Ing. Jan Mrkos

As electric vehicles (EVs) are slowly becoming a common occurrence on roads, commercial EV charging is becoming a standard commercial service. With this development, charging station operators are looking for ways to make their charging services more profitable or allocate the available resources optimally. Dynamic pricing is a proven technique to increase revenue in markets with heterogeneous demand. This paper proposes a Markov Decision Process (MDP)-based approach to revenue- or utilization- maximizing dynamic pricing for charging station operators. We implement the method using a Monte Carlo Tree Search (MCTS) algorithm and evaluate it in simulation using a range of problem instances based on a real-world dataset of EV charging sessions. We show that our approach provides near-optimal pricing decisions in milliseconds for large-scale problems, significantly increasing revenue or utilization over the flat-rate baseline under a range of parameters.

The charging infrastructure for electric vehicles faces the challenges of insufficient capacity and long charging duration. These challenges decrease the electric vehicle users’ satisfaction and lower the profits of infrastructure providers. Reservation systems can mitigate these issues. We introduce a reference architecture for interoperable reservation systems. The advantages of the proposed architecture are: it (1) considers the needs of the most relevant electric mobility stakeholders, (2) satisfies the interoperability requirements of existing technological heterogeneity, and (3) provides a classification of reservation types based on a morphological methodology. We instantiate the reference architecture and verify its interoperability and fulfillment of stakeholder requirements. Further, we demonstrate a proof-of-concept by instantiating and implementing an ad-hoc reservation approach. Our validation was based on simulations of real-world case studies for various reservation deployments in the Netherlands. We conclude that, in certain high demand situations, reservations can save significant time for electric vehicle trips. The findings indicate that a reservation system does not directly increase the utilization of the charging infrastructure.

As the number of electric vehicles on the roads increases, new technologies and concepts such as fast/super-fast charging and dynamic pricing are developed and implemented respectively. With those innovations on the rise, reservation of charging stations for electric vehicles will play a pivotal role in seamlessly integrating them into the transportation and mobility system. In this paper we derive basic requirements for building interoperable reservation systems and identify four generic approaches to reservation. For designing the system model and engineering the charging station reservation system, we utilize the E-Mobility Systems Architecture framework. For one of the reservation types, we implement a proof-of-concept and demonstrate its usefulness by conducting a showcase in Bavaria, Germany. Further, we set up and conduct a simulation-based evaluation to compare the four different reservation types regarding their benefit to users and providers as well as overall system efficiency. To the best of our knowledge, this is the first contribution proposing an interoperable reservation system for electric vehicle charging. The results presented in this paper provide insights regarding the feasibility of the different reservation types under varying conditions.

Routing of a fleet of automated unit-occupancy vehicles in a capacitated transportation network is an emerging problem that needs to be addressed to realize large-scale automated transportation systems. We adopt an existing network-flow-based model for the problem and present a new reformulation based on Dantzig-Wolfe decomposition. This reformulation allows us to apply the column generation solution technique which, in turn, enables us to solve large-scale problem instances with tens of thousands of requests on networks with thousands of links. We empirically compare our method to the state-of-the-art approach on several standard benchmark instances and find that the computational time of our solution approach scales qualitatively better in all tested problem instance parameters: namely, in the size of the transportation network, in the magnitude of demand intensity, and in the number of demand flows.

Efficient allocation of charging capacity to electric vehicle (EV) users is a key prerequisite for large-scale adaption of electric vehicles. Dynamic pricing represents a flexible framework for balancing the supply and demand for limited resources. In this paper, we show how dynamic pricing can be employed for allocation of EV charging capacity. Our approach uses Markov Decision Process (MDP) to implement demand-response pricing which can take into account both revenue maximization at the side of the charging station provider and the minimization of cost of charging on the side of the EV driver. We experimentally evaluate our method on a real-world data set. We compare our dynamic pricing method with the flat rate time-of-use pricing that is used today by most paid charging stations and show significant benefits of dynamically allocating charging station capacity through dynamic pricing.

With the increasing prevalence of electric vehicles (EVs), the provision of EV charging is becoming a standard commercial service. With this shift, EV charging service providers are looking for ways to make their business more profitable. Dynamic pricing is a proven technique to increase revenue in markets with time-variant, heterogeneous demand. In this paper, we propose a Markov Decision Process (MDP)-based approach to revenue-maximizing dynamic pricing for charging service providers. We implement the approach using an ensemble of policy iteration MDP solvers and evaluate it using a simulation based on real-world data. We show that our proposed method achieves significantly higher revenue than methods utilizing flat-based pricing. In addition to achieving higher revenue for charging service providers, the method also increases the efficiency of allocation measured in terms of the total utilization of the charging station.

On-demand transport has been disrupted by Uber and other providers, which are challenging the traditional approach adopted by taxi services. Instead of using fixed passenger pricing and driver payments, there is now the possibility of adaptation to changes in demand and supply. Properly designed, this new approach can lead to desirable tradeoffs between passenger prices, individual driver profits and provider revenue. However, pricing and allocations known as mechanisms are challenging problems falling in the intersection of economics and computer science. In this paper, we develop a general framework to classify mechanisms in ondemand transport. Moreover, we show that data is key to optimizing each mechanism and analyze a dataset provided by a real-world on-demand transport provider. This analysis provides valuable new insights into efficient pricing and allocation in on-demand transport.