amul&’scaledesignandcontrolframeworkfor ...stein/research-nsf-v2g/efri... ·...
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A Mul&-‐Scale Design and Control Framework for Dynamically Coupled Sustainable and Resilient Infrastructures,
with Applica&on to Vehicle-‐to-‐Grid Integra&on (EFRI – RESIN, Award Number: 0835995)
People PI: Jeffrey Stein [[email protected]] (University of Michigan) Co-‐PIs: Zoran Filipi [[email protected]], Gregory Keoleian [[email protected]], Huei Peng [[email protected]] (U-‐M) and Mariesa Crow [[email protected]] (Missouri University of Science and Technology) Par&cipa&ng Inves&gators: Duncan Callaway [[email protected]] (Berkeley), Hosam Fathy [[email protected]] (U-‐M), Carl Simon [[email protected]] (U-‐M), John Sullivan [[email protected]] (U-‐M) and Jing Sun [[email protected]] (U-‐M)
Good and frequent communicaRon and interacRon is considered key to our project's success and this occurs primarily at biweekly research meeRngs. At each meeRng a presentaRon is given by of one of the task area researchers. This allows team members see what each task is focused on, what their results mean, how it affects all tasks projects, and the potenRal for task integraRon. Offsite team members aVend via teleconferencing and receive slides via email. Offsite team members can visit U of M as oWen and for as long as they would to because office space has been made available for them.
Research Objec&ves Project Descrip&on
Enabling Poten&ally Transforma&ve Results Status of Research
Managing a Mul&-‐Disciplinary and Mul&-‐Ins&tu&onal Project
Vehicle to Grid (V2G) may poten&ally improve resilience by: -‐CreaRng a redundancy of power sources and flow paths. -‐Improving grid integrity to disturbances through energy storage. -‐Decreasing the load through peak shaving and reacRve power.
V2G provides sustainability through increased energy storage: -‐Allowing the grid to beVer absorb renewable electricity. -‐RedistribuRng power demand over Rme in both infrastructures. -‐Possibly decreasing use of expensive grid peaking units.
Sustainability and Resilience
FUEL PUMP
PHEV
BATTERY Outlet
Personal TransportaRon Infrastructure
Electric Power Infrastructure
Mul&-‐Role Intermediaries (MRIs) An infrastructure’s sustainability and resilience oWen depend on how strongly coupled it is to other infrastructures through the exchange of commodiRes, resources, services, or informaRon. This exchange oWen takes place through MulR-‐Role Intermediaries which may be organizaRons, individuals or intelligent devices.
Plug-‐in hybrid electric vehicles are an important MRI because they couple the personal transportaRon infrastructure with the electric power infrastructure. This is the project’s test bed applicaRon.
0 2000 4000 6000 8000
10000 12000 14000 16000
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
deman
d, M
W
hour of day
Electricity Demand, Effect of DSM
non-‐PHEV demand
total demand, no DSM
total demand, w/ DSM 50%
total demand w/ DSM 100%
No DSM
50% DSM
100% DSM
PHEV charging, gCO2/mi 43 41 41
Tailpipe GHGs, gCO2/mi (Fleet avg) 258 254 254
5.9 Michigan million drivers, 50% of vehicles are PHEVs
Monte Carlo SimulaRon for Lyapunov Stability
Angle
Lyapunov Energy
AsymptoRc bounds for the probability of load curtailment for a large populaRon system with controllable loads
Time to ramp MRI response up and down, versus probabilisRc bound
Probabilis&c Control of Charging PHEV Fleets
Demand Side Management (DSM) -‐ X% PHEV's disallowed from charging between 2 and 9PM
Task 2: Infrastructure Resilience Modeling
Task 3: MRI Design OpRmizaRon
Task 1: Infrastructure Sustainability Modeling (ABM/LCA)
Task 5: Infrastructure Control Task 6: Model IntegraRon/ReducRon
Above Figure from: T. Yoshida, M. Takahashi, S. Morikawa, et al. 2006
Li-‐ion Ba\ery Electrochemical Health SimulaRon
Controlled PHEV charging will beVer uRlize the generaRon assets and renewable resources during light load hours, and help prevent increases in peak load and grid instabiliRes.
Total energy [GJ] Losses [GJ] Peak load [pu] Peak hr
Uncontrolled 19.9898 0.1284 (0.64%) 0.3350 6:12 PM
Min. losses 19.8818 0.0373 (0.19%) 0.2457 3:04 AM
Dual Tariff 19.9898 0.1284 (0.64%) 0.5235 8:24 PM
Task 4: Intermediary Control