.png){kind=icon}
返回
Duality-Free Decomposition Based Data-Driven Stochastic Security-Constrained Unit Commitment
.png){kind=icon}
OA
AI
DOI：10.1109/TSTE.2018.2825361.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
To incorporate the superiority of both stochastic and robust approaches, a data-driven stochastic optimization is employed to solve the security-constrained unit commitment model. This approach makes themost use of the historical data to generate a set of possible probability distributions for wind power outputs and then it optimizes the unit commitment under the worst-case probability distribution. However, this model suffers from huge computational burden, as a large number of scenarios are considered. To tackle this issue, a duality-free decomposition method is proposed in this paper. This approach does not require doing duality, which can save a large set of dual variables and constraints, and therefore reduces the computational burden. In addition, the inner max-min problem has a special mathematical structure, where the scenarios have the similar constraint. Thus, the max-min problem can be decomposed into independent subproblems to be solved in parallel, which further improves the computational efficiency. A numerical study on an IEEE 118-bus system with practical data of a wind power system has demonstrated the effectiveness of the proposal.
Keyword:
Data-driven stochastic optimization
duality-free decomposition
security-constrained unit commitment
distributionally robust optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.4
论文数:
6.8K
被引数:
1.5W
机构
引用论文
.png){kind=icon}
Adaptive Robust Optimization for the Security Constrained Unit Commitment Problem安全约束机组组合问题的自适应鲁棒优化
OAAI
OAAI分享收藏Distributionally Robust Solution to the Reserve Scheduling Problem With Partial Information of Wind Power含风电部分信息的备用调度问题的分布式鲁棒解
PREAI
分享收藏Economic Valuation of Reserves in Power Systems With High Penetration of Wind Power风电高渗透率电力系统中储量的经济评估
PREAI
分享收藏
查看更多内容