Smart Charging of Plug-in Vehicles and Driver Engagement for Demand Management and Participation in Electricity Markets Agreement #EPC-14-057 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

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.

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.

AlCo Park Weekday 15-min Demand Feb 2013

AlCo Park Weekday 15-min Demand Feb 2015

Example of Shifting EV Charging Demand at AlCo Park

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

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

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.

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).

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

Additional Slides

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

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

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

Fleet Management System – Reservation

Charge Control Display