A Distributed Algorithm for Managing Residential Demand Response in Smart Grids

Abstract

Demand response enabled by time-varying prices can propel the power industry toward a greater efficiency. However, a noncoordinated response of customers may lead to severe peak rebounds at periods with lower prices. In this regard, a coordinated demand response scheme can mitigate concerns about the peak rebounds. This paper presents a system-wide demand response management model to coordinate demand response provided by residential customers. The objective of the model is to flatten the total load profile that is subject to minimum individual cost of customers. The model is first formulated as a bi-level optimization problem. It is then casted into equivalent single-level problems, which are solved via an iterative distributed algorithm. Home load management (HLM) modules embedded in customers' smart meters are autonomous agents associated with the algorithm. In the algorithm, at first, HLM modules, in response to prices announced by the utility, optimize the daily operation of household appliances and send back the scheduled load profiles. Then, the total load profile is calculated and released by the utility. Thereafter, the HLM modules asynchronously update their schedule such that, given their least energy expenses, the most evenly distributed total load profile is achieved. The mutual interaction between the utility and HLM modules is continued to the point in which no further improvement is obtained. Convergence and optimality of the algorithm are proved.