Using a Particle Swarm Optimization (PSO) algorithm, the study found that coordinated sizing and dispatch of distributed energy resources can substantially reduce system cost, diesel dependence, and carbon dioxide emissions. Compared with the HOMER simulation platform, the PSO-based method reduced Net Present Cost (NPC), Cost of Energy (COE), diesel fuel consumption, and CO₂ emissions by 12.01%, 16.09%, 50%, and 17.65%, respectively.
Residential microgrids are increasingly viewed as an important solution for integrating renewable energy into homes and communities, especially as countries seek to reduce emissions from electricity generation. Solar and wind resources can lower dependence on fossil fuels, while distributed energy resources make power systems more flexible and locally responsive. However, renewable generation is intermittent, and residential demand changes throughout the day. Without proper storage, backup generation, and dispatch rules, microgrids may face high operating costs, energy waste, unstable supply, and continued dependence on diesel generators. These challenges make optimized energy management essential for balancing reliability, affordability, and environmental performance.
A study (DOI: 10.48130/een-0026-0005) published in Energy & Environment Nexus on 10 April 2026 by Richard Oladayo Olarewaju's team, University of Ibadan, shows that a PSO-based optimization strategy can improve the economic and environmental performance of a hybrid residential microgrid integrating PV, wind, diesel generation, and battery storage.
The researchers first built mathematical models for each component of the hybrid microgrid, including wind turbine output, PV power generation, diesel generator fuel consumption, and battery charging and discharging behavior. Hourly residential load demand, solar irradiance, and wind speed data were generated using stochastic models to represent realistic but non-site-specific operating conditions. The study then formulated an objective function to minimize the total NPC, including capital cost, operation and maintenance cost, replacement cost, fuel cost, emission cost, and penalties for unmet load. The PSO algorithm, implemented in MATLAB, was used to search for the optimal size and operation of each distributed energy resource. The energy management strategy prioritized renewable energy at all times. When renewable output exceeded demand, surplus energy was stored in the battery before any energy was dumped. When renewable output was insufficient, the battery was dispatched first, and the diesel generator was activated only when both renewable generation and stored energy could not meet the load. The strategy also prevented simultaneous battery charging and discharging, unnecessary load curtailment, and avoidable diesel operation. Six configurations were tested: diesel generator only, diesel plus wind, diesel plus PV, wind plus battery, PV plus battery, and the full PV/wind/diesel/battery system. The diesel-only case had the highest cost, fuel use, and emissions. PV-only or wind-only combinations improved performance but remained limited by intermittency or lack of storage. The full hybrid configuration performed best, achieving an NPC of US$85.54 million, COE of US$0.73/kWh, diesel consumption of 2.1 million L/year, and CO₂ emissions of 8.4 million kg/year. Compared with the diesel-only scenario, battery storage reduced diesel fuel consumption by 74.44%, CO₂ emissions by 80.81%, and COE by 46.34%. Against HOMER, the PSO approach also delivered lower NPC, lower COE, lower fuel use, and lower emissions, although it required more fine-tuning and computational effort.
Overall, the study demonstrates that intelligent optimization can help residential microgrids make better use of renewable energy while maintaining reliable power supply. By combining solar, wind, diesel backup, and battery storage under a coordinated dispatch strategy, the proposed PSO-based method reduces both economic and environmental burdens. The results highlight the importance of battery storage in smoothing renewable variability, cutting diesel runtime, and improving the long-term viability of hybrid microgrids. Such strategies could support future residential energy planning, especially in communities seeking cleaner, more resilient, and cost-effective electricity systems.
