Day-Ahead Optimal Scheduling for a Full-Scale PV–Energy Storage Microgrid: From Simulation to Experimental Validation
সংরক্ষণ করুন:
| প্রকাশিত: | Electronics vol. 14, no. 8 (2025), p. 1509 |
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| প্রধান লেখক: | |
| অন্যান্য লেখক: | |
| প্রকাশিত: |
MDPI AG
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| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| ট্যাগগুলো: |
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| সার সংক্ষেপ: | Microgrids facilitate the complementary and collaborative operation of various distributed energy resources. Implementing effective day-ahead scheduling strategies can significantly enhance the economic efficiency and operational stability of microgrid systems. In this study, the long short-term memory (LSTM) neural network is first employed to forecast photovoltaic (PV) power generation and load demand, using operational data from a full-scale microgrid system. Subsequently, an optimization model for a full-scale PV–energy storage microgrid is developed, integrating a PV power generation system, a battery energy storage system, and a specific industrial load. The model aims to minimize the total daily operating cost of the system while satisfying a set of system operational constraints, with particular emphasis on the safety requirements for grid exchange power. The formulated optimization problem is then transformed into a mixed-integer linear programming (MILP) model, which is solved using a computational solver to derive the day-ahead economic scheduling scheme. Finally, the proposed scheduling scheme is validated through field experiments conducted on the full-scale PV–energy storage microgrid system across various operational scenarios. By comparing the simulation results with the experimental outcomes, the effectiveness and practicality of the proposed day-ahead economic scheduling scheme for the microgrid are demonstrated. |
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| আইএসএসএন: | 2079-9292 |
| ডিওআই: | 10.3390/electronics14081509 |
| সম্পদ: | Advanced Technologies & Aerospace Database |