1. Modelling Electric Vehicles in Optimal Power Flow Problems 9 th January, 2012 Dr. Salvador Acha – Imperial College Research Associate

2. 2 Presentation Contents Introduction, context and research questions Agent based models Optimal power delivery to EVs Case study Conclusions Q&A

3. 3 Introduction EV deployment represents a challenging task in power systems since its intrinsic mobility characteristic means loads can appear and disappear in different parts of the network. Addressing this issue will require thorough research in driving patterns and vehicle usage; thus providing utilities the capacity to forecast when and where EVs may require energy for their travelling needs while also guaranteeing they are capable of supplying stochastic demand whenever required.

4. 4 Context Commercial EVs signal that the electrification of the transport sector is imminent. This represents a good opportunity to reduce carbon emissions from transport activities. However, EVs can only be effective mitigating carbon if the electricity used to charge the batteries are through low-carbon technologies. EVs offer great load flexibility potential: –Vehicles are idle 95% of the time; –40 miles/day average urban travel (EU data); –Spatial and temporal considerations must be taken.

5. 5 Context Demand response refers to “deliberate load control during times of system need, such as periods of peak demand or high market prices, thus creating a techno-economical effective balance between supply and demand”. In order for demand response programs to be effective and unbiased it is necessary to apply a holistic approach in assessing and quantifying the tradeoffs EVs bring to power systems. In this work an ABMEVOPF framework is formulated.

6. 6 Energy W2W Efficiency Comparing km/lt vs. km/kWh is elemental. The energy W2W equation follows the energy content of the fuel from its original source up to its point of consumption. EVs are more energy efficient than ICE units even when fuelled with power generated from coal

7. 7 Carbon W2W Efficiency No electric car is carbon free. The carbon W2WCO 2 equation translates the W2W efficiency into carbon emissions per vehicle model (kg/km). Based on the calculations EVs when fuelled with power generated from coal will pollute more than ICE vehicles

8. 8 The UK Fuel Mix Today the UK fuel mix is composed from 48% gas, 26% coal, 18% nuclear, 6% low carbon. Figure. Exemplifies the differences in the carbon emitted for each MWh of generated once wind power is prominent. The increased presence of wind power will naturally decrease the carbon content of the fuel mix in the UK

9. 9 The UK Power Market Renewable sources not only affect emissions, but also wholesale prices of electricity. Figure. The incursion of intermittent energy sources in the UK fuel mix will have an increasing influence in the bids and offers of the spot market. Wind power will displace marginal plant, thus if the wind blows spot prices mostly at peak times will be reduced

10. 10 Research Questions How can we model this complex problem that merges transport and power systems? How can EVs and network operators coordinate their needs for the overall benefit of the infrastructure? How can EVs become incentivised to charge when electricity is cheaper and less dirty? Is it possible to manage EV profiles in order to minimise negative network impact such as peak demand?

11. 11 Power Market Interactions Figure. Illustrates the interactions a global coordinator should consider in order to provide optimal load control signals to EV users.

12. 12 Modelling Framework A combined formulation is presented in this work in order to represent and address the research questions at hand. Figure. Integration of agent-based and optimal power flow models to analyse the effects of EVs on an electricity network.

13. 13 Agent Based Model The ABM describes the behaviour of EV owners as their activities bring them to various parts of the city at different times. Random distribution is employed to determine departure times and next activity. At each interval plugged EVs are aggregated to give SOC and estimated rate of charge. ABM was implemented in Repast Symphony –Input: driver profiles, EV capacity and travelling efficiency, city layout (building, road, and network) –Assumption: EV parked = EV plugged –Result: EV energy forecast to coordinator

14. 14 EV Power Flow A coordinated optimal power flow is used to determine when, where and by how much EVs should charge in a local network. EV demand is estimated based on the data given by ABM simulation. The multi-objective function problem tries to consider key drivers that look to influence how EV technology can be charged efficiently from day to day. These are represented in monetary terms as follows: –Day ahead spot electricity market prices –Cost from charging with carbon electricity –Network operating costs for energy delivery

15. Multi-objective Function OPF model was implemented in gPROMS. For total energy cost minimisation For spot and carbon market costs For operating network costs (1) (2) (3)

16. OPF Constraints

17. Case Study Goal: demonstrate that through ABM the (temporal and spatial) load flexibility on electrical networks can be estimated and exploited. Driver profiles (14 profiles): –People with a job, either at an office or leisure centre, who may or may not have kids of school-going age (10). –People without a job, but who have kids of school- going age (2). –Pensioners and other people who do not have to go to work and/or to a school (2).

18. Case Study EV characteristics (250 agents): –EV unit is similar to the Nissan Leaf; 24 kWh capacity, 100 miles on a full charged battery. –Max charging rate per EV is 3.84 kW, 16 A, 240 V City layout (3 areas): –All stored geographical data is stored using GIS. Figure. Map of the urban area considered in the case study.

19. Case Study Network features and load profiles (4 nodes): –11 kV level with a radial topology, operating under balanced conditions and represented by its positive sequence network. The load in each node is assumed to be a 3 phase balanced load Figure. Residential and commercial load profiles used for the urban area. MW

20. Case Study Scenarios The case study explores 2 scenarios: –1 st scenario takes the current power generation portfolio in the UK to depict how optimal EV charging would look like within the present context –2 nd scenario attempts to look at a future in which power is more expensive, renewable energies have an important presence (20%) and carbon emissions are taxed as well –In all scenarios the same load profiles and number of EVs are used, the only changing variables are fuel mix and energy costs/emissions –Simulation runs from Thursday 6 am to Sunday 6 am

21. ABM Results Figure. ABM allows DNOs to estimate the influence EVs can play in local networks – penetration and SOC levels give load flexibility.

22. EV OPF Results Figure. Load profiles seen from the supply point will evolve over time as EV penetration increases and random prices occur – the solver is quite efficient in indicating ideal charging times.

23. EV OPF Results Figure. The solver provides valuable granular detail on nodal charging of EVs.

24. Conclusions The ability to determine optimal charging profiles of EVs is paramount in developing an efficient and reliable smart-grid. This work has attempted to merge transport related modelling concepts from EV travel with traditional optimal power flow issues in order to identify ideal EV charging strategies that enhance power network performance. Power system engineers need to work hard to tackle the many issues surrounding EVs, but we must be confident we can model such complicated problems. A simplified EV + OPF model was presented here in order for engineers to grasp key concepts.

25. Conclusions The work presented makes new formulations to address what energy spot and carbon markets bring to future power systems. Coordination between power and transport sectors will be paramount. In this work ABM and OPF worked hand in hand. Stakeholders need to address carbon issues both in ‘real costs’ and W2W analysis, otherwise EV technology will not actively contribute to reduce GHG emissions. Results suggest incentivised EV charging via power markets can yield meaningful results if methodologies such as the ones presented here are continued to be explored.

26. Thank you for your attention!