A semivectorial bilevel programming approach to optimize electricity dynamic time-of-use retail pricing

Abstract

Presently, residential electricity consumers are, in general, charged at flat or dual time-of-use tariffs along the day, which are defined by the retailer for long periods (e.g., one year). These pricing schemes do not convey price signals reflecting generation costs and grid conditions. Hence, consumers lack the incentives to engage in different consumption patterns using the flexibility they generally have in the operation of some end-use loads. Dynamic tariffs, i.e. energy prices varying possibly with significant magnitude in short periods of time, are expected to become an applicable pricing scheme in smart grids. In this setting, home energy management systems can play an important role to help end-users optimizing the usage of appliances to minimize energy costs without compromising comfort. This can be advantageous also from the perspective of grid management.A semivectorial bilevel programming approach is developed to model the interaction between electricity retailers and consumers in order to optimize electricity time-of-use retail pricing. The aim is to support the retailer in finding good decisions for the prices. The retailer (upper level decision maker) establishes dynamic time-of-use electricity prices to maximize profits. The consumer (lower level decision maker) responds by selecting, under that price setting, a load scheduling decision leading to a nondominated solution balancing his objectives of minimizing the electricity bill (cost dimension) and minimizing the dissatisfaction in face of his preferences and requirements (comfort dimension).The lower level optimization problem is formulated as a bi-objective mixed-integer linear programming problem. A hybrid approach is proposed, which consists of a genetic algorithm for the upper level problem and an exact solver to solve surrogate scalar problems at the lower level. A case study is presented and discussed.