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Publication# Experimental Validation of the Real-Time Control of an Electric-Vehicle Charging Station

Sherif Alaa Salaheldin Fahmy, Jean-Yves Le Boudec, Mario Paolone, Roman Rudnik

*IEEE, *2021

Article de conférence

Article de conférence

Résumé

The high penetration of electric vehicles (EVs) charging stations (CSs), together with the progressive availability of distributed energy resources, increases the risk of grid overloading and power-quality degradation. Real-time control has been advocated by the recent literature as an alternative to costly grid reinforcement. Performance of most of the existing control methods is assessed via simulations, and real-field validation is rarely performed. We present the experimental validation of a recently proposed real-time control method for EVs CS. This method works at sub-second scale and allows the CS to adapt to the rapidly changing state of the grid caused by highly volatile energy resources, such as photo-voltaic (PV) plants. The method fairly allocates the available power, proportional to the EVs needs, while minimizing the EVs battery wear due to frequent charging power variations. It tracks an aggregated power-setpoint that comes from the main grid controller by solving a mixed-integer optimization problem. Our main goal is to show that the method works in the field, i.e., it can control the charge of commercial EVs that are connected to a real grid through a CS. The field validation has two challenges. The first one is to study the real-time capabilities of the method and by analysing how fast it computes the control power-setpoints. The second one refers to the handling of the non-ideal response of EVs to the control power-setpoints due to implementation and reaction delays and inaccuracies. The experimental results demonstrate the performance of the method and show that it can be deployed in the field.

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Jagdish Prasad Achara, Jean-Yves Le Boudec, Mario Paolone, Lorenzo Enrique Reyes Chamorro, Roman Rudnik, Cong Wang

We consider the problem of controlling the charging of electric vehicles (EVs) connected to a single charging station that follows an aggregated power setpoint from a main controller of the local distribution grid. To cope with volatile resources such as load or distributed generation, this controller manages in real time the flexibility of the energy resources in the distribution grid and uses the charging station to adapt its power consumption. The aggregated power setpoint might exhibit rapid variations due to other volatile resources of the local distribution grid. However, large power jumps and minicycles could increase the EV battery wear. Hence, our first challenge is to properly allocate the powers to EVs so that such fluctuations are not directly absorbed by EV batteries. We assume that EVs are used as flexible loads and that they do not supply the grid. As the EVs have a minimum charging power that cannot be arbitrarily small, and as the rapid fluctuations of the aggregated power setpoint could lead to frequent disconnections and reconnections, the second challenge is to avoid these disconnections and reconnections. The third challenge is to fairly allocate the power in the absence of the information about future EVs arrivals and departures, as this information might be unavailable in practice. To address these challenges, we formulate an online optimization problem and repeatedly solve it by using a mixed-integer-quadratic program. To do so in real time, we develop a heuristic that reduces the number of integer variables. We validate our method by simulations with real-world data.

2020Jagdish Prasad Achara, Jean-Yves Le Boudec, Mario Paolone, Lorenzo Enrique Reyes Chamorro, Roman Rudnik, Cong Wang

An electric vehicle (EV) charging method which considers the EVs heterogeneity, taking into account the absence of any information about future arrivals and departures, and of the amount of time any charging will take. The method further considers both switch on and off possibilities and not an arbitrarily small minimum charging power. In order to achieve all these objectives, the invention defines novel metrics and uses them to construct a dedicated optimization problem. As the charging power is discontinuous, the minimum charging power not being arbitrarily small, the optimization problem is mixed integer by nature. Further, because the mixed-integer optimization is difficult to perform in real-time, the invention proposes a heuristic for reducing the number of integer variables, thus reducing the complexity of the problem.

2020Sherif 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