Energy system digitization in the era of AI: A three-layered approach toward carbon neutrality.

The transition toward carbon-neutral electricity is one of the biggest game changers in addressing climate change since it addresses the dual challenges of removing carbon emissions from the two largest sectors of emitters: electricity and transportation. The transition to a carbon-neutral electric grid poses significant challenges to conventional paradigms of modern grid planning and operation. Much of the challenge arises from the scale of the decision-making and the uncertainty associated with the energy supply and demand. Artificial intelligence (AI) could potentially have a transformative impact on accelerating the speed and scale of carbon-neutral transition, as many decision-making processes in the power grid can be cast as classic, though challenging, machine-learning tasks. We point out that to amplify AI's impact on carbon-neutral transition of the electric energy systems, the AI algorithms originally developed for other applications should be tailored in three layers of technology, markets, and policy.

Targeted demand response for mitigating price volatility and enhancing grid reliability in synthetic Texas electricity markets.

Demand response (DR) is rapidly gaining attention as a solution to enhance the grid reliability with deep renewable energy penetration. Although studies have demonstrated the benefits of DR in mitigating price volatility, there is limited work considering the choice of locations for DR for maximal impact. We reveal that very small load reductions at a handful of targeted locations can lead to a significant decrease in price volatility and grid congestion levels based on a synthetic Texas grid model. We achieve this through exploiting the highly nonlinear nature of congestion dynamics and by strategically selecting DR locations. We demonstrate that we can similarly place energy storage to achieve an equivalent impact. Our findings suggest that targeted DR at specific locations, rather than across-the-board DR, can have substantial benefits to the grid. These findings can inform energy policy makers and grid operators how to target DR initiatives for improving grid reliability.