European Conference on Nanoelectronics and Embedded Systems for Electric Mobility ecocity emotion 24-25 th September 2014, Erlangen, Germany Internet of Energy Ecosystems Solutions Dr. Randolf Mock, Siemens AG, Germany
Internet of Energy for Electric Mobility Presentation Outline: Project Key Figures Project Objectives Initial Approach 38 Partners, 1 Demonstrator? Building Concept; IoE Building Ecosystems Demos I to IV: Internet Connection & Data Exchange Demonstrators: Key Results Grid Impact of EVSE Infrastructure Summary
IoE: Key Figures Pan-European Cooperation 38 Partners 10 Countries 44 M Budget
IoE: Project Objectives The Objectives of the IoE Project: Connecting the Internet with the energy grids and with applications in the area of electric mobility Implement a real time interface between the power grid and the Internet Develop hardware, software and middleware for seamless, secure connectivity and interoperability Develop reference designs and embedded systems architectures for high efficiency smart network systems Managing key topics: Demand response, modeling/simulation, usage monitoring, real time energy balance and billing Creation of value added services using both wired and wireless devices with access to the Internet
Initial Approach Initial Approach to Tackle the Project Objectives: Organization into 13 Supply Chains Transfer into ONE demonstrator at Siemens Erlangen
IoE - 38 Partners, 1 Demonstrator?
The IoE Building Concept The IoE Building Concept BEM (plus backend system, where appropriate) Energy gateway Internet gateway Renewable energy Energy storage Energy charging, Electric vehice Smart appliances Energy gateway Backend system Internet gateway BEM Renewable energy Energy storage Energy charging Electric vehicle Smart appliances
The Four IoE Building Ecosystems Demo sites framework overview Energy gateway Backend system Internet gateway BEM Renewable energy Energy storage Energy charging Electric vehicle Smart appliances Demo Public, Demo office and I industrial Public, building office ecosystem and industrial building ecosystem Demo II Charging Demo station II post ecosystem Charging station post ecosystem Demo III Micro grid ecosystem Demo III Micro grid ecosystem Demo IV Residential Demo building IV ecosystem Residential building ecosystem
Subdivision into Modules/Cohorts
Demos I to IV: Internet Connection Internet interconnection of the demonstrators and service platforms (a) SOAP - TCP/IP over Ethernet (b) SOAP - WEB service Specify SOAP WEB Services Reference implementation. 3G/4G Base Station? Internet b DSO Virtual Power Plant Aggregator Energy Markets Smart building in smart grid 3G/4G Router a Smart building in smart grid 3G/4G Router a Smart building in smart grid 3G/4G Router a Smart building in smart grid 3G/4G Router a BEM a BEG BEM a BEG BEM a BEG BEM a BEG Demo I Public, office and industrial building ecosystem Demo II Charging station post ecosystem Demo III Micro grid ecosystem Demo IV Residential building ecosystem
Data Exchange via Internet Data Exchange between Building and Energy Supplier: Building Energy Manager calculates energy consumption / production forecast based on: weather forecast (temperature, wind, solar irradiation/clouds,...) scheduled EV charging events (e.g. employees) Behavioral data (employees) Energy Supplier aggregates building data Calculates pricing data using Energy Market information
Demonstrators: Key Results Demo I (Office Building, Erlangen): Novel comm. interface between building and energy provider BACnet/EVSE Gateway; extensions to OCPP protocol Fast IEC 61851 Charger; BiDi On-Board Charger; Flywheel Demo II (Charging Station Post, Torino): Charging post environment, with EV and Charging station Smart BiDi chargers; distributed on-board energy management Smart grid comunication services from mobile devices
Demonstrators: Key Results (cont d.) Demo III (Micro Grid, Seville): Demonstrate micro grid stabilization ebroker: Energy trading among micro grid agents Secure charging process by Smartphone Demo IV (Residential Building, Haslar UK): Integration of partner systems in a residential environment (i.e. management of all the fiddly details...) Increase the interoperability among the heterogeneous residential automation systems Increase quality of service (QoS) and quality of experience (QoE) for both users and stakeholders
Grid Impact of EVSE Infrastructure EV / ICV Fleet Simulation Simulation of vehicular traffic network: arrival time profiles travelled distances at a specific location Charging power profiles are inferred from arrival times Specification of Road Network: Topology (junctions, edges, edge type, lanes, lane connections, max. velocity, ) Altitude info (experimental) Traffic light specs (signal duration, type) Traffic signs
Calculation of EV Arrival Times Road Network Definition SUMO Traffic Simulation Arrival Time Table Aerial View (maps.google.com) Imported Road network (OpenStreetmap) Topology, traffic lights, speed limits,... Macroscopic Statistical Data Total number of inhabitants Probability of unemployment Incoming traffic Work Place Density... SUMO: Microscopic traffic simulation Siemens & UniBo: Simulation of SoC Arrival/Departure times & SOCs atspecific network node
Power (kw) Calculation of EVSE Load Profiles Arrival Time Table Load profile of EVSE station 250 200 Reactive power and harmonics generation 150 100 50 0 9:00 9:55 10:50 11:45 12:40 13:35 14:30 15:25 Time Arrival/Departure times & SOCs atspecific network node Load profiles due to EV charging, EVSE Type (AC or DC, max. pwr rating)? Controlled or uncontrolled Charging? Reactive power and THD measurements of on-board chargers (for AC charging) off-board DC chargers in conjunction with load profiles Grid properties (X, SCR) allow estimation of reactive power & harmonics generation. Sample Load Profile: Supermarket 6 DC fast charging stations with 50 kw maximum charging power 85 EVs with 30-60 minutes stay
Enabler for Grid Planning incl. EV Fleets Simulated charging profiles as enabler for grid layout calculations incl. EVs Enabler for EVSE power management (charging process scheduling) at charging stations and in buildings Yields forecasts of arrival times, departure times, travelled distances Current use case: Erlangen City P Load EVSE PV t
Summary Organizing the set of deliverables into four demonstrators proved to be advantageous in several regards: The complexity of the demonstrators became manageable Country, history and application specific aspects could be treated in a focused way The co-ordinated Internet interaction of the four demonstrators via a DSO turned out to be a promising approach for energy management in smart grids Quantifying the interaction between fleets of electric vehicles allows to plan future grid infrastructures based on reliable EVSE load characteristics helps to avoid overengineered grids and hence unnecessary cost for grid re-construction
Use Case: Erlangen City Loop Detectors included in Simulation 8 7 6 5 SUMO Simulation 10 2 4 3 1 9 Reference: Stadt Erlangen Verkehrsbelastungsplan 2012