Sierra Leone is suffering from a persistent electricity gap that has crippled its economic growth and prevented it from attaining several health and education development goals. This persistent electricity gap has generated significant interest in tackling the country’s long-lasting energy deficiency. Providing electricity in a reliable, sustainable, and cost-effective manner in Sierra Leone requires adopting robust integrated energy planning and appropriate technologies. Despite various interventions by the government, a balance between electricity demand and supply has yet to be achieved. Using the Long-range Energy Alternatives Planning System (LEAP), this work assesses Sierra Leone’s energy supply and demand for 2019–2040. We developed three case scenarios (Base, Middle, and High) based on forecasted demand, resource potential, techno-economic parameters, and CO₂ emissions. The Base case considers the electricity sector as business as usual, the Middle case examines the electricity sector reform roadmap and the prospect of integrating renewable energy into the power system, and the High case examines the sustainable development of the power generation system considering the electricity sector roadmap. As part of this study, we analyze potential alternatives to conventional electricity generation systems aimed at providing electricity in a sustainable, reliable, and affordable manner, including the use of renewable energy sources and technologies with less CO₂ emissions. Model results estimate an increase in electricity demand of 1812.5 GWh, 1936 GWh, and 2635.8 GWh for Base, Middle, and High cases respectively. Also, there is a reduction in production, fuel cost, and CO₂ emission in the High case to the Base case by 67.15%, 35.79%, and 51.8%, respectively. This paper concludes with recommendations devised from the study results for the power system of Sierra Leone.

Weather-driven uncertainties and other extreme events, particularly with the increasing reliance on variable renewable energy (VRE), have made achieving a reliable microgrid operation increasingly challenging. This research proposes a comprehensive and integrated planning strategy for capacity sizing and operational planning, incorporating forecasting and demand response program (DRP) strategies to address microgrid operation under various conditions, accounting for uncertainties. The microgrid includes photovoltaic systems, wind turbines, and battery energy storage. Uncertainties in VREs and load fluctuations are modeled using Monte Carlo simulations (MCSs), while forecasting is based on the long short-term memory (LSTM) model. To determine the best techno-economic planning approach, six cases are formulated and solved using a multi-objective particle swarm optimization with multi-criteria ranking for these three objectives: total lifecycle costs (TLCC), reliability criteria, and surplus VRE curtailment. Shortage/surplus adaptive pricing combined with variable peak critical peak pricing (SSAP VP-CPP) DRP is devised and compared with a time-of-use VP-CPP DRP in mitigating the impacts of both critical and non-critical events in the system. The simulation results show that the integrated planning, which combines LSTM forecasting with DRP strategies, achieved about 7% and 5% TLCC reductions for deterministic and stochastic approaches, respectively. The approach allowed optimal sizing and operation planning, improving the utilization of VREs and effectively managing uncertainty, resulting in the most cost-effective and robust VRE-based microgrid with enhanced resilience and reliability.

In response to the growing need to reduce greenhouse gas emissions and achieve a decarbonized society, this study focuses on the optimal design of electric vehicle (EV) charging stations powered by renewable energy sources. Battery electric vehicles (BEVs), which emit no CO2 during operation, are particularly effective when combined with Park and Ride (P&R) systems that mitigate urban traffic congestion. This paper investigates the optimal operation and configuration of P&R BEV charging stations in Okinawa, Japan, comparing two power supply methods: on-grid and off-grid systems using photovoltaic (PV), wind power generation (WG), and battery energy storage systems (BESS). A multi-objective optimization model is developed to minimize the annual operating cost while meeting EV charging demand. To identify the optimal configuration, an exhaustive search of equipment unit combinations is conducted. The resulting pareto front reveals trade-offs between total annual EV charging energy and parking operator profit. In addition, three representative charging levels are analyzed in detail. Results show that on-grid systems achieve higher profit margins under moderate charging demand, leveraging grid power to maintain stable operations. In contrast, off-grid systems, while requiring higher initial investment, exhibit greater self-sufficiency and perform better in high demand scenarios or in remote areas with limited grid access. Thus, the optimal power supply method should be selected based on site-specific conditions, such as EV charging scale, regional infrastructure, and resilience requirements. Among the options, the on-grid system is identified as the most practical and economically viable solution under current technological and institutional conditions.