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This paper formulates a Stackelberg game between a coordination agent andparticipating homes to control the overall load consumption of a residentialneighborhood. Each home optimizes a comfort-cost trade off to determine a loadschedule of its available appliances in response to a price vector set by thecoordination agent. The goal of the coordination agent is to find a pricevector that will keep the overall load consumption of the neighborhood aroundsome target value. After transforming the bilevel optimization problem into asingle level optimization problem by using Karush-Kuhn-Tucker (KKT) conditions,we model how each home reacts to any change in the price vector by using theimplicit function theorem. By using this information, we develop a distributedoptimization framework based on gradient descent to attain a better pricevector. We verify the load shaping capacity and the computational performanceof the proposed optimization framework in a simulated environment establishingsignificant benefits over solving the centralized problem using commercialsolvers.

A Stackelberg Game Approach to Control the Overall Load Consumption of a Residential Neighborhood