A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast Uncertainty
The active distribution network management (ADNM) equipped by active distribution networks (ADNs) can enhance the resilience of the network after failure. This paper proposes a novel comprehensive post-event network restoration model and its chance-constrained variant for ADN that considering the di...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9525391/ |
| _version_ | 1819088786522374144 |
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| author | Guanghe Wang Xiang Lei Han Wu Kai Sun Lijun Wang Yi Ding Che Wang |
| author_facet | Guanghe Wang Xiang Lei Han Wu Kai Sun Lijun Wang Yi Ding Che Wang |
| author_sort | Guanghe Wang |
| collection | DOAJ |
| description | The active distribution network management (ADNM) equipped by active distribution networks (ADNs) can enhance the resilience of the network after failure. This paper proposes a novel comprehensive post-event network restoration model and its chance-constrained variant for ADN that considering the dispatch of distributed generation (DG), energy storage system (ESS), demand response (DR), static var compensator (SVC), and network reconfiguration to fully investigate the potential of the ADNM in network restoration. In this paper, the line failure, bus failure, and the uncertainty from load and DG output forecast error are considered. Specifically, the line and bus failures are modeled by an enhanced fictitious network technique, while the uncertainty of load and DG output forecast is modeled by the chance-constrained optimization. The power flow is described by a linearized DistFlow model, and thus the deterministic and chance-constrained network restoration models are programmed by the mixed-integer linear programming (MILP). The proposed deterministic and chance-constrained restoration models are tested on the IEEE 33 bus system. Results demonstrate the effectiveness of the proposed deterministic and chance-constrained network restoration model. |
| first_indexed | 2024-12-21T21:57:34Z |
| format | Article |
| id | doaj.art-7b19ade54b5f49cbbbdc4170174272b0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-21T21:57:34Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7b19ade54b5f49cbbbdc4170174272b02022-12-21T18:48:56ZengIEEEIEEE Access2169-35362021-01-01913099713100510.1109/ACCESS.2021.31090719525391A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast UncertaintyGuanghe Wang0Xiang Lei1Han Wu2https://orcid.org/0000-0001-9653-4573Kai Sun3https://orcid.org/0000-0001-8820-4005Lijun Wang4Yi Ding5Che Wang6Huai’an Hongze District Power Supply Branch, State Grid Jiangsu Province Electric Power Co., Ltd., Huai’an, ChinaHuai’an Hongze District Power Supply Branch, State Grid Jiangsu Province Electric Power Co., Ltd., Huai’an, ChinaSmart Grid Research Institute, Nanjing Institute of Technology, Nanjing, ChinaHuai’an Hongze District Power Supply Branch, State Grid Jiangsu Province Electric Power Co., Ltd., Huai’an, ChinaHuai’an Hongze District Power Supply Branch, State Grid Jiangsu Province Electric Power Co., Ltd., Huai’an, ChinaHuai’an Hongze District Power Supply Branch, State Grid Jiangsu Province Electric Power Co., Ltd., Huai’an, ChinaHuai’an Hongze District Power Supply Branch, State Grid Jiangsu Province Electric Power Co., Ltd., Huai’an, ChinaThe active distribution network management (ADNM) equipped by active distribution networks (ADNs) can enhance the resilience of the network after failure. This paper proposes a novel comprehensive post-event network restoration model and its chance-constrained variant for ADN that considering the dispatch of distributed generation (DG), energy storage system (ESS), demand response (DR), static var compensator (SVC), and network reconfiguration to fully investigate the potential of the ADNM in network restoration. In this paper, the line failure, bus failure, and the uncertainty from load and DG output forecast error are considered. Specifically, the line and bus failures are modeled by an enhanced fictitious network technique, while the uncertainty of load and DG output forecast is modeled by the chance-constrained optimization. The power flow is described by a linearized DistFlow model, and thus the deterministic and chance-constrained network restoration models are programmed by the mixed-integer linear programming (MILP). The proposed deterministic and chance-constrained restoration models are tested on the IEEE 33 bus system. Results demonstrate the effectiveness of the proposed deterministic and chance-constrained network restoration model.https://ieeexplore.ieee.org/document/9525391/Active distribution networkchance-constrained optimizationmicrogrid formulationnetwork restorationuncertainty |
| spellingShingle | Guanghe Wang Xiang Lei Han Wu Kai Sun Lijun Wang Yi Ding Che Wang A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast Uncertainty IEEE Access Active distribution network chance-constrained optimization microgrid formulation network restoration uncertainty |
| title | A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast Uncertainty |
| title_full | A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast Uncertainty |
| title_fullStr | A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast Uncertainty |
| title_full_unstemmed | A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast Uncertainty |
| title_short | A Comprehensive Network Restoration Model for Active Distribution Network Considering Forecast Uncertainty |
| title_sort | comprehensive network restoration model for active distribution network considering forecast uncertainty |
| topic | Active distribution network chance-constrained optimization microgrid formulation network restoration uncertainty |
| url | https://ieeexplore.ieee.org/document/9525391/ |
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