Two-Stage Self-Healing Restoration Strategy Considering Operating PerformanceSource: Journal of Energy Engineering:;2020:;Volume ( 146 ):;issue: 004Author:Kaiyuan Pang
,
Chongyu Wang
,
Fushuan Wen
,
Ivo Palu
,
Changsen Feng
,
Zeng Yang
,
Minghui Chen
,
Hongwei Zhao
,
Huiyu Shang
DOI: 10.1061/(ASCE)EY.1943-7897.0000683Publisher: ASCE
Abstract: Power system resilience requires highly effective self-healing restoration strategies, and calls for superior performance both in system restoration and operation. Most existing restoration methods focus only on restoring performance, such as generation capacity, restored load capacity, and recovery time, but ignore operating performance that can enhance resilience significantly. This paper sheds light on a two-stage self-healing system restoration strategy considering operating performance, which includes voltage balance, transmission loss, and generation cost, to enhance resilience. The first stage aims to determine the restoration sequence of generators, power lines, and loads with maximum restored load capacity. The second stage of the optimization problem focuses on readjusting the power outputs of generators to improve single or multiple operating performance indicators. The optimization model of the first stage is formulated as a linear integer programming (IP) problem and solved by the branch-and-bound method, while the second-stage problem considering AC optimal power flow is solved by the interior point method. The proposed two-stage strategy also provides a possible solution for an online restoration decision support system with superior computational performance. Case studies on the IEEE 30-bus and 118-bus power systems have demonstrated the correctness and effectiveness of the proposed strategy.
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| contributor author | Kaiyuan Pang | |
| contributor author | Chongyu Wang | |
| contributor author | Fushuan Wen | |
| contributor author | Ivo Palu | |
| contributor author | Changsen Feng | |
| contributor author | Zeng Yang | |
| contributor author | Minghui Chen | |
| contributor author | Hongwei Zhao | |
| contributor author | Huiyu Shang | |
| date accessioned | 2022-01-30T21:40:06Z | |
| date available | 2022-01-30T21:40:06Z | |
| date issued | 8/1/2020 12:00:00 AM | |
| identifier other | %28ASCE%29EY.1943-7897.0000683.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4268634 | |
| description abstract | Power system resilience requires highly effective self-healing restoration strategies, and calls for superior performance both in system restoration and operation. Most existing restoration methods focus only on restoring performance, such as generation capacity, restored load capacity, and recovery time, but ignore operating performance that can enhance resilience significantly. This paper sheds light on a two-stage self-healing system restoration strategy considering operating performance, which includes voltage balance, transmission loss, and generation cost, to enhance resilience. The first stage aims to determine the restoration sequence of generators, power lines, and loads with maximum restored load capacity. The second stage of the optimization problem focuses on readjusting the power outputs of generators to improve single or multiple operating performance indicators. The optimization model of the first stage is formulated as a linear integer programming (IP) problem and solved by the branch-and-bound method, while the second-stage problem considering AC optimal power flow is solved by the interior point method. The proposed two-stage strategy also provides a possible solution for an online restoration decision support system with superior computational performance. Case studies on the IEEE 30-bus and 118-bus power systems have demonstrated the correctness and effectiveness of the proposed strategy. | |
| publisher | ASCE | |
| title | Two-Stage Self-Healing Restoration Strategy Considering Operating Performance | |
| type | Journal Paper | |
| journal volume | 146 | |
| journal issue | 4 | |
| journal title | Journal of Energy Engineering | |
| identifier doi | 10.1061/(ASCE)EY.1943-7897.0000683 | |
| page | 11 | |
| tree | Journal of Energy Engineering:;2020:;Volume ( 146 ):;issue: 004 | |
| contenttype | Fulltext |