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Residential PV capacity estimation and power disaggregation using net metering measurements

2023-01-11

【论文推荐】天津大学刘博等：净负荷数据下的居民光伏容量估算和功率分解

原创英文期刊编辑部全球能源互联网期刊 2023-01-11 08:00

摘要

随着越来越多的分布式光伏发电设备接入电网，它的间歇性和不确定性给电力系统的运行带来了挑战，准确的光伏发电量估计对于配电运行、维护和需求响应计划的实施具有重要意义。目前，大多数住宅的光伏发电安装在表后，只有净负荷数据可以提供给电力公司。因此，需要一种将居民光伏发电量从净负荷数据中分解出来的方法来提高电网公司对光伏设备的了解。对此，本文首先提出了基于净负荷数据的分布式光伏发电容量无监督估计方法，根据用户日间和夜间净负荷极值的分布规律自适应估计目标用户的光伏发电容量。而后，建立了基于净负荷数据的分布式光伏功率分解方法，在分析用户日间和夜间实际负荷之间相关性的基础上，通过多个典型用户分布式光伏实例线性拟合估计目标用户光伏电量，并利用典型用户的光伏功率曲线，将电量估计值映射到小时级时间分辨率上实现光伏功率分解。最后，利用容量估计值对光伏功率分解异常值进行校验与修正。在含260家用户的真实数据集上的测试结果表明，在小时级数据下，所提出的光伏发电容量估计方法准确率较高，且具有复杂度低、鲁棒性强等优点；与现有方法相比，所提出方法的光伏功率分解的平均绝对百分比误差总体降低15%以上，均方根误差降低超过20%。

Residential PV capacity estimation and power disaggregation using net metering measurements

净负荷数据下的居民光伏容量估算和功率分解

Bo Liu1, Jianmin Tian1, Wenpeng Luan1, Yi Gao2, Xiaohui Wang3, Shuai Luo2

(1 School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, P. R. China

2 Economic and Technical Research Institute, State Grid Tianjin Electric Power Company, Tianjin,300171, P. R. China

3 Artificial Intelligence Application Department, China Electric Power Research Institute, Beijing 100089, P. R. China)

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Residential PV capacity

Abstract

As the intermittency and uncertainty of photovoltaic (PV) power generation poses considerable challenges to the power system operation, accurate PV generation estimates are critical for the distribution operation, maintenance, and demand response program implementation because of the increasing usage of distributed PVs. Currently, most residential PVs are installed behind the meter, with only the net load available to the utilities. Therefore, a method for disaggregating the residential PV generation from the net load data is needed to enhance the grid-edge observability. In this study, an unsupervised PV capacity estimation method based on net metering data is proposed, for estimating the PV capacity in the customer’s premise based on the distribution characteristics of nocturnal and diurnal net load extremes. Then, the PV generation disaggregation method is presented. Based on the analysis of the correlation between the nocturnal and diurnal actual loads and the correlation between the PV capacity and their actual PV generation, the PV generation of customers is estimated by applying linear fitting of multiple typical solar exemplars and then disaggregating them into hourly-resolution power profiles. Finally, the anomalies of disaggregated PV power are calibrated and corrected using the estimated capacity. Experiment results on a real-world hourly dataset involving 260 customers show that the proposed PV capacity estimation method achieves good accuracy because of the advantages of robustness and low complexity. Compared with the state- of-the-art PV disaggregation algorithm, the proposed method exhibits a reduction of over 15% for the mean absolute percentage error and over 20% for the root mean square error.

Keywords

Behind-the-meter, Residential photovoltaic, Capacity estimation, Power disaggregation, Net metering.

Fig.1 Time division and the net load composition

Fig.2 Overall structure of the proposed power disaggregation method

Fig.3 Diagram of power extremum combinations

Fig.4 Capacity characteristic curve

Fig.5 PV capacity and monthly PV generation

Fig.6 Monthly nocturnal and diurnal actual load

Fig.7 Distribution of PV capacity estimation errors

Fig.8 PV power disaggregation result

Fig.9 Actual load disaggregation result

Fig.10 Comparison of the results with or without postprocessin

Fig.11 Daily PV power disaggregation results for one customer

Fig.12 Performance comparison of power disaggregation results

(Left: Proposed method, Middle:reference [17], Right: reference [18])

Fig.13 Comparison of PV power profile disaggregation results from different methods

本文引文信息

Liu B, Tian JM, Luan WP, et al. (2022) Residential PV capacity estimation and power disaggregation using net metering measurements, 5(6): 590-603

刘博，田建民，栾文鹏等 (2022) 净负荷数据下的居民光伏容量估算和功率分解. 全球能源互联网（英文）, 5(6): 590-603

Biographies

Bo Liu

Bo Liu is a lecturer with the School of Electrical and Information Engineering, Tianjin University. He is the author of more than 20 articles and more than 20 inventions. His research interests include the non-intrusive power load monitoring and disaggregation, big data analytics and applications, AI in Smart Grid, and ubiquitous power Internet of Things, etc.

Jianmin Tian

Jianmin Tian received his B.S. degree in electrical engineering from Tianjin University in 2021, and he is pursuing his master degree in electrical engineering at Tianjin University. His research interests include non- intrusive load monitoring, the renewable energy integration and smart meter data analytics.

Wenpeng Luan

Wenpeng Luan (SM’05) is a professor with the School of Electrical and Information Engineering, Tianjin University. His research interests include smart metering data analytics, distribution system analysis, renewable energy resource integration, and utility advanced applications, etc.

Yi Gao

Yi Gao received the Ph.D. degree in College of Electrical Engineering, Tianjin University. He works in State Grid Tianjin Electric Power Company Economy and Technology Research Institute. His research interests include power system planning, clean energy system, electric big data, etc.

Xiaohui Wang

Xiaohui Wang received the Ph.D. degree at North China Electric Power University, Beijing, 2012. He is working in China Electric Power Research Institute Co. Ltd., Haidian district, Beijing. His research interests include power big data technology, artificial intelligence, active distributed network, energy internet, etc.

Shuai Luo

Shuai Luo received the Ph.D. degree in College of Management and Economics, Tianjin University. He currently is a researcher in State Grid Tianjin Electric Power Company Economy and Technology Research Institute. His research interests include deep learning, few-shot learning, and their applications in energy, low-carbon economy, etc.

编辑：王彦博

审核：王伟

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