Multiobjective optimization-based design and dispatch of islanded, hybrid microgrids for remote, off-grid communities in sub-Saharan Africa

TL;DR

This paper develops a multiobjective optimization framework for designing and dispatching hybrid microgrids in remote sub-Saharan African communities, balancing cost, emissions, reliability, and renewable use.

Contribution

It introduces a novel multiobjective, multiperiod optimization approach tailored to SSA conditions, benchmarking algorithms, and analyzing trade-offs and sensitivities.

Findings

Particle swarm optimization offers the best runtime-quality trade-off.

Optimal microgrid configuration achieves 94% renewable penetration with a $0.46 USD/kWh LCOE.

Cost-only optimization performs worse than multiobjective approaches.

Abstract

Reliable, affordable electricity remains inaccessible to over 600 million people in sub-Saharan Africa (SSA), where islanded hybrid microgrids combining renewable generation, battery storage, and diesel backup offer a viable electrification pathway. This paper presents a multiobjective, multiperiod optimization framework for the design, sizing, and dispatch of such systems, with a case study for a remote community in Kenya. System sizing is optimized over a one-year horizon and dispatch over a representative day, both at hourly resolution. The formulation jointly minimizes lifecycle levelized cost of energy (LCOE), emissions, lost load, and dumped energy, while maximizing renewable penetration. Seven optimization algorithms are benchmarked; particle swarm optimization (PSO) achieves the best trade-off between runtime (63 s) and solution quality (normalized objective 0.146) and is used for subsequent analyses. The optimal configuration of solar PV, wind, lithium-ion battery storage, and diesel backup achieves a normalized LCOE of 0.46 USD per kWh with over 94 percent renewable penetration, outperforming alternatives. Pareto fronts highlight trade-offs between cost, emissions, and reliability, showing that cost-only optimization yields inferior outcomes. Sensitivity analyses identify fuel prices and discount rates as the most influential parameters in SSA contexts. A break-even distance analysis shows microgrids are economically competitive with grid extension at the study site. The dispatch model produces day-ahead schedules that are robust to short-term uncertainty, though extended wind lulls increase diesel reliance. This work fills a critical gap by providing a comprehensive multiobjective design and dispatch framework tailored to SSA resource, economic, and operational conditions.