论文标题
多周期随机最佳功率流的分析不确定性传播
Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow
论文作者
论文摘要
可再生能源（RESS）（如风能或太阳能）的增加也会在传输网格中增长。这通过波动的能量供应和超载线的可能性增加了网格稳定性。应对这种不确定性的一种关键策略是使用分布式储能系统（ESSS）。为了安全地操作电源系统范围的可再生能源并使用存储，需要优化模型来处理不确定性和应用ESS。本文介绍了紧凑的动态随机机会约束的DC最佳功率流（CC-OPF）模型，该模型可最大程度地减少发电成本并包括分布式ESS。假设高斯的不确定性，我们使用仿射政策来获得可拖延的，分析的重新印度ASA二阶锥体问题（SOCP）。我们在五个不同的IEEE网络上测试了新模型，其尺寸为5、39、57、118和300节点，并包括复杂性分析。结果表明该模型在计算上是有效的，并且在约束违规风险方面具有稳健性。 TheDistrib的储能系统可通过扁平的生成概况导致更稳定的操作。存储吸收的不确定性和降低的发电成本。
The increase in renewable energy sources (RESs), like wind or solar power, results in growinguncertainty also in transmission grids. This affects grid stability through fluctuating energy supplyand an increased probability of overloaded lines. One key strategy to cope with this uncertainty isthe use of distributed energy storage systems (ESSs). In order to securely operate power systemscontaining renewables and use storage, optimization models are needed that both handle uncertaintyand apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained DC optimalpower flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. AssumingGaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation asa second-order cone problem (SOCP). We test the new model on five different IEEE networks withvarying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results showthat the model is computationally efficient and robust with respect to constraint violation risk. Thedistributed energy storage system leads to more stable operation with flattened generation profiles.Storage absorbed RES uncertainty, and reduced generation cost.
