Title: Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow Show Chinese title

Title: 多时段随机最优潮流的解析不确定性传播

Abstract: 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. Show Chinese abstract

Abstract: 可再生能源来源（如风能或太阳能）的增加也导致输电网络中的不确定性增加。 这通过波动的能源供应和过载线路概率的增加影响电网稳定性。 应对这种不确定性的关键策略之一是使用分布式储能系统（ESS）。 为了安全地运行包含可再生能源的电力系统并利用储能，需要优化模型既能处理不确定性又能应用储能系统。 本文介绍了一种紧凑型动态随机机会约束直流最优潮流（CC-OPF）模型，该模型以最小化发电成本并包括分布式储能系统为目标。 假设高斯不确定性，我们采用仿射策略，将其转化为一个可处理且分析上精确的二阶锥问题（SOCP）。 我们在五个不同规模的IEEE网络（分别为5、39、57、118和300个节点）上测试了新模型，并进行了复杂性分析。 结果显示，该模型在计算效率和约束违反风险方面具有鲁棒性。 分布式储能系统实现了更稳定的运行，并且发电曲线趋于平缓。储能吸收了可再生能源的不确定性，降低了发电成本。