计及光伏分层消纳与离网供电可靠性的农村微电网光储协同规划方法

刘一欣; 李彦榕; 郭力; 马良; 姜世公; 李鹏

doi:10.19912/j.0254-0096.tynxb.2025-0225

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

计及光伏分层消纳与离网供电可靠性的农村微电网光储协同规划方法

更新时间：2026-07-22

- 计及光伏分层消纳与离网供电可靠性的农村微电网光储协同规划方法
- Vol. 47, Issue 6, Pages: 131-143(2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.19912/j.0254-0096.tynxb.2025-0225
  CLC： TM715
- Online First：08 July 2026，
  
  Published：2026
- 稿件说明：
移动端阅览
刘一欣, 李彦榕, 郭力, et al. 计及光伏分层消纳与离网供电可靠性的农村微电网光储协同规划方法[J]. 2026, 47(6): 131-143.
DOI：

刘一欣, 李彦榕, 郭力, et al. 计及光伏分层消纳与离网供电可靠性的农村微电网光储协同规划方法[J]. 2026, 47(6): 131-143. DOI： 10.19912/j.0254-0096.tynxb.2025-0225.

摘要

为提升农村分布式光伏消纳能力和供电可靠性

提出一种农村微电网光储分层协同规划方法。首先

构建基于DenseNet-Autoformer网络的中长期负荷预测模型

基于自相关机制深度提取时序特征

综合考虑气象、经济等要素得到月用电量预测结果;其次

采用具有梯度惩罚的Wasserstein生成对抗网络扩充源荷场景样本

并基于高斯混合聚类得到规划期内的典型源荷场景;最后

在台区层面考虑并网电量自平衡能力和离网重要负荷的可靠供电需求

基于机会约束量化极端场景下的供电可靠性

得到台区光储配置方案;同时

考虑10 kV馈线整体自平衡能力

优化得到馈线级光储配置方案。仿真结果表明

所提方法可提升农村微电网自平衡能力和分布式光伏就地消纳水平

并改善极端场景下重要负荷的供电可靠性。

Abstract

To enhance the consumption capacity of distributed photovoltaic （PV） and improve the power supply reliability of rural areas

this paper proposes a hierarchical coordinated planning method of PV and energy storage system for rural microgrids. Firstly

the medium and long-term load forecasting model based on the DenseNet-Autoformer network is proposed

which uses the autocorrelation mechanism to extract the deep time-series features

and comprehensively considers factors such as meteorology and economy on long-term monthly electricity consumption; Secondly

the Wasserstein generative adversarial network-gradient penalty method is used to expand the PV power samples. On this basis

the typical source and load scenarios are obtained via Gaussian mixture clustering; Finally

taking into account the electricity self-sufficiency and the off-grid reliability

the PV and storage configuration plan of the distribution transformer area is optimized

and the power supply reliability under extreme scenarios is quantified based on chance constraints. Meanwhile

considering the overall energy self-sustained ability of the 10 kV feeder

the feeder-level PV and storage configuration plan is optimized. The simulation results show that the proposed method can enhance the energy self-sustained ability and PV consumption capacity of rural microgrids and improve the power supply reliability in extreme scenarios.

关键词

Keywords

references

NIU D X, SONG Z Y.Medium and long-term load forecasting based on system dynamics and electricity elasticity coefficient method in China[J]. Energy education science and technology part a: energy science and research, 2014, 32(3): 2085-2094.

MYSTAKIDIS A, TSALIKIDIS N, KOUKARAS P, et al.Power load forecasting: a time-series multi-step ahead and multi-model analysis[C]//2023 58th International Universities Power Engineering Conference (UPEC). Dublin, Ireland, 2023: 1-6.

JIANG X, LIU X H, GAO R.Hybrid load forecasting method based on fuzzy support vector machine and linear extrapolation[C]//Proceedings of the 10th World Congress on Intelligent Control and Automation. Beijing, China, 2012: 2431-2435.

ZHAO J, LIU X J.A hybrid method of dynamic cooling and heating load forecasting for office buildings based on artificial intelligence and regression analysis[J]. Energy and buildings, 2018, 174: 293-308.

FEKRI M N, PATEL H, GROLINGER K, et al.Deep learning for load forecasting with smart meter data: online adaptive recurrent neural network[J]. Applied energy, 2021, 282: 116177.

XIAO W J, MO L, XU Z X, et al.A hybrid electric load forecasting model based on decomposition considering fisher information[J]. Applied energy, 2024, 364: 123149.

邱宜彬, 李诗涵, 刘璐, 等. 基于场景D藤Copula模型的多风电场出力相关性建模[J]. 太阳能学报, 2019, 40(10): 2960-2966.

GUO L, HOU R S, LIU Y X, et al.A novel typical day selection method for the robust planning of stand-alone wind-photovoltaic-diesel-battery microgrid[J]. Applied energy, 2020, 263: 114606.

