Optimal Bidding Strategy for GENCO with Green Power in Day-ahead Electricity Market

The electricity market has evolved from a regulated monopoly to a more liberalized competitive market, which allows a generating company (GENCO) to bid to provide energy. The two-period structure of the electricity market (day-ahead and real-time market) introduces a mechanism for determining the GENCO’s optimal bidding strategy. The difference between clearing prices for each period adds uncertainty to GENCO’s determination of its optimal bid. In addition, the fast growth of renewable energy sources (wind and solar power) and their increasing penetration to the power system adds uncertainty over how much energy the GENCO can actually produce in the realtime market. Based on the two-period market structure, we develop an optimization model for a single GENCO with green power to derive an optimal strategy to bid a price and quantity for the day-ahead market with the objective of maximizing its expected profit. Furthermore, we apply the optimization model with risk-aversion attitude to reduce chance of negative profits for GENCO. We fit probability distributions to historical data to reflect the uncertainties, and Monte-Carlo simulation allows us to solve the stochastic optimization problem. The optimization model and corresponding algorithm are verified in Southern California Edison, a GENCO in California ISO.