Dominance Constraints for Risk Control of a VPP’s Optimal Bidding Strategy

The Virtual Power Plant (VPP) serves as the primary form of participation for distributed power generation resources in electricity markets. Throughout this market engagement, uncertainties stemming from renewable energy sources and market prices present significant revenue risks for VPPs. Addressing this challenge, the paper proposes a VPP bidding risk management model centered on dominance constraints. In contrast to conventional methods reliant on risk metrics like Conditional Value at Risk, the proposed model offers improved control over downside and tail risks while reducing optimization iterations to just one, thus substantially reducing computational cost associated with risk management. The study also contrasts the efficacy and computational costs of first-order and second-order dominance models. For instance, in a three-scenario benchmark, the first-order dominance model exhibits a 16.7% lower overall risk level compared to the second-order model. Similarly, in a nine-scenario benchmark, the computational time of the first-order model increases by 0.96% due to the introduction of binary variables, and this increase scales with the number of benchmark scenarios. The paper additionally incorporates mean deviation of profits and expected excess returns to develop a bi-objective optimization model. This model not only fulfills risk control requisites but also shapes return distributions to align more closely with VPP decision-makers’ profit preferences. The case study is based on a VPP comprising conventional thermal units, wind turbines, energy storage stations, and flexible loads.