Quantity-price bidding optimization strategy considering risk cost of virtual power plant in the day-ahead electricity market

Considering that virtual power plant (VPP) participates in the market as a price maker, and the distributed wind and solar power, as the main component of VPP, have strong randomness and volatility, and that load forecasting error will also bring certain risk costs to VPP, an optimization strategy for the bidding of VPP participating in the day-ahead electricity market considering the risk cost is established. Firstly, the uncertainty inside VPP is processed to obtain the expected scenario of the prediction error, and the risk cost caused by the prediction error is modeled to quantify the risk. Then, based on the guidance mechanism of VPP to internal resources, the quantity-price method of VPP in the day-ahead electricity market is designed, and the optimization model of VPP in the day-ahead electricity market is established. Monte Carlo and K-means clustering are used to deal with prediction errors. The particle swarm optimization algorithm is used to solve the model. Finally, a case study demonstrates that the proposed method can effectively manage the internal resources of VPP and avoid risks to improve the income.