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Exploring the Influence of Residential Electric Vehicle Charging on Distribution System Hosting Capacity -- A Case-Study in Arizona

Mohammad Golgol, Anamitra Pal, Vijay Vittal, Christine Fini, Ernest Palomino, Kyle Girardi

TL;DR

Driven by rising residential EV adoption, the paper tackles distribution hosting capacity (HC) assessment under uncertainty. It develops a data-driven, end-to-end framework that coordinates residential charging using AMI data from a 120-transformer feeder, along with realistic commuting patterns, SOC distributions, and TOU pricing. The optimization minimizes charging cost while enforcing transformer capacity, introduces a switching-cost mechanism, and provides a confidence metric for stochasticity; a case study demonstrates the method can accommodate substantial EVs and identifies upgrade priorities. The results reveal seasonal effects and, in some cases, advantages to higher-power chargers, offering practical guidance for utility planning and targeted infrastructure upgrades.

Abstract

The installation of high-capacity fast chargers for electric vehicles (EVs) is posing a significant risk to the distribution grid as the increased demand from widespread residential EV charging could exceed the technical limits of the distribution system. Addressing this issue is critical, given that current infrastructure upgrades to enhance EV hosting capacity are both costly and time-consuming. Moreover, the inherent uncertainties associated with EV charging parameters make it challenging for power utilities to accurately assess the impact of EVs added to specific locations. To address these knowledge gaps, this study (a) introduces an algorithm to coordinate residential EV charging, and (b) proposes a comprehensive framework that evaluates all transformers within a feeder. The proposed method is applied to a real-world feeder, which includes 120 transformers of varying capacities. The results demonstrate that this approach effectively manages a substantial number of EVs without overloading any of the transformers, while also pinpointing locations that must be prioritized for future upgrades. This framework can serve as a valuable reference for utilities when conducting distribution system evaluations for supporting the growing EV penetration.

Exploring the Influence of Residential Electric Vehicle Charging on Distribution System Hosting Capacity -- A Case-Study in Arizona

TL;DR

Driven by rising residential EV adoption, the paper tackles distribution hosting capacity (HC) assessment under uncertainty. It develops a data-driven, end-to-end framework that coordinates residential charging using AMI data from a 120-transformer feeder, along with realistic commuting patterns, SOC distributions, and TOU pricing. The optimization minimizes charging cost while enforcing transformer capacity, introduces a switching-cost mechanism, and provides a confidence metric for stochasticity; a case study demonstrates the method can accommodate substantial EVs and identifies upgrade priorities. The results reveal seasonal effects and, in some cases, advantages to higher-power chargers, offering practical guidance for utility planning and targeted infrastructure upgrades.

Abstract

The installation of high-capacity fast chargers for electric vehicles (EVs) is posing a significant risk to the distribution grid as the increased demand from widespread residential EV charging could exceed the technical limits of the distribution system. Addressing this issue is critical, given that current infrastructure upgrades to enhance EV hosting capacity are both costly and time-consuming. Moreover, the inherent uncertainties associated with EV charging parameters make it challenging for power utilities to accurately assess the impact of EVs added to specific locations. To address these knowledge gaps, this study (a) introduces an algorithm to coordinate residential EV charging, and (b) proposes a comprehensive framework that evaluates all transformers within a feeder. The proposed method is applied to a real-world feeder, which includes 120 transformers of varying capacities. The results demonstrate that this approach effectively manages a substantial number of EVs without overloading any of the transformers, while also pinpointing locations that must be prioritized for future upgrades. This framework can serve as a valuable reference for utilities when conducting distribution system evaluations for supporting the growing EV penetration.

Paper Structure

This paper contains 21 sections, 11 equations, 8 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Problem Background
  3. Type of Chargers
  4. Commuter Departure and Return Patterns
  5. Type of EV
  6. State of Charge (SOC)
  7. Pricing Plan
  8. Proposed Optimization Formulation
  9. Implementation Procedure
  10. Feeder Evaluation
  11. Coordination in Practice
  12. Simulation Results
  13. Case Study and Data Preparation
  14. Findings from AMI Data Analysis
  15. Performance of Proposed Coordination Scheme under Different Values of Charging Power
  16. ...and 6 more sections

Figures (8)

  • Figure 1: Joint PMF of trip Start and End Times for EV commuters
  • Figure 2: Proposed methodology for evaluating EV hosting capacity in distribution feeders
  • Figure 3: Daily profiles of aggregated power drawn from a specific transformer in July with a maximum capacity of 50 kVA
  • Figure 4: Daily profiles of aggregated power drawn from a specific transformer in March with a maximum capacity of 50 kVA
  • Figure 5: Impact of setting all EV chargers to a fixed power of 7.2 kW on a transformer in July
  • ...and 3 more figures