A Day-ahead Optimal Market Bidding Strategy for Risk-averse Virtual Power Plants Based on Stochastic Dominance Constraints

Risk management is a key step in the development of bidding strategies for risk-averse virtual power plants, and tail risk is a focal point of risk management. Traditional risk management methods such as variance, loss probability, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR) are difficult to directly and effectively control tail risk, which may lead to large losses for virtual power plants under extreme conditions. Therefore, this paper chooses to directly manage risk by using stochastic dominance constraints to formulate bidding strategies for virtual power plants in the day-ahead market. This paper establishes risk-averse virtual power plant day-ahead bidding models based on CVaR and stochastic dominance constraints, respectively, and conducts numerical simulations. The simulation results demonstrate that stochastic dominance constraints can better control tail risk of returns.