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• W4293720110 abstract "Optimal scheduling of a distributed energy resources system (DERs) can be beneficial for utilizing renewable energy and relieving the pressures upon the main electric grid. One of the most popular DER systems is the Building mount photovoltaic (BMPV) system. In this paper, a large office building is taken as a case study to develop an optimal day-ahead strategy for the BMPV system considering 24 hours ahead of energy consumption and PV generation uncertainty. This paper mainly includes BMPV system capacity determination, 24 hours ahead of energy usage and PV generation forecasting, and day-ahead optimal strategy development. Firstly, based on collected history energy demands, weather data, and building information, the install capacity of PV is defined by roof area. Furthermore, the battery capacity is constructed by China's latest photovoltaic energy storage capacity standard. Secondly, the ensemble learning model is established using a time-series algorithm and Long Short-term memory (LSTM) to predict day-ahead energy consumption. Moreover, an interval prediction method is used to forecast the PV generation for the next 24 hours. Finally, the accurate energy consumption prediction and PV generation interval are introduced to establish the BMPV system scheduling strategy. The day-ahead optimization scheduling strategy was developed based on Mixed integer linear programming (MILP), targeting minimal economic cost. As a result of the optimization process, the proposed strategy could save 4.8% in economic costs and reduce the peak load of the main grid effectively. Moreover, it can effectively optimize the energy consumption structure of buildings or even distributed energy systems and can provide feasibility to achieve net-zero energy consumption without sacrificing any comfort of occupancy." @default.
• W4293720110 created "2022-08-31" @default.
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• W4293720110 date "2022-01-01" @default.
• W4293720110 modified "2023-10-14" @default.
• W4293720110 title "A Case Study for BMPV Systems Optimal Day-Ahead Scheduling and performance evaluation" @default.
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• W4293720110 doi "https://doi.org/10.1051/e3sconf/202235601013" @default.
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