Abstract
Renewable energy (RE) systems play a key role in producing electricity worldwide. The integration of RE systems is carried out in a distributed aspect via an autonomous hybrid microgrid (A-HMG) system. The A-HMG concept provides a series of technological solutions that must be managed optimally. As a solution, this paper focuses on the application of a recent nature-inspired metaheuristic optimization algorithm named a multimodal delayed particle swarm optimization (MDPSO). The proposed algorithm is applied to an A-HMG to find the minimum levelized cost of energy (LCOE), the lowest loss of power supply probability (LPSP), and the maximum renewable factor (REF). Firstly, a smart energy management scheme (SEMS) is proposed to coordinate the power flow among the various system components that formed the A-HMG. Then, the MDPSO is integrated with the SEMS to perform the optimal sizing for the A-HMG of a fishing village that is located in the coastal city of Essaouira, Morocco. The proposed A-HMG comprises photovoltaic panels (PV), wind turbines (WTs), battery storage systems, and diesel generators (DGs). The results of the optimization in this location show that A-HMG system can be applied for this location with a high renewable factor that is equal to 90%. Moreover, the solution is very promising in terms of the LCOE and the LPSP indexes that are equal to 0.17$/kWh and 0.12%, respectively. Therefore, using renewable energy can be considered as a good alternative to enhance energy access in remote areas as the fishing village in the city of Essaouira, Morocco. Furthermore, a sensitivity analysis is applied to highlight the impact of varying each energy source in terms of the LCOE index.
Highlights
Electricity has become one of the most essential parts of modern life
Based on the results presented by the studies mentioned above, the prime aim of the present work is adopting an efficient variant of PSO algorithm referred to as a multimodal delayed particle swarm optimization (MDPSO), which is developed recently by Song et al [2]. e MDPSO is applied to design an optimal autonomous hybrid microgrid (A-HMG) with a high renewable factor (REF) and minimal values of the levelized cost of energy (LCOE) and the loss of power supply probability (LPSP) indexes
The simulation results are presented. e smart energy management scheme (SEMS) for an A-HMG system is performed to fulfill the energy demand of a fishing village that is located in the coastal city of Essaouira, Morocco. e MDPSO method is applied to obtain the best configuration of A-HMG system and for sizing the components. e LCOE, LPSP, and REF are defined as objective functions
Summary
Electricity has become one of the most essential parts of modern life. Nowadays, the electricity sector is facing serious challenges, such as ensuring sufficient supply to keep up with the ever-increasing demand of electrical energy, reducing its costs, and limiting polluting emissions. E authors in [4] proposed a generalized formulation for intelligent energy management of a HMG in using multiobjective optimization to minimize the operation cost and the environmental impact, where they applied artificial neural network to predict RE generation and load demand. (2) e stability and the convergence of the adopted MDPSO algorithm were evaluated to build A-HMG with a high REF and minimal values in terms of the LCOE and the LPSP indexes. 2. Autonomous Hybrid Microgrid System Description e proposed A-HMG system is composed of wind turbines (WTs), photovoltaic (PV) panels, diesel generator (DGs), and battery storage bank as shown, which can vary greatly depending on specific parameters, such as the availability of renewable resources and desired services to provide [13]. (i) ηoverall represents the overall efficiency of the DG (ii) ηbrak−thermal is the brake thermal efficiency of the DG
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