article · Energy Reports
An improved particle swarm optimisation algorithm has been developed to optimise the sizing of components in autonomous hybrid microgrid systems, tested using a case study of Shlateen, Egypt. The algorithm incorporates adaptive mutation and chaos-based initialisation to prevent premature convergence and avoid becoming trapped in local minima, thereby improving global search efficiency. It assesses hourly variations across an entire year for solar radiation, wind speed, and electrical load. Evaluating four system configurations containing solar photovoltaics, wind turbines, diesel generators, and battery energy storage, the optimisation targets three objectives: cost of energy, renewable energy contribution, and loss of power supply probability. The algorithm outperforms multi-objective differential evolution and standard particle swarm methods in cutting costs. A configuration combining photovoltaics with backup devices proved most cost-effective, achieving an energy cost of 0.27185 dollars per kilowatt-hour and a loss of power supply probability under 0.52 percent.
Remote and autonomous communities often struggle with high electricity costs and unreliable power supplies. By improving the computer algorithms used to size hybrid energy systems, planners can better balance economic costs against reliability risks. This ensures that installations deliver dependable power using local renewable sources while keeping backup diesel generation and battery investments at an affordable minimum.
The method is an applied computational tool relevant to microgrid designers, energy planners, and engineering consultancies sizing stand-alone hybrid power installations. Based on simulation and optimization using one year of hourly operational data, the work represents early-stage software modelling rather than a deployed physical system, meaning further field validation would be required before commercial adoption.
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This research aims to develop a novel approach called improved particle swarm optimization method (IPSO) for optimizing the sizes of an autonomous hybrid microgrid energy systems (HESs) in Shlateen, Egypt. IPSO uses adaptive mutation and chaos-based initialization algorithm to eliminate premature convergence and avoid local minima trapping to enhances the global search efficiency. IPSO aims to strike a balance between technical and economic requirements, considering uncertain system parameters (load variation, solar radiation, and wind speed) over all hours in one year. Three objective functions are considered, the cost of energy (COE), the use of RES and the loss of power supply probability (LPSP). The designs of four different grid-connected configurations include photovoltaic (PV), wind turbine (WT), diesel generator (DG), and battery storage (BESU) are simulated and optimized. The results show that the suggested IPSO approach reduces COE more effectively than other well-known algorithms such as the Multi-Objective Differential Evolution (MODE) and original PSO method. The PSO approach reduces LPSP with outstanding results and provides the highest contribution of renewable energy (RF). The first design (PV with both backup devices) provides best cost-effective configuration at 0.27185$/kWh and shows excellent dependability with LPSP values less than 0.52%. This work demonstrates the role of Information Technology in smart energy optimization.
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DOI: 10.1016/j.egyr.2026.109605
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