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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Abstract:Recently, the off-grid smart house has been attracting attention in Japan for considering global warming. Moreover, the selling price of surplus power from the renewable energy system by Feed-In Tariff (FIT) has declined. Therefore, this paper proposes an off-grid smart house with the introduced Photovoltaic (PV) system, Solar Collector (SC) system, Hot Water Heat Pump (HWHP), fixed battery and Electric Vehicle (EV). In this research, a multi-objective optimization problem is considered to minimize the introduced capacity and shortage of the power supply in the smart house. It can perform the electric power procurement from the EV charging station for the compensation of a shortage of power supply. From the simulation results, it is shown that the shortage of the power supply can be reduced by the compensation of the EV power. Furthermore, considering the uncertainty for PV output power, reliable simulation results can be obtained.

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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.

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