How artificial intelligence can help achieve a clean energy future – MIT News

The Role of Artificial Intelligence in Advancing Sustainable Development Goals for Energy

Artificial intelligence (AI) presents a dual impact on the global energy landscape. While the increasing energy demand from AI-powering data centers poses a challenge to achieving clean energy objectives, AI also offers significant opportunities to accelerate the energy transition. This report outlines how AI applications are contributing to key Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action).

Enhancing Grid Operations for Clean and Reliable Energy (SDG 7 & SDG 11)

AI is instrumental in modernizing electricity grids, making them more efficient, resilient, and capable of integrating renewable energy sources. This directly supports the goal of ensuring access to affordable, reliable, and sustainable energy for all.

Real-Time Control and Optimization

• Renewable Integration: AI algorithms help manage the intermittency of solar and wind power, ensuring a stable balance between electricity supply and demand.
• Demand-Side Management: AI enables smart responses to grid conditions, such as shifting electric vehicle charging to off-peak hours or adjusting smart thermostats, promoting efficient energy consumption.
• Efficiency and Cost Reduction: By optimizing the storage and distribution of energy, AI increases overall grid efficiency and reduces operational costs.

Predictive Maintenance and Resilience

To maintain a reliable energy supply, a core component of SDG 7 and SDG 11, AI is deployed for predictive maintenance. This capability contributes to building resilient infrastructure as outlined in SDG 9.

1. AI algorithms monitor key performance data from grid equipment.
2. Deviations from normal operation trigger alerts for operators.
3. Proactive intervention prevents equipment failures and potential blackouts.
4. This process extends the lifetime of critical infrastructure and improves worker productivity.

Strategic Infrastructure Planning for a Sustainable Future (SDG 9 & SDG 13)

AI provides powerful tools for planning long-term energy infrastructure, ensuring it is both sustainable and resilient to the impacts of climate change.

Forecasting and System Modeling

• Future Needs Assessment: AI assists in forecasting future energy demands to guide strategic investments in generation, storage, and transmission infrastructure.
• Climate Resilience: AI models can predict the increasing frequency of extreme weather events, allowing planners to design infrastructure that can withstand climate-related risks, directly supporting climate action (SDG 13).
• Regulatory Streamlining: Large language models can analyze regulatory documents to accelerate the approval process for new clean energy projects, speeding up the transition.

Accelerating Materials Innovation for Clean Technology (SDG 7 & SDG 9)

The development of novel materials is critical for advancing clean energy technologies. AI is significantly accelerating the pace of materials discovery and optimization, fostering innovation as called for in SDG 9.

Advanced Simulation and Discovery

• AI enables faster physics-based simulations at the atomic level, providing a deeper understanding of material properties.
• This knowledge guides the design of new materials for applications such as advanced nuclear reactors, high-capacity batteries, and efficient solar cells.

AI-Guided Experimentation

AI is transforming the research workflow by creating an active learning loop that combines machine intelligence with human expertise and robotics.

1. AI analyzes existing scientific literature and experimental data to form hypotheses.
2. It suggests the most informative experiments to conduct next.
3. Robotic systems execute the experiments, synthesizing and testing materials.
4. This accelerated process can shorten material development timelines from decades to years, fast-tracking the deployment of clean energy solutions.

Collaborative Efforts and Research Initiatives (SDG 17)

Addressing the complex relationship between AI and energy requires collaboration across sectors. Initiatives led by institutions like the MIT Energy Initiative (MITEI) exemplify the partnerships needed to achieve the SDGs (SDG 17).

Targeted Research Programs

• Data Center Efficiency: MITEI’s Data Center Power Forum convenes member companies to address the challenges of rising energy demand from AI.
• Clean Energy Applications: Research projects are using AI to model fusion energy, design adaptive grid planning tools, optimize solar cells, and develop robots for maintaining renewable energy infrastructure.

Fostering Partnerships

By bringing together experts from academia, industry, and government, these initiatives facilitate the exchange of knowledge and drive collective action. This collaborative approach is essential for harnessing AI’s potential to solve pressing energy challenges and advance the global sustainable development agenda.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

1. SDG 7: Affordable and Clean Energy

   • The article’s central theme is the transition to clean energy and the role of AI in this process. It discusses the optimization of renewable energy sources like wind and solar, increasing energy efficiency in various sectors, and developing new materials for clean energy technologies such as batteries and nuclear reactors. It also addresses the challenge of increased energy demand from AI data centers, which directly relates to ensuring sustainable energy for all.

2. SDG 9: Industry, Innovation, and Infrastructure

   • The article extensively covers innovation and technological advancement through AI. It highlights how AI is being used to build resilient and efficient infrastructure, specifically the electric power grid. The text discusses upgrading this infrastructure to handle intermittent renewable energy sources, using AI for predictive maintenance to extend the life of key equipment, and fostering research to discover advanced materials, all of which are core components of SDG 9.

3. SDG 11: Sustainable Cities and Communities

   • The reliability of the electricity grid is crucial for the functioning of cities and communities. The article addresses the risk of “customer prices and service interruptions” and “possible blackouts.” By using AI to ensure a continuous supply of electricity and manage demand through smart devices like thermostats, the article touches upon making urban infrastructure more resilient and sustainable.

