The value of storage and renewables plus storage can be significantly increased through analytics that continuously optimize operations through a combination of AI techniques. With application of reinforced learning for bid optimization and forecasting future market conditions with supervised learning, AI has become a key ingredient to maximizing value. This presentation will address analytic techniques to improve the economic intelligence of ISO bids (both day-ahead and real-time) along with direct override control signals to further advance storage economics. The presentation will show a case study in ERCOT of the optimization techniques and AI structure to increase storage project revenues by 30% traditional approaches. The AI methods we apply will address techniques for next-day and next-hour probabilistic price spikes. The dynamic optimization structure creates economically optimal offer strategies for the day-ahead and real-time markets that maximizes revenue given the price forecasts while managing the state of charge of the system.
