In this paper, we study the optimal sizing planning of renewable distributed generation (RDG) in distribution networks to minimize the long-term cost, including the investment cost, maintenance cost, and operating cost. In particular, the operating cost itself is optimized by solving an optimal power flow (OPF) problem at each time $t$ based on uncertain time-varying RDG output and load demand. As a result, the sizing planning problem is a bilevel stochastic programming problem, which is hard to solve. Instead of resorting to conventional meta-heuristic algorithms, this paper first proposes a novel data-driven approach based on the philosophy of online convex optimization to solve the problem with drastically lower complexity. As a key step to facilitate the algorithm, we derive a closed-form expression to iteratively update the sizing solution upon drawing each data sample. With sufficient data samples, the proposed algorithm guarantees to converge to the global optimal solution regardless of the underlying probabilistic distribution of RDG output and load demand. Numerical results on the IEEE 13-bus test feeder, the IEEE 33-bus test feeder, and the Southern California Edison (SCE) 56-bus feeder show that our data-driven method drastically outperforms the other methods in terms of both the solution optimality and computational complexity.

中文翻译：

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具有最优性保证的配电网中可再生分布式发电的数据驱动规模规划

在本文中，我们研究了配电网中可再生分布式发电（RDG）的最佳规模规划，以最大程度地减少长期成本，包括投资成本，维护成本和运营成本。特别是，通过每次解决最佳功率流（OPF）问题来优化运营成本本身$ t $基于不确定的时变RDG输出和负载需求。结果，规模规划问题是一个双层随机规划问题，很难解决。本文没有采用传统的元启发式算法，而是提出了一种基于在线凸优化原理的新颖的数据驱动方法，以解决复杂度大大降低的问题。作为简化算法的关键步骤，我们得出了一个封闭形式的表达式，以便在绘制每个数据样本时迭代更新大小调整解决方案。有了足够的数据样本，无论RDG输出和负载需求的潜在概率分布如何，所提出的算法都可以保证收敛到全局最优解。IEEE 13总线测试馈线，IEEE 33总线测试馈线的数值结果，