Artificial Intelligence: A Catalyst to Transform Energy Sector for Sustainable Future

Commentary by Akanksha Golchha

Published October 29, 2024

Amid rapidly growing demand, India's energy sector faces two key challenges-providing affordable energy for domestic and industrial consumers and preparing the energy infrastructure for its expanding renewable energy (RE) base. India's grid infrastructure is aging and the transmission and distribution (T&D) losses are above 15 percent annually, more than three times higher than the United States, underlining the urgency for reforms.

Artificial intelligence (AI) can be a powerful tool to improve how India generates, distributes, and consumes energy. Policymakers can use AI to design policies that are impact driven while focusing on developing governance frameworks for the use of AI in energy systems. AI can be used to optimize the grid, reduce inefficiencies, and integrate decentralized RE sources. AI can also play a critical role in how public-private partnerships (PPPs) are designed to integrate technology into the energy system and build capacities of the workforce that are fit for the purpose.

Use of AI for Effective Policy Design

Both system-level and consumer-level energy management can be optimized through AI integration. Unlike traditional policymaking approaches, AI enables agent-based modeling that can create various scenarios to supplement the decisionmaking tools used by policymakers to prioritize various interventions. For instance, these models can anticipate how incentives for renewable energy adoption may affect different sectors, optimizing for both impact and cost efficiency. As a result, government expenditure can be minimized resources can be allocated for other areas.

The United States has identified priority areas including grid planning and resilience to deploy AI. In addition, the United States is exploring how AI can be used as a tool to increase the pace of the deployment of clean energy projects. This includes using AI tools to engage with communities and other stakeholders effectively, analyzing data to ensure better social and environmental outcomes, and identifying project sites conducive to environmental goals. India can use this as a precedent to create tailored solutions for its unique energy landscape.

AI for Cleaner Power Systems and Distributed RE Integration

AI can be used to integrate distributed renewable energy sources such as rooftop solar, wind, and bioenergy with the grid. This is critical given the policy momentum in India to add up to 30 gigawatts (GW) of distributed solar capacity under the prime minister's Surya Ghar Yojana (solar rooftop scheme). AI algorithms can optimize the performance of these systems, predict renewable energy generation, and enhance grid stability through real-time data analytics.

For instance, India aims to install 250 million smart meters that not only aim to enhance the operational efficiency of energy systems but also generate invaluable data that can be used to design better policies and programs through AI integration.

A demonstrated example of using AI for improved grid management is the integrated virtual power plant (VPP) systems, which allow consumers to aggregate decentralized energy resources like rooftop solar panels and battery storage systems into a single controllable entity. The supply and demand of energy can be managed in real-time by using AI algorithms for VPPs and in turn maintain grid stability. For example, Australia has successfully used VPPs to balance loads, manage its grid, and integrate RE. By adopting AI-based VPP systems, India can more effectively manage decentralized RE, reduce the strain on its aging energy infrastructure, and defer costly upgrades to transmission systems.

Mainstreaming AI in Public-Private Partnerships

An effective collaboration between technology companies and energy providers is crucial to scale AI applications across the sector. Government programs can be designed to bring new technologies and capacity-building opportunities to India through PPP models. For instance, these partnerships can pave the way for AI-driven predictive maintenance to reduce grid downtime, and improve energy efficiency, thereby reducing T&D losses.

Simultaneously, it is important to invest in training programs to skill and reskill the workforce in AI-driven energy systems. India can potentially create 3.4 million jobs by 2030 to achieve its 500 GW RE target. The momentum towards AI and energy is an opportune time to develop skilling programs that ensure that India stays competitive in the global AI landscape by developing relevant skills in the workforce. The recently announced AI regulations in California provide necessary lessons that require rigorous testing of AI to prevent misuse and enhance data transparency. The five bills passed introduce stricter privacy controls for consumer opt-outs and regulations on AI model transparency, usage, and accountability. Similar practices can be adopted for AI in energy systems and India can take a cue.

Integration of AI-driven technologies can optimize grid operations, reduce T&D losses, and facilitate the large-scale integration of RE into the grid. AI is poised to be a game-changing technology that can facilitate India's energy independence goals while ensuring that clean, affordable, and reliable energy is available to all. To do this, the government should focus on employing AI to create suitable policies and mechanisms that are impactful in enhancing system efficiencies and innovation.

Akanksha Golchha is a fellow with the Chair on India and Emerging Asia Economics at the Center for Strategic and International Studies in Washington, D.C.

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