Data-Driven interval robust optimization method of VPP Bidding strategy in spot market under multiple uncertainties

The participation of Virtual Power Plants (VPPs) in the spot market enhances the flexibility of modern power systems as renewable energy penetration increases. However, multiple uncertainties on the market, load, and generation sides can significantly affect the bidding strategies and operational efficiency of VPPs. This paper employs interval numbers generated by a data-driven model to capture the uncertainty and correlation of electricity prices in the spot market. Additionally, uncertainty sets are utilized to represent the variability in the number of electric vehicles (EVs) and photovoltaic (PV) power generation. A two-stage interval robust optimization model considering arbitrage opportunity is established to optimize the bidding strategies of a VPP that includes gas turbines, energy storage, PV systems, and EVs. An improved column-and-constraint generation (C&CG) algorithm is developed to solve this model. The results demonstrate that the interval numbers of electricity prices produced by the proposed data-driven model can reduce VPP cost fluctuations by 9.3%. The two-stage interval robust optimization model reduces costs by 2.5% compared to a single-stage robust method and 52.0% compared to robust method ignoring arbitrage opportunities. As parameters change, the advantages of the proposed model become more significant. The improved C&CG algorithm shows superior convergence and accuracy. Unlike stochastic optimization methods that generate n scenarios, the computational time for the interval optimization method can be reduced to 1/n. This study offers a feasible solution for the bidding strategies of VPPs considering multiple uncertainties in the spot market.