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Impact of Work Schedule Flexibility on EV Hosting Capacity: Insights from Analyzing Field Data

Marco Iorio, Mohammad Golgol, Anamitra Pal

Abstract

Uncoordinated electric vehicle (EV) charging is altering residential load patterns and pushing distribution transformers to operate beyond their limits. These outcomes can be offset by exploiting the flexibility in work schedules (hybrid, remote vs. in-person) of EV owners, particularly when combined with rooftop photovoltaic (PV) generation. However, this phenomenon has not been explored in-depth yet. This paper addresses this research gap by introducing weekly work schedule-aware robust and chance-constrained optimization formulations for EV charging coordination to determine a transformer's EV hosting capacity. The results obtained using data from a residential feeder in Arizona indicate that an intelligent combination of work schedule flexibility with PV generation can help power utilities effectively manage changing grid demands.

Impact of Work Schedule Flexibility on EV Hosting Capacity: Insights from Analyzing Field Data

Abstract

Uncoordinated electric vehicle (EV) charging is altering residential load patterns and pushing distribution transformers to operate beyond their limits. These outcomes can be offset by exploiting the flexibility in work schedules (hybrid, remote vs. in-person) of EV owners, particularly when combined with rooftop photovoltaic (PV) generation. However, this phenomenon has not been explored in-depth yet. This paper addresses this research gap by introducing weekly work schedule-aware robust and chance-constrained optimization formulations for EV charging coordination to determine a transformer's EV hosting capacity. The results obtained using data from a residential feeder in Arizona indicate that an intelligent combination of work schedule flexibility with PV generation can help power utilities effectively manage changing grid demands.
Paper Structure (11 sections, 17 equations, 3 figures)

This paper contains 11 sections, 17 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Model Development and Considerations
  3. Optimization Model and Data Preliminaries
  4. Parametric Uncertainty Quantification
  5. Mathematical Formulation
  6. Simulation Results
  7. Transformer EV HC Under Different Work Schedules
  8. Effect of PV Penetration and Chance-Constraints
  9. Case-Study 1: Mixed EV Charging Optimization Model
  10. Case-Study 2: Impact of Coordinated Charging on Transformer Loading and Reverse Power Flows
  11. Conclusion

Figures (3)

  • Figure 1: EV HC results for robust and chance-constrained formulations of the in-person, hybrid, and remote EV charging optimization models on 40 transformers at 50 kVA capacity each, located on an SRP feeder in Arizona. The numbers in black are transformers. The numbers in red are EV HC results for the robust formulation. The numbers in green are EV HC results for the chance-constrained formulation. Transformers circled in blue ovals have loads with PV.
  • Figure 2: EV HC results for four stochastic optimization models.
  • Figure 3: Charging coordination impact on a transformer supporting loads with PV for four EV charging optimization models.