As energy shortages and environmental pollution continue to worsen, household photovoltaics has gradually become an important part of household energy management. However, with the penetration rate of household photovoltaics increasing, the access of the high‐proportion household photovoltaics (HPHP) will seriously endanger the operation of the distribution network. Therefore, it is urgent to stabilize the voltage fluctuations after the HPHP connected to the grid. This paper proposes a high‐proportion household photovoltaic optimal configuration method based on integrated–distributed energy storage system. After analyzing the adverse effects of HPHP connected to the grid, this paper uses modified K‐means clustering algorithm to classify energy storage in an integrated and distributed manner. Then based on the modified particle swarm optimization (MPSO), the integrated and distributed energy storage system is configured to realize the voltage stability of HPHP. The analysis of the example shows that the method proposed in this paper can effectively improve the power quality. At the same time, it can achieve the goal of economic optimization, which can effectively solve the adverse impact of HPHP grid connection. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

With the development of the automobile industry, the extensive use of automobiles is a key factor in the global energy crisis and environmental pollution, and the clean and environmentally friendly characteristics of electric vehicles have become an ideal means to deal with these two major problems. Connecting household electric vehicles to the home will not only affect the distribution network, but also adversely affect the home electricity network. Therefore, it is necessary to connect electric vehicles to household charging load for quantitative analysis, so as to design energy control methods for electric vehicles to connect to households, and minimize the adverse effects of electric vehicles connected to household power grids. First of all, this article analyzes the uncertainty of electric vehicle access to the grid. After considering the random characteristics of electric vehicle charging load, an electric vehicle charging load model is built based on Monte Carlo simulation. Secondly, according to the electric vehicle charging load power curve obtained by the above method, combined with the daily load probability model of the grid, a household electricity model is established. Finally, based on the Particle Swarm Optimization(PSO), a family energy control method is proposed, and the optimization goal is to minimize the daily load fluctuation of the family, so as to minimize the impact of electric vehicles connected to the grid.

Background: With the tremendous changes in the world’s fuel structure, the Electric Vehicle (EV) has become a powerful means of mitigating energy and environmental issues. Objective: However, when an electric vehicle is connected to home, it will cause load fluctuation, which threatens the safe and smooth operation of the user's electricity. Method: Therefore, in order to solve the problem of power instability when electric vehicle is connected to home, this paper proposes an optimization strategy for household charging based on Smart Load(SL). Result: After the daily load fluctuation model of electric vehicle family charging is constructed, the Particle Swarm Optimization(PSO) algorithm is combined to establish the electric vehicle family charging optimization model. Conclusion: The analysis of the example shows that the proposed method can stabilize the household power, which can effectively solve the adverse effects caused by excessive fluctuation of daily load in the family.

Background: Fast and reliable fault detection methods are the main technical challenges faced by photovoltaic grid-connected systems through modular multilevel converters (MMC) during the development. Objective: Existing fault detection methods have many problems, such as the inability of non-linear elements to form accurate analytical expressions, the difficulty of setting protection thresholds and the long detection time. Method: Aiming at the problems above, this paper proposes a rapid fault detection method for photovoltaic grid-connected systems based on Recurrent Neural Network (RNN). Results: The phase-to-mode transformation is used to extract the fault feature quantity to get the RNN input data. The hidden layer unit of the RNN is trained through a large amount of simulation data, and the opening instruction is given to the DC circuit breaker. Conclusion: The simulation verification results show that the proposed fault detection method has the advantage of faster detection speed without difficulties in setting and complicated calculation.