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

• Published in: Journal of energy storage Vol. 25; p. 100877
• Main Authors: Paliwal, Navin Kumar, Singh, Asheesh Kumar, Singh, Navneet Kumar
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2019
• Subjects: Artificial bee colony; Battery storage system; Energy scheduling; Market bid; Microgrid; Renewable energy sources; Energy scheduling; Market bid; Renewable energy sources; Artificial bee colony; Microgrid; Battery storage system
• ISSN: 2352-152X; 2352-1538
• DOI: 10.1016/j.est.2019.100877

•This paper deals the optimal energy scheduling in a remote MG under the uncertainty in nature, dynamic market bid and load profiles.•Three different policies of MG are executed for continuous and discrete operation of DEs to identify most economic and environment friendly operation.•The precisely formulated operational and fixed costs of MG elements are utilized, for better insight on optimal revenue and operation of MG.•The operation based on dynamic market bid, load and RES gives optimal revenue and lesser GHG emission than other policies in each mode of DEs.•The results also conclude that GABC performed better to solve complex objective with constraints in realistic situation than other algorithms. 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.