期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 38, 期 2, 页码 1350-1365出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2022.3176024
关键词
Resilience; Load modeling; Distribution networks; Planning; Costs; Control systems; Investment; Co-deployment; distribution network; multiple recovery stages; resilience improvement; remote-controlled switch; soft open point
This paper proposes a co-deployment framework for soft open points (SOPs) and remote-controlled switches (RCSs) to improve the resilience and management of flexible resources in distribution networks. The model optimizes the investment cost of SOPs and RCSs, as well as the cost caused by de-energized loads, while considering operational constraints. It also analyzes the tradeoff between resilience and cost.
Soft open points (SOPs) can perform load transferring and voltage support, helping to expand the range of service restoration in distribution networks (DNs) under fault scenarios. In addition, in the field of DN restoration, SOPs are usually controlled in coordination with switch actions (e.g., the network reconfiguration). However, in the planning level, the coordinated deployment of SOPs and remote-controlled switches (RCSs) has never been studied in the literature. To this end, for the first time, this paper proposes a co-deployment framework for SOPs and RCSs incorporating active management of flexible resources for resilience improvement in DNs, in which the coupled multiple recovery stages are also modeled and included. The objective is to minimize the investment cost of SOPs and RCSs, along with the cost caused by de-energized loads, with the consideration of exhaustive DN operation constraints over proactive islanding, degradation, isolation and restoration stages. Furthermore, resilience-cost tradeoff is analyzed to help DN managers determine equipment investment plans. The formulated model is further transformed into a mixed-integer second-order cone programming model that can be thus efficiently solved. Simulation results on modified IEEE 34-node and IEEE 123-node test systems verify the effectiveness of the proposed model.
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