A risk-based multi-objective energy scheduling and bidding strategy for a technical virtual power plant

The impact of climate change on Renewable Energy Sources (RES) is not negligible. System operators have different attitudes to overcoming uncertainties. Risk-averse attitudes under robust optimization are reliable solutions to cover the effects of uncertainties, but the cost of operation would be increased dramatically under a rigid pessimistic attitude. This paper proposes a multi-objective programming model to make a tradeoff between the regret level of risk for uncertainties and the cost of operation. Integration of RESs into the power systems imposes several challenges for system operation to keep the security constraints of the system. A technical Virtual Power Plant (VPP) is considered to cope with the operational challenges. The proposed stochastic model considering the uncertain parameters of RESs, load, and market prices, determine the bidding strategies for VPP to participate in the day-ahead market. A fuzzy satisfying method incorporating the p-robust stochastic programming model is proposed to provide optimal bidding/offering curves according to the worst-case scenario under minimizing the regret level. The results show that by using the proposed model, with a total profit reduction of about 0.46%, the maximum regret level would decrease about 50%, which is a suitable response considering the cost and risk analysis aspects.