| 引用本文: | 申洪涛,刘宝铭,任 鹏,等.基于辨识解耦的含高渗透分布式光伏用户集群基线负荷估计方法[J].电力系统保护与控制,2022,50(3):164-173.[点击复制] |
| SHEN Hongtao,LIU Baoming,REN Peng,et al.Aggregated baseline load estimation method under high distributed photovoltaic penetration based on identification and decoupling[J].Power System Protection and Control,2022,50(3):164-173[点击复制] |
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| 摘要: |
| 近年来，表后分布式光伏迅猛发展，其不可观特性给需求响应集群基线负荷(Aggregated baseline load, ABL)估计带来巨大挑战。为提升高渗透分布式光伏下ABL的估计精度，提出了一种基于辨识解耦的ABL估计方法。首先，提出了一种基于天气状态驱动特征的分布式光伏用户辨识方法，将光伏用户与非光伏用户解耦分离。其次，根据两类用户净负荷的特点，分别建立估计模型：针对光伏用户建立了天气类型分类估计模型，针对非光伏用户建立了温度与负荷的分段线性回归模型。最后，将两类用户的估计值相加，得到最终ABL估计值。仿真结果表明，与直接估计法和其他分类估计方法相比，所提方法在平均绝对百分比误差指标上平均降低42.07%，在标准均方根误差指标上平均降低23.93 %。 |
| 关键词: 基线负荷估计 分布式光伏 辨识解耦 天气类型 |
| DOI：DOI: 10.19783/j.cnki.pspc.210540 |
| 投稿时间：2021-05-08修订日期：2021-08-27 |
| 基金项目:国家电网公司科技项目资助(SGHEYX00SCJS2000037) |
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| Aggregated baseline load estimation method under high distributed photovoltaic penetration based on identification and decoupling |
| SHEN Hongtao,LIU Baoming,REN Peng,ZHANG Yangrui,ZHANG Chao,ZHAO Junpeng,WANG Fei |
| (1. Marketing Service Center, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050022, China;
2. North China Electric Power University, Baoding 071003, China) |
| Abstract: |
| In recent years, behind-the-meter distributed photovoltaic (PV) systems have developed rapidly, and their unobservable characteristics pose huge challenges to demand response aggregated baseline load (ABL) estimation. To improve the ABL estimation accuracy under high penetration of distributed PVs, an ABL estimation method based on identification and decoupling is proposed. First, a PV user identification and decoupling method based on weather state-driven features is proposed. This separates PV users from non-PV users. Secondly, estimation models are established respectively according to the characteristics of two types of users' net load: the estimation model based on weather type classification is established for PV users, and the piecewise linear regression model of temperature and load is established for non-PV users. Finally, a final ABL estimation is obtained by adding the estimated values of the two types of users. The simulation results show that, compared with the direct estimation methods and other classification estimation methods, the proposed method has an average decrease of 42.07% on the mean absolute percentage error (MAPE) and an average decrease of 23.93% on the normalized root mean square error (NRMSE).
This work is supported by the Science and Technology Project of State Grid Corporation of China (No. SGHEYX00SCJS2000037). |
| Key words: baseline load estimation distributed photovoltaic identification and decoupling weather type |
