论文标题
与非高斯分布式不确定性的机会约束的DC最佳功率流
Chance-constrained DC Optimal Power Flow with Non-Gaussian Distributed Uncertainties
论文作者
论文摘要
机会受限的编程（CCP）是处理最佳功率流（OPF）中不确定性的一种有前途的方法。但是，传统的CCP通常假设不确定性遵循高斯分布，这可能与现实不符。一些论文采用高斯混合模型（GMM）将CCP扩展到具有非高斯不确定性的病例，但它们仅适用于右侧不确定性的情况，但不适用于包含左侧不确定性的DC OPF。为了解决这个问题，我们开发了一种基于CHMM的频率约束DC OPF模型。在该模型中，我们不仅利用GMM来捕获非高斯分布式不确定性的概率特征，而且还开发了一种线性化技术来重新将左侧非高斯分布式不确定性的机会约束重新制定为可拖动形式。进一步提供了数学证明，以证明相应的重新制作是对原始问题的安全近似，这确保了解决方案的可行性。
Chance-constrained programming (CCP) is a promising approach to handle uncertainties in optimal power flow (OPF). However, conventional CCP usually assumes that uncertainties follow Gaussian distributions, which may not match reality. A few papers employed the Gaussian mixture model (GMM) to extend CCP to cases with non-Gaussian uncertainties, but they are only appropriate for cases with uncertainties on the right-hand side but not applicable to DC OPF that containing left-hand side uncertainties. To address this, we develop a tractable GMM-based chance-constrained DC OPF model. In this model, we not only leverage GMM to capture the probability characteristics of non-Gaussian distributed uncertainties, but also develop a linearization technique to reformulate the chance constraints with non-Gaussian distributed uncertainties on the left-hand side into tractable forms. A mathematical proof is further provided to demonstrate that the corresponding reformulation is a safe approximation of the original problem, which guarantees the feasibility of solutions.
