Robust Optimal Operation of Smart Microgrid Considering Source–Load Uncertainty
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| Publicat a: | Processes vol. 13, no. 8 (2025), p. 2458-2484 |
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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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|---|---|---|---|
| 001 | 3244057504 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2227-9717 | ||
| 024 | 7 | |a 10.3390/pr13082458 |2 doi | |
| 035 | |a 3244057504 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231553 |2 nlm | ||
| 100 | 1 | |a Qiu Zejian |u Guangdong Power Grid Corp, Dongguan Power Supply Bureau, Dongguan 523000, China | |
| 245 | 1 | |a Robust Optimal Operation of Smart Microgrid Considering Source–Load Uncertainty | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The uncertainties arising from high renewable energy penetration on both the generation and demand sides pose significant challenges to distribution network security. Smart microgrids are considered an effective way to solve this problem. Existing studies exhibit limitations in prediction accuracy, Alternating Current (AC) power flow modeling, and integration with optimization frameworks. This paper proposes a closed-loop technical framework combining high-confidence interval prediction, second-order cone convex relaxation, and robust optimization to facilitate renewable energy integration in distribution networks via smart microgrid technology. First, a hybrid prediction model integrating Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), and Quantile Regression (QR) is designed to extract multi-frequency characteristics of time-series data, generating adaptive prediction intervals that accommodate individualized decision-making preferences. Second, a second-order cone relaxation method transforms the AC power flow optimization problem into a mixed-integer second-order cone programming (MISOCP) model. Finally, a robust optimization method considering source–load uncertainties is developed. Case studies demonstrate that the proposed approach reduces prediction errors by 21.15%, decreases node voltage fluctuations by 16.71%, and reduces voltage deviation at maximum offset nodes by 17.36%. This framework significantly mitigates voltage violation risks in distribution networks with large-scale grid-connected photovoltaic systems. | |
| 653 | |a Load | ||
| 653 | |a Accuracy | ||
| 653 | |a Distributed generation | ||
| 653 | |a Voltage | ||
| 653 | |a Optimization | ||
| 653 | |a Signal processing | ||
| 653 | |a Power flow | ||
| 653 | |a Closed loops | ||
| 653 | |a Renewable energy | ||
| 653 | |a Long short-term memory | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Uncertainty | ||
| 653 | |a Probability distribution | ||
| 653 | |a Climate change | ||
| 653 | |a Prediction models | ||
| 653 | |a Robustness | ||
| 653 | |a Wind power | ||
| 653 | |a Electric potential | ||
| 653 | |a Confidence intervals | ||
| 653 | |a Neural networks | ||
| 653 | |a Relaxation method (mathematics) | ||
| 653 | |a Renewable resources | ||
| 653 | |a Error reduction | ||
| 653 | |a Mixed integer | ||
| 653 | |a Alternating current | ||
| 653 | |a Alternative energy sources | ||
| 653 | |a Decision making | ||
| 653 | |a Energy distribution | ||
| 700 | 1 | |a Zhu Zhuowen |u Guangdong Power Grid Corp, Dongguan Power Supply Bureau, Dongguan 523000, China | |
| 700 | 1 | |a Yu, Lili |u Guangdong Power Grid Corp, Dongguan Power Supply Bureau, Dongguan 523000, China | |
| 700 | 1 | |a Han Zhanyuan |u Guangdong Power Grid Corp, Dongguan Power Supply Bureau, Dongguan 523000, China | |
| 700 | 1 | |a Shao Weitao |u Guangdong Power Grid Corp, Dongguan Power Supply Bureau, Dongguan 523000, China | |
| 700 | 1 | |a Zhang, Kuan |u State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China; kuanzhang@ncepu.edu.cn | |
| 700 | 1 | |a Ma Yinfeng |u State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China; kuanzhang@ncepu.edu.cn | |
| 773 | 0 | |t Processes |g vol. 13, no. 8 (2025), p. 2458-2484 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244057504/abstract/embedded/Y2VX53961LHR7RE6?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244057504/fulltextwithgraphics/embedded/Y2VX53961LHR7RE6?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244057504/fulltextPDF/embedded/Y2VX53961LHR7RE6?source=fedsrch |