material that would otherwise go to waste. Geothermal: Used for thousands of years in some countries for cooking and heating, geothermal energy is derived from Earth’s internal heat. On a large scale, underground reservoirs of steam and hot water can be tapped through wells that can go a two kilometers deep or more to generate electricity. On a smaller scale, some buildings have geothermal heat pumps that use temperature differences several meters below ground for heating and cooling. Unlike solar and wind energy, geothermal energy is always available, but it has side effects that need to be managed, such as the rotten-egg smell that can accompany released hydrogen sulfide. Ways To Boost Renewable Energy Cities, states, and federal governments around the world are instituting policies aimed at increasing renewable energy. At least 29 U.S. states have set renewable portfolio standards—policies that mandate a certain percentage of energy from renewable sources. More than 100 cities worldwide now boast receiving at least 70 percent of their energy from renewable sources, and still others are making commitments to reach 100 percent. Other policies that could encourage renewable energy growth include carbon pricing, fuel economy standards, and building efficiency standards. Corporations are making a difference too, purchasing record amounts of renewable power in 2018. Wonder whether your state could ever be powered by 100 percent renewables? No matter where you live, scientist Mark Jacobson believes it's possible. That vision is laid out here , and while his analysis is not without critics , it punctuates a reality with which the world must now reckon. Even without climate change, fossil fuels are a finite resource, and if we want our lease on the planet to be renewed, our energy will have to be renewable.

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With the advantages of clean, renewable energy, offshore wind power has become one of the fastest-growing energy sources and one of the most economical solutions for electricity generation. Consequently, the number and scale of offshore wind farms have been developing rapidly. In light of climate change, demand continues to grow. However, to address challenges related to availability, production, and cost reduction for wind farm owners, it is critically urgent to investigate and explore advanced intelligent techniques for the monitoring, operation, and maintenance of offshore wind farms. These techniques are essential for improving the safety and reliability of wind farm systems, reducing operation and maintenance costs, enhancing power generation efficiency, and accelerating the implementation of offshore wind farms. Advanced techniques have made tremendous progress in the monitoring, operation, and maintenance of offshore wind farms. However, current approaches are often fully data-driven, making it difficult to handle the massive amounts of monitoring and detection data effectively and incorporating limited domain knowledge in areas such as data processing, fault diagnosis, and fault prediction. Additionally, robust manufacturing technologies and continuous data monitoring for early warning in these safety-critical power assets often result in complex fault mechanisms and fewer fault samples for key components of offshore wind farms. Consequently, current methods frequently exhibit poor generalization performance and provide unreliable diagnostic and predictive decisions for wind farm owners. In this context, it is significantly necessary to explore and examine advanced and innovative paradigms for data processing, condition monitoring, fault diagnosis, fault prediction, and maintenance decision-making. These paradigms have the potential to substantially enhance the flexibility and reliability of advanced techniques, thereby improving their application in the monitoring, operation, and maintenance of offshore wind farms. The primary objective of this Research Topic is to present collective insights and novel methodologies related to advanced techniques for monitoring, operation, and maintenance, aimed at enhancing the high reliability and low maintenance costs of offshore wind farms. We welcome a variety of article types, including Original Research, Reviews, and Perspectives. Contributions are sought on the following topics, but are not limited to: • Advanced techniques for monitoring offshore wind farms • Advanced techniques for the operation and maintenance of wind farms • Trustworthy decision-making support for the operation and maintenance of wind farms • Advanced techniques for fault diagnosis of wind farms • Advanced techniques for wind power forecasting • Integrated physical models and data-driven methods for wind farm management • Innovative inspection technologies for offshore wind farms

Keywords : operation and maintenance, renewable energy integration, offshore wind farms, wind power forecasting

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Wind turbine blades and connected photovoltaic panels are seen in the tidal flat wetland of Yancheng City, Jiangsu province, September 23, 2023.

7 Steps to What a Real Renewable Energy Transition Looks Like

Historically, an overhaul for humanity's energy system would take hundreds or many thousands of years. the rapid shift to cleaner, more sustainable sources of power generations will easily be the most ambitious enterprise our species has ever undertaken..

Humanity’s transition from relying overwhelmingly on fossil fuels to instead using alternative low-carbon energy sources is sometimes said to be unstoppable and exponential . A boosterish attitude on the part of many renewable energy advocates is understandable: overcoming people’s climate despair and sowing confidence could help muster the needed groundswell of motivation to end our collective fossil fuel dependency. But occasionally a reality check is in order.

