Section: New Results

Optimal control of energy storage

Energy storage revenue estimation is essential for analyzing financial feasibility of investment in batteries. In [22], we quantify the cycles of operation considering depth-of-discharge (DoD) of operational cycles and provide an algorithm to calculate equivalent 100% DoD cycles. This facilitates in comparing cycles of different DoDs. The battery life is frequently defined as a combination of cycle and calendar life. We propose a battery capacity degradation model based on the cycle and the calendar life and operational cycles. Using equivalent 100% DoD cycles and revenue generated, we calculate the dollars per cycle revenue of storage performing electricity price based arbitrage and ancillary services for load balancing in real time. Using PJM’s (a regional transmission organization in the United States) real data we calculate short term and long term financial potential for the year of 2017. We observe that participating in ancillary services is significantly more beneficial for storage owners compared to participating in energy arbitrage.

Battery life is often described a combination of cycle life and calendar life. In [21], we propose a mechanism to limit the number of cycles of operation over a time horizon in an optimal arbitrage algorithm proposed in our previous work. The cycles of operation have to be tuned based on price volatility to maximize the battery life and arbitrage gains.

In [23], we analyze the effect of real time electricity price (RTP) on the amount of ancillary services required for load balancing in presence of responsive users, information asymmetry and forecast errors in demand and renewable energy sources (RES) generation. We consider a RTP that is determined by the forecasted generation and ramping cost. A community choice aggregator manages the load of all the consumers by setting the price. The consumer’s objective is to minimize their overall cost of consumption. Ancillary services are called upon to balance the load in real time. With zero RES in the power network and a high degree of load flexibility, the proposed RTP flattens and the volatility in demand vanishes. However, in presence of RES the volatility in price and demand is reduced up to an extent and ancillary services are required for load balancing. The amount of ancillary services required increases with forecast errors. We also propose a real time algorithm that approximates the optimal consumer behavior under the complete information setting. Extensive numerical simulations are provided using real data from Pecan Street and Elia Belgium.