Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 36, Issue 3, Pages 2281-2292Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2020.3032830
Keywords
Planning; Uncertainty; Stochastic processes; Load modeling; Substations; Programming; Investment; Distribution network planning; uncertainty; distributed energy resources; multi-stage stochastic programming
Categories
Funding
- National Key Research and Development Program of China [2016YFB0901900]
- National Natural Science Foundation of China [51977166]
- China Postdoctoral Science Foundation [2017T100748]
- Natural Science Foundation of Shaanxi Province [2020KW022]
- U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344, LLNL-JRNL-815905]
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This paper introduces a multi-period active distribution network planning (ADNP) method with distributed generation (DG) to minimize total planning cost while considering DG uncertainties. It utilizes a multi-stage stochastic optimization model and a nested decomposition method to address computational challenges, and its effectiveness is verified on practical distribution systems.
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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