Optimal joint demand and virtual bidding for a strategic retailer in the short-term electricity market

Abstract

This paper proposes a bilevel stochastic optimization model for generating the optimal joint demand and virtual bidding strategy for a strategic retailer in the short-term electricity market, where virtual bidding is used to improve the retailer's market power in the day-ahead (DA) electricity market. In the proposed model, virtual bidding can be used at multiple buses, which are not limited to the locations of the demands of the strategic retailer. In the bilevel stochastic optimization model, the upper level problem maximizes the total profit of demand and virtual bidding, while the lower level problem represents the DA electricity market clearing process; the uncertain demands of the strategic retailer and real-time (RT) electricity prices in the market are represented by scenarios; and the Conditional Value at Risk (CVaR) is used for risk management. By using the duality theory, Karush–Kuhn–Tucker (KKT) conditions and big M method, the proposed bilevel nonlinear optimization model is converted into a single-level mixed-integer linear programming (MILP) problem, which can be solved efficiently by existing commercial solvers. Case studies are performed to validate the proposed model and study the impacts of various model parameters on the strategic retailer's joint demand and virtual bidding strategy.