Voltage Regulation Strategy for Distributed Photovoltaic Considering Power Quality in the Substation Area

GUAN Yongming; SHI Yuliang; WANG Jihu; LYU Liang; CHEN Zhiyong; LI Hui

doi:10.12263/DZXB.20250980

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Voltage Regulation Strategy for Distributed Photovoltaic Considering Power Quality in the Substation Area

PAPERS | 更新时间：2026-06-04

- Voltage Regulation Strategy for Distributed Photovoltaic Considering Power Quality in the Substation Area
- ACTA ELECTRONICA SINICA Vol. 54, Issue 1, Pages: 206-218(2026)
- 作者机构：
  
  1.山东大学软件学院，山东济南 250101
  2.地纬智能科技股份有限公司，山东济南 250100
  3.山东大学控制科学与工程学院，山东济南 250101
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62376135;62406176);China Postdoctoral Science Foundation(2024M751810);Shandong Provincial Key Research and Development Plan (Competitive Innovation Platform) Project(2025CXPT101);Shandong Provincial Natural Science Foundation(ZR2024QF091)
- DOI：10.12263/DZXB.20250980
  CLC： TP391;
- Received：25 December 2025，
  
  Accepted：10 January 2026，
  
  Published：25 January 2026
- 稿件说明：
移动端阅览
管永明, 史玉良, 王继虎, 等. 计及台区电能质量的分布式电源电压调控策略[J]. 电子学报, 2026, 54(01): 206-218.

GUAN Yongming, SHI Yuliang, WANG Jihu, et al. Voltage Regulation Strategy for Distributed Photovoltaic Considering Power Quality in the Substation Area[J]. Acta Electronica Sinica, 2026, 54(01): 206-218.
管永明, 史玉良, 王继虎, 等. 计及台区电能质量的分布式电源电压调控策略[J]. 电子学报, 2026, 54(01): 206-218. DOI：10.12263/DZXB.20250980

GUAN Yongming, SHI Yuliang, WANG Jihu, et al. Voltage Regulation Strategy for Distributed Photovoltaic Considering Power Quality in the Substation Area[J]. Acta Electronica Sinica, 2026, 54(01): 206-218. DOI：10.12263/DZXB.20250980

摘要

针对高渗透率分布式光伏接入配电台区引发的电压波动问题，提出一种计及台区电能质量的分布式电源电压调控策略。首先，基于台区拓扑结构构建分布式光伏-负荷节点连接关系图，以此为依据进行动态调控区域划分、关键电压节点筛选及多目标优化函数设计。在此基础上，构建了一种基于时频分类与混合专家网络（Mixture of Experts，MoE）的光伏出力预测模型，通过融合时域变化特征与频域周期规律，增强出力波动的分类表征能力，并借助MoE结构实现数据分类下的专业定向预测，从而显著提升预测精度与稳定性。进一步，以预测结果为输入，采用模型预测控制方法，将电压约束、有功功率输出及调控频率等多重限制条件直接嵌入滚动优化目标，生成前瞻式协同调控策略，以解决传统逆变器调控滞后、动作频繁及出力降额等问题。为提升台区级调控效率并降低计算负担，设计了具备自趋优更新机制的经验回放区，结合调控边界自感知规则，在预测完成后可触发精简调控模式，直接选取相似历史策略执行，并通过奖励机制持续优化策略库，从而在保证调控稳定性的同时大幅提升响应速度。仿真结果表明，所提方法在预测准确性上显著优于多种对比方案，其测试准确率达99.29%，标准差仅0.71%，波动范围控制在3.27%。在电压调控效果方面，该方法在负荷突增导致电压越下限、光伏出力波动引起电压越上限等多类场景中，均能实现快速且平稳的电压恢复：在电压越下限2%~10%场景下，调控完成速度较现有方法提升2.4倍以上；在越上限2%~7%场景中，调控速度快1.5倍以上，且全过程电压偏差始终维持在±2%以内，有效避免了频繁调控与发电损失；在电压越上限7%~10%场景中，通过降低有功功率输出实现压降，所提方法在2 s内即可完成调控，且有功功率输出较传统方式提升约3%，显著缓解了因过压保护导致的停机风险。综上所述，文章所提出的融合精准预测、滚动优化与经验回放机制的电压调控策略，不仅具有较高的预测精度与响应速度，而且能有效保障台区电压稳定并提升光伏出力，为分布式新能源从“规模扩张”向“质量提升”转型提供了可行的技术支撑。

Abstract

Addressing the voltage fluctuation issues caused by high-penetration distributed photovoltaic (PV) integration into distribution areas

a distributed PV voltage regulation strategy that takes into account the power quality of the area is proposed. Firstly

based on the topology structure of the area

a distributed PV-load node connection graph is constructed. This serves as the basis for dynamic regulation area division

key voltage node screening

and multi-objective optimization function design. On this basis

a PV output prediction model based on time-frequency classification and mixture of experts(MoE) is developed. By integrating time-domain variation characteristics and frequency-domain periodic patterns

the classification and representation ability of output fluctuations is enhanced. Additionally

MoE is utilized to improve prediction accuracy and stability. Furthermore

using the prediction results as input

a model predictive control method is adopted to directly embed multiple constraints such as voltage constraints

active power output

and regulation frequency into the rolling optimization objective

generating a forward-looking collaborative regulation strategy to address issues such as lagging regulation

frequent actions

and output derating of traditional inverters. To enhance the efficiency of area-level regulation and reduce computational burden

an experience replay area with a self-optimizing update mechanism is designed. Combined with regulation boundary self-sensing rules

a simplified regulation mode can be triggered after prediction completion

directly selecting similar historical strategies for execution. The strategy library is continuously optimized through a reward mechanism

significantly improving response speed while ensuring regulation stability. Simulation results show that the proposed method significantly outperforms multiple comparative schemes in prediction accuracy

achieving a test accuracy of 99.29%

a standard deviation of only 0.71%

and a fluctuation range controlled within 3.27%. In terms of voltage regulation effects

the method achieves rapid and smooth voltage recovery in various scenarios such as voltage undershoot caused by sudden load increases and voltage overshoot caused by PV output fluctuations. Specifically

in scenarios where the voltage undershoots by 2%~10%

the regulation completion speed is increased by more than 2.4 times compared to existing methods. In scenarios where the voltage overshoots by 2%~7%

the regulation speed is increased by more than 1.5 times

and the voltage deviation throughout the entire process remains within ±2%

effectively avoiding frequent regulation and power generation losses. In scenarios where the voltage overshoots by 7%~10%

the proposed method achieves regulation within 2 seconds by reducing active power output

and the active power output is increased by about 3% compared to traditional methods

significantly mitigating the risk of downtime caused by overvoltage protection. In summary

the voltage regulation strategy that integrates precise prediction

rolling optimization

and experience replay mechanisms not only exhibits high prediction accuracy and response speed

but also effectively ensures voltage stability in the substation area and enhances photovoltaic output. This provides feasible technical support for the transformation of distributed renewable energy from “scale expansion” to “quality improvement”.

关键词

Keywords

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