Abstract
One of the few methods electric utilities can use to motivate and change customer energy consumption is through retail rate structures. Utilities are increasingly moving toward more dynamic rate plans to encourage energy conservation, utilization of onsite renewable generation, peak demand reduction and flattening of demand profiles. This paper creates a set of rate-oriented load metrics that are the determinants of customers' bills under four unique rate plans. These metrics are not only indicative of which rate structure can provide customer bill reductions based on their load profile characteristics, but also convey useful information about load consumption behavior. With these metrics, utilities can analyze their customers and identify classes that are rewarded under each rate plan. This can help inform utilities whether the customers 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 a set of 300 customer profiles, examining four different rate plans, different numbers of clusters, customer bills and cluster load profile characteristics.
Original language | American English |
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Number of pages | 5 |
DOIs | |
State | Published - Aug 2019 |
Event | 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, United States Duration: 4 Aug 2019 → 8 Aug 2019 |
Conference
Conference | 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 |
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Country/Territory | United States |
City | Atlanta |
Period | 4/08/19 → 8/08/19 |
Bibliographical note
See NREL/CP-5D00-72655 for preprintNREL Publication Number
- NREL/CP-5D00-76231
Keywords
- Customer Clustering
- Distribution Networks
- Load Analysis
- Price-Plans
- Retail Tariffs