Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables
Chance-Constrained AC Optimal Power Flow for Distribution Systems With Renewables
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DOI：
10.1109/tpwrs.2017.2656080
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发表时间：
2017-01
影响因子：
6.6
通讯作者：
E. Dall’Anese;K. Baker;T. Summers
中科院分区：
文献类型：
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作者：
E. Dall’Anese;K. Baker;T. Summers
本文聚焦于具有可再生能源（RES）和储能系统的配电系统，并提出一种交流最优潮流（OPF）方法，以在应对可再生能源发电和负荷的不确定性的同时优化系统级性能目标。所提出的方法基于机会约束型交流最优潮流公式，其中利用概率约束以规定的概率强制进行电压调节。通过采用交流潮流方程的适当线性近似以及机会约束的凸近似，开发出一种计算上更易于处理的凸重构形式。近似机会约束提供了对预测误差的任意分布都成立的保守界限。然后通过将所提出的交流最优潮流任务嵌入到模型预测控制框架中获得一种自适应策略。最后，开发了一种分布式求解器，以便在公用事业公司和用户之间策略性地分配优化问题的解。
This paper focuses on distribution systems featuring renewable energy sources (RESs) and energy storage systems, and presents an AC optimal power flow (OPF) approach to optimize system-level performance objectives while coping with uncertainty in both RES generation and loads. The proposed method hinges on a chance-constrained AC OPF formulation, where probabilistic constraints are utilized to enforce voltage regulation with prescribed probability. A computationally more affordable convex reformulation is developed by resorting to suitable linear approximations of the AC power-flow equations as well as convex approximations of the chance constraints. The approximate chance constraints provide conservative bounds that hold for arbitrary distributions of the forecasting errors. An adaptive strategy is then obtained by embedding the proposed AC OPF task into a model predictive control framework. Finally, a distributed solver is developed to strategically distribute the solution of the optimization problems across utility and customers.