Demand response-oriented virtual power plant evaluation based on AdaBoost and BP neural network
As an advanced technology that efficiently aggregates and optimizes renewable energy, controllable loads, and energy storage systems, virtual power plants (VPPs) can effectively promote the green and low-carbon transformation of power systems. A comprehensive assessment of VPPs is important for VPP...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-09-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723007539 |
| _version_ | 1797688383221268480 |
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| author | Dawei Hu Hengyu Liu Yidong Zhu Jiazheng Sun Zhe Zhang Luyu Yang Qihuitianbo Liu Bo Yang |
| author_facet | Dawei Hu Hengyu Liu Yidong Zhu Jiazheng Sun Zhe Zhang Luyu Yang Qihuitianbo Liu Bo Yang |
| author_sort | Dawei Hu |
| collection | DOAJ |
| description | As an advanced technology that efficiently aggregates and optimizes renewable energy, controllable loads, and energy storage systems, virtual power plants (VPPs) can effectively promote the green and low-carbon transformation of power systems. A comprehensive assessment of VPPs is important for VPP investment and operation. However, most of the existing evaluation methods focus on the reliability, economy, and mobilizability of VPPs. In this paper, in order to better address the characteristics of demand response-oriented VPPs, three aspects of VPP operation indices, new energy indices, and demand response indices are analyzed. In this manner, it is possible to meet the principles of the construction of VPP evaluation system and also to measure the effect of demand response of the VPP. Then, on the basis of AdaBoost algorithm, combined with back propagation (BP) neural network for the evaluation and classification of demand response-oriented VPPs. The entropy value method and gray correlation are also compared to validate the superiority of the proposed method. |
| first_indexed | 2024-03-12T01:29:52Z |
| format | Article |
| id | doaj.art-018fada206024cd394958d28b2950c78 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-12T01:29:52Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-018fada206024cd394958d28b2950c782023-09-12T04:16:03ZengElsevierEnergy Reports2352-48472023-09-019922931Demand response-oriented virtual power plant evaluation based on AdaBoost and BP neural networkDawei Hu0Hengyu Liu1Yidong Zhu2Jiazheng Sun3Zhe Zhang4Luyu Yang5Qihuitianbo Liu6Bo Yang7Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd, Shenyang, 110000, ChinaElectric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd, Shenyang, 110000, China; Corresponding author.Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd, Shenyang, 110000, ChinaElectric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd, Shenyang, 110000, ChinaElectric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd, Shenyang, 110000, ChinaElectric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd, Shenyang, 110000, ChinaShenyang EPIC Technology Co., Ltd, Shenyang 110000, ChinaShenyang EPIC Technology Co., Ltd, Shenyang 110000, ChinaAs an advanced technology that efficiently aggregates and optimizes renewable energy, controllable loads, and energy storage systems, virtual power plants (VPPs) can effectively promote the green and low-carbon transformation of power systems. A comprehensive assessment of VPPs is important for VPP investment and operation. However, most of the existing evaluation methods focus on the reliability, economy, and mobilizability of VPPs. In this paper, in order to better address the characteristics of demand response-oriented VPPs, three aspects of VPP operation indices, new energy indices, and demand response indices are analyzed. In this manner, it is possible to meet the principles of the construction of VPP evaluation system and also to measure the effect of demand response of the VPP. Then, on the basis of AdaBoost algorithm, combined with back propagation (BP) neural network for the evaluation and classification of demand response-oriented VPPs. The entropy value method and gray correlation are also compared to validate the superiority of the proposed method.http://www.sciencedirect.com/science/article/pii/S2352484723007539Virtual power plantDemand responseComprehensive evaluation index systemAdaBoost |
| spellingShingle | Dawei Hu Hengyu Liu Yidong Zhu Jiazheng Sun Zhe Zhang Luyu Yang Qihuitianbo Liu Bo Yang Demand response-oriented virtual power plant evaluation based on AdaBoost and BP neural network Energy Reports Virtual power plant Demand response Comprehensive evaluation index system AdaBoost |
| title | Demand response-oriented virtual power plant evaluation based on AdaBoost and BP neural network |
| title_full | Demand response-oriented virtual power plant evaluation based on AdaBoost and BP neural network |
| title_fullStr | Demand response-oriented virtual power plant evaluation based on AdaBoost and BP neural network |
| title_full_unstemmed | Demand response-oriented virtual power plant evaluation based on AdaBoost and BP neural network |
| title_short | Demand response-oriented virtual power plant evaluation based on AdaBoost and BP neural network |
| title_sort | demand response oriented virtual power plant evaluation based on adaboost and bp neural network |
| topic | Virtual power plant Demand response Comprehensive evaluation index system AdaBoost |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723007539 |
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