A two-stage optimal energy management by using ADP and HBB-BC algorithms for microgrids with renewable energy sources and storages

Energy management system (EMS) plays a key role to control and maintain efficiency and reliability of microgrids (MGs) at satisfactory levels. In this paper, a two-stage optimal EMS is designed for MGs with renewable energy sources and storages, and residential and industrial loads, with the capability to predict stochastic electrical demands and electricity prices with respect to operational and model uncertainties. A two-stage optimization method, based on Approximate Dynamic Programing (ADP) and Hybrid Big Bang Big Crunch (HBB-BC) algorithm, is developed to optimally manage power generation, distribution and saving within MGs. Conditions of the main grid, operational cost functions are fully addressed into the design. In particular, Time of Use (ToU) tariffs and curtailable loads are considered to implement a Demand Response (DR) program to efficiently enhance the optimization performance. The proposed method is applied to a residential MG benchmark system, and simulation results are discussed and compared in various scenarios.