Abstract
The expansion planning of distribution network is based on the implementation information fusion management of distributed generation in distribution network. In order to improve the management and scheduling capability of distributed generation, an implementation information management platform for distributed generation in distribution network based on information integration is proposed, and a comprehensive optimization model for distributed generation and energy storage of distribution network is constructed. The load change from the planning base year to the level year is simulated by Monte Carlo Method; According to the installed capacity of distributed generation, Monte Carlo Method is used to simulate the output of distributed generation, and the planning model of township distribution network expansion is established to meet all power supply needs. The line loss calculation is based on the simulation results of load and distributed generation output. The corresponding steady state model for all kinds of distributed generations is established, and the type of interface connected to the power grid should be confirmed. The information integration method is used to optimize the design of the information management platform for distributed generation in the distribution network. The test shows that the designed implementation information management platform for distributed generation in distribution network can accurately realize the information fusion and management of distributed generation, solve the simultaneity problem caused by the fluctuation and interval of load data and distributed generation output on power flow calculation, and integrate distribution network planning with distributed generation planning.
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Yuping Yan and Yutian Hong wrote the main manuscript text. All authors reviewed the manuscript.
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Yan, Y., Hong, Y. Design of the implementation information management platform for distributed generation in distribution network based on information integration. Cluster Comput 27, 3293–3305 (2024). https://doi.org/10.1007/s10586-023-04153-0
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DOI: https://doi.org/10.1007/s10586-023-04153-0