1. Smart Charging of Plug-in Vehicles and Driver Engagement for Demand Management and Participation in Electricity Markets Agreement #EPC
Doug Black, Samveg Saxena, and Jason MacDonald
Lawrence Berkeley National Lab
Second Annual California Multi-Agency Update
on Vehicle-Grid Integration Research
December 14, 2015

2. Project Overview
Partners: LBNL, Alameda County, Kisensum, ChargePoint, and Prospect Silicon Valley-Bay Area Climate Collaborative.
Alameda County (AlCo) objectives:
Offer free or low-cost charging to the public
Aim to reduce costs, particularly demand charges for both fleet and privately-owned PEVs that use existing AlCo charging stations.
Several AlCo buildings participate in Auto DR.
Need similar Auto DR of fleet EVs, particularly EVs charging during peak periods, many of which are not fleet.

3. Alameda County PEV Charging
~45 fleet EVs in 7 locations
Majority at AlCo Park garage in Oakland
66 L2 charging ports (and 40 L1) in 10 locations
Most located at AlCo Park
AlCo estimates that monthly demand charges have increased from ~$100 to ~$1500 at the five locations with the most charging stations.

4. AlCo Park Weekday 15-min Demand Feb 2013

5. AlCo Park Weekday 15-min Demand Feb 2015

6. Example of Shifting EV Charging Demand at AlCo Park

7. Smart Charging Control System Overview… …
Public/
Employee
Fleet
(66 Ports)

8. Smart Charging Control System
Kisensum
Fleet Management System
LBNL V2G-Sim
(Vehicle Powertrain Model)
Driver App
LBNL
Optimization Algorithms
Kisensum
Charge Controller
ChargePoint
API
ChargePointCharging
Stations
Building
Demand
Data

9. Alameda County Approach Summary
Goal is to minimize charging costs to increase uptake of EVs by municipal and corporate fleets and increase public EVSE infrastructure.
Provide smart charging solutions for fleet and public EVs.
Guarantee mobility needs are met while providing optimal smart charging to maximize value / minimize costs.
Leverage fleet management and smart charging control optimization methods developed in LA AFB V2G project.
Use OEM (ChargePoint) APIs to control charging.

10. VGI Research Directions
EV/EVSE hardware and software that provides both bulk system services and feeder level power quality control.
Probabilistic forecasting of available grid service capacity that could be provided by EVs.
Inform grid service communication standards by identifying minimal set of EV/EVSE data and marginal value of additional data.
Low-cost metering to meet needs of verifying grid service provision compared to existing baseline methods.
Controlled lab testing of bi-directional grid service impact on EV batteries with protocols developed from pilot(s).

11. Thanks! Any questions: Doug Black DRBlack@lbl.gov
Samveg Saxena
Jason MacDonald

12. Additional Slides

13. V2G-Sim: A Platform Enabling Cross-Disciplinary Research in VGI
Vehicle-to-Grid Simulator (V2G-Sim)
Vehicle and vehicle-grid simulation code developed at Berkeley Lab, available for use by all stakeholders e- $
V2G-Sim models the driving & charging of many individual vehicles to temporally & spatially predict how vehicles can benefit the electricity grid and how the grid will affect vehicles
Bottom-up Approach
Core objective: a platform to develop and test any user-defined charge / discharge control approach and co-simulate with complementary models (e.g. distribution, transmission, market, etc.)
V2G-Sim is a platform that enables cross-disciplinary research to address the uncertainties and barriers facing vehicle-grid integration in a systematic, validated, and quantitative way.
V2G-Sim follows up a bottom up approach that models phenomena at the individual vehicle level to make grid-scale predictions with fine temporal and spatial resolution
There are two version of V2G-Sim under development:
1. V2G-Sim Analysis
2. V2G-Sim Operations
I will focus on V2G-Sim Analysis during this presentation
Points about timing…
Model Architecture

14. EV Energy & Availability
DER-CAM for LA AFB
Inputs:
Forecasts:
Outputs:
Site load data
Regulation market prices
Site weather data
Base Load & Reg Prices
Optimal market bidding schedule
Optimal vehicle charging schedule
DER-CAM
Electricity tariff data
Objectives:
EV travel schedules
EV technology data
EV Energy & Availability
Minimize total cost
Uncertainty in EV availability, EV charging requirements, Non-EV load, and regulation market prices are handled through forecasts and a robust approach to optimization in DER-CAM

15. Trip SOC / Charging Flexibility Forecaster
Module within Berkeley Lab’s MyGreenCar tool, being applied to quantify flexibility for EVs to alter charging patterns within AlCo VGI project
How the MyGreenCar Trip Planner Works:
Specify trip origin and destination
Trip forecaster determines likely route
Trip forecaster constructs a probabalistic drive cycle for the route (speed vs. time, and terrain vs. time profile)
Trip forecaster leverage’s MyGreenCar’s calibrated vehicle powertrain models to calculate required battery charge for the trip
Returns results to users of their SOC needs to make the trip

16. Fleet Management System – Reservation

17. Charge Control Display