What does this research mean for the field?

The multiobjective salp swarm algorithm (MOSSA) outperforms other metaheuristic algorithms in optimizing the sizing of hybrid renewable energy microgrids, achieving superior cost of energy and power supply reliability. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

May 13, 2022Energies86 citationsOpen Access

Multiobjective Optimization of a Hybrid PV/Wind/Battery/Diesel Generator System Integrated in Microgrid: A Case Study in Djelfa, Algeria

ZBZakaria BelboulAutoliv (Sweden)BTBelgacem ToualZiane Achour University of Djelfa AKAbdellah KouzouZiane Achour University of Djelfa

Key Points

Key points are not available for this paper at this time.

Abstract

Hybrid Renewable Energy Sources (HRES) integrated into a microgrid (MG) are a cost-effective and convenient solution to supply energy to off-grid and rural areas in developing countries. This research paper focuses on the optimization of an HRES connected to a stand-alone microgrid system consisting of photovoltaics (PV), wind turbines (WT), batteries (BT), diesel generators (DG), and inverters to meet the energy demand of fifteen residential housing units in the city of Djelfa, Algeria. In this context, the multiobjective salp swarm algorithm (MOSSA), which is among the latest nature-inspired metaheuristic algorithms recently introduced for hybrid microgrid system (HMS) optimization, has been proposed in this paper for solving the optimization of an isolated HRES. The proposed multiobjective optimization problem takes into account the cost of energy (COE) and loss of power supply probability (LPSP) as objective functions. The proposed approach is applied to determine three design variables, which are the nominal power of photovoltaic, the number of wind turbines, and the number of battery autonomy days considering higher reliability and minimum COE. In order to perform the optimum size of HMG, MOSSA is combined with a rule-based energy management strategy (EMS). The role of EMS is the coordination of the energy flow between different system components. The effectiveness of using MOSSA in addressing the optimization issue is investigated by comparing its performance with that of the multiobjective dragonfly algorithm (MODA), multiobjective grasshopper optimization algorithm (MOGOA), and multiobjective ant lion optimizer (MOALO). The MATLAB environment is used to simulate HMS. Simulation results confirm that MOSSA achieves the optimum system size as it contributed 0.255 USD/kW h of COE and LPSP of 27.079% compared to MODA, MOGOA, and MOALO. In addition, the optimization results obtained using the proposed method provided a set of design solutions for the HMS, which will help designers select the optimal solution for the HMS.

Ask AI

Helpful

Bookmark

View Full Paper