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

Tianli Song, Yang Li, Xiao-Ping Zhang, Jianing Li, Cong Wu, Qike Wu and Beibei Wang
Additional contact information
Tianli Song: School of Electrical Engineering, Southeast University, Nanjing 210096, China
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

Energies, 2018, vol. 12, issue 1, 1-17

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.

Keywords: customer baseline load; individual customer; power usage patterns; demand response (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/1/64/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/1/64/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2018:i:1:p:64-:d:193319

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().