Bilevel programming approach to optimizing a time-variant electricity tariff for demand response

This paper proposes a bilevel programming model to day-ahead electricity tariff optimization in smart grids to balance grid-level demand and supply at all times. In this Stackelberg game approach, the leader is the grid operator, who aims to set the tariff to ensure the balance of supply and demand. The followers are groups of consumers, who, in response to the observed tariff, schedule their controllable consumption and determine the charging/discharging policy of their batteries to minimize their cost. The bilevel optimization problem is reformulated into a single-level quadratically constrained quadratic program (QCQP), which is then solved by a successive linear programming (SLP) algorithm. The approach is illustrated on an example with three different consumer groups.