What does this research mean for the field?

The improved particle swarm optimization (IPSO) method reduces the cost of energy more effectively than Multi-Objective Differential Evolution and original PSO methods while maintaining high dependability in hybrid microgrid energy systems. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop an improved particle swarm optimization (IPSO) framework for optimally sizing stand-alone hybrid renewable microgrids while balancing economic cost and supply reliability under hourly environmental uncertainty.

August 22, 2026Energy ReportsOpen Access

Optimal design of stand-alone microgrid hybrid system with energy storage using improved particle swarm algorithm

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Authors

ASAyat Ali Saleh SASalem Alkhalaf MFMasahiro Furukakoi

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Overview

Simulation study reveals superior cost and reliability outcomes for hybrid microgrid sizing using an improved particle swarm algorithm, suggesting enhanced deployment of renewable energy systems.

Key Points

To develop an improved particle swarm optimization (IPSO) framework for optimally sizing stand-alone hybrid renewable microgrids while balancing economic cost and supply reliability under hourly environmental uncertainty.
Incorporated chaos-based initialization and adaptive mutation into the particle swarm optimization algorithm to avoid premature convergence and local minima.
Simulated one year of hourly load demand, wind speed, and solar irradiance across four distinct microgrid architectures combining solar PV, wind turbines, diesel generators, and battery energy storage.
Compared IPSO performance against standard PSO and Multi-Objective Differential Evolution (MODE) across three objectives: cost of energy (COE), loss of power supply probability (LPSP), and renewable fraction.
IPSO achieved lower cost of energy and loss of power supply probability while maximizing the renewable fraction compared to standard PSO and MODE algorithms.
A hybrid photovoltaic system with dual backup from diesel generators and battery storage yielded the optimal cost of energy at $0.27185/kWh with an LPSP below 0.52%.

Cite This Study

Saleh et al. (2026) studied this question.

synapsesocial.com/papers/6a895f9cca7ade938187e567 https://doi.org/10.1016/j.egyr.2026.109605

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August 22, 2026Energy ReportsOpen Access

Optimal design of stand-alone microgrid hybrid system with energy storage using improved particle swarm algorithm

View Full Paper

Ask AI

Bookmark

Authors

ASAyat Ali Saleh SASalem Alkhalaf MFMasahiro Furukakoi

Discussion

Member takes

Overview

Simulation study reveals superior cost and reliability outcomes for hybrid microgrid sizing using an improved particle swarm algorithm, suggesting enhanced deployment of renewable energy systems.

Key Points

To develop an improved particle swarm optimization (IPSO) framework for optimally sizing stand-alone hybrid renewable microgrids while balancing economic cost and supply reliability under hourly environmental uncertainty.
Incorporated chaos-based initialization and adaptive mutation into the particle swarm optimization algorithm to avoid premature convergence and local minima.
Simulated one year of hourly load demand, wind speed, and solar irradiance across four distinct microgrid architectures combining solar PV, wind turbines, diesel generators, and battery energy storage.
Compared IPSO performance against standard PSO and Multi-Objective Differential Evolution (MODE) across three objectives: cost of energy (COE), loss of power supply probability (LPSP), and renewable fraction.
IPSO achieved lower cost of energy and loss of power supply probability while maximizing the renewable fraction compared to standard PSO and MODE algorithms.
A hybrid photovoltaic system with dual backup from diesel generators and battery storage yielded the optimal cost of energy at $0.27185/kWh with an LPSP below 0.52%.

Cite This Study

Saleh et al. (2026) studied this question.

synapsesocial.com/papers/6a895f9cca7ade938187e567 https://doi.org/10.1016/j.egyr.2026.109605

View Full Paper

Ask AI

Bookmark