【论文推荐】俄罗斯乌拉尔联邦大学安德鲁·帕兹德林等：基于状态估计方法和计量数据的潮流计算研究

原创英文期刊编辑部全球能源互联网期刊 2022-11-23 08:00

摘要

潮流分布模型对电力系统中精确的电能测量和计费估计非常重要。通常情况下，电网的网络状态和拓扑可能会随时间而波动，特别是在拓扑结构发生变化的情况下，从遥测获得的瞬时功率值来求解经典电气工程方程会导致严重的建模误差。改进的潮流模型可能更为适用。论文的研究涉及从分钟到年的范围内确定电力系统潮流。该方法使用了基于电能表测量数据的状态估计。该研究在电表数据校验、不平衡电量计费和非技术性电能损耗检查等方面有着广泛的应用场景。

Energy flow problem solution based on state estimation approaches and smart meter data

基于状态估计方法和计量数据的潮流计算研究

Andrew V. Pazderin¹, Ilya D. Polyakov², Vladislav O. Samoylenko¹

(1.Department of Automated Electrical Systems, Ural Federal University, Yekaterinburg, Mira 19, Russia 2.System Operator of United Power System, Yekaterinburg, Tolmacheva 6, Russia)

Abstract

Accurate electric energy (EE) measurements and billing estimations in a power system necessitate the development of an energy flow distribution model. This paper summarizes the results of investigations on a new problem related to the determination of EE flow in a power system over time intervals ranging from minutes to years. The problem is referred to as the energy flow problem (EFP). Generally, the grid state and topology may fluctuate over time. An attempt to use instantaneous (not integral) power values obtained from telemetry to solve classical electrical engineering equations leads to significant modeling errors, particularly with topology changes. A promoted EFP model may be suitable in the presence of such topological and state changes. Herein, EE flows are determined using state estimation approaches based on direct EE measurement data in Watt-hours (Volt-ampere reactive-hours) provided by electricity meters. The EFP solution is essential for a broad set of applications, including meter data validation, zero unbalance EE billing, and nontechnical EE loss check.

Keywords

Automatic meter reading, Advanced metering infrastructure, Energy flow distribution, Electricity losses, Energy measurements, State estimation.

Fig.1 EFD for a DC circuit: (a) first hour, (b) second hour,(с) third hour, (d) resulting EFD over three hours

Fig.2 EFD averaging over T = 3 h using equation (2) and Fig. 1, d

Fig.3 Distribution of (a) power flow and losses – state 1 (MWi%+ jMVAr), (b) power flow and losses – state 2 (MW + jMVAr) (the branch 1-2 is switched off), (с) electric energy flows and losses for 2000 h (MWh + jMVArh), (d) calculated energy flow distribution for 2000 h (MWh + jMVArh), (e) electric energy flow and losses for 2000 h (MWh + jMVArh); the consumption of node 5 is understated; its correction by means of energy flow distribution.

Fig.4 Estimation residual distribution density: (a) measurements with a strong negative bias; (b) measurements with Gaussian-distributed errors and small bias

本文引文信息

Pazderin AV, Polyakov ID, Vladislav OS (2022) Energy flow problem solution based on state estimation approaches and smart meter data. Global Energy Interconnection, 5(5): 551-565

安德鲁•帕兹德林，伊利亚•波利亚科夫，弗拉迪斯拉夫•萨莫伊连科. (2022) 基于状态估计方法和计量数据的潮流计算研究. 全球能源互联网（英文）, 5(5): 551-565

Biographies

Andrew V. Pazderin

Andrew V. Pazderin is Sc.D., professor, head of the Department of Automated electrical systems at Ural Federal University, Yekaterinburg, Russia since 2005. His research interests includes branch flow-based models, electric energy transmission and distribution.

Ilya D. Polyakov

Ilya D. Polyakov is a specialist at dispatch systems of System Operator of the United Power System since 2016. He defended Ph.D. at 2019. His research interests includes dispatch systems and state estimation.

Vladislav O. Samoylenko

Vladislav O. Samoylenko defended Ph.D. at 2017. Now he is associate professor at the Department of Automated electrical systems at Ural Federal University, Yekaterinburg, Russia. His research interests includes active distribution systems and distributed generation.

编辑：王彦博

审核：王伟

郑重声明

根据国家版权局相关规定，纸媒、网站、微博、微信公众号转载、摘编本网站作品，需包含本网站名称、二维码等关键信息，并在文首注明《全球能源互联网》原创。个人请按本网站原文转发、分享。

Energy flow problem solution based on state estimation approaches and smart meter data

【论文推荐】俄罗斯乌拉尔联邦大学 安德鲁·帕兹德林等：基于状态估计方法和计量数据的潮流计算研究

郑重声明

【论文推荐】俄罗斯乌拉尔联邦大学安德鲁·帕兹德林等：基于状态估计方法和计量数据的潮流计算研究