Demand charges and user flexibility – Exploring differences in electricity consumer types and load patterns within the Swedish commercial sector

Fouad El Gohary,Cajsa Bartusch,Vera Van Zoest,Edith C.H Ngai

doi:10.1016/j.apenergy.2021.117543

Fouad El Gohary, Cajsa Bartusch + Show 2 more

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https://doi.org/10.1016/j.apenergy.2021.117543

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Journal: Applied Energy	Publication Date: Aug 10, 2021
Citations: 10	License type: cc-by

Affiliation: Uppsala University, University of Hong Kong

Abstract

Demand-based charges have been employed as a tool intended to reduce electricity users’ maximum demand but there is a lack of consensus regarding their efficacy. One reason for this may be the diversity in the flexibility potential of different types of users. This study explores the flexibility potential of different types of electricity consumers in the small to medium-sized commercial sector (35-63A) in response to a compulsory demand charge. The objective is to characterize varying levels of flexibility with respect to different types of commercial users with different load patterns. A multivariate clustering technique was used to group commercial users with comparable load patterns based on a year of hourly data before the tariff change was introduced. This method was used to: (1) match users from the intervention area and reference area with similar load patterns, without losing any user data, and (2) compare how users with different load patterns react differently to the tariff change. We found clear distinctions in the types of commercial users in each cluster and their response to the tariff, demonstrating the extent to which demand flexibility may be dependent on the nature of an organization’s activities and its respective load patterns. The highest demand flexibility was found in clusters which had a large share of users in the IT sector, commerce and public administration. The lowest demand flexibility was found in the real estate and education sectors. Future research should further investigate these variations and explore the possibilities of tailoring interventions to the specific types of users.

Highlights

We propose using a multivariate clustering technique to be able to avoid the beforementioned problems, as well as to be able to explore the effects of the tariff change on different types of users, characterized by the clusters
The time series for each group were visually inspected to evaluate whether the within-cluster variability was small enough to make the intervention group and reference group comparable in terms of their average electricity consumption, weekly consumption patterns and seasonality
Effects of the new tariff through decreases in mean and maximum monthly electricity consumption were detectable in three of the four clusters. Both these results point to disparities between users who make up the sample that are overlooked when the sample is treated homogenously

Summary

Introduction

As countries strive to reduce their CO2 emissions through increased electrification, they face new chal lenges in adapting their electricity grids to higher levels of renewable energy penetration. Electricity grids are becoming increasingly strained by bottlenecks in the grid’s transmission capacity. Capacity shortages, induced by high surges in demand during peak hours of consumption, are responsible for vulnerabilities in the electricity system that require expensive upgrades in infrastructure. One proposed remedy to both these problems is increasing the flexibility of the demand side, through demand side management (DSM) measures that involve motivating users to shift their electricity usage [2]. Demand response (DR) is a subset of these measures that typically relies on using the price of electricity to incentivize end users to make changes in their electricity usage [2]

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