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Multi-Objective Demand Response Model Considering the Probabilistic Characteristic of Price Elastic Load

Author

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  • Shengchun Yang

    (School of Electrical & Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
    China Electric Power Research Institute-Nanjing Branch, Nanjing 210003, Jiangsu, China)

  • Dan Zeng

    (China Electric Power Research Institute-Nanjing Branch, Nanjing 210003, Jiangsu, China)

  • Hongfa Ding

    (School of Electrical & Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China)

  • Jianguo Yao

    (China Electric Power Research Institute-Nanjing Branch, Nanjing 210003, Jiangsu, China)

  • Ke Wang

    (China Electric Power Research Institute-Nanjing Branch, Nanjing 210003, Jiangsu, China)

  • Yaping Li

    (China Electric Power Research Institute-Nanjing Branch, Nanjing 210003, Jiangsu, China)

Abstract

Demand response (DR) programs provide an effective approach for dealing with the challenge of wind power output fluctuations. Given that uncertain DR, such as price elastic load (PEL), plays an important role, the uncertainty of demand response behavior must be studied. In this paper, a multi-objective stochastic optimization problem of PEL is proposed on the basis of the analysis of the relationship between price elasticity and probabilistic characteristic, which is about stochastic demand models for consumer loads. The analysis aims to improve the capability of accommodating wind output uncertainty. In our approach, the relationship between the amount of demand response and interaction efficiency is developed by actively participating in power grid interaction. The probabilistic representation and uncertainty range of the PEL demand response amount are formulated differently compared with those of previous research. Based on the aforementioned findings, a stochastic optimization model with the combined uncertainties from the wind power output and the demand response scenario is proposed. The proposed model analyzes the demand response behavior of PEL by maximizing the electricity consumption satisfaction and interaction benefit satisfaction of PEL. Finally, a case simulation on the provincial power grid with a 151-bus system verifies the effectiveness and feasibility of the proposed mechanism and models.

Suggested Citation

  • Shengchun Yang & Dan Zeng & Hongfa Ding & Jianguo Yao & Ke Wang & Yaping Li, 2016. "Multi-Objective Demand Response Model Considering the Probabilistic Characteristic of Price Elastic Load," Energies, MDPI, vol. 9(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:2:p:80-:d:62984
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    Citations

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    Cited by:

    1. Antti Alahäivälä & Matti Lehtonen, 2016. "Increasing the Benefit from Cost-Minimizing Loads via Centralized Adjustments," Energies, MDPI, vol. 9(12), pages 1-13, November.
    2. Ovidiu Ivanov & Samiran Chattopadhyay & Soumya Banerjee & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2020. "A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks," Mathematics, MDPI, vol. 8(8), pages 1-24, July.
    3. Yi Tang & Yuqian Liu & Jia Ning & Jingbo Zhao, 2017. "Multi-Time Scale Coordinated Scheduling Strategy with Distributed Power Flow Controllers for Minimizing Wind Power Spillage," Energies, MDPI, vol. 10(11), pages 1-15, November.
    4. Ying-Yi Hong, 2016. "Electric Power Systems Research," Energies, MDPI, vol. 9(10), pages 1-4, October.
    5. Jiafu Yin & Dongmei Zhao, 2018. "Fuzzy Stochastic Unit Commitment Model with Wind Power and Demand Response under Conditional Value-At-Risk Assessment," Energies, MDPI, vol. 11(2), pages 1-18, February.

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