Day-Ahead Optimal Scheduling for a Full-Scale PV–Energy Storage Microgrid: From Simulation to Experimental Validation
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| Publicat a: | Electronics vol. 14, no. 8 (2025), p. 1509 |
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| Autor principal: | |
| Altres autors: | |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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|---|---|---|---|
| 001 | 3194571311 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14081509 |2 doi | |
| 035 | |a 3194571311 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Wang, Zixuan | |
| 245 | 1 | |a Day-Ahead Optimal Scheduling for a Full-Scale PV–Energy Storage Microgrid: From Simulation to Experimental Validation | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Linear programming | ||
| 653 | |a Electrical loads | ||
| 653 | |a Distributed generation | ||
| 653 | |a Energy sources | ||
| 653 | |a Integer programming | ||
| 653 | |a Optimization | ||
| 653 | |a Solar power generation | ||
| 653 | |a Batteries | ||
| 653 | |a Energy storage | ||
| 653 | |a Energy resources | ||
| 653 | |a Energy consumption | ||
| 653 | |a Photovoltaic cells | ||
| 653 | |a Optimization models | ||
| 653 | |a Mathematical programming | ||
| 653 | |a Scheduling | ||
| 653 | |a Simulation | ||
| 653 | |a Dynamic programming | ||
| 653 | |a Solar energy | ||
| 653 | |a Neural networks | ||
| 653 | |a Electricity | ||
| 653 | |a Renewable resources | ||
| 653 | |a Effectiveness | ||
| 653 | |a Resource scheduling | ||
| 653 | |a Algorithms | ||
| 653 | |a Mixed integer | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Operating costs | ||
| 700 | 1 | |a Shi Libao | |
| 773 | 0 | |t Electronics |g vol. 14, no. 8 (2025), p. 1509 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3194571311/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3194571311/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3194571311/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |