Calculation of Maximum Total Supply Capacity of Three-Phase Unbalance Distribution Network Based on Mixed Integer Second-Order Cone Article Swipe

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Jieyun Zheng , Shiyuan Ni , Pengjia Shi , Guilian Wu , Ri’an Wang , Chenying Yi , Zhijian Hu ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.20944/preprints201911.0280.v1

Considering the fault "N-1" checksum and the power flow, the single-phase power flow model is further transformed into a three-phase power flow model, and the asymmetry of the three-phase power flow is measured by the three-phase unbalance factor. The calculation model is linearized by the second-order cone relaxation and the Big-M method. At the same time, the load response and distribution network reconstruction are used to improve the reliability of the power supply network to cope with the power failure. The relationship between power supply capability and power flow constraints, main transformer capacity and distributed power parameters is analyzed by IEEE 33-node three-phase power distribution system. The feasibility of the proposed model and the accuracy of the second-order cone relaxation are verified by numerical examples, which provides a technical reference for distribution network planning.

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Electrical Engineering

Voltage

Physics

Structural Engineering

Seismology

Artificial Intelligence

Quantum Mechanics

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Power (physics) Power-flow study Mathematical optimization Node (physics) Fault (geology) Mathematics Control theory (sociology) Power factor Engineering Computer science Electrical engineering Voltage Physics Structural engineering Seismology Control (management) Artificial intelligence Quantum mechanics Geology

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Type: preprint
Language: en
Landing Page: https://doi.org/10.20944/preprints201911.0280.v1
PDF: https://www.preprints.org/manuscript/201911.0280/v1/download
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Title: Calculation of Maximum Total Supply Capacity of Three-Phase Unbalance Distribution Network Based on Mixed Integer Second-Order Cone

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Language: en

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Publication year: 2019

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Publication date: 2019-11-24

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Authors: Jieyun Zheng, Shiyuan Ni, Pengjia Shi, Guilian Wu, Ri’an Wang, Chenying Yi, Zhijian Hu

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Landing page: https://doi.org/10.20944/preprints201911.0280.v1

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Concepts: Power (physics), Power-flow study, Mathematical optimization, Node (physics), Fault (geology), Mathematics, Control theory (sociology), Power factor, Engineering, Computer science, Electrical engineering, Voltage, Physics, Structural engineering, Seismology, Control (management), Artificial intelligence, Quantum mechanics, Geology

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Cited by: 1

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abstract_inverted_index.proposed	110
abstract_inverted_index.provides	126
abstract_inverted_index.response	58
abstract_inverted_index.verified	121
abstract_inverted_index.asymmetry	25
abstract_inverted_index.examples,	124
abstract_inverted_index.numerical	123
abstract_inverted_index.planning.	133
abstract_inverted_index.reference	129
abstract_inverted_index.technical	128
abstract_inverted_index.unbalance	36
abstract_inverted_index.capability	85
abstract_inverted_index.linearized	42
abstract_inverted_index.parameters	96
abstract_inverted_index.relaxation	47, 119
abstract_inverted_index.Considering	0
abstract_inverted_index.calculation	39
abstract_inverted_index.distributed	94
abstract_inverted_index.feasibility	107
abstract_inverted_index.reliability	68
abstract_inverted_index.three-phase	19, 28, 35, 102
abstract_inverted_index.transformed	16
abstract_inverted_index.transformer	91
abstract_inverted_index.constraints,	89
abstract_inverted_index.distribution	60, 104, 131
abstract_inverted_index.relationship	81
abstract_inverted_index.second-order	45, 117
abstract_inverted_index.single-phase	10
abstract_inverted_index.reconstruction	62
cited_by_percentile_year.max	94
cited_by_percentile_year.min	90
countries_distinct_count	1
institutions_distinct_count	7
sustainable_development_goals[0].id	https://metadata.un.org/sdg/7
sustainable_development_goals[0].score	0.8199999928474426
sustainable_development_goals[0].display_name	Affordable and clean energy
citation_normalized_percentile.value	0.48769035
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False