Fast Distributed Demand Response Algorithm in Smart Grid

This paper proposes a fast distributed demand response (DR) algorithm for future smart grid based on primaldual interior method and Gaussian belief propagation (GaBP) solver. At the beginning of each time slot, each end-user/energy-supplier exchanges limited rounds of messages that are not private with its neighbors, and computes the amount of energy consumption/generation locally. The proposed demand response algorithm converges rapidly to a consumption/generation decision that yields the optimal social welfare when the demands of end-users are low. When the demands are high, each end-user/energy-supplier estimates its energy consumption/generation quickly such that a sub-optimal social welfare is achieved and the power system is ensured to operate within its capacity constraints. The impact of distributed computation errors on the proposed algorithm is analyzed theoretically. The simulation results show a good performance of the proposed algorithm.