The reality is that energy transitions are a big deal, and they typically take centuries to unfold. Historically, they’ve been transformative for societies—whether we’re speaking of humanity’s taming of fire hundreds of thousands of years ago, the agricultural revolution 10,000 years ago, or our adoption of fossil fuels starting roughly 200 years ago. Given (1) the current size of the human population (there are eight times as many of us alive today as there were in 1820, when the fossil fuel energy transition was getting underway), (2) the vast scale of the global economy, and (3) the unprecedented speed with which the transition will have to be made in order to avert catastrophic climate change, a rapid renewable energy transition is easily the most ambitious enterprise our species has ever undertaken.

As we’ll see, the evidence shows that the transition is still in its earliest stages, and at the current rate, it will fail to avert a climate catastrophe in which an unimaginable number of people will either die or be forced to migrate, with most ecosystems transformed beyond recognition.

Implementing these seven steps will change everything. The result will be a world that’s less crowded, one where nature is recovering rather than retreating, and one in which people are healthier (because they’re not soaked in pollution) and happier.

We’ll unpack the reasons why the transition is currently such an uphill slog. Then, crucially, we’ll explore what a real energy transition would look like, and how to make it happen.

Why This Is (So Far) Not a Real Transition

Despite trillions of dollars having been spent on renewable energy infrastructure, carbon emissions are still increasing , not decreasing, and the share of world energy coming from fossil fuels is only slightly less today than it was 20 years ago. In 2024, the world is using more oil, coal, and natural gas than it did in 2023.

While the U.S. and many European nations have seen a declining share of their electricity production coming from coal, the continuing global growth in fossil fuel usage and CO2 emissions overshadows any cause for celebration .

Why is the rapid deployment of renewable energy not resulting in declining fossil fuel usage? The main culprit is economic growth, which consumes more energy and materials . So far, the amount of annual growth in the world’s energy usage has exceeded the amount of energy added each year from new solar panels and wind turbines. Fossil fuels have supplied the difference.

So, for the time being at least, we are not experiencing a real energy transition. All that humanity is doing is adding energy from renewable sources to the growing amount of energy it derives from fossil fuels. The much-touted energy transition could, if somewhat cynically, be described as just an aspirational grail.

How long would it take for humanity to fully replace fossil fuels with renewable energy sources, accounting for both the current growth trajectory of solar and wind power, and also the continued expansion of the global economy at the recent rate of 3 percent per year? Economic models suggest the world could obtain most of its electricity from renewables by 2060 (though many nations are not on a path to reach even this modest marker). However, electricity represents only about 20 percent of the world’s final energy usage; transitioning the other 80 percent of energy usage would take longer—likely many decades.

However, to avert catastrophic climate change, the global scientific community says we need to achieve net-zero carbon emissions by 2050—i.e., in just 25 years. Since it seems physically impossible to get all of our energy from renewables that soon while still growing the economy at recent rates, the IPCC (the international agency tasked with studying climate change and its possible remedies) assumes that humanity will somehow adopt carbon capture and sequestration technologies at scale—including technologies that have been shown not to work —even though there is no existing way of paying for this vast industrial build-out. This wishful thinking on the part of the IPCC is surely proof that the energy transition is not happening at sufficient speed.

Why isn’t it? One reason is that governments, businesses, and an awful lot of regular folks are clinging to an unrealistic goal for the transition. Another reason is that there is insufficient tactical and strategic global management of the overall effort. We’ll address these problems separately, and in the process uncover what it would take to nurture a true energy transition.

The Core of the Transition is Using Less Energy

At the heart of most discussions about the energy transition lie two enormous assumptions: that the transition will leave us with a global industrial economy similar to today’s in terms of its scale and services, and that this future renewable-energy economy will continue to grow, as the fossil-fueled economy has done in recent decades. But both of these assumptions are unrealistic. They flow from a largely unstated goal: we want the energy transition to be completely painless, with no sacrifice of profit or convenience. That goal is understandable, since it would presumably be easier to enlist the public, governments, and businesses in an enormous new task if no cost is incurred (though the history of overwhelming societal effort and sacrifice during wartime might lead us to question that presumption).

But the energy transition will undoubtedly entail costs. Aside from tens of trillions of dollars in required monetary investment, the energy transition will itself require energy—lots of it. It will take energy to build solar panels, wind turbines, heat pumps, electric vehicles, electric farm machinery, zero-carbon aircraft, batteries, and the rest of the vast panoply of devices that would be required to operate an electrified global industrial economy at current scale.

