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Title: Development and Clustering of Rate-Oriented Load Metrics for Customer Price-Plan Analysis: Preprint

Abstract

One of the few methods electric utilities can use to motivate and change customer energy consumption is through their retail rate structures. Utilities are increasingly moving towards more dynamic rate-plans to achieve the objectives of motivating energy conservation, increasing self-consumption from onsite renewable generation, reducing peak demand and to flattening the demand profiles. This paper presents a set of rate-oriented customer load metrics that are the determinants of customers bill under four unique rate-plans. These metrics are not only indicative of which rate structure a customer should choose based on their load profiles, but also convey useful information about load consumption behavior. With these metrics, the utility can analyze their customers and identify customer classes that are rewarded under each rate-plan. This can help inform utilities whether the customer profiles being rewarded under each rate-plans are meeting their original objectives. To develop these customer classes, we calculate these rate-oriented load metrics for each customer and perform k-means clustering. The analysis is conducted on from a set of 300 customer profiles, examining the impact of four different rate-plans, different numbers of clusters and looking at customer bills and cluster load profile characteristics.

Authors:
 [1];  [1];  [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
Salt River Project (SRP)
OSTI Identifier:
1566039
Report Number(s):
NREL/CP-5D00-72655
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Conference
Resource Relation:
Conference: Presented at the IEEE Power and Energy Society (PES) General Meeting, 4-8 August 2019, Atlanta, Georgia
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; distribution networks; customer clustering; load analysis; price-plans; retail tariffs

Citation Formats

Abraham, Sherin Ann, McKenna, Killian K, and Nagarajan, Adarsh. Development and Clustering of Rate-Oriented Load Metrics for Customer Price-Plan Analysis: Preprint. United States: N. p., 2019. Web.
Abraham, Sherin Ann, McKenna, Killian K, & Nagarajan, Adarsh. Development and Clustering of Rate-Oriented Load Metrics for Customer Price-Plan Analysis: Preprint. United States.
Abraham, Sherin Ann, McKenna, Killian K, and Nagarajan, Adarsh. Tue . "Development and Clustering of Rate-Oriented Load Metrics for Customer Price-Plan Analysis: Preprint". United States. https://www.osti.gov/servlets/purl/1566039.
@article{osti_1566039,
title = {Development and Clustering of Rate-Oriented Load Metrics for Customer Price-Plan Analysis: Preprint},
author = {Abraham, Sherin Ann and McKenna, Killian K and Nagarajan, Adarsh},
abstractNote = {One of the few methods electric utilities can use to motivate and change customer energy consumption is through their retail rate structures. Utilities are increasingly moving towards more dynamic rate-plans to achieve the objectives of motivating energy conservation, increasing self-consumption from onsite renewable generation, reducing peak demand and to flattening the demand profiles. This paper presents a set of rate-oriented customer load metrics that are the determinants of customers bill under four unique rate-plans. These metrics are not only indicative of which rate structure a customer should choose based on their load profiles, but also convey useful information about load consumption behavior. With these metrics, the utility can analyze their customers and identify customer classes that are rewarded under each rate-plan. This can help inform utilities whether the customer profiles being rewarded under each rate-plans are meeting their original objectives. To develop these customer classes, we calculate these rate-oriented load metrics for each customer and perform k-means clustering. The analysis is conducted on from a set of 300 customer profiles, examining the impact of four different rate-plans, different numbers of clusters and looking at customer bills and cluster load profile characteristics.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

Conference:
Other availability
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