ABDOOS A A, ABDOOS H, KAZEMITABAR J, et al.An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction[J]. Energy, 2023, 278: 127914.

WANG J Z, HU J M.A robust combination approach for short-term wind speed forecasting and analysis-Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model[J]. Energy, 2015, 93: 41-56.

YU Y, LIU M, CHEN D Y, et al.Dynamic grouping control of electric vehicles based on improved k-means algorithm for wind power fluctuations suppression[J]. Global energy interconnection, 2023, 6(5): 542-553.

段晓磊, 王宇飞, 魏云峰, 等. 考虑新能源消纳的配电网中分布式光伏及储能联合优化配置[J]. 现代电力, 2026, 43(1): 181-192, I0014, I0015.

张靠社, 冯培基, 张刚, 等. 考虑机会约束的多能源微电网双层优化配置[J]. 太阳能学报, 2021, 42(8): 41-48.

陈明昊, 朱月瑶, 孙毅, 等. 计及高渗透率光伏消纳与深度强化学习的综合能源系统预测调控[J]. 电工技术学报, 2024, 39(19): 6054-6071, 6103.

宁可儿, 刘俊杉, 张超逸, 等. 考虑光伏消纳的配电网储能与直流选址定容协同规划[J]. 全球能源互联网, 2023, 6(1): 45-53.

张志刚, 唐巍, 李宗晟, 等. 考虑光伏消纳和时空多层级功率均衡的供电网格线路升级改造[J]. 电网技术, 2025, 49(8): 3156-3166, I0036-I0039.

姜恩宇, 陈周, 史雷敏, 等. 计及多重不确定性与综合贡献率的多微网合作运行策略[J]. 太阳能学报, 2023, 44(10): 80-89.

陈璨, 樊小伟, 张文浩, 等. 促进分布式光伏消纳的两阶段源网荷储互动优化运行策略[J]. 电网技术, 2022, 46(10): 3786-3796.

张涛, 杨建华, 靳开元, 等. 基于Stackelberg博弈的配电网分布式光伏低碳化消纳方法[J]. 电力自动化设备, 2023, 43(1): 48-54, 63.

唐冬来, 倪平波, 李玉, 等. 基于互信共识标识的县域屋顶光伏消纳交易策略[J]. 电力系统自动化, 2022, 46(22): 41-50.

范杏蕊, 李元诚. 基于改进Autoformer模型的短期电力负荷预测[J]. 电力自动化设备, 2024, 44(4): 171-177.

方豪, 魏业文, 张子洵, 等. 基于格拉姆角和场和改进DenseNet的配电网故障选线方法[J]. 电力系统及其自动化学报, 2025, 37(9): 109-118.

黄棋悦, 严楠, 钟旭佳. 基于生成对抗网络的风电爬坡功率预测[J]. 太阳能学报, 2023, 44(1): 226-231.

廖一帆, 武志刚. 基于迁移学习与Wasserstein生成对抗网络的静态电压稳定临界样本生成方法[J]. 电网技术, 2021, 45(9): 3722-3728.

赵洪山, 孙承妍, 温开云. 基于多气象类型加权和改进高斯混合模型的光伏出力超短期概率预测[J]. 太阳能学报, 2024, 45(7): 567-576.

吴雄, 贺明康, 何雯雯, 等. 考虑储能寿命的风-光-火-储打捆外送系统容量优化配置[J]. 电力系统保护与控制, 2023, 51(15): 66-75.

杨修宇, 杨呈, 彭建, 等. 考虑储能循环里程成本的风-火-储协调优化调度方法[J]. 太阳能学报, 2024, 45(9): 122-130.

东亚光, 李凤婷, 王森. 跟踪计划出力的新能源场站与汇集站储能协调运行策略[J]. 电网技术, 2023, 47(4): 1579-1588.

李圣清, 高泽华, 乔靖潇, 等. 基于改进麻雀搜索算法的微电网容量优化配置[J]. 太阳能学报, 2025, 46(10): 424-433.

赵靖英, 乔珩埔, 姚帅亮, 等. 考虑储能SOC自恢复的风电波动平抑混合储能容量配置策略[J]. 电工技术学报, 2024, 39(16): 5206-5219.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

基于层级嵌套博弈的微电网独立储能与供需双响应协同优化策略

基于改进SAC算法的多微电网经济优化调度研究

碳-绿证联合交易机制下多微网系统多能源互济优化调度模型研究

高渗透率可再生能源接入下考虑网络重构和需求响应的微电网低碳优化调度

考虑共享储能的多微电网低碳经济优化研究

Related Author

邹阳

姚雨佳

石松浩

黄煜

方梦泓

金涛

赵志华

倪欢

Related Institution

Key Laboratory of Smart Grid of Ministry of Education, Tianjin University

Tianjin Key Laboratory of Information Sensing & Intelligent Control, Tianjin University ofTechnology and Education

School of Electrical and Electronic Engineering, North China Electric Power University

Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration

CMA Wind and Solar Energy Centre

AI问答

⁰