A Mul&Scale Design and Control Framework for Dynamically Coupled Sustainable and Resilient Infrastructures, with Applica&on to VehicletoGrid Integra&on (EFRI – RESIN, Award Number: 0835995) People PI: Jeffrey Stein [[email protected]] (University of Michigan) CoPIs: Zoran Filipi [fi[email protected]], Gregory Keoleian [[email protected]], Huei Peng [[email protected]] (UM) 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]] (UM), Carl Simon [[email protected]] (UM), John Sullivan [[email protected]] (UM) and Jing Sun [[email protected]] (UM) 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 MulRRole Intermediaries which may be organizaRons, individuals or intelligent devices. Plugin 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 101112131415161718192021222324 demand, MW hour of day Electricity Demand, Effect of DSM nonPHEV demand total demand, no DSM total demand, w/ DSM 50% total demand w/ DSM 100% No DSM 50% DSM 100% DSM PHEV charging, gCO 2 /mi 43 41 41 Tailpipe GHGs, gCO 2 /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 Liion 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