Optimal bidding strategy of renewable-based virtual power plant in the day-ahead market

This paper proposes an optimal bidding strategy model of a virtual power plant (VPP) in the day-ahead market (DAM) that contains energy, reserve, and regulation markets. The VPP aggregates the wind farm (WF), photovoltaic power (PV), energy storage (ES), gas turbine (GT), and hydropower station (HS). Based on the uncertainty modeling for the output of uncontrollable power sources (UCPSs), such as the renewable energy in terms of WF and PV, this research develops countermeasures to reduce the penalty caused by the deviation between actual and predicted outputs of UCPSs. The other three controllable power sources (CPSs) are required to remain a certain reserve capacity for compensating the deviation to maximize the expected benefits of the whole VPP. By means of the quantile and superquantile theory, the proposed model considers the economic penalties beyond the reserve capacity and optimizes the allocation of reserve capacity to maximize the whole profit. With the construction of a mixed-integer nonlinear programming model, the best profits of the VPP in a variety of cases are reached and discussed. The experimental results demonstrate the effectiveness of diverse power sources integrated into a VPP, and the optimal bidding strategy of such renewable-based VPP in the DAM.