A new algorithm for hybrid microgrids boosts energy access and sustainability in remote communities, cutting costs and emissions by optimizing solar, wind and diesel resources
More than 4,000 remote communities worldwide rely on diesel generators for their electricity needs. This dependence on diesel is a strain on their finances and on the environment, with significant emissions limiting sustainable growth in isolated regions. To address these challenges, a team of researchers from Khalifa University has developed an innovative energy management system that optimizes hybrid microgrids, balancing solar, wind, and diesel power sources to make energy access more reliable, cost-effective, and environmentally sustainable.
Adel Merabet, Dr. Ahmed Al-Durra, Dr. Tarek El-Fouly and Prof. Ehab El-Saadany, with Sujoy Barua, Saint Mary’s University, Canada, focused on a novel optimization method called the Levy Arithmetic Algorithm (LAA). This enhanced algorithm builds on traditional arithmetic optimization techniques by improving the search capabilities and avoiding common pitfalls in optimization, offering a robust solution for managing multiple power sources within a microgrid, especially under the complex conditions found in off-grid communities. The team published their results in, a top 1% journal.
The hybrid microgrid model integrates solar photovoltaic panels, wind turbines, and multiple diesel generators to meet the load demands of remote areas. The LAA optimizes energy use by prioritizing renewable energy sources whenever they are available, thus reducing reliance on diesel generators. The team demonstrated that this method is sufficient to lower the energy costs and reduce the emissions significantly — up to 10% compared to diesel-only microgrids.
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“With the pressing global need for sustainable energy solutions, especially in isolated areas, our model offers a promising path forward. By reducing fuel dependency and emissions while ensuring steady electricity access, this technology has the potential to transform lives in remote regions, paving the way for a cleaner, more resilient energy future.”
— Prof. Ehab El-Saadany, Professor of Electrical Engineering, KU
“Reducing the reliance on diesel is especially critical given that fuel transportation and storage in remote areas often entails additional costs and logistical complexities, compounding the environmental and financial burden on these communities,” Prof. El-Saadany explained.
The model’s breakthrough lies in the LAA’s ability to minimize both cost and emissions in energy dispatch decisions. Traditional economic load dispatch strategies have focused primarily on balancing load and costs, neglecting the emission impact of diesel power generation. The research team’s system weighs emission penalties and economic costs, resulting in a greener and more cost-effective solution, managing the timing and scale of generator use based on projected renewable energy availability.
“Our results underscore the potential of optimized hybrid microgrids as a scalable solution for isolated and underserved communities,” Prof. El-Saadany said. “By reducing diesel dependency and maximizing the use of renewables, our model enhances resilience, lowering the environmental impact of energy generation and improving access to stable power.”
The research team plans to continue refining the system for real-world implementation, with a focus on integrating electric vehicle charging and storage solutions, which could provide valuable backup power during low renewable output periods. Advanced load-shifting techniques within the LAA framework could also enable the system to anticipate demand changes more effectively, optimizing energy use over longer cycles.
Jade Sterling
Science Writer
