A bi-level stochastic framework for VPP decision making in a joint market considering a novel demand response scheme

Abstract

Summary This paper presents a stochastic decision-making framework for an optimal bidding strategy of a virtual power plant (VPP) in a joint day-ahead and regulation (balancing) market. The VPP seeks to maximize its expected profit in day-ahead market and compensates its deviation in balancing market. Due to the inherent uncertainties of renewable energy resources and also other market participant strategies, a stochastic programming is used for underlying optimization problem. In this regard, a 2-stage bi-level problem is presented in which upper level represents VPP profit maximization and lower level deals with market clearing problem in which power transfer distribution factors are used to incorporate transmission constraints. Uncertainties related to stochastic generation and market participant offer/bid curves are modeled via scenarios. A mathematical programming with equilibrium constraints is obtained by reformulating the lower-level problem using Karush-Kuhn-Tucker optimality conditions. The resulting mathematical programming with equilibrium constraints is converted into a tractable mixed-integer linear programming problem with strong duality theorem. The considered VPP is assumed to be commercial and consists of stochastic generation units (wind and solar), conventional power plant, energy system storage, and adjustable internal loads. A novel demand response scheme is introduced into the VPP portfolio in which VPP is penalized through shifting load amount and operating time interval as well. Finally, a trade-off between profit and risks associated with uncertainties is explicitly taken into account using the conditional value at risk. To assess the validity and the effectiveness of the proposed model, a 6-bus test system is chosen to apply the model.