In the early stages of the transition, most of that energy for building new low-carbon infrastructure will have to come from fossil fuels, since those fuels still supply over 80 percent of world energy (bootstrapping the transition—using only renewable energy to build transition-related machinery—would take far too long). So, the transition itself, especially if undertaken quickly, will entail a large pulse of carbon emissions. Teams of scientists have been seeking to estimate the size of that pulse; one group suggests that transition-related emissions will be substantial, ranging from 70 to 395 billion metric tons of CO2 “with a cross-scenario average of 195 GtCO2”—the equivalent of more than five years’ worth of global carbon CO2 emissions at current rates. The only ways to minimize these transition-related emissions would be, first, to aim to build a substantially smaller global energy system than the one we are trying to replace; and second, to significantly reduce energy usage for non-transition-related purposes—including transportation and manufacturing, cornerstones of our current economy—during the transition.

In addition to energy, the transition will require materials. While our current fossil-fuel energy regime extracts billions of tons of coal, oil, and gas, plus much smaller amounts of iron, bauxite, and other ores for making drills, pipelines, pumps, and other related equipment, the construction of renewable energy infrastructure at commensurate scale would require far larger quantities of non-fuel raw materials —including copper, iron, aluminum, lithium, iridium, gallium, sand, and rare earth elements.

While some estimates suggest that global reserves of these elements are sufficient for the initial build-out of renewable-energy infrastructure at scale, there are still two big challenges. First: obtaining these materials will require greatly expanding extractive industries along with their supply chains. These industries are inherently polluting, and they inevitably degrade land. For example, to produce one ton of copper ore, over 125 tons of rock and soil must be displaced. The rock-to-metal ratio is even worse for some other ores . Mining operations often take place on Indigenous peoples’ lands and the tailings from those operations often pollute rivers and streams. Non-human species and communities in the global South are already traumatized by land degradation and toxification; greatly expanding resource extraction—including deep-sea mining —would only deepen and multiply the wounds.

The second materials challenge: renewable energy infrastructure will have to be replaced periodically— every 25 to 50 years . Even if Earth’s minerals are sufficient for the first full-scale build-out of panels, turbines, and batteries, will limited mineral abundance permit continual replacements? Transition advocates say that we can avoid depleting the planet’s ores by recycling minerals and metals after constructing the first iteration of solar-and-wind technology. However, recycling is never complete, with some materials degraded in the process. One analysis suggests recycling would only buy a couple of centuries’ worth of time before depletion would bring an end to the regime of replaceable renewable-energy machines—and that’s assuming a widespread, coordinated implementation of recycling on an unprecedented scale. Again, the only real long-term solution is to aim for a much smaller global energy system.

The transition of society from fossil fuel dependency to reliance on low-carbon energy sources will be impossible to achieve without also reducing overall energy usage substantially and maintaining this lower rate of energy usage indefinitely. This transition isn’t just about building lots of solar panels, wind turbines, and batteries. It is about organizing society differently so that is uses much less energy and gets whatever energy it uses from sources that can be sustained over the long run.

How We Could Actually Do It, In Seven Concurrent Steps

Step one: Cap global fossil fuel extraction through global treaty, and annually lower the cap. We will not reduce carbon emissions until we reduce fossil fuel usage—it’s just that simple. Rather than trying to do this by adding renewable energy (which so far hasn’t resulted in a lessening of emissions), it makes far more sense simply to limit fossil fuel extraction. I wrote up the basics of a treaty along these lines several years ago in my book, The Oil Depletion Protocol .

Step two: Manage energy demand fairly. Reducing fossil fuel extraction presents a problem. Where will we get the energy required for transition purposes? Realistically, it can only be obtained by repurposing energy we’re currently using for non-transition purposes. That means most people, especially in highly industrialized countries, would have to use significantly less energy, both directly and also indirectly (in terms of energy embedded in products, and in services provided by society, such as road building). To accomplish this with the minimum of societal stress will require a social means of managing energy demand.

The fairest and most direct way to manage energy demand is via quota rationing . Tradable Energy Quotas ( TEQs ) is a system designed two decades ago by British economist David Fleming; it rewards energy savers and gently punishes energy guzzlers while ensuring that everyone gets energy they actually need. Every adult would be given an equal free entitlement of TEQs units each week. If you use less than your entitlement of units, you can sell your surplus. If you need more, you can buy them. All trading takes place at a single national price, which will rise and fall in line with demand.

Step three: Manage the public’s material expectations . Persuading people to accept using less energy will be hard, if everyone still wants to use more. Therefore, it will be necessary to manage the public’s expectations. This may sound technocratic and scary, but in fact society has already been managing the public’s expectations for over a century via advertising—which constantly delivers messages encouraging everyone to consume as much as they can. Now we need different messages to set different expectations.

