Retail electric provider selection for prosumers with local distributed energy resources and plug-in electric vehicles using data mining techniques

In a deregulated retail electricity market, the residential customers typically have the liberty to choose an electricity service plan from a list of retail electric providers (REPs) that best matches their monthly energy consumption. This process is called “retail choice”. The selection of a suitable REP plan creates uncertainty for the residential customer, especially when numerous plans with various rate structures exist. The customers who intend to become prosumer (producers and consumers of electric power) face an expected change in their monthly energy consumption. This builds on the uncertainty of purchasing local distributed energy resources (L-DERs) and/or plug-in electric vehicles (PEVs) that are feasible for their REP plan and may result in savings on their energy bills. Therefore, this thesis designs and implements a personalized tool to guide residential customers and those who intend to become prosumers in selecting a suitable REP plan that maximizes their energy bill savings. In this study, 48 annual home profiles from the Pecan Street dataset and 24 REP plans with both time-invariant and time-variant plans, from the state of Texas, were evaluated. A feature-based approach was used to create the monthly energy bills from which a suitable REP plan could be selected for a customer. Additionally, the residential customers who intend to become prosumers are considered through clustering the profiles of rooftop solar photovoltaic (PV), home battery energy storage system (HBESS) and/or PEV and finding the representative profiles, which are then used to identify the most feasible combination of resources and plan to maximize their energy bill savings. The results revealed that the presence of PV is essential in achieving significant savings on the energy bills. Furthermore, if the customer plans on adding a HBESS and/or PEV, the solar PV capacity installed should be at a high capacity so to achieve savings on the energy bill. The results have also revealed that time-invariant plans are more suitable for customers with high energy usage that exceeds 1,000 kWh/month while time-variant and time-invariant plans may be suitable for customers with energy usage that is under 1,000 kWh/month to help them achieving significant savings on their energy bills.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Mabuggwe, Daniel Jacob

Advisor dc:contributor.advisor

• Ibrahim, Walid Morsi

• Retail electric providers
• Distributed energy resources
• Electric vehicles
• Energy bill savings
• Net metering

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1420

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1420