Data-Aided Offline and Online Screening for Security Constraint
Data-Aided Offline and Online Screening for Security Constraint
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DOI：
10.1109/tpwrs.2020.3040222
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发表时间：
2021-05
影响因子：
6.6
通讯作者：
Shubo Zhang;Hongxing Ye;Fengyu Wang;Yonghong Chen;Steve Rose;Yaming Ma
中科院分区：
文献类型：
--
作者：
Shubo Zhang;Hongxing Ye;Fengyu Wang;Yonghong Chen;Steve Rose;Yaming Ma
安全约束是机组组合中保证可靠发电商组合的关键组成部分。大量的安全约束使问题难以解决，这是臭名昭著的。约束筛选，即过滤掉非支配约束，被认为是解决这一挑战的有力工具。这项工作提出了一种安全约束筛选，可以有效地集成虚拟交易并在线捕获实时或前瞻性市场的变化。所提出的方法同时利用了确定性和统计学方法，这利用了数学建模和历史数据。使用中大陆独立系统运营商(MISO)的数据验证了有效性。
Security constraint is a key component in unit commitment to guarantee reliable generator commitment. Large set of security constraints are notorious for making the problem difficult to solve. Constraint screening, i.e., filtering out non-dominating constraints, is regarded as a powerful tool to address this challenge. This work presents a security-constraint screening that can effectively integrate virtual transaction and capture changes online in real-time or look-ahead markets. The proposed approach takes advantage of both deterministic and statistical methods, which leverages mathematical modeling and historical data. Effectiveness are verified using Midcontinent Independent System Operator (MISO) data.