1. Frankfurt (Germany), 6-9 June 2011 Steven Inglis – United Kingdom – RIF Session 5 – Paper 0434 Multi-Objective Network Planning tool for the optimal integration of Electric Vehicles as Responsive Demands and Dispatchable Storage Steven Inglis, Allan Smith, Graham Ault Department for Electrical and Electronic Engineering University of Strathclyde, Glasgow, United Kingdom

2. Frankfurt (Germany), 6-9 June 2011 Background  General goal of sustainable and resilient highly distributed energy future  Supergen Highly Distributed Energy Future (HiDEF) programme  Vision of a decentralised energy system in the period 2025 - 2050  The research vision is one of: Decentralised resources (EVs, PV panels, Wind turbine), Control Market participation to include end users at system extremities

3. Frankfurt (Germany), 6-9 June 2011 Research Goal  Extend existing network planning tool to analyse the integration of EVs into the distribution N/W when used as a responsive demand and dispatchable storage: Minimise electricity purchase costs Minimise network reinforcement requirement Minimise network investment and operation costs

4. Frankfurt (Germany), 6-9 June 2011 SPEA2 DER evaluation framework

5. Frankfurt (Germany), 6-9 June 2011 Responsive Electric Vehicle Charging  Hypothesis: Suitably located and sized EV charging sites with smart EV charging can meet multi-stakeholder objectives.  Hypothesis being tested using a SPEA2 optimisation based evaluation framework  Different EV charging/scheduling methods will be applied to a generic distribution network model

6. Frankfurt (Germany), 6-9 June 2011 Network Planning using SPEA2  Using Strength Pareto Evolutionary Algorithm (SPEA2) technique  Multiple and conflicting objectives  Elitism and non-truncation attributes  SPEA2 (and other MOEA techniques) analyse complex, non- linear and convex objective functions offering ‘true’ multi- objective approach

7. Frankfurt (Germany), 6-9 June 2011 Simulation Background  EVs aggregated into larger capacity storage blocks  Located in distribution network model  Parameter of energy import is minimised to make use of local renewable energy  Trade offs for EV benefits are identified  results generated from 20 GA generations  Good spread of results evident and clear Pareto front convergence through generations  IEEE 34 bus network

8. Frankfurt (Germany), 6-9 June 2011 Case A: distribution of DG and EV D: renewable DG (wind) E: EV connection point 854 840 848 Transmission Network Bus 1000 800802 806810 808 812 814816 818820 822 824 826 890 828 832 888 858 864 852 830 834860 836 862 838 856 842844846 850 E E E E D D D D D D

9. Frankfurt (Germany), 6-9 June 2011 Results: Case A  Knee point: 745 MWh imported energy with storage of 60 MWh

10. Frankfurt (Germany), 6-9 June 2011 Case B: DG and EV close to supply substation D: renewable DG (wind) E: EV connection point Transmission Network Bus 1000 800802 806810 808 812 814816 818820 822 824 826 890 828 832 854 888 858 864 852 830 834860 836 840 862 838 856 842844846 848 850 E E EE D D D D D D

11. Frankfurt (Germany), 6-9 June 2011 Results: Case B  Knee point: 750 MWh imported energy with storage of 30 MWh

12. Frankfurt (Germany), 6-9 June 2011 Conclusions & Further Work  Early results show strong influence on EV benefits of charging location and proximity to grid supply and DG connections  Smart charging strategies need to be explored further to identify how much the result can be improved  Optimisation objectives to be expanded to fully represent the objectives of EV stakeholders  The use of the SPEA2 based network planning tool seems appropriate to the ‘location, sizing and operating’ problem  Results can inform policy and DNO mechanisms for EV network integration