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A Cluster-Based Baseline Load Calculation Approach for Individual Industrial and Commercial Customer

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  • Tianli Song

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Yang Li

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Xiao-Ping Zhang

    (Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Jianing Li

    (Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Cong Wu

    (Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Birmingham B15 2TT, UK)

  • Qike Wu

    (Guangzhou Power Supply Bureau Limited Company, Guangzhou 510620, China)

  • Beibei Wang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

Demand response (DR) in the wholesale electricity market provides an economical and efficient way for customers to participate in the trade during the DR event period. There are various methods to measure the performance of a DR program, among which customer baseline load (CBL) is the most important method in this regard. It provides a prediction of counterfactual consumption levels that customer load would have been without a DR program. Actually, it is an expected load profile. Since the calculation of CBL should be fair and simple, the typical methods that are based on the average model and regression model are the two widely used methods. In this paper, a cluster-based approach is proposed considering the multiple power usage patterns of an individual customer throughout the year. It divides loads of a customer into different types of power usage patterns and it implicitly incorporates the impact of weather and holiday into the CBL calculation. As a result, different baseline calculation approaches could be applied to each customer according to the type of his power usage patterns. Finally, several case studies are conducted on the actual utility meter data, through which the effectiveness of the proposed CBL calculation approach is verified.

Suggested Citation

  • Tianli Song & Yang Li & Xiao-Ping Zhang & Jianing Li & Cong Wu & Qike Wu & Beibei Wang, 2018. "A Cluster-Based Baseline Load Calculation Approach for Individual Industrial and Commercial Customer," Energies, MDPI, vol. 12(1), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:12:y:2018:i:1:p:64-:d:193319
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    References listed on IDEAS

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    1. Sadiq Ahmad & Ayaz Ahmad & Muhammad Naeem & Waleed Ejaz & Hyung Seok Kim, 2018. "A Compendium of Performance Metrics, Pricing Schemes, Optimization Objectives, and Solution Methodologies of Demand Side Management for the Smart Grid," Energies, MDPI, vol. 11(10), pages 1-33, October.
    2. Saehong Park & Seunghyoung Ryu & Yohwan Choi & Jihyo Kim & Hongseok Kim, 2015. "Data-Driven Baseline Estimation of Residential Buildings for Demand Response," Energies, MDPI, vol. 8(9), pages 1-21, September.
    3. Hung-po Chao, 2011. "Demand response in wholesale electricity markets: the choice of customer baseline," Journal of Regulatory Economics, Springer, vol. 39(1), pages 68-88, February.
    4. Seyedeh Narjes Fallah & Ravinesh Chand Deo & Mohammad Shojafar & Mauro Conti & Shahaboddin Shamshirband, 2018. "Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions," Energies, MDPI, vol. 11(3), pages 1-31, March.
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    Cited by:

    1. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.

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