Abstract
The objective of this work is to 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.
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