Steven Inglis – United Kingdom – RIF Session 5 – Paper 0434

1. 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 Steven Inglis – United Kingdom – RIF Session 5 – Paper 0434

2. 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. 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. SPEA2 DER evaluation framework

5. 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. 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. 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. 848 842 844 846 818 820 Transmission Network Bus 1000 864 D E 840 822 E 808 858 812 850 836 D 824 826 888 834 860 E E D D 890 832 814 816 862 800 802 806 810 D 852 838 828 856 D 830 854 Case A: distribution of DG and EV D: renewable DG (wind) E: EV connection point

9. Results: Case A • Knee point: 745 MWh imported energy with storage of 60 MWh

10. 842 844 846 818 820 Transmission Network Bus 1000 864 E E D 848 822 808 858 812 850 836 D 840 824 826 888 834 860 D D 890 832 862 E 814 816 800 802 806 810 D E 852 838 828 856 D 830 854 Case B: DG and EV close to supply substation D: renewable DG (wind) E: EV connection point

11. Results: Case B • Knee point: 750 MWh imported energy with storage of 30 MWh

12. 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