A day-ahead optimal energy scheduling in a remote microgrid alongwith battery storage system via global best guided ABC algorithm

Abstract

Abstract Since last decade, the concept of microgrid (MG) is growing rapidly with increasing electricity generation through renewable energy sources (RES) and small dispatchable sources. A stand-alone MG is a better option for growing unserved electricity demand especially in remote areas, where classical power transmission system is not economically and technically feasible. The energy scheduling of RES and small dispatchable sources can be efficiently handled by the inclusion of battery storage system (BSS) along with RES. Further, the BSS in MG includes some degree of complexity in the objective function of optimal scheduling strategy. This paper deals the optimal energy scheduling in stand-alone MG consisting of wind turbine (WT), photovoltaic (PV), diesel engine generators (DEs) and BSS, which is not an easy task because of uncertainty in nature, dynamic market bid, and demand profiles. The BSS is operated in three different strategies based on fluctuation in RES power, load, and market bid to obtain the optimal energy scheduling of MG in continuous and discrete operation modes of DEs. The scheduling strategies maximize the arbitrage of MG system in both modes of DEs operation. The problem is simulated in MATLAB® environment, using artificial bee colony (ABC) and its variant global best (Gbest) guided ABC (GABC) algorithms, and other existing algorithms as particle swarm optimization (PSO) and genetic algorithm (GA). The obtained results depict that the GABC provides better revenue and, exploration, and exploitation capabilities in all operational strategies, than ABC, PSO and GA algorithms. The operational strategy based on variable market bid, load, and RES power gives optimal revenue and reduced or equal green house gases (GHG) emissions than other strategies in the considered modes of DEs operation.