Virtual power plants participating in day-ahead electricity market bidding strategy considering carbon trading

A virtual power plant (VPP) is proposed to aggregate various resources to participate as a whole in both electricity and carbon trading markets. As the scale of VPPs continues to expand, they are transitioning from being price takers to price makers. To this end, this paper treats the VPP as a price maker and proposes a bi-level bidding strategy in the day-ahead electricity market, considering the impact of carbon trading. Firstly, an introduction and analysis of the day-ahead electricity market mechanism, considering carbon trading, are provided. Secondly, based on the Stackelberg game theory, a bi-level bidding model in the day-ahead electricity market is established with the VPP as the bidding entity. The upper-level model aims to maximize the anticipated profit of the VPP, while the lower-level model aims to minimize the system's clearing cost. Considering the uncertainty in wind farm output predictions within the VPP, operators are provided with two bidding strategies: risk-averse and opportunity seeker strategies based on the information gap decision theory (IGDT). Then, utilizing the strong duality theory, the Karush-Kuhn-Tucker (KKT) optimality conditions, and the big-M method, the bi-level model is simplified into a mixed-integer linear programming problem for resolution. Finally, an example is provided to illustrate the optimal bidding strategy and operation plan for the VPP, along with an analysis of how uncertainty in wind farm output predictions within the VPP affects the expected profit of the VPP. The example shows that VPPs can influence market prices through strategic bidding decisions. After considering carbon trading, the expected revenue of the VPP increased by 5.1% compared to the scenario without carbon trading.