iminds the conference 2012: michel tilman
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TRANSCRIPT
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SPARCiMinds 08-11-2012
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SPARC
Smart Algorithms, security, connectivity
Plug-in EV, PHEV
Automobile Cars (Kangoo, Ampera), scooter (QWIC)
Renewable Charge using wind energy Balance supply and demand
Charging services Platform, algorithms, security, charging stations, mobile
device integration, simulations
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Participants
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REstore
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Research activities
User profiles Survey and workshops on smart charging and billing of
electric vehicles Clustering of potential EV customers Qualitative analysis of different profiles
Future-proof architecture Scalable platform Pluggable algorithms Steering and monitoring via REST services Mobile device integration Local and remote security services Charging stations supporting real and simulated charging
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Research activities (continued)
Multi-party security models for identification and billing Triple authentication, authorization, auditing SAML / XACML, secure auditing
Design and evaluation of smart charging algorithms Trivial, Intelligator, Intelligator+, Dual Decomposition Different KPI’s
User satisfaction, balancing supply and demand, green energy usage Simulation framework
Business models and techno-economic studies EV-leasing Smart charging infrastructure
8 November 2012 5
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Field trial
Setup Parkings Zand and Station (Interparking)
2 charging stations in each parking SPARC servers embedded in master poles
Global server (Sony) Replays real wind data
Test Trivial and smart algorithms Authentication over powerline (Dolphin) Smart connectivity (VITO scooter) Multi-party security Degraded mode operation
Log data
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Field trial (continued)
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Field trial (continued)
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Field trial (continued)
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Field trial (continued)
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ArchitectureZand (Local Server)
RESTful Services
Local Optimizer Scheduler BeCharged Station
BeCharged StationDolphin
Sony (Global Server)
RESTful Services
Global OptimizerWind
TestPlanSimulator / JMeterAndroid App
Dolphin Reader
Kouter (Local Server)
Local Optimizer Scheduler Virtual Station
Virtual StationRESTful Services
QWIC Scooter
iMinds App
Security
Security
Authentication / Authorization / Audit
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Evaluation KPIs
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Evaluation KPIs (continued)
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Evaluation KPIs (continued)Setting• 100 EV’s – 1 month – 3 algorithms
Intelligator: multi-agent system with central coordination. Comfort settings are guaranteed locally.Dual decomposition: local optimization, coordination through virtual prices.
• Realistic behaviour / real wind data
0:00 15:00 6:00 21:0012:00 3:00 18:00 9:00 0:00 14:59 6:00 21:00-50005000
150002500035000450005500065000
Wind Trivial IntelligatorDual decomposition
Time
Pow
er
(W)
KPI1 – User satisfaction
• Are EV’s full (95% or more) at departure?• Yes when departure time is known• Degeneration when leaving early
0% 5% 25% 50% 75%
70%
75%
80%
85%
90%
95%
100%
KPI
Max deviation expected duration
KPI2 – Imbalance reduction
• Works best with diverse profiles• Different algorithms are comparable
KPI3 – Green energy
• Fraction of demand supplied by renewable energy• Algorithms again quite comparable
TrivialIntelligator
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