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.

This paper proposes energy management systems for micro-grids. In recent years, the use of renewable energy sources in micro-grids has become an effective means of power decentralization especially in remote areas where the extension of the main power grid is an impediment. But a mixture of renewable energy sources and conventional generation poses serious challenges in the operation and control of micro-grids as a result of the uncertainty associated with renewable sources. Therefore, excellent energy management with regards to the power production, control, reliability, and consumption is needed in the power system. The main objective of the energy management system is to minimize the system cost as well as meeting the demand. In order to take into account the uncertainties of distributed generators (DGs) and load consumption, demand response participation and load shedding is taken into account. Two different types of algorithms are used to solve the smart energy management system of the micro-grid. To minimize system costs, which includes unit commitment and demand response, a genetic algorithm is used. To further balance the supply and demand during extreme cases, load shedding is employed through the use of the artificial neural network. The simulation results displayed corroborate the merit of the proposed method.

In recent years, the use of renewable energy sources in micro-grids has become an effectivemeans of power decentralization especially in remote areas where the extension of the main power gridis an impediment. Despite the huge deposit of natural resources in Africa, the continent still remains inenergy poverty. Majority of the African countries could not meet the electricity demand of their people.Therefore, the power system is prone to frequent black out as a result of either excess load to the systemor generation failure. The imbalance of power generation and load demand has been a major factor inmaintaining the stability of the power systems and is usually responsible for the under frequency andunder voltage in power systems. Currently, load shedding is the most widely used method to balancebetween load and demand in order to prevent the system from collapsing. But the conventional methodof under frequency or under voltage load shedding faces many challenges and may not perform asexpected. This may lead to over shedding or under shedding, causing system blackout or equipmentdamage. To prevent system cascade or equipment damage, appropriate amount of load must beintentionally and automatically curtailed during instability. In this paper, an effective load sheddingtechnique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system isproposed. The combined techniques take into account the actual system state and the exact amount ofload needs to be curtailed at a faster rate as compared to the conventional method. Also, this methodis able to carry out optimal load shedding for any input range other than the trained data. Simulationresults obtained from this work, corroborate the merit of this algorithm.

Japan contains a vast number of isolated islands. Majority of these islands are poweredby diesel generators (DGs), which are operationally not economical. Therefore, the introduction of renewableenergy systems (RESs) into these area is very much vital. However, the variability of RESs asa result of weather condition as well as load demand , battery energy storage system (BESS) is broughtinto play. Demand response (DR) programs have also been so attractive in the energy management systemsfor the past decades. Among them, the real-time pricing (RTP) has been one of the most effectivedemand response program being utilized. This program encourages the customer to increase or reducethe load consumption by varying the electricity price. Also, due to the increase in power transactionmarket, Japan electric power exchange (JEPX) has established spot (day-ahead), intraday hour-ahead,and forward market programs. This paper utilizes day-ahead and hour-ahead markets, since these marketscan make it possible to deal with uncertainty related to generated power fluctuations. Therefore,this paper presents the optimal operation method coping with the uncertainties of RESs in multi-areasmall power systems. The proposed method enables flexibility to correspond to the forecasting error byproviding two kinds of power markets among multi-area small power systems and trading the shortageand surplus powers. Furthermore, it accomplishes a stable power supply and demand by RTP. Thus, theproposed method was able to reduce operational cost for multi-area small power systems. The processof creating operational plan for RTP, power trading at the markets and the unit commitment of DGs arealso presented in this paper. Simulation results corroborate the merit of the proposed program

Due to the depletion of fossil resources and the increase in carbon dioxide emissions, renewable energy systems (RESs) such as photovoltaic (PV) and wind generator (WG) have become an active player in power systems (PSs). In Japan, diesel generators (DGs) are the means of power generation in most isolated islands. Therefore, the introduction of RESs into these islands is of great importance. However, due to the uncertainty of RESs, battery energy storage systems (BESSs), fuel cells (FCs), and exploiting the electric vehicles (EVs) owned by customers are expected as vital power sources for the next generation. Moreover, demand response programs are utilized in this paper. As a result of reforming PSs in recent years, transactions on power markets have also been increasing in Japan. The Japan electric power exchange provides day-ahead market, intraday hour-ahead market, and forward market. It is expected that these markets make it possible to deal with uncertainties related to the generated power fluctuation. This paper presents the optimal operation method for multi-area PSs consisting of DG, PV, WG, BESS, and FC by using chance-constrained programming. The proposed method has achieved reduction of the total operational cost of the multi-area small power systems by using FC, household EVs, and two power markets.