A distributed coevolution algorithm for black box optimization of demand response

Abstract

Demand side management is an efficient way of reducing the grid fluctuation and enhancing the net profit in the smart grid. As the increasing complexity of the real-world applications, e.g., NP-hard and non-polynomial problems, a distributed coevolution algorithm is proposed to cope with this challenge. In the proposed algorithm, the management system is modeled as a hierarchical structure. The user side locates at the lowest level and upper levels represent the system operators. The system operators manage users' energy consumption behavior through the real-time pricing strategy. The genetic algorithm and particle swarm optimization are modified to play the role of the smart scheduler and the smart pricing generator. The end users conduct appliance commitment using the smart scheduler which considers the users' comfort level and the electricity cost. The decision of the lower level depends directly on the real-time pricing strategy released by its adjacent upper level. The real-time pricing is developed by the smart pricing generator which considers the net profit and the grid fluctuation. Three types of experiments are conducted on a distributed platform to investigate and ascertain the performance of the proposed algorithm. For the operator side, the experimental results indicate that the proposed algorithm improves the grid fluctuation, and enhances the net profit. As for the user side, it improves the comfort level and achieves budget saving.