Robust Mean-Variance Optimization for Self-scheduling of Thermal Producer and Its Price of Robustness

In deregulated electricity markets, generation companies with the aim of maximum revenue need to provide trading strategies to the electricity trading market, which contributed to a self-scheduling model. When considering the uncertainty of price, the trading strategies are required to maximize the revenue as well as minimizing the risks brought by uncertainties. In this paper, a multi-objective robust mean-variance model was proposed to solve the above problem and the Pareto frontier of the multi-objective optimization was obtained. Moreover, the proposed robust mean-variance model could be equivalently transformed into a non-robust mean-variance model which was casted as a second-order cone programming (SOCP) optimization. The price of robustness to benefits, risks, and the Pareto frontier were analyzed. Finally, the robust mean-variance model based self-scheduling model optimization and its budget of robustness were tested on a 30-bus system. The simulation results demonstrate the effectiveness of the proposed method and analysis.