Chance Constraints for Improving the Security of AC Optimal Power Flow
Chance Constraints for Improving the Security of AC Optimal Power Flow
复制标题
DOI：
10.1109/tpwrs.2018.2890732
复制
发表时间：
2018-03
影响因子：
6.6
通讯作者：
Miles Lubin;Y. Dvorkin;Line A. Roald
中科院分区：
文献类型：
--
作者：
Miles Lubin;Y. Dvorkin;Line A. Roald
本文提出了一种可扩展的方法，用于改善交流最优潮流（AC OPF）的解决方案，相对于预测的功率注入从风和其他不确定的发电资源的偏差。本文的目的是提供解决方案，更强大的短期偏差，并优化初始操作点和参数化的响应政策，在波动期间的控制。我们制定这作为一个机会约束优化问题。为了获得一个易于处理的机会约束表示，我们引入了一些建模假设，并利用最近的理论结果重新制定的问题作为一个凸，二阶锥程序，这是有效的解决，即使是大型的实例。我们的实验表明，所提出的方法提高了OPF解决方案的可行性和性价比，而额外的计算时间是在同一数量级上作为一个单一的确定性AC OPF计算。
This paper presents a scalable method for improving the solutions of ac optimal power flow (AC OPF) with respect to deviations in predicted power injections from wind and other uncertain generation resources. The aim of this paper is on providing solutions that are more robust to short-term deviations, and that optimize both the initial operating point and a parametrized response policy for control during fluctuations. We formulate this as a chance-constrained optimization problem. To obtain a tractable representation of the chance constraints, we introduce a number of modeling assumptions and leverage recent theoretical results to reformulate the problem as a convex, second-order cone program, which is efficiently solvable even for large instances. Our experiments demonstrate that the proposed procedure improves the feasibility and cost performance of the OPF solution, while the additional computation time is on the same magnitude as a single deterministic AC OPF calculation.