Decentralized Voltage Prediction in Multi-Area Distribution Systems: A Privacy-Preserving Collaborative Framework
With the high penetration of renewable energy and the deregulation of electricity markets, future active distribution networks are anticipated to comprise areas with diverse ownership. Frequent power exchange between these areas leads to strong interarea influences, making it essential to account fo...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10993425/ |
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| author | Jianfeng Yan Beibei Wang Zhiqiang Wu Zhengkai Ding |
| author_facet | Jianfeng Yan Beibei Wang Zhiqiang Wu Zhengkai Ding |
| author_sort | Jianfeng Yan |
| collection | DOAJ |
| description | With the high penetration of renewable energy and the deregulation of electricity markets, future active distribution networks are anticipated to comprise areas with diverse ownership. Frequent power exchange between these areas leads to strong interarea influences, making it essential to account for voltage coupling in decentralized voltage prediction (DVP). To ensure the accuracy of DVP, collaborative modeling using power information from neighboring areas is imperative. However, potential privacy concerns pose significant challenges to data sharing and interarea collaboration. To address these challenges, this paper proposes a privacy-preserving collaborative prediction framework. First, a privacy-preserving multi-objective XGBoost algorithm is developed to mitigate curious behavior through homomorphic encryption and minimal information sharing. Then, an equitable incentive mechanism is introduced to allocate rewards based on the relative marginal contributions, which discourages potential adversarial behavior. The proposed framework further modularizes subproblems, enabling parallelization to improve computational efficiency. Finally, case studies demonstrate that the proposed framework can achieve prediction accuracy comparable to centralized methods while ensuring privacy preservation and collaboration reliability, highlighting its potential for broad adoption in multi-area systems. |
| format | Article |
| id | doaj-art-91b6d0ab7a1f45a7aa8fd0c1e00a0c33 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-91b6d0ab7a1f45a7aa8fd0c1e00a0c332025-08-20T02:19:38ZengIEEEIEEE Access2169-35362025-01-0113923059231810.1109/ACCESS.2025.356802510993425Decentralized Voltage Prediction in Multi-Area Distribution Systems: A Privacy-Preserving Collaborative FrameworkJianfeng Yan0https://orcid.org/0009-0004-0226-9940Beibei Wang1https://orcid.org/0000-0002-1030-3756Zhiqiang Wu2Zhengkai Ding3School of Cyber Science and Engineering, Southeast University, Nanjing, Jiangsu, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, Jiangsu, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, Jiangsu, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing, Jiangsu, ChinaWith the high penetration of renewable energy and the deregulation of electricity markets, future active distribution networks are anticipated to comprise areas with diverse ownership. Frequent power exchange between these areas leads to strong interarea influences, making it essential to account for voltage coupling in decentralized voltage prediction (DVP). To ensure the accuracy of DVP, collaborative modeling using power information from neighboring areas is imperative. However, potential privacy concerns pose significant challenges to data sharing and interarea collaboration. To address these challenges, this paper proposes a privacy-preserving collaborative prediction framework. First, a privacy-preserving multi-objective XGBoost algorithm is developed to mitigate curious behavior through homomorphic encryption and minimal information sharing. Then, an equitable incentive mechanism is introduced to allocate rewards based on the relative marginal contributions, which discourages potential adversarial behavior. The proposed framework further modularizes subproblems, enabling parallelization to improve computational efficiency. Finally, case studies demonstrate that the proposed framework can achieve prediction accuracy comparable to centralized methods while ensuring privacy preservation and collaboration reliability, highlighting its potential for broad adoption in multi-area systems.https://ieeexplore.ieee.org/document/10993425/Interarea couplingsecure data sharingdecentralized voltage predictionhomomorphic encryptionprivacy preservation |
| spellingShingle | Jianfeng Yan Beibei Wang Zhiqiang Wu Zhengkai Ding Decentralized Voltage Prediction in Multi-Area Distribution Systems: A Privacy-Preserving Collaborative Framework IEEE Access Interarea coupling secure data sharing decentralized voltage prediction homomorphic encryption privacy preservation |
| title | Decentralized Voltage Prediction in Multi-Area Distribution Systems: A Privacy-Preserving Collaborative Framework |
| title_full | Decentralized Voltage Prediction in Multi-Area Distribution Systems: A Privacy-Preserving Collaborative Framework |
| title_fullStr | Decentralized Voltage Prediction in Multi-Area Distribution Systems: A Privacy-Preserving Collaborative Framework |
| title_full_unstemmed | Decentralized Voltage Prediction in Multi-Area Distribution Systems: A Privacy-Preserving Collaborative Framework |
| title_short | Decentralized Voltage Prediction in Multi-Area Distribution Systems: A Privacy-Preserving Collaborative Framework |
| title_sort | decentralized voltage prediction in multi area distribution systems a privacy preserving collaborative framework |
| topic | Interarea coupling secure data sharing decentralized voltage prediction homomorphic encryption privacy preservation |
| url | https://ieeexplore.ieee.org/document/10993425/ |
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