MC-based Risk Analysis on the Capacity of Distribution Grids to Charge PEVs on 3-ph 0.4-kV Distribution Grids Considering Time and Location Uncertainties Sven Bohn, Daniel Beyer, Robert Feustel, Michael Agsten Fraunhofer IOSB-AST Power Systems Research Group Am Vogelherd 50 98684 Ilmenau European Union / Germany Company Logo Here

Fraunhofer IOSB Director Prof. Dr.-Ing. habil. Jürgen Beyerer Lemgo Director Prof. Dr.-Ing. habil. Jürgen Beyerer Operational costs in 2014 41 Mio € Permanent employees 442 of which scientists and engineers 327 Graduate assistants 175 Ilmenau The IOSB is connected to Karlsruhe Institute of Technology KIT Department of Computer Sciences, Institute for Anthropomatics, Vision and Fusion Laboratory Beijing Paper # 2015-01-0305

Fraunhofer IOSB Advanced Systems Technology Branch Director Prof. Dr.-Ing. habil. Thomas Rauschenbach Deputies Dr.-Ing. Peter Bretschneider Prof. Dr.-Ing. habil. Jürgen Wernstedt Focus Energy, water and mobile systems related consulting of enterprises, governmental and non-governmental organizations Science transfer, applied research and development projects International activities, networking and scientific cooperation What’s behind systems technology – an example – a view on the German power system? Multi-Modal Energy Systems Electricity Grid Capacity and Quality Private and Industrial Demand and Behavior Business Models and Business related Interactions Time Scale Dependent Electric System Behavior Fluctuating Generation and Demand Liberalized Energy Markets and Legal Frameworks Paper # 2015-01-0305

Introduction Overview Project Managed Charging 3.0 Project Objectives Objective TSO: Negative Secondary Control Power Objective DSO: Integration of one-phase charging electric vehicles into the tri-phase system (EU) Architecture TSO Smart Charging Control System DSO 4 1 2 3 1 Negative Secondary Control Power Request (TSO) Distribution Grid Capacity (DSO) Up to 7,2 kVA additional load 4 Smart Charging Control Systems contains: 4.1 Aggregator 4.2 EV Pool 4.3 Intelligent EVSE EV 2 3 Paper # 2015-01-0305

DSO Typical Grid Layouts in EU / Germany 0.4-kV 3ph Urban Distribution Grid 270m 250m 0.4-kV 3ph Rural Distribution Grid 400m 700m Legend Voltage Loading Transformers Loading Cables/Lines KPI Urban Grid Rural Grid # Households 176 10 Peak Demand [single household] 2.52 KW cosphi=0.95 7.5 kW cosphi=0.95 Transformator Loading 436 kVA / ~70% 172 kVA / ~70% Topology Meshed Radial Balanced Condition V2/V1 0% Longest distance between transformer and load ~460m ~800m Paper # 2015-01-0305

DSO Possible Charging Technologies Grid unfriendly Worst case Grid friendly L1 L2 L3 Alternating chosen phase Optimal solutions for network disturbances when 1- and 2-Phase loading are available Must be ensured by the electrical installation and/or by infrastructure functions (e.g. coordination of charging planes) Worst case All PEV‘s are loading @ the same phase Follows conventional regulations 2-Phase 16A 8A 5.33A AC/DC 5.33A AC Best Case PEV represents a symmetric load Each phase will be loaded with 1/3 of the total load 1-Phase 3-Phase N Loading Type TR: 20/ 0.4 kV Paper # 2015-01-0305

DSO Typical Charging Use Cases and Solutions UC 1: Charging of dispersed fleets UC 2: Charging of aggregated fleets UC 1 Solution: Active Management PLM UC 2 Solution: Active Management LLM Paper # 2015-01-0305

