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）的增加也会导致不确定性在传输网格中的不确定性越来越确定。这会通过波动的能源供应和超载线的可能性增加来影响电网稳定性。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）。为了安全地操作包含可再生能源的电源系统，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进行的，分析的精确重新重新制定作为二阶锥体问题（SOCP）。我们在五个不同的IEEE网络上测试了新模型，其大小为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是强大的。分布式的储能系统可通过扁平的生成轮廓实现更稳定的操作。存储吸收了不确定性，并降低了发电成本。
The increase in renewable energy sources (RESs), like wind or solar power, results in growing uncertainty also in transmission grids. This affects grid stability through fluctuating energy supply and an increased probability of overloaded lines. One key strategy to cope with this uncertainty is the use of distributed energy storage systems (ESSs). In order to securely operate power systems containing renewables and use storage, optimization models are needed that both handle uncertainty and apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained optimal power flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. Assuming Gaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation as a second-order cone problem (SOCP). We test the new model on five different IEEE networks with varying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results show that the model is computationally efficient and robust with respect to constraint violation risk. The distributed energy storage system leads to more stable operation with flattened generation profiles. Storage absorbed RES uncertainty, and reduced generation cost.
