LAPSE:2023.12954
Published Article

LAPSE:2023.12954
Line Loss Interval Algorithm for Distribution Network with DG Based on Linear Optimization under Abnormal or Missing Measurement Data
February 28, 2023
Abstract
Data collection is more difficult in distribution network than transmission networks since the structure of distribution networks is more complex. As a result, data could be partly abnormal or missing, which cannot completely describe the operation status of distribution network. In addition, access of distributed generation (DG) to distribution network further aggravates the variability of power flow in distribution network. The traditional deterministic line loss calculation method has some limitations in accurately estimating the line loss of distribution network with DG. A line loss interval calculation method based on power flow calculation and linear optimization is proposed, considering abnormal data collection and distribution network power flow variability. The linear optimization model is established according to sensitivity of line loss to the injected power and sensitivity of transmission power of first branch to the injected power. Introducing the scheduling information into the optimization model, a reliable line loss fluctuation interval can be obtained which actual line loss locates. The effectiveness of the proposed algorithm is verified in IEEE 33-bus distribution network system.
Data collection is more difficult in distribution network than transmission networks since the structure of distribution networks is more complex. As a result, data could be partly abnormal or missing, which cannot completely describe the operation status of distribution network. In addition, access of distributed generation (DG) to distribution network further aggravates the variability of power flow in distribution network. The traditional deterministic line loss calculation method has some limitations in accurately estimating the line loss of distribution network with DG. A line loss interval calculation method based on power flow calculation and linear optimization is proposed, considering abnormal data collection and distribution network power flow variability. The linear optimization model is established according to sensitivity of line loss to the injected power and sensitivity of transmission power of first branch to the injected power. Introducing the scheduling information into the optimization model, a reliable line loss fluctuation interval can be obtained which actual line loss locates. The effectiveness of the proposed algorithm is verified in IEEE 33-bus distribution network system.
Record ID
Keywords
abnormal data collection, line loss interval calculation, linear optimization model, power flow variability
Subject
Suggested Citation
Liang C, Chen C, Wang W, Ma X, Li Y, Jiang T. Line Loss Interval Algorithm for Distribution Network with DG Based on Linear Optimization under Abnormal or Missing Measurement Data. (2023). LAPSE:2023.12954
Author Affiliations
Liang C: Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou 730050, China
Chen C: State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Wang W: Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou 730050, China
Ma X: Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou 730050, China; School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Li Y: State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Jiang T: State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Chen C: State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Wang W: Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou 730050, China
Ma X: Electric Power Research Institute of State Grid Gansu Electric Power Company, Lanzhou 730050, China; School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Li Y: State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Jiang T: State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
Journal Name
Energies
Volume
15
Issue
11
First Page
4158
Year
2022
Publication Date
2022-06-06
ISSN
1996-1073
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Original Submission
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PII: en15114158, Publication Type: Journal Article
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LAPSE:2023.12954
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https://doi.org/10.3390/en15114158
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Feb 28, 2023
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