Simulating smart charging optimization for electric vehicles

Simulating smart charging optimization for electric vehicles: A quantification and statistical analysis of the cost reduction and emission reduction potential of an aggregated Dutch EV fleet

Paanakker, Marnix (TU Delft Electrical Engineering, Mathematics and Computer Science)

Ghotge, R. (mentor)
Westerhof, Oscar (mentor)
van Wijk, A.J.M. (graduation committee)
Nane, G.F. (graduation committee)
Correia, Gonçalo (graduation committee)

Delft University of Technology

Electrical Engineering | Sustainable Energy Technology

2019-08-30

The objective of this research is to understand what smart charging can bring business and society at large. In close collaboration with the largest fleet operator in the world and a commercial aggregator, the impact of smart charging on both cost reduction and carbon reduction was simulated for an electric vehicle (EV) fleet in 2018. The simulation is designed to quantify the cost reduction of EV smart charging in The Netherlands as realistic as possible. Besides cost reduction quantification, the objective is to create a better understanding of what variables influence smart charging cost reduction. This is done via a statistical analysis of the smart charging simulations output. Furthermore, this research also has the objective find the direct impact of smart charging on carbon intensity of the electricity used for personal transport.
In this research, an EV aggregators perspective is leading. The EV aggregators could utilizing available flexibility in an EV fleet to deliver flexibility services. The strategy chosen to simulate is based on day ahead market optimization and passive balancing on the imbalance market. The EV fleet is assumed to be an isolated portfolio handled by the balance responsible party (BRP). The average synthetic load profile over 2018 was €41,56 per MWh and this is used as benchmark to quantify the smart charging savings in the simulations.
Different smart charging simulation set-up scenarios are designed and executed. In all simulations a real-world charging data set with 300.000 historic charging sessions was used. For each session, a new smart charging profile is determined by the optimization algorithms. The session price and session carbon intensity is calculated for both the smart charging scenarios as the business-as-usual scenarios. To compare the results of the different smart charging set-up scenarios, the average session price of all session in that simulations is used. At the same time, the smart charging savings is calculated based on the defined benchmark. The findings within this thesis support the conclusion that the used smart charging algorithms work properly and could decrease the electricity purchase price in The Netherlands. Additionally we found that the carbon intensity of the charged electricity during the smart charging schedule decreases compared to a business as usual scenario. This is a direct result of a correlation between the carbon intensity in the grid and day ahead prices in The Netherlands. EV aggregators are able to add flexibility to the demand side of the electricity system by means of smart charging, if a strong price incentive is provided. If stakeholders across the mobility and the energy sector work together, a real-world commercial implementation based on the price incentives on day ahead market and imbalance market in The Netherlands is possible. In the statistical analysis, multiple regression models show a linear relation between three independent variables (the session duration, session volume and maximum power of the charge point) and two dependent variables (the average session purchase price and savings per session). The key insights from the models empowered three main recommendations to EV aggregators to optimize the smart charging savings in the future: 1) Encourage longer session length. 2) Encourage regular overnight charging sessions behaviour, independent from the charging needs. 3) Stimulate access to high charging power. The data showed compelling differences between the 20% BEVs and the 80% PHEVs and their results were separated accordingly in this research. In all simulation set-up scenarios are the PHEVs outperforming the BEVs in terms of a lower average session price and higher cost reduction. If the smart charging strategy is executed as proposed in this thesis, the EV aggregator is exposed to the day ahead market and imbalance settlements for its portfolio. The EV aggregator is able to decrease the electricity purchase price, while acting as BRP. The exposure to the markets brings significant risk. Collaboration with an electricity supplier or BRP could potentially increase the smart charging savings for the EV aggregator. Furthermore, other revenue streams to utilize flexibility could be investigated. If stacking different flexibility strategies is possible, it could increase the smart charging value in the future.

Electric Vehicle
Smart Charging
optimization algorithms
Aggregator
carbon reduction
cost reduction
flexibility

http://resolver.tudelft.nl/uuid:6011234f-ce54-43f2-b254-0f8d65747291

2023-09-01

Student theses

master thesis

© 2019 Marnix Paanakker