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A Chance-Constrained Multistage Planning Method for Active Distribution Networks

Author

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  • Nikolaos Koutsoukis

    (School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece)

  • Pavlos Georgilakis

    (School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Zografou, Greece)

Abstract

This paper introduces a multistage planning method for active distribution networks (ADNs) considering multiple alternatives. The uncertainties of load, wind and solar generation are taken into account and a chance constrained programming (CCP) model is developed to handle these uncertainties in the planning procedure. A method based on a k-means clustering technique is employed for the modelling of renewable generation and load demand. The proposed solution methodology, which is based on a genetic algorithm, considers multiple planning alternatives, such as the reinforcement of substations and distribution lines, the addition of new lines, and the placement of capacitors and it aims at minimizing the net present value of the total operation cost plus the total investment cost of the reinforcement and expansion plan. The active network management is incorporated into planning method in order to exploit the control capabilities of the output power of the distributed generation units. To validate its effectiveness and performance, the proposed method is applied to a 24-bus distribution system.

Suggested Citation

  • Nikolaos Koutsoukis & Pavlos Georgilakis, 2019. "A Chance-Constrained Multistage Planning Method for Active Distribution Networks," Energies, MDPI, vol. 12(21), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:21:p:4154-:d:281955
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    References listed on IDEAS

    as
    1. Arman Oshnoei & Rahmat Khezri & Mehrdad Tarafdar Hagh & Kuaanan Techato & SM Muyeen & Omid Sadeghian, 2018. "Direct Probabilistic Load Flow in Radial Distribution Systems Including Wind Farms: An Approach Based on Data Clustering," Energies, MDPI, vol. 11(2), pages 1-19, February.
    2. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    3. Fichera, Alberto & Pluchino, Alessandro & Volpe, Rosaria, 2018. "A multi-layer agent-based model for the analysis of energy distribution networks in urban areas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 710-725.
    4. Rui Li & Wei Wang & Zhe Chen & Jiuchun Jiang & Weige Zhang, 2017. "A Review of Optimal Planning Active Distribution System: Models, Methods, and Future Researches," Energies, MDPI, vol. 10(11), pages 1-27, October.
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    Cited by:

    1. Vasileios Evangelopoulos & Panagiotis Karafotis & Pavlos Georgilakis, 2020. "Probabilistic Spatial Load Forecasting Based on Hierarchical Trending Method," Energies, MDPI, vol. 13(18), pages 1-25, September.
    2. Yu Zhang & Xiaohui Song & Yong Li & Zilong Zeng & Chenchen Yong & Denis Sidorov & Xia Lv, 2020. "Two-Stage Active and Reactive Power Coordinated Optimal Dispatch for Active Distribution Network Considering Load Flexibility," Energies, MDPI, vol. 13(22), pages 1-13, November.
    3. Zhang, Houwang & Wu, Qiuwei & Chen, Jian & Lu, Lina & Zhang, Jiangfeng & Zhang, Shuyi, 2023. "Multiple stage stochastic planning of integrated electricity and gas system based on distributed approximate dynamic programming," Energy, Elsevier, vol. 270(C).

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