Risk-Constrained Optimal Operation Strategy for Virtual Power Plants Considering Incentive-Based Demand Response

Incentive-based demand response (IBDR) and Energy Storage Systems (ESSs) can play an important role in addressing operation risks of the virtual power plant (VPP) participating in the short-term electricity market. In this paper, we propose a multi-level incentive electricity prices mechanism of IBDR and an interaction model of components in the VPP. Our objective is to maximize the profit of the VPP while analyzing the impact of decision makers' risk attitudes on their profits. A two-stage scenario-based risk-constrained stochastic program is formulated for the VPP participating in the market to deal with uncertainties of renewable energy resources (RESs). Monte Carlo simulation (MCS) is applied to generate scenarios and k-means clustering algorithm is employed to reduce the number of the scenarios for the sake of problem simplification. Moreover, applying conditional value at risk (CVaR) to measure economic risk attitudes of the VPP operators. The proposed method is implemented in a VPP and the numerical simulation results show its effectiveness and advantages.