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

Tianli Song,Yang Li,Jianing Li,Xiao-Ping Zhang,Beibei Wang,Cong Wu,Qike Wu

doi:10.3390/en12010064

Tianli Song, Yang Li + Show 5 more

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https://doi.org/10.3390/en12010064

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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.

Highlights

In most modern power systems, the problems of periodic and structural power shortage and sharper peak-valley difference exist for a long time
In order to ensure the benefits of both sides in demand response (DR) programs, it is necessary to calculate customer baseline load (CBL)
The DR event day can be classified into one certain type of power data-selection windows are set five

Summary

Introduction

In most modern power systems, the problems of periodic and structural power shortage and sharper peak-valley difference exist for a long time. These raise concerns over resource depletion and bridging the gap between power supply and demand. DR appeals customers to temporarily reduce, shift, or shed their demand in response to price signals or other market incentives during the event period [4]. To this end, quantifying the demand reduction is becoming a major issue for both electrical unities and customers.

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