A source-load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefit
Energy-intensive industrial load offers substantial capacity and rapid adjustment capabilities, which can be effectively coordinated with deep peak regulation (DPR) methods of thermal power to optimize the peak regulation state of the system. The uncertainty of electricity prices and the current pea...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525001814 |
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| author | Xiaoyu Yue Lijun Fu Siyang Liao Jian Xu Deping Ke Huiji Wang Shuaishuai Feng Jiaquan Yang Xuehao He |
| author_facet | Xiaoyu Yue Lijun Fu Siyang Liao Jian Xu Deping Ke Huiji Wang Shuaishuai Feng Jiaquan Yang Xuehao He |
| author_sort | Xiaoyu Yue |
| collection | DOAJ |
| description | Energy-intensive industrial load offers substantial capacity and rapid adjustment capabilities, which can be effectively coordinated with deep peak regulation (DPR) methods of thermal power to optimize the peak regulation state of the system. The uncertainty of electricity prices and the current peak regulation compensation mechanism significantly affect the economic viability of industrial load regulation. In this study, electrolytic aluminum load (EAL) is used as a representative industrial load. This paper combines the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), whale optimization algorithm (WOA), and long short-term memory network (LSTM) to propose a CEEMDAN-WOA-LSTM prediction model for electricity price scenarios. Subsequently, comprehensive cost and fine adjustment models for electrolytic aluminum load (EAL) are developed, incorporating the current peak regulation compensation mechanism. Finally, a source-load collaborative stochastic optimization method is proposed, integrating the scenario method and chance constraints. The effectiveness of the proposed scheme is verified using a real regional system, demonstrating significant reductions in total social peak regulation costs, a substantial decrease in renewable energy (RE) abandonment rates, reduced frequency of thermal power DPR, and improved economic efficiency of thermal power. Additionally, the current peak regulation compensation mechanism effectively guarantees the benefits of EAL and encourages its adjustment willingness. |
| format | Article |
| id | doaj-art-446c23e3a36d4eb499882f76ecf9cb63 |
| institution | DOAJ |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-446c23e3a36d4eb499882f76ecf9cb632025-08-20T03:09:03ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-06-0116711063010.1016/j.ijepes.2025.110630A source-load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefitXiaoyu Yue0Lijun Fu1Siyang Liao2Jian Xu3Deping Ke4Huiji Wang5Shuaishuai Feng6Jiaquan Yang7Xuehao He8Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, The School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaNational Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430072, ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, The School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China; Corresponding author at: Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China.Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, The School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, The School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, The School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaHubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network, The School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, ChinaElectric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 430072, ChinaElectric Power Research Institute of Yunnan Power Grid Co. Ltd, Kunming 430072, ChinaEnergy-intensive industrial load offers substantial capacity and rapid adjustment capabilities, which can be effectively coordinated with deep peak regulation (DPR) methods of thermal power to optimize the peak regulation state of the system. The uncertainty of electricity prices and the current peak regulation compensation mechanism significantly affect the economic viability of industrial load regulation. In this study, electrolytic aluminum load (EAL) is used as a representative industrial load. This paper combines the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN), whale optimization algorithm (WOA), and long short-term memory network (LSTM) to propose a CEEMDAN-WOA-LSTM prediction model for electricity price scenarios. Subsequently, comprehensive cost and fine adjustment models for electrolytic aluminum load (EAL) are developed, incorporating the current peak regulation compensation mechanism. Finally, a source-load collaborative stochastic optimization method is proposed, integrating the scenario method and chance constraints. The effectiveness of the proposed scheme is verified using a real regional system, demonstrating significant reductions in total social peak regulation costs, a substantial decrease in renewable energy (RE) abandonment rates, reduced frequency of thermal power DPR, and improved economic efficiency of thermal power. Additionally, the current peak regulation compensation mechanism effectively guarantees the benefits of EAL and encourages its adjustment willingness.http://www.sciencedirect.com/science/article/pii/S0142061525001814Electricity price scenario predictionElectrolytic aluminum load regulationPeak regulation compensation benefitCollaborative stochastic optimizationRenewable energy accommodation |
| spellingShingle | Xiaoyu Yue Lijun Fu Siyang Liao Jian Xu Deping Ke Huiji Wang Shuaishuai Feng Jiaquan Yang Xuehao He A source-load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefit International Journal of Electrical Power & Energy Systems Electricity price scenario prediction Electrolytic aluminum load regulation Peak regulation compensation benefit Collaborative stochastic optimization Renewable energy accommodation |
| title | A source-load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefit |
| title_full | A source-load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefit |
| title_fullStr | A source-load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefit |
| title_full_unstemmed | A source-load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefit |
| title_short | A source-load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefit |
| title_sort | source load collaborative stochastic optimization method considering the electricity price uncertainty and industrial load peak regulation compensation benefit |
| topic | Electricity price scenario prediction Electrolytic aluminum load regulation Peak regulation compensation benefit Collaborative stochastic optimization Renewable energy accommodation |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525001814 |
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