Toward Ultrafast Charging of Electric Vehicles

The paper is devoted to the problems arising from the ultrafast (≤ 10 min) charging of an electric vehicle (EV). An ultrafast charging station (UFCS) must provide high power output with minimal influence on the electricity transmission system, which can only be achieved by the application of energy storage acting as an additional buffer between the vehicle and the grid. Besides storage, interfaces between a fast charging station and the outside environment (vehicle, utility grid) must be designed to fulfil a set of requirements. The main challenge is to be found within the specification of parameters for the design of future energy supply systems, providing for fast charging of the vehicle batteries while avoiding solicitations of the local distribution system which exceed its instantaneous power capabilities. The possible impact of an UFCS on the power distribution system is analysed with the stochastic approach, based on the utilisation of such a station. The general aspects of highly variable load profile clearly include the use of energy storage means that must be specified regarding both the energy storage and the instant power capabilities. Different technologies are analysed in terms of performances and costs. In conclusion, one of the important problems facing electric vehicles is the possibility of short-time charging, both as seen from the EV battery itself as well as from the local supply system. In this context, large load variations as seen from the local power system, at multiple levels, must be carefully assessed with special attention to feasible load changes at the coupling points. By addressing these technical issues as well as the financial constraints, the research aims at providing viable solution for broad ultrafast charging systems integration into the power distribution infrastructure.

Grid-aware distributed control of electric vehicle charging stations in active distribution grids

Sherif Alaa Salaheldin Fahmy, Rahul Kumar Gupta, Mario Paolone

The penetration of electric vehicle (EV) charging stations (CSs), along with the progressive connection of stochastic distributed generation, is increasing the probability of violating the power distribution grid operational constraints and deteriorate the quality of power supply. To this end, the paper proposes a real-time control scheme for allocating power set-points to EV CSs while accounting for the grid operational requirements. In the proposed problem formulation the grid and the power injections are modelled accounting for their unbalanced 3-phase nature, thus enabling to formulate the problem objective and its constraints adopting the sequence decomposition. The EVs’ users need, along with the stochastic nature of other uncontrollable injections (e.g. loads and generation from photovoltaic generation units), are also taken into account. A distributed control scheme, with a minute-scale control horizon, is proposed where local controllers, operating at EV aggregation level, compute EV battery-secure power set-points. These controllers send their set-points to a central controller operating at the grid aggregation level. The central controller solves a scenario-based linearised optimal power flow accounting for grid operational and power quality constraints. Then, it sends back its solution to the respective local controllers. The obtained iterative algorithm is efficiently solved until convergence. We analyse the performance of the proposed control scheme via a simulation ran on the IEEE-34 feeder. Comparisons with two other control algorithms, a grid-unaware local controller and a myopic maximum power controller, are included to benchmark the proposed control scheme.

2020

Vehicle-to-Grid for Reliable Frequency Regulation

Dirk Lauinger

Future low-carbon societies will need to store vast amounts of electricity to stabilize electricity grids and to power electric vehicles. Vehicle-to-grid allows vehicle owners and grid operators to share the costs of electricity storage by making the batteries of electric vehicles available to the grid. In practice, vehicle owners decide when to reserve their cars for driving and when to make them available for grid services. Vehicle aggregators then decide how to commit the vehicles to grid services. For vehicle-to-grid to succeed, both vehicle owners and grid operators must be able to trust aggregators, i.e., vehicles should be available for driving and for grid services when the aggregators promise they will be. In this thesis, we solve a decision-making problem that ensures reliable commitments by vehicle aggregators for a particular grid service known as primary frequency regulation, considered one of the most profitable applications of vehicle-to-grid. Mathematically, we first formulate a robust optimization problem with functional uncertainties that maximizes the expected profit from selling primary frequency regulation to grid operators and guarantees that vehicle owners can meet their market commitments for all frequency deviation trajectories in an uncertainty set that encodes applicable European Union regulations. Functional uncertainties ensure that vehicle owners and grid operators can trust the decisions of aggregators at all times. Faithfully modeling the energy conversion losses during battery charging and discharging renders the optimization problem nonconvex. By exploiting a total unimodularity property of the proposed uncertainty sets and an exact linear decision rule reformulation, we prove that the nonconvex robust optimization problem with functional uncertainties is equivalent to a tractable linear program. Somewhat counterintuitively, the underlying deterministic problem for a known frequency deviation trajectory does not reduce to a linear program but results in a large-scale mixed-integer linear program, even if time is discretized. We believe that we have thus discovered the first practically interesting class of optimization problems that become dramatically easier through robustification. Through extensive numerical experiments using real-world data, we quantify the economic value of vehicle-to-grid and elucidate the financial incentives of vehicle owners, aggregators, equipment manufacturers, and regulators. In particular, we find that the prevailing penalties for non-delivery of promised regulation power are too low to incentivize aggregators to honor their promises toward grid operators. For general electricity storage devices, we then solve a simplified version of the decision-making problem analytically to understand how the optimal frequency regulation commitments depend on the roundtrip efficiency of the storage device, the dispersion of the frequency deviations, and the EU delivery guarantee. We show how the marginal cost of frequency regulation decreases with roundtrip efficiency and increases with frequency deviation dispersion. For energy-constrained storage devices, we find that the profits from frequency regulation are inversely proportional to the length of time for which storage operators commit regulation power. Establishing an intra-day market for frequency regulation would thus make electricity storage devices more competitive with other regulation providers, such as thermal power plants.

EPFL2022

Distributed Routing and Charging Scheduling Optimization for Internet of Electric Vehicles

Xiaoying Tang

In this paper, we consider an Internet of Electric Vehicles (IoEV) powered by heterogeneous charging facilities in the transportation network. In particular, we take into account the state-of-the-art vehicle-to-grid (V2G) charging and renewable power generation technologies implemented in the charging stations, such that the charging stations differ from each other in their energy capacities, electricity prices, and service types (i.e., with or without V2G capability). In this case, each electric vehicle (EV) user needs to decide which path to take (i.e., the routing problem) and where and how much to charge/discharge its battery at the charging stations in the chosen path (i.e., the charging scheduling problem) such that its journey can be accomplished with the minimum monetary cost and time delay. From the system operator's perspective, we formulate a joint routing and charging scheduling optimization problem for an IoEV network, and show that the problem is NP-hard in general. To tackle the NP-hardness, we propose an approximate algorithm that can achieve affordable computational complexity in large-size IoEV networks. The proposed algorithm allows the routing and charging solution to be calculated in a distributed manner by the system operator and EV users, which can effectively reduce the computational complexity at the system operator and protect the EV users' privacy and autonomy. Besides, a proximal method is introduced to improve the convergence rate of the proposed algorithm. Extensive simulations using real world data show that the proposed distributed algorithm can achieve near-optimal performance with relatively low computational complexity in different system set-ups.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Vehicle-to-Grid for Reliable Frequency Regulation

Dirk Lauinger

EPFL2022