Data-Driven Pricing Strategy for Demand-Side Resource Aggregators

doi:10.1109/tsg.2016.2544939

Title: Data-Driven Pricing Strategy for Demand-Side Resource Aggregators

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Abstract

We consider a utility who seeks to coordinate the energy consumption of multiple demand-side flexible resource aggregators. For the purpose of privacy protection, the utility has no access to the detailed information of loads of resource aggregators. Instead, we assume that the utility can directly observe each aggregator's aggregate energy consumption outcomes. Furthermore, the utility can leverage resource aggregator energy consumption via time-varying electricity price profiles. Based on inverse optimization technique, we propose an estimation method for the utility to infer the energy requirement information of aggregators. Subsequently, we design a data-driven pricing scheme to help the utility achieve system-level control objectives (e.g., minimizing peak demand) by combining hybrid particle swarm optimizer with mutation algorithm and an iterative algorithm. Case studies have demonstrated the effectiveness of the proposed approach against two benchmark pricing strategies-a flat-rate scheme and a time-of-use scheme.

Authors:

Xu, Zhiwei; Deng, Tianhu;

; Song, Yonghua; Wang, Jianhui

Publication Date:: 2018-01-01

Research Org.:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Org.:: National Natural Science Foundation of China (NNSFC)

OSTI Identifier:: 1465139

DOE Contract Number:: AC02-06CH11357

Resource Type:: Journal Article

Journal Name:: IEEE Transactions on Smart Grid

Additional Journal Information:: Journal Volume: 9; Journal Issue: 1; Journal ID: ISSN 1949-3053

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: data-driven optimization; demand response; dynamic pricing; inverse optimization; particle swarm optimization (PSO)

Citation Formats


                    Xu, Zhiwei, Deng, Tianhu, Hu, Zechun, Song, Yonghua, and Wang, Jianhui. Data-Driven Pricing Strategy for Demand-Side Resource Aggregators.  United States: N. p., 2018. 
        Web.  doi:10.1109/tsg.2016.2544939.

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                    Xu, Zhiwei, Deng, Tianhu, Hu, Zechun, Song, Yonghua, & Wang, Jianhui. Data-Driven Pricing Strategy for Demand-Side Resource Aggregators.  United States.  doi:10.1109/tsg.2016.2544939.

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                    Xu, Zhiwei, Deng, Tianhu, Hu, Zechun, Song, Yonghua, and Wang, Jianhui. Mon .  
        "Data-Driven Pricing Strategy for Demand-Side Resource Aggregators".  United States.  doi:10.1109/tsg.2016.2544939.

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@article{osti_1465139,

  title        = {Data-Driven Pricing Strategy for Demand-Side Resource Aggregators},

  author       = {Xu, Zhiwei and Deng, Tianhu and Hu, Zechun and Song, Yonghua and Wang, Jianhui},

  abstractNote = {We consider a utility who seeks to coordinate the energy consumption of multiple demand-side flexible resource aggregators. For the purpose of privacy protection, the utility has no access to the detailed information of loads of resource aggregators. Instead, we assume that the utility can directly observe each aggregator's aggregate energy consumption outcomes. Furthermore, the utility can leverage resource aggregator energy consumption via time-varying electricity price profiles. Based on inverse optimization technique, we propose an estimation method for the utility to infer the energy requirement information of aggregators. Subsequently, we design a data-driven pricing scheme to help the utility achieve system-level control objectives (e.g., minimizing peak demand) by combining hybrid particle swarm optimizer with mutation algorithm and an iterative algorithm. Case studies have demonstrated the effectiveness of the proposed approach against two benchmark pricing strategies-a flat-rate scheme and a time-of-use scheme.},

  doi          = {10.1109/tsg.2016.2544939},

  journal      = {IEEE Transactions on Smart Grid},

  issn         = {1949-3053},

  number       = 1,

  volume       = 9,

  place        = {United States},

  year         = {2018},

  month        = {1}

}

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DOI: 10.1109/tsg.2016.2544939

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