A Holistic State Estimation Framework for Active Distribution Network with Battery Energy Storage System

doi:10.35833/MPCE.2020.000613

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A Holistic State Estimation Framework for Active Distribution Network with Battery Energy Storage System

DOI:

10.35833/MPCE.2020.000613

Author:

Shaojian Song ^¹
Shaojian Song
School of Electrical Engineering, Guangxi University , Nanning 530004, China
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Huangjiao Wei ^¹
Huangjiao Wei
School of Electrical Engineering, Guangxi University , Nanning 530004, China
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Yuzhang Lin ^²
Yuzhang Lin
Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01852, USA
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Cheng Wang ^³
Cheng Wang
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
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Antonio Gómez-Expósito ^⁴
Antonio Gómez-Expósito
Laboratory of Engineering for Energy and Environmental Sustainability, University of Seville, Spain
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Affiliation:

1.School of Electrical Engineering, Guangxi University , Nanning 530004, China;2.Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01852, USA;3.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;4.Laboratory of Engineering for Energy and Environmental Sustainability, University of Seville, Spain

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Abstract:

Battery energy storage systems (BESSs) are expected to play a crucial role in the operation and control of active distribution networks (ADNs). In this paper, a holistic state estimation framework is developed for ADNs with BESSs integrated. A dynamic equivalent model of BESS is developed, and the state transition and measurement equations are derived. Based on the equivalence between the correction stage of the iterated extended Kalman filter (IEKF) and the weighted least squares (WLS) regression, a holistic state estimation framework is proposed to capture the static state variables of ADNs and the dynamic state variables of BESSs, especially the state of charge (SOC). A bad data processing method is also presented. The simulation results show that the proposed holistic state estimation framework improves the accuracy of state estimation as well as the capability of bad data detection for both ADNs and BESSs, providing comprehensive situational awareness for the whole system.

Key words:Active distribution network (ADN) ; anomaly detection ; battery energy storage system (BESS) ; Kalman filtering ; situational awareness ; state estimation ; state of charge (SOC).

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Received:August 15,2020
Revised:November 28,2020
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Online: May 12,2022
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