A hybrid genetic algorithm for the interaction of electricity retailers with demand response

Abstract

In this paper a bilevel programming model is proposed for modeling the interaction between electricity retailers and consumers endowed with energy management systems capable of providing demand response to variable prices. The model intends to determine the optimal pricing scheme to be established by the retailer (upper level decision maker) and the optimal load schedule adopted by the consumer (lower level decision maker) under this price setting. The lower level optimization problem is formulated as a mixed-integer linear programming (MILP) problem. A hybrid approach consisting of a genetic algorithm and an exact MILP solver is proposed. The individuals of the population represent the retailer’s choices (electricity prices). For each price setting, the exact optimal solution to the consumer’s problem is obtained in a very efficient way using the MILP solver. An illustrative case is analyzed and discussed.