4. SDG 13: Climate Action

   • The “clean energy transition” is a primary strategy for climate change mitigation. The article discusses how AI can accelerate this transition by reducing energy consumption and associated emissions. Furthermore, it mentions AI’s role in strengthening resilience to climate-related hazards by predicting “low-probability but high-risk events such as hurricanes, floods, and wildfires” that threaten the energy infrastructure.

5. SDG 17: Partnerships for the Goals

   • The article emphasizes the need for collaboration to address the complex challenges at the intersection of AI and energy. It mentions MITEI’s role as a “convenor, bringing together interested parties” and its symposium that gathered experts from “academia, industry, government, and nonprofit organizations.” This highlights the multi-stakeholder partnerships necessary to achieve sustainable development.

2. What specific targets under those SDGs can be identified based on the article’s content?

1. Targets under SDG 7 (Affordable and Clean Energy)

   • Target 7.2: “By 2030, increase substantially the share of renewable energy in the global energy mix.” The article directly supports this by explaining how AI helps “integrate the growing share of renewables” and optimizes the “design and siting of new wind and solar installations.”
   • Target 7.3: “By 2030, double the global rate of improvement in energy efficiency.” The article states that “use of AI is reducing energy consumption and associated emissions in buildings, transportation, and industrial processes” and that AI algorithms are “increasing efficiency” on electric power grids.
   • Target 7.a: “By 2030, enhance international cooperation to facilitate access to clean energy research and technology… and promote investment in energy infrastructure and clean energy technology.” The article describes research at MIT and the MITEI Data Center Power Forum, which brings together companies to address energy challenges. It also details how AI is helping researchers “discover or design novel materials for nuclear reactors, batteries, and electrolyzers.”

2. Targets under SDG 9 (Industry, Innovation, and Infrastructure)

   • Target 9.4: “By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency…” The article discusses upgrading the electric grid to be more efficient and reliable. The use of AI for “predictive maintenance” to prevent equipment failures and “extend the lifetime of key equipment” is a clear example of increasing resource-use efficiency in infrastructure.
   • Target 9.5: “Enhance scientific research, upgrade the technological capabilities of industrial sectors… encouraging innovation…” The entire article is a testament to this target, focusing on how AI research and innovation can solve energy problems. The section on “harnessing AI to discover and exploit advanced materials” by speeding up the discovery process from “decades… to just a few years” directly relates to enhancing scientific research and technological capabilities.

3. Targets under SDG 11 (Sustainable Cities and Communities)

   • Target 11.5: “By 2030, significantly reduce… the number of people affected and… economic losses… caused by disasters…” The article mentions that AI can help ensure grid reliability during “low-probability but high-risk events such as hurricanes, floods, and wildfires,” thereby reducing the impact of these disasters on communities by preventing blackouts.

4. Targets under SDG 13 (Climate Action)

   • Target 13.1: “Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.” The article’s discussion of using AI to predict extreme weather events and ensure the grid remains stable during such events is a direct example of strengthening resilience to climate-related hazards.

5. Targets under SDG 17 (Partnerships for the Goals)

   • Target 17.16: “Enhance the Global Partnership for Sustainable Development, complemented by multi-stakeholder partnerships…” The article describes MITEI’s symposium which “brought together AI and energy experts from across academia, industry, government, and nonprofit organizations,” exemplifying a multi-stakeholder partnership to share knowledge and address a common challenge.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

1. Indicators for SDG 7

   • Share of renewable energy in the grid: The article mentions integrating a “growing share of renewables,” which is a direct measure of progress towards Target 7.2.
   • Reduction in energy consumption: The text states AI is “reducing energy consumption” in various sectors, which can be quantified to measure improvements in energy efficiency (Target 7.3).
   • Speed of material discovery: The article implies an indicator by stating AI could shorten the material discovery process from “decades… to just a few years.” This acceleration can be measured to track progress in clean energy technology research (Target 7.a).

2. Indicators for SDG 9

   • Grid operational efficiency: The article mentions AI is “increasing efficiency and reduce costs” on power grids. This can be measured through metrics like transmission loss reduction or cost per kilowatt-hour.
   • Rate of equipment failure: The use of “predictive maintenance” to “prevent equipment failures” implies that a reduction in the frequency and duration of equipment downtime can be used as an indicator of more resilient infrastructure.

3. Indicators for SDG 11 & 13

   • Frequency and duration of power outages (blackouts): The article’s focus on preventing “possible blackouts” and ensuring a “continuous supply of electricity” suggests that a reduction in service interruptions, especially during extreme weather, is a key indicator of community and infrastructure resilience.
   • Accuracy of extreme weather prediction: The article states that “AI can help by predicting such events” (hurricanes, floods). Improvements in the accuracy and lead time of these predictions would be an indicator of enhanced adaptive capacity.

4. Indicators for SDG 17

   • Number of multi-stakeholder collaborations: The formation of initiatives like the “Data Center Power Forum” and the hosting of symposia involving “academia, industry, government, and nonprofit organizations” serve as indicators of active partnerships.

4. Summary Table of SDGs, Targets, and Indicators

Source: news.mit.edu