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Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique

Author

Listed:
  • Yajing Gao

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Yanping Sun

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Xiaodan Wang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Feifan Chen

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Ali Ehsan

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Hongmei Li

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Hong Li

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

To fully investigate the characteristics and the complementarities of demand side resources (DSRs), and to achieve efficient utilization of resources, the aggregation of DSRs is studied in this paper. Considering the uncertainty of DSRs, the characteristics analysis and the selection of relevant daily feature corresponding to various types of DSR are carried out. Then a multi-scenario model based on quarter division and self-organizing map (SOM) neural network algorithm is proposed. In the model, the clustering feature vector is selected as the input vector of the SOM algorithm to perform DSR clustering analysis to get the different scenarios. In addition, to obtain the resource aggregation (RA) with good load characteristics, response characteristics and distributed generation (DG) consumption, a multi-scenario objective optimization aggregation model of DSR based on scenario partition is established, and an the model is solved by an improved niche evolutionary multi-objective immune algorithm. Finally, the case studies are given to verify the validity of the model.

Suggested Citation

  • Yajing Gao & Yanping Sun & Xiaodan Wang & Feifan Chen & Ali Ehsan & Hongmei Li & Hong Li, 2017. "Multi-Objective Optimized Aggregation of Demand Side Resources Based on a Self-organizing Map Clustering Algorithm Considering a Multi-Scenario Technique," Energies, MDPI, vol. 10(12), pages 1-20, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2144-:d:123101
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    References listed on IDEAS

    as
    1. Yajing Gao & Huaxin Cheng & Jing Zhu & Haifeng Liang & Peng Li, 2016. "The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather," Sustainability, MDPI, vol. 8(1), pages 1-22, January.
    2. Jianchao Zhang & Boon-Chong Seet & Tek Tjing Lie, 2016. "An Event-Based Resource Management Framework for Distributed Decision-Making in Decentralized Virtual Power Plants," Energies, MDPI, vol. 9(8), pages 1-19, July.
    3. Pedro Faria & Zita Vale & José Baptista, 2015. "Demand Response Programs Design and Use Considering Intensive Penetration of Distributed Generation," Energies, MDPI, vol. 8(6), pages 1-17, June.
    4. Mingchao Xia & Qingying Lai & Yajiao Zhong & Canbing Li & Hsiao-Dong Chiang, 2016. "Aggregator-Based Interactive Charging Management System for Electric Vehicle Charging," Energies, MDPI, vol. 9(3), pages 1-14, March.
    5. Pengwei Cong & Wei Tang & Lu Zhang & Bo Zhang & Yongxiang Cai, 2017. "Day-Ahead Active Power Scheduling in Active Distribution Network Considering Renewable Energy Generation Forecast Errors," Energies, MDPI, vol. 10(9), pages 1-20, August.
    6. Jingpeng Yue & Zhijian Hu & Chendan Li & Juan C. Vasquez & Josep M. Guerrero, 2017. "Economic Power Schedule and Transactive Energy through an Intelligent Centralized Energy Management System for a DC Residential Distribution System," Energies, MDPI, vol. 10(7), pages 1-14, July.
    7. Yajing Gao & Jing Zhu & Huaxin Cheng & Fushen Xue & Qing Xie & Peng Li, 2016. "Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories," Energies, MDPI, vol. 9(7), pages 1-15, July.
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    Cited by:

    1. Siqing Sheng & Qing Gu, 2019. "A Day-ahead and Day-in Decision Model Considering the Uncertainty of Multiple Kinds of Demand Response," Energies, MDPI, vol. 12(9), pages 1-26, May.
    2. Hongwei Tang & Anping Lin & Wei Sun & Shuqi Shi, 2020. "An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments," Energies, MDPI, vol. 13(12), pages 1-18, June.
    3. Daiva Stanelyte & Neringa Radziukyniene & Virginijus Radziukynas, 2022. "Overview of Demand-Response Services: A Review," Energies, MDPI, vol. 15(5), pages 1-31, February.

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