Stochastic risk-constrained decision-making approach for a retailer in a competitive environment with flexible demand side resources

Abstract

This paper presents a risk‐averse stochastic bi‐level programming approach to solve decision‐making of a retailer in a competitive market under uncertainties. The retailer decides the level of involvement in day‐ahead (DA) and regulation markets by making an optimal bidding strategy with the goal of expected profit maximization. Uncertainties associated with DA prices, up/ down regulation market prices, customers' demand, and rival retailers' behaviors are tackled through a stochastic programming model. In the proposed model, responsive loads and electric vehicles (EVs) track the real‐time prices and choose the proper retailer to minimize their payments in the competitive trading floor. The obtained nonlinear stochastic model is transformed into an equivalent linear single‐level program by replacing the lower‐level problem with its Karush‐Kuhn‐Tucker optimality conditions and using duality theory. Finally, the proposed methodology is evaluated by applying to a realistic case study, and the results demonstrate the effectiveness of the proposed framework.