Intelligent evaluation method for power system stability with demand response resources

董波; 崔景侠; 徐纬河

doi:10.3969/j.issn.1009-1831.2019.04.014

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Intelligent evaluation method for power system stability with demand response resources

更新时间：2026-03-05

- Intelligent evaluation method for power system stability with demand response resources
- Vol. 21, Issue 4, (2019)
- 作者机构：
  
  国网连云港供电公司,江苏,连云港,222004
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2019.04.014
  CLC：
- Published：2019
- 稿件说明：
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董波, 崔景侠, 徐纬河. Intelligent evaluation method for power system stability with demand response resources[J]. 2019, 21(4).
DOI：

董波, 崔景侠, 徐纬河. Intelligent evaluation method for power system stability with demand response resources[J]. 2019, 21(4). DOI： 10.3969/j.issn.1009-1831.2019.04.014.

摘要

首先，阐述了需求响应资源的概率分布特性，并以IES运行成本为优化目标，综合考虑电力系统、天然气系统运行约束及能量耦合约束。建立IES 最优能量流(optimal energy flow，OEF)模型，用于求取发电机和耦合环节功率，并将其作为电力系统稳定器的输入；其次，通过搭建不同负荷水平下的暂态仿真模型，得到故障情况下的系统稳定情况；然后，提出基于堆栈降噪自动编码器（stacked denoising auto-encoders，SDAE）的电力系统稳定性评估器的训练方法；最后，在IEEE-39节点电力系统和修改的比利时20节点天然气系统组成的IES中，进行电力系统稳定性智能化评估的算例分析。仿真结果表明，基于SDAE的电力系统稳定性评估器识别精度较高，同时计算效率也较优。

Abstract

Firstly

the probability distribution characteristics of demand response resources are expounded. The IES（integrated energy system）operating cost is taken as the optimization goal and the power system

natural gas system operation constraints and energy coupling constraints are considered. The IES optimal energy flow（OFF）model is established

which is used as the input of the power system stability estimator to calculate the generator and coupling link power. Secondly

by setting up the transient simulation model under different load levels

the system stability under fault conditions is obtained. The training of power system stability estimator based on stack denoising auto-encoders（SDAE）is proposed. Finally

the IES comprised of the IEEE?39 node power system and the modified Belgian 20?node natural gas system is used to analyze the intelligent evaluation of power system stability. The simulation results show that the SDAE-based power system stability estimator has high recognition accuracy and excellent computational efficiency.

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