The transition towards 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. Introduction To grapple with climate change, many countries are striving to achieve carbon-neutrality of their electricity sectors.

Technological advancements continue to create new opportunities in Ontario's electricity sector, and many businesses are turning to artificial intelligence (AI) to enhance their operations. If you're an organization that is currently using AI, or exploring the use of it, the IESO wants to hear from you. By taking a short survey, you can help inform a whitepaper the IESO is developing that will provide insight into the opportunities AI can provide for Ontario's electricity market participants and ratepayers. The whitepaper is the latest in the Innovation and Sector Evolution White Paper Series, and an overview was presented to stakeholders at a webinar held yesterday. The survey is a key first step that will provide a foundational understanding of where various segments of Ontario's electricity sector are in their AI journey, and where they expect to be in five years.

Now that energy storage technologies are coming close to commercial reality, decades of work should result in artificial intelligence (AI) emerging as the third key technology in the transformation of the electricity sector. Combined with scalable generation and storage, it will blur the distinction between suppliers and consumers, with excess local generation being fed into the grid so that entities from individual homeowners to business and municipalities will become "producer-consumers" or "prosumers". Demand management systems will also have a role to play. The introduction of multiple players of widely varying consumption and production patterns connecting into a single nationwide grid is impossible until we have software able to predict and manage energy flows to ensure that supply and demand balance at all times. There are obvious drivers also for energy storage at small scale, particularly for remote locations. Apart from the potential for autonomy, and the ability to smooth draw from the grid (avoiding or at least reducing demand-based charges), local storage could relieve grid congestion and add flexibility to power generation requirements, potentially improving network stability.