frankfurt (germany), 6-9 june 2011 steven inglis – united kingdom – rif session 5 – paper 0434...
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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 asResponsive Demands and Dispatchable Storage
Steven Inglis, Allan Smith, Graham Ault
Department for Electrical and Electronic Engineering
University of Strathclyde, Glasgow, United Kingdom
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
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
Frankfurt (Germany), 6-9 June 2011
SPEA2 DER evaluation framework
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
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
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
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
800 802
806 810
808
812
814 816
818 820
822
824
826
890
828
832
888
858
864
852
830
834 860
836
862
838
856
842 844 846
850
E
E
E
E
D
D
D
D
D
D
Frankfurt (Germany), 6-9 June 2011
Results: Case A
Knee point: 745 MWh imported energy with storage of 60 MWh
Frankfurt (Germany), 6-9 June 2011
Case B: DG and EV close to supply substationD: renewable DG (wind)E: EV connection point
Transmission Network Bus 1000
800 802
806 810
808
812
814 816
818 820
822
824
826
890
828
832
854
888
858
864
852
830
834 860
836840
862
838
856
842 844 846
848
850
E
E
EED
D
D
D
D
D
Frankfurt (Germany), 6-9 June 2011
Results: Case B
Knee point: 750 MWh imported energy with storage of 30 MWh
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