What’s our objective in life? Is it to have as much stuff as possible, or to be happy and secure? Our current economic system assumes the former, and we have instituted an economic goal (constant growth) and an indicator (gross domestic product, or GDP) to help us achieve that goal. But ever-more people using ever-more stuff and energy leads to increased rates of depletion, pollution, and degradation, thereby imperiling the survival of humanity and the rest of the biosphere. In addition, the goal of happiness and security is more in line with cultural traditions and human psychology . If happiness and security are to be our goals, we should adopt indicators that help us achieve them. Instead of GDP, which simply measures the amount of money changing hands in a country annually, we should measure societal success by monitoring human well-being. The tiny country of Bhutan has been doing this for decades with its Gross National Happiness ( GNH ) indicator, which it has offered as a model for the rest of the world.

Step four: Aim for population decline . If population is always growing while available energy is capped, that means ever-less energy will be available per capita. Even if societies ditch GDP and adopt GNH, the prospect of continually declining energy availability will present adaptive challenges. How can energy scarcity impacts be minimized? The obvious solution: welcome population decline and plan accordingly.

Global population will start to decline sometime during this century . Fertility rates are falling worldwide, and China, Japan, Germany, and many other nations are already seeing population shrinkage. Rather than viewing this as a problem, we should see it as an opportunity. With fewer people, energy decline will be less of a burden on a per capita basis. There are also side benefits: a smaller population puts less pressure on wild nature, and often results in rising wages . We should stop pushing a pro-natalist agenda; ensure that women have the educational opportunities, social standing, security, and access to birth control to make their own childbearing choices; incentivize small families, and aim for the long-term goal of a stable global population closer to the number of people who were alive at the start of the fossil-fuel revolution (even though voluntary population shrinkage will be too slow to help us much in reaching immediate emissions reduction targets).

Step five: Target technological research and development to the transition. Today the main test of any new technology is simply its profitability. However, the transition will require new technologies to meet an entirely different set of criteria, including low-energy operation and minimization of exotic and toxic materials. Fortunately, there is already a subculture of engineers developing low-energy and intermediate technologies that could help run a right-sized circular economy .

Step six: Institute technological triage . Many of our existing technologies don’t meet these new criteria. So, during the transition, we will be letting go of familiar but ultimately destructive and unsustainable machines.

Some energy-guzzling machines—such as gasoline-powered leaf blowers —will be easy to say goodbye to. Commercial aircraft will be harder. Artificial intelligence is an energy guzzler we managed to live without until very recently; perhaps it’s best if we bid it a quick farewell. Cruise ships? Easy: downsize them, replace their engines with sails, and expect to take just one grand voyage during your lifetime. Weapons industries offer plenty of examples of machines we could live without . Of course, giving up some of our labor-saving devices will require us to learn useful skills—which could end up providing us with more exercise. For guidance along these lines, consult the rich literature of technology criticism.

Step seven: Help nature absorb excess carbon . The IPCC is right: if we’re to avert catastrophic climate change we need to capture carbon from the air and sequester it for a long time. But not with machines. Nature already removes and stores enormous amounts of carbon; we just need to help it do more (rather than reducing its carbon-capturing capabilities, which is what humanity is doing now). Reform agriculture to build soil rather than destroy it. Restore ecosystems , including grasslands, wetlands, forests, and coral reefs.

Granted, this seven-step program appears politically unachievable today. But that’s largely because humanity hasn’t yet fully faced the failure of our current path of prioritizing immediate profits and comfort above long-term survival—and the consequences of that failure. Given better knowledge of where we’re currently headed, and the alternatives, what is politically impossible today could quickly become inevitable.

Social philosopher Roman Krznaric writes that profound social transformations are often tied to wars, natural disasters, or revolutions. But crisis alone is not positively transformative. There must also be ideas available for different ways to organize society, and social movements energized by those ideas. We have a crisis and (as we have just seen) some good ideas for how to do things differently. Now we need a movement.

Building a movement takes political and social organizing skills, time, and hard work. Even if you don’t have the skills for organizing, you can help the cause by learning what a real energy transition requires and then educating the people you know; by advocating for degrowth or related policies; and by reducing your own energy and materials consumption . Calculate your ecological footprint and shrink it over time, using goals and strategies, and tell your family and friends what you are doing and why.

Even with a new social movement advocating for a real energy transition, there is no guarantee that civilization will emerge from this century of unraveling in a recognizable form. But we all need to understand: this is a fight for survival in which cooperation and sacrifice are required, just as in total war. Until we feel that level of shared urgency, there will be no real energy transition, and little prospect for a desirable human future.

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