期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 36, 期 6, 页码 5032-5043出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2021.3071088
关键词
Power system reliability; Integrated circuit modeling; Capacity planning; Substations; Optimization; Integrated circuit reliability; Redundancy; Capacity value; distribution network; effective load carrying capability; reliability; soft open point
资金
- Northern Powergrid, U.K. as part of the Pragmatic Security Assessment NIA Project [NIA_NPG_029]
- Northern Powergrid, U.K. as part of the Enhanced Understanding of Network Losses Project
- Engineering and Physical Sciences Research Council (EPSRC) [EP/T021969/1]
- National Science Foundation of China (NSFC) under NSFC-EPSRC Collaborative Research Initiative in Sustainable Power Supply, as part of the Multi-energy Control of Cyber-Physical Urban Energy Systems (MC2) Project [520616336103]
- EPSRC [EP/T021969/1] Funding Source: UKRI
This study proposes a reliability-based method to quantify the capacity value of Soft Open Points (SOPs) using the Effective Load Carrying Capability method and second-order cone programming. Results demonstrate that operating under a relaxed redundancy level can significantly increase the capacity value of a given SOP capacity relative to the standard redundancy level.
Soft open points (SOPs) are power electronic devices which provide interconnection between two feeders in place of normally open points in electricity distribution networks. SOPs can continuously control active power flow between feeders and inject reactive power controllably at both nodes, which can be used to provide substantial capacity support to the system. This paper provides a reliability-based method to quantify the capacity value (the additional load which can be accommodated without reducing reliability) of SOPs using the Effective Load Carrying Capability method within a Monte Carlo Simulation (MCS). Optimization of post-fault active/reactive power injections by SOPs to minimize energy not supplied is formulated (directly in matrix form) as a second-order cone programming problem. This results in very low computational times which enables embedding of the optimization problem within the MCS. The proposed methodology is applied to a modified real-world distribution network considering three different SOP sizes (5 SOPs totalling 2.5, 5, 10 MVA) across three redundancy levels (N-1, N-0.75, N-0.5), and on an unbalanced network with distributed generation. Results demonstrate capacity values ranging from 2.4-12.84 MVA. When operating under a relaxed redundancy level, the capacity value of a given SOP capacity can more than double relative to N-1.
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