The Virtual Power Plant Bidding Strategy Model based on Multi-stage Semi-anticipativity Distributionally Robust Optimization

Abstract

For virtual power plants (VPPs), the strength of their market competitiveness largely depends on their ability to dispatch various distributed resources. An efficient bidding strategy model that can provide superior decision-making solutions is the key to enhancing this competitiveness. This paper focuses on price-making VPP and constructs an efficient three-period VPP bidding strategy model (3P-VPP) with a bi-level optimization framework for the day-ahead energy market by introducing three-period generator output constraints and generator ramping constraints. Furthermore, to address the uncertainty of renewable resources, we introduce the theory of distributionally robust optimization (DRO) and the concept of semi-anticipativity, refining the 3P-VPP model into a multi-stage DRO VPP bidding strategy model based semi-anticipativity (SADRO-VPP). The model possesses dynamic adaptation capabilities, tailoring the optimal decision-making solutions based on the accuracy of the forecast data. When faced with unreliable prediction data, the model will offer a more robust decision-making plan to guard against potential extreme scenarios. Conversely, when the forecast data is credible, the model adeptly utilizes this information to provide more economical decision-making strategies. The experimental results demonstrate that the 3P-VPP model exhibits higher computational efficiency compared to traditional model, while the SADRO-VPP model provides effective scheduling solutions and bidding strategies for VPP.