Energy management and optimized operation of renewable sources and electric vehicles based on microgrid using hybrid gravitational search and pattern search algorithm. (December 2021)

• Record Type: Journal Article
• Authors: Li, Ning; Su, Zhanguo; Jerbi, Houssem; Abbassi, Rabeh; Latifi, Mohsen; Furukawa, Noritoshi
• Is Part Of: Sustainable cities and society. Volume 75(2021)
• Publication Date: 2021-12
• Subjects: Optimal operation -- Microgrid -- Storage system -- Electric vehicles -- Uncertainty; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.103279
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Considering renewable energy resources. Reporting superior solutions in the case of cost and execution time Reporting superior solutions in Short-Term Scheduling and Micro-grid Energy Management Presenting Hybrid Gravitational Search and Pattern Search Algorithm Abstract: The use of plug-in hybrid electric vehicles (PHEVs) can solve many environmental problems and energy crises around the world. Using a large number of PHEVs with high storage and control capabilities can enhance the flexibility of distribution networks. However, optimal management PHEVs with the presence of renewable energy sources (RESs) is one of the main challenges that must be addressed. The optimal management of various RESs along with PHEVs is possible in the form of a microgrid (MG). Moreover, the uncertainties of input parameters are successfully considered in the model development by Monte Carlo simulation (MCS). In the modelling, the uncertainties in PHEVs, load, RESs and energy price are considered and simulated for 24 h. The NiMH-Battery is also used to investigate the role of the storage device. The objective function is minimizing the total cost of the grid-connected MG including the costs of load supply, PHEVs charging demand and power losses. The optimization problem of this paper is solved using the hybrid gravitational search and pattern search (GSA-PS) algorithms. Simulation confirm the efficiency of the GSA-PS technique compared with conventional schemes. The results also show that the generation costs of the GSA-PS are considerably reduced than the classical optimization algorithms.