Energy management of multi-microgrids with renewables and electric vehicles considering price-elasticity based demand response: A bi-level hybrid optimization approach

Abstract

This article develops an energy management scheme for multi-microgrid systems involving the scheduling strategy of distributed resources, renewables and plug-in electric vehicles penetration to enhance the economic-environmental benefits of the system. The proposed framework also integrates the price elasticity-based augmented demand response program into the energy management (EM) problem to investigate the influence of dynamic energy pricing and incentives provided to consumers. The local power trading between adjacent microgrids and external trading with the primary grid through each microgrid's generation resources scheduling facilitates the microgrids’ cost reduction while maintaining supply-demand balance in the system. But, the ubiquitous intermittent nature of renewables, load demands, and vehicle charging imply complexities in the system. Hence, this work formulates the EM problem incorporating the uncertainties of renewables and load demands by the worst-case realization and employs a bi-level hybrid grey wolf-whale optimization; the first level is optimized from the microgrid's standpoint to obtain the dynamic pricing, and the load demands modification, whereas the operational cost of the multi-microgrid system and the utility profit are optimized in the second level. The proposed approach is studied on a test system consisting of residential, commercial and industrial microgrids; substantial simulation results are presented to validate the effectiveness.