An empirical regard on integrated smart grids and smart mobility pilot projects (MeRegio Mobil) Hartmut Schmeck Institute + KIT Focus COMMputation Research Center for Information Technology FZI INSTITUTE FOR APPLIED INFORMATICS AND FORMAL DESCRIPTION METHODS - KIT University of the State of Baden-Württemberg and National Research Center of the Helmholtz Association www.kit.edu
Moving towards Minimum Emission Regions Research Question / Scenario Energy Technology Smart Metering Hybrid Generation Demand Side Management Distribution Grid Management Energy Markets Decentralized Trading Price incentives at the power plug Premium Services System Optimization Objectives Optimize power generation & usage from producers to end consumers Intelligent combination of new generator technology, DSM and ICT Price signals for efficient energy allocation Combined Heat and Power MEREGIO-Certificate: Best practice + information dissemination Partners 2 ICT Real-time measurement Safety & Security System Control & Billing Non Repudiable Transactions Pilot Region with ~ 1 Participants (Freiamt + Göppingen) (5 chairs at KIT: Energy Economics, Informatics, Telematics, Management, Law)
4 Phases of MeRegio Phase 1 Phase 2 Phase 3 Phase 4 Q4/ 29 Q1 / 21 Q2-Q3 / 21 Q4 /21 Q1 / 211 Q2 / 211 to Q1 / 212 Measure & Respond Control Storage Market place Insights on consumer response to dynamic price signal Hour-based price signal for testing sensitivity of standard demand profile Price elasticity Control of consumers and decentrlal producers using control boxes and complex price and control signals First local optimisation; testing control methods for intelligent components Combining (partially) flexible consumption und storage of decentrally generated power Testing interaction of components and preparation for market entry Simulation of grid events, bottlenecks, management Automatic interconnection of interested participants (consumer, producer) via market place. MeRegio certification Offering different roles / degrees of freedom for participating in energy trading Number of test customers 1, 84 4 98 5 1 Phase 1 Phase 2 Phase 3 Phase 4 3
1 9 17 25 33 41 49 57 65 73 81 89 97 15 113 121 129 137 145 153 161 1 9 17 25 33 41 49 57 65 73 81 89 97 15 113 121 129 137 145 153 161 Phase 1: First results on user response Demand profile before testing 12 Demand Last MeRegio-Testkunden test Demand Last Referenzgruppe reference 1 8 6 4 2 Demand profile during testing 14 Demand Last MeRegio-Testkunden test Demand Last Referenzgruppe reference 12 1 8 6 4 2 2.% 15.% 1.% 5.%.% -5.% -1.% -15.% -2.% -25.% Relative changes compared to reference group 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 SNT NT HT 4
ICT for Electromobility Research Question / Scenario [source: EnBW AG] Objectives Intelligent & efficient integration of electric vehicles into the grid Technology assessment & feasibility under real life conditions Seamless integration into MEREGIO pilot region Center of competence at KIT (demo and research lab) Partners Methodology Computer Simulations Field trial with about 1 PEV Living Lab (11 chairs at KIT: Electrical Engineering (2), Energy Economics, Informatics (5), Telematics, Management, Law) 5
Effects of electric vehicles (EVs) on power grid Germany, 28: average daily car usage < 1 h Average netto capacity of currently available EVs: 2 KWh At 1 Million BEVs (German objective for 22): available storage capacity of ~ 2 GWh At charging/discharging power of 3 KW: ~ 3 GW potential power Consequently: high demand for power, potentially also high supply (if power feedback is possible) Average time for charging: Single phase 3.7 KW: 6 to 8 hours. Three phase 1 KW: ~ 2 hours (but high risk of grid overload!) Potential of high flexibility for load shifting, but also potential of high peak load! Using intelligent control high potential for stabilizing the grid. 6
6: 12: 18: 6: 12: 18: Power P in kw Power P in kw 6: 12: 18: 6: 12: 18: Power P in kw Power P in kw Integration Strategies: Load Balancing Potential 15 original grid load curve Uncontrolled EV energy charging 1 2 1 1 15 5 5 Controlled EV charging -5 Solar power infeed -5 Time Time 3 4 15 15 resulting load curve 1 5 EV <-> Grid Exchange Charging/Infeed 1 5-5 -5 Time Time 7
Smart Home e-mobility Lab: Testing smart integration of EVs into various grid profiles bedroom I kitchen technical room bedroom II living room 8
Challenges Battery charging infrastructure needs standardization and interoperability (at private, public, semi-public charging stations) Effective bidirectional control of batteries needs knowledge on next drive privacy protection problems? Need of incentives (regulations?) for leaving charging control to external provider. Limited range of BEVs needs new energy-aware services, e.g.: remaining driving distance energy-optimized routing and driving reservation of next charging station (coordination and booking) Exploit potential of effective system services utilizing virtualized storage. Security and safety issues Validity of billing for bidirectional charging? Denial of service attacks, viruses, worms all the problems known from data communication networks. 9
Problems: Fluctuations in demand and supply Small Scale Short Term Variations Mismatch Dead Calm Variations at different time scales, widely unpredictable How to deal with fluctuations? How to compensate for a dead calm?? 1