Chance-constrained Optimal Dispatch for Distribution Networks Based on Trustworthy Gaussian Mixture Linearization

DAI Guanquan; NIU Zhenyong; ZHOU Rongsheng; ZHU Jie; XU Jing; LYU Jiaxin; ZHU Yiying

doi:10.20204/j.sp.2026.04010

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Chance-constrained Optimal Dispatch for Distribution Networks Based on Trustworthy Gaussian Mixture Linearization

更新时间：2026-04-20

- Chance-constrained Optimal Dispatch for Distribution Networks Based on Trustworthy Gaussian Mixture Linearization
- Vol. 54, Issue 4, Pages: 78-86(2026)
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- DOI：10.20204/j.sp.2026.04010
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- Published：2026
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戴观权,牛振勇,周荣生,朱杰,徐菁,吕佳欣,朱怡莹.基于可信高斯混合线性化的配电网机会约束优化调度[J].智慧电力,2026,54(4):78-86.

doi:10.20204/j.sp.2026.04010
戴观权,牛振勇,周荣生,朱杰,徐菁,吕佳欣,朱怡莹.基于可信高斯混合线性化的配电网机会约束优化调度[J].智慧电力,2026,54(4):78-86. DOI： 10.20204/j.sp.2026.04010.

doi:10.20204/j.sp.2026.04010 DOI：

摘要

针对可再生能源与电动汽车等负荷侧灵活性资源接入导致配电网向主动配电网转型过程中出现的不确定性优化调度问题，提出一种基于共形校正高斯混合模型线性化的机会约束优化方法。首先，采用机会约束规划刻画可再生能源预测误差的不确定性，并基于高斯混合模型精确描述其概率分布特征，进而将机会约束重构为各高斯分量累积分布函数的线性加权形式。其次，通过对累积分布函数进行分段线性逼近，将原非凸的机会约束转化为可高效求解的线性约束。最后，引入共形预测技术对机会约束进行可靠性校准，以消除高斯混合模型拟合误差导致的违反概率偏差，确保调度方案严格满足预设的可靠性要求。算例结果表明，所提方法在严格满足预设风险水平的前提下，有效降低了运行成本并显著提升了可再生能源消纳率。

Abstract

To address the uncertainty optimization dispatch problem arising from the transition of distribution networks into active distribution networks due to the integration of load-side flexible resources such as renewable energy and electric vehicles

this paper proposes a chance-constrained optimization method based on conformal correction and Gaussian mixture model linearization. First

chance-constrained programming is employed to characterize the uncertainty of renewable energy forecast errors

and a Gaussian mixture model is used to accurately describe their probabilistic distribution

thereby reformulating the chance constraints into a linearly weighted form of cumulative distribution functions of the Gaussian components. Second

by applying piecewise linear approximation to the cumulative distribution functions

the original non-convex chance constraints are transformed into efficiently solvable linear constraints. Finally

conformal prediction is introduced to calibrate the reliability of the chance constraints

eliminating the violation probability bias caused by the fitting error of the Gaussian mixture model and ensuring that the dispatch solution strictly meets the prescribed reliability requirements. Case study results demonstrate that the proposed method effectively reduces operational costs and significantly improves renewable energy accommodation while strictly satisfying the preset risk levels.

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references

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