Bidding Strategy for Participation of Virtual Power Plant in Energy Market Considering Uncertainty of Generation and Market Price

Bidding Strategy for Participation of Virtual Power Plant in Energy Market Considering Uncertainty of Generation and Market Price

Due to the small capacity of DGs, their individual participation in the energy market is not beneficial. In the case of wind and solar plants, their uncertain power generation is another issue for their participation in the market, especially when their capacity is low. Commercial Virtual Power Plant (CVPP) is a new market participant, which represents a group of various DGs in the market, and bids to the market. This paper proposes a new bidding strategy approach for the participation of CVPP in the day-ahead energy market, considering uncertainties of wind turbine generation and Market Clearing Price (MCP). The market is pay as bid, and each participant bids a multi-step price-power curve. The uncertainty of MCP has formulated analytically, while the wind uncertainty is modeled by a quantized Rayleigh probability distribution function. Particle Swarm Optimization (PSO) algorithm is utilized for optimizing the objective function, which is the expected benefit of the CVPP. Numerical results are provided to evaluate the performance of proposed approach in increasing the benefit of VPP.

Due to the small capacity of DGs, their individual participation in the energy market is not beneficial. In the case of wind and solar plants, their uncertain power generation is another issue for their participation in the market, especially when their capacity is low. Commercial Virtual Power Plant (CVPP) is a new market participant, which represents a group of various DGs in the market, and bids to the market. This paper proposes a new bidding strategy approach for the participation of CVPP in the day-ahead energy market, considering uncertainties of wind turbine generation and Market Clearing Price (MCP). The market is pay as bid, and each participant bids a multi-step price-power curve. The uncertainty of MCP has formulated analytically, while the wind uncertainty is modeled by a quantized Rayleigh probability distribution function. Particle Swarm Optimization (PSO) algorithm is utilized for optimizing the objective function, which is the expected benefit of the CVPP. Numerical results are provided to evaluate the performance of proposed approach in increasing the benefit of VPP.