1. Charilaos Latinopoulos Centre for Transport Studies, Imperial College London Smart parking facilities for electric vehicles 20 th European Conference on Mobility Management, Athens, 1-3 June 2016

2. Motivation Electric vehicles add complexity to power network operations and potential problems (overloads, bottlenecks, local disruptions) Need for better understanding of spatiotemporal distribution of electro- mobility and the choice mechanisms Smart charging (or charging coordination) with demand-side management methods by definition should explicitly treat demand Design new charging services and business models that will facilitate the use of EVs and turn them into a competitive alternative. There is ambiguity in everyday choices of EV drivers (range anxiety, restricted charging infrastructure, TOU electricity tariffs) Off-street parking in London Demand-side management with EVs 2 Smart parking facilities for electric vehicles

3. The Charging Service Provider (CSP) Range anxiety is “the fear or concern that the battery level is not enough to cover the daily driving needs” Policy makers should encourage EV adoption by investing in public charging infrastructure Drivers can top-up their vehicle’s battery (on-road, workplace, shopping malls etc.) and reduce their uncertainty An intermediate agent is possibly required for the management of charging infrastructure Multiple objectives for the CSP (EV drivers, DNOs and contracted parking facilities) 3 Smart parking facilities for electric vehicles

4. 4 Vehicle-to-Grid (V2G)

5. 5 Smart parking facilities for electric vehicles Research objectives Develop a stated preference methodology to characterize joint preferences for parking and charging associated with out-of-home activities Introduce the concept of reservation in advance Develop techniques for revenue maximization (capacity allocation, dynamic pricing) Identify segments of the market and their latent characteristics Quantify the various levels of uncertainty (range anxiety, price volatility) and explore forward-looking behaviour Understand the dynamic interaction of supply and demand (strategic customers)

6. EV-PLACE Recruitment process 6 Smart parking facilities for electric vehicles 21,000 plug-in cars and vans in the UK (0,06% of all registered vehicles) EV drivers and “considerers” (considered to buy an EV during the last 12 months) 263 respondents (118 after processing)

7. Revealed charging preferences 7 Smart parking facilities for electric vehicles

8. Stated preference exercises 8 Smart parking facilities for electric vehicles CHARGING GAME BOOKING GAME

9. Charging choices 9 Smart parking facilities for electric vehicles Charging parameters are in agreement with a priori expectations and systematic heterogeneity is captured by demographics and trip attributes EV drivers can be segmented in distinctive groups (price-sensitive and time-sensitive) and segmentation depends on latent constructs (pre- planning for travel activities) For variable electricity prices there is a tendency towards risk-aversion Individuals that are likely to belong to the “price conscious” group tend to be more risk-averse

10. Revenue management 10 Smart parking facilities for electric vehicles Segmentation of customers and price differentiation Explicit modelling of demand with discrete choice models (Choice-based revenue management) Deviates in the treatment of load flexibility. EV drivers pre-book a “charging bundle” This inflexibility is not limiting because optimal allocation can be performed in advance Airlines Charging service provider

11. 11 Smart parking facilities for electric vehicles Hybrid Panel Latent Class model (HPLC) Modelling framework

12. 12 Smart parking facilities for electric vehicles Choice-based price optimization Two heterogeneous latent classes Imbalance between supply and demand Imbalance factor Total number of EVs that request the service Capacity in charging posts Capacity in power Incidence matrices

13. 13 Smart parking facilities for electric vehicles Services for Optimal Charging Simulator (SOCSim)

14. 14 Smart parking facilities for electric vehicles Main results Optimal pricing resulted in higher price for charging bundles with higher charging rates, longer charging durations and consumption of peak period resources Sensitivity analysis highlights the structural properties of the model and the capabilities of the tool For scenarios with V2G, the dynamic allocation method enabled the accommodation of drivers during peak hours

15. What is new? First explicit estimates of out-of-home charging behaviour Integration of parking and charging choices Charging control with explicit demand modelling The simulation demonstrates a 5%-10% revenue increase compared to the uncontrolled scenario Recommendations to CSPs for revenue management implementation: o Type of attitudinal data required (past reservations, impulsive choices) o Areas to reduce uncertainty and increase revenue predictability (smart meters) o Examine the acceptability of dynamic pricing from the customers 15 Smart parking facilities for electric vehicles

16. Target groups and transferability CSP Contracted parking facilities DSO Driver Methods and elements transferrable to other cities and regions Potentially the estimated sensitivities are context-specific (UK, Ireland) Cross-examination with similar datasets Application to small urban (or rural) areas depends on local needs 16 Smart parking facilities for electric vehicles

17. 17 Smart parking facilities for electric vehicles Potential applications CSPs can sign V2G contracts with EV owners Introduction of “mixed charging/discharging bundles” for frequency regulation Challenging due to the real- time nature of regulation CSPs could modify their pricing schemes (game- theoretical methods) to take account of strategic behaviour

18. THANK YOU FOR YOUR ATTENTION QUESTIONS? Charilaos Latinopoulos charilaos.latinopoulos10@imperial.ac.uk