Module summary

In this lecture series we have discussed several advanced business models for electric vehicles. In the first lecture we have seen how the trends for electrification go hand-in-hand with automation and sharing. In the lecture Roland Steinmetz discussed several scenarios about how an automated future might look like. He discussed the location dependencies, e.g. the differences between highway and inner-city traffic to show that automation will happen at a different pace at different places. Using this information he explained how this will impact the business models for electric vehicles and the charging infrastructure.

In the second lecture the business model behind ‘smart charging’ was explained. Using the definition of smart charging Roland Steinmetz introduced five different ways of smart charging:

• • • Load balancing
    • Energy Markets
    • Smart charging for DSO
    • Balancing services
    • Vehicle-2-grid

Each of these concepts were explained in more detail during the lecture. Finally we looked at some of the barriers of smart charging such as a lack of a market place for DSO smart charging and strict regulations at the balancing services and energy markets. In the lecture notes the value chain for smart charging could be studied in more detail.

In the third lecture one of the ways of smart charging that differs from the others by its bi-directional nature was studied in more detail. Vehicle-2-X is a concept in which energy stored in the battery of an electric vehicle can also be fed back to a load. This is especially applicable when a car is parked for a long time and the peak in energy demand would normally be supplied by fossil fuel plants. Roland Steinmetz explained the three different aggregation levels of V2X, the vehicle-2-grid both at the TSO and DSO level and Vehicle-2-Home of Building at a very local level. Finally we provided some insights into the main barriers for V2X and gave some examples of pilot projects for you to study in more detail.

In the last lecture we discussed how to be able to predict the implications of the large scale transition to electric vehicles in the near future. What is the required charging infrastructure? What is the generated load on the grid? We introduced the concept of Agent Based Modelling in which each unique inhabitant is simulated. Using the case of the SparkCity model which was developed especially to look at the buying and charging behaviour of electric vehicles we explored several case studies that can be investigated with such a model.

Further reading

• • • Eurelectric (2015). Smart charging: steering the charge, driving the change. Retrieved from: https://kipdf.com/smart-charging-steering-the-charge-driving-the-change_5ac19d531723dde774eb8ff1.html
    • Develder, C., Sadeghianpourhamami, N., Strobbe, M. & Refa, N. (2016). Quantifying flexibility in EV charging as DR potential: Analysis of two real-world data sets, 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, Australia
    • Tamis, M., Hoed, R. van den, & Thorsdottir, H. (2017). Smart Charging in the Netherlands. European Battery, Hybrid & Electric Fuel Cell Electric Vehicle Congress, Geneva, Switzerland
    • SAE International (2014). AUTOMATED DRIVING LEVELS OF DRIVING AUTOMATION ARE DEFINED IN NEW SAE INTERNATIONAL STANDARD J3016
    • McKinsey & Bloomberg New Energy Finance (2016). An integrated perspective on the future of mobility. Retrieved from: https://www.mckinsey.com/business-functions/sustainability-and-resource-productivity/our-insights/an-integrated-perspective-on-the-future-of-mobility