Uncontrolled Charging Research Articles

Development and Evaluation of a Smart Charging Strategy for an Electric Vehicle Fleet Based on Reinforcement Learning

Governments are currently subsidizing growth in the electric car market and the associated infrastructure in order to accelerate the transition to more sustainable mobility. To avoid the grid overload that results from simultaneously charging too many electric vehicles, there is a need for smart charging coordination systems. In this paper, we propose a charging coordination system based on Reinforcement Learning using an artificial neural network as a function approximator. Taking into account the baseload present in the power grid, a central agent creates forward-looking, coordinated charging schedules for an electric vehicle fleet of any size. In contrast to optimization-based charging strategies, system dynamics such as future arrivals, departures, and energy consumption do not have to be known beforehand. We implement and compare a range of parameter variants that differ in terms of the reward function and prioritized experience. Subsequently, we use a case study to compare our Reinforcement Learning algorithm with several other charging strategies. The Reinforcement Learning-based charging coordination system is shown to perform very well. All electric vehicles have enough energy for their next trip on departure and charging is carried out almost exclusively during the load valleys at night. Compared with an uncontrolled charging strategy, the Reinforcement Learning algorithm reduces the variance of the total load by 65%. The performance of our Reinforcement Learning concept comes close to that of an optimization-based charging strategy. However, an optimization algorithm needs to know certain information beforehand, such as the vehicle’s departure time and its energy requirement on arriving at the charging station. Our novel Reinforcement Learning-based charging coordination system therefore offers a flexible, easily adaptable