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.

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The provision of electricity in a reliable and sustainable manner in provincial towns and villages in the small West Africa state of Sierra Leone requires the adoption of appropriate technologies. The rapid increase in electricity demand has generated great interest in how to tackle a possible long-lasting energy deficiency in the country. This paper aims at analyzing the techno-economic feasibility of a hybrid renewable energy system (HRES) for the sustainable rural electrification of Lungi Town, Port Loko District, Sierra Leone. Optimization, economic, reliability, and sustainability analyses were carried out using a genetic algorithm (GA), with the main objectives of minimizing the loss of power supply probability (LPSP) and cost of energy (COE). Three different case scenarios were configured, using a diesel generator (DG), wind/PV/DG/battery, and wind/PV/battery. Various combinations of these case scenarios were compared to determine which option was the most economically viable. In order to determine the case scenario with the lowest LPSP and COE, the operations and maintenance costs of the three cases were calculated. Using only DG for case one, the operations and maintenance cost amounted to USD 1,050,348.12/year. The operation and maintenance cost for case two, which included wind/PV/DG/battery, was found to be USD 561,674.06/year. The operations and maintenance cost for case three, which included wind/PV/battery, was found to be USD 36,000/year. In standalone microgrids, however, the use of renewable energy sources is not reliable due to the uncertainty of renewable energy sources. Consequently, the simulation results show that the wind/PV/DG/battery-based HRES is the most cost-effective, reliable, and sustainable for the specific location in comparison to the current traditional method of electricity generation. Since there is abundant solar radiation with substantial wind speeds across the country, this HRES can be applied in most rural and remote areas in place of the current diesel generators (DGs) that are widely deployed in the country.

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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.