Transcript 12 EPIC LBNL Black.pptx

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
THE GRID INTEGRATION GROUP
http://gig.lbl.gov
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.
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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.
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AlCo Park Weekday 15-min Demand Feb 2013
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AlCo Park Weekday 15-min Demand Feb 2015
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Example of Shifting EV Charging Demand at
AlCo Park
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Smart Charging Control System Overview
Smart Charging
Control System
…
…
Fleet
(66 Ports)
THE GRID INTEGRATION GROUP
Public/
Employee
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Smart Charging Control System
Kisensum
Fleet
Management
System
LBNL V2G-Sim
(Vehicle Powertrain
Model)
LBNL
Optimization
Algorithms
Building
Demand
Data
THE GRID INTEGRATION GROUP
Driver App
Kisensum
Charge
Controller
ChargePoint
API
ChargePoint
Charging
Stations
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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.
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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).
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Thanks!
Any questions:
Doug Black [email protected]
Samveg Saxena [email protected]
Jason MacDonald [email protected]
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Additional Slides
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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 userdefined charge / discharge
control approach and cosimulate with complementary
models (e.g. distribution,
transmission, market, etc.)
Grid-scale impacts
PEV 1
PEV 2
PEV N
Individual PEV driving/
charging/V2G profile
Inputs of
fleet usage
statistics
Travel Itinerary
Generation for
Each Vehicle
(Sub-models for each vehicle) x N vehicles
Detailed Battery
Models
Automated
Drive-Cycle
Generation
OR
Inputs of
Travel Itinerary
for Each Vehicle
Model Architecture
THE GRID INTEGRATION GROUP
Battery
Degradation
Models
Vehicle
Powertrain
Models
Charging
Models
SmartCharging
Controllers
Temporally- and Spatially-resolved
PEV-Grid Energy, Power, and Monetary
Interactions (Distribution, Wholesale, etc.)
DER-CAM for LA AFB
Inputs:
Forecasts:
Outputs:
Site load data
Site weather
data
Base Load &
Reg Prices
Regulation
market prices
DERCAM
Electricity
tariff data
EV travel
schedules
EV technology
data
●
EV Energy &
Availability
Optimal vehicle
charging schedule
Optimal market
bidding schedule
Objectives:
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
THE GRID INTEGRATION GROUP
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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:
1. Specify trip origin and destination
2. Trip forecaster determines likely route
3. Trip forecaster constructs a probabalistic drive cycle for the route
(speed vs. time, and terrain vs. time profile)
4. Trip forecaster leverage’s MyGreenCar’s calibrated vehicle
powertrain models to calculate required battery charge for the trip
5. Returns results to users of their SOC needs to make the trip
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Fleet Management System – Reservation
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Charge Control Display
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