A. OBJECTIVES
The main objective is to evaluate and enhance Electric Vehicle Charging Stations (EVCS) resilience in Indianapolis, IN, Dayton, OH, and Ames, IA. We will provide technical solutions to: (a) Quantify the resilience of EVCS and associated distribution grid infrastructure (substations, feeders <35kV) using multiple data sources, where the resilience metrics are outage probability and expected restoration time; (b) Visualize resilience as a customizable heat map in Geospatial Energy Mapper (GEM); (c) Provide grid hardening plans to enhance EVCS resilience; (d) Provide investment plans of three EVCS resilience enhancement technologies, including mobile energy storage (MES), solar plus storage, and double feeding, where the goal is to use real data to assess their economic viability and effectiveness by quantifying resilience improvements under extreme events and economic benefits under normal operations; (e) Develop storm preparation and restoration plans by classifying EVCS into two categories (priority and regular loads), where the plans outline actions to be taken before, during, and after extreme events to improve EVCS resilience. The expected EVCS resilience enhancement by implementing the project outcomes can be summarized in the following Table 1.
Evaluation Metrics | How to Obtain Baseline | Targeted Improvement | |
Individual EVCS | EVCS outage frequency | Historical OMS data | 75% |
Expected restoration time | Historical OMS data | 50% | |
Expected EV charging load loss per year | AMI recorded charging data | 85% | |
Expected EV charging hour loss per year | AMI recorded charging data | 85% | |
EVCS regions | Regional outage probability | Historical OMS data | 75% |
Expected regional EV charging load loss | AMI recorded charging data | 85% | |
Expected regional EV charging hour loss | AMI recorded charging data | 85% |
B. SCOPE OF WORK
This project will be conducted in two budget period:
Budget Period 1: (1) Set up an Industry Advisory Board (IAB); (2) Establish Non-Disclosure Agreements (NDAs) with project partners; (3) Collect and prepare real utility data including geospatial locations of distribution substations/feeders, EVCS charging data, and outage management system (OMS) historians; (4) Develop data-driven fragility models that correlate outages of EVCS and associated grid infrastructure with weather intensities, and probability models that correlate restoration time with number of outages; the models will provide outage numbers and restoration times for given weather events; (5) Enhance GEM by integrating spatial information of distribution substations/feeders (<35kV), EVCS charging demands, and the developed data-driven resilience metrics for EVCS and associated grid infrastructure; (6) Provide suitability evaluations of potential sites for future EVCS using GEM-based composite scores considering various criteria including resilience and energy justice (EJ); (7) Identify existing EVCS that need grid hardening using the GEM-based composite scores; (8) Develop models to optimize grid infrastructure hardening and make investment plans for utility partners.
Budget Period 2: Develop investment plans of three resilience enhancement technologies (MES, solar plus storage, double feeding) and compare their benefits and costs; in addition, we will develop storm preparation and restoration plans considering EVCS. (1) Develop algorithms to cluster EVCS into multiple regions according to utility depots’ locations; (2) Develop models to optimize MES investment (number and capacity) in each region and provide benefit-cost analysis considering both resilience benefits and normal operation benefits; (3) Develop algorithms to classify EVCS into multiple categories and optimize investments in solar plus storage and double feeding, and provide benefit-cost analysis; (4) Calibrate EVCS restoration priorities using multi-criterion composite scores and update utilities’ restoration plans; (5) Develop storm preparation plans with resource allocations considering EVCS; (6) Validate developed methods/plans at community and utility levels.