Multi-Period Active Distribution Network Planning Using Multi-Stage Stochastic Programming and Nested Decomposition by SDDIP

This paper presents a multi-period active distribution network planning (ADNP) with distributed generation (DG). The objective of the proposed ADNP is to minimize the total planning cost, subject to both investment and operation constraints. The paper proposes a multi-stage stochastic optimization m...

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Veröffentlicht in:IEEE transactions on power systems Jg. 36; H. 3; S. 2281 - 2292
Hauptverfasser: Ding, Tao, Qu, Ming, Huang, Can, Wang, Zekai, Du, Pengwei, Shahidehpour, Mohammad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Decomposition
distributed energy resources
Distributed generation
Distribution network planning
ENGINEERING
Integer programming
Investment
Load modeling
multi-stage stochastic programming
Optimization
Planning
POWER TRANSMISSION AND DISTRIBUTION
Programming
Stochastic processes
Stochastic programming
Substations
Uncertainty
ISSN:0885-8950, 1558-0679
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Zusammenfassung:This paper presents a multi-period active distribution network planning (ADNP) with distributed generation (DG). The objective of the proposed ADNP is to minimize the total planning cost, subject to both investment and operation constraints. The paper proposes a multi-stage stochastic optimization model to address DG uncertainties over several periods, in which the decisions are made sequentially by only using the present-stage information. A nested decomposition method is proposed which applies the stochastic dual dynamic integer programming (SDDIP) method to address computational intractabilities of the proposed ADNP approach. The presented numerical results and discussions on a 33-bus distribution system and a large-scale 906-bus system verify the effectiveness of the proposed ADNP method and its solution method.
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Natural Science Foundation of Shaanxi Province
AC52-07NA27344; 2016YFB0901900; 51977166; 2017T100748; 2020KW-022
National Natural Science Foundation of China (NSFC)
National Key Research and Development Program of China
USDOE National Nuclear Security Administration (NNSA)
LLNL-JRNL-815905
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2020.3032830