DSO Open Question: “What is the real grid capacity for EVs?” Definition of Electric Grid Capacity Grid Capacity Limiting KPIs [Time scales] Steady state Transients and Dynamics Subtransients µs..ms s...min min…h Slow voltage changes Flicker/Voltage Dips Voltage level Thermal rating L/C/T under n. cond. Thermal rating lines/cables/transformers under failure conditions Protection Fast voltage control Changes of generation Harmonics Changes of demand KPI Value Max. voltage level +10% [EN50160] Min. voltage level -10% [EN50160] Max. unbalance V2/V1 [neg. seq./pos. seq] <=2% [EN50160] Thermal rating transformers typical between 60% … 100% Thermal rating cables Power quality ref. EN50160 Paper # 2015-01-0305

DSO Open Question: “What are the uncertainties in EV grid integration” Which car a customer buys in near or far future? Examples from the market: Car Charging time Charging Technology Tesla Models 1 up to 8h 11 up to 22 kVA, tri-phase AC Renault Zoe between 1 and 2h BMWi3 up to 4h 3,6 up to 7,2 kVA, one-phase AC Where are the charging spots located over time in near or far future? Which spots are used simultaneously? Influenced only by the customers! On which one of the three phases are charging one-phase AC EVs? Can be influenced by the customers, installer (and the DSO)! L2N Dispersed Concentrated L1N L2N L3N Paper # 2015-01-0305

DSO Challenge Customer behavior Application to the grid Possible Scenarios 1..176 EVs charge simultaneously 1ph 9.17E105 1..176 EVs charge simultaneously 3ph 9.57E52 It is impossible for a grid planner to calculate enough representative scenarios to determine the grid capacity for EVs, and the right decisions Paper # 2015-01-0305

DSO Solution MC based risk analysis MC based grid capacity estimator N0% N100% no actions required actions depending on the DSOs risk strategy DSO must act Paper # 2015-01-0305

DSO Example Analysis Scenario Description 0.4-kV 3ph Urban Distribution Grid 0.4-kV 3ph Rural Distribution Grid Criteria Value Analyse Area Whole grid Loading Peak Max. PEVs per Node 2 Charging Technology 4,6 kVA1ph cosphi=0,95 Max. Cable Loading 80% Max. Transformer Loading 100% Max. voltage deviatation ±10% Max. unbalance 2% KPI Urban Grid Rural Grid # EV N0% 3 # EV N30% 6 # EV N100% 10 1 # Cable Loading failed in 698 / 1000 simulated scenarios failed in 219 / 1000 simulated scenarios # Transformer Loading failed in 370 / 1000 simulated scenarios failed in 113 / 1000 simulated scenarios # max. voltage deviatation failed in 343 / 1000 simulated scenarios failed in 999 / 1000 simulated scenarios # Unbalance failed in 12 / 1000 simulated scenarios failed in 114 / 1000 simulated scenarios Paper # 2015-01-0305

DSO Conclusion EVs in the EU charge at a AC tri-phase system EVs are in the EU available as AC tri-phase or one-phase loads EVs are a volatile load over space, grid phases and time Planning the grid of tomorrow with EVs becomes very complex The number of possible development paths in a single distribution grid is extremely high It is impossible for a grid planner to calculate enough representative scenarios to determine the grid capacity for EVs, and the right decisions With a MC based approach a small sample of all possible scenarios can be chosen to estimate the grid capacity for EVs Benefits for DSOs Estimate the capacity for every single Low Voltage Distribution Grid (e.g. only in Germany round about 500000  Big Data Problem) Classify critical and uncritical distribution grids Actually the best available input for DSOs strategy An available evaluator of the grid capacity for EVs (…and naturally PV, CHP, Storages, Voltage Controlled Transformers, etc.) Paper # 2015-01-0305

Acknowledgments The work done by S. Bohn and M. Agsten is partly funded by the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB) under the program “Erneuerbar Mobil”, grand no. 16EM1074. Paper # 2015-01-0305

Contact Information Thank you for your attention Dr.-Ing. Michael Agsten Head of the Power Systems Research Group Energy Department Fraunhofer Advanced Systems Technology Branch (IOSB-AST) Am Vogelherd 50 98693 Ilmenau European Union / Germany Phone +49 (0)3677 461 1520 Mail michael.agsten@iosb-ast.fraunhofer.de Paper # 2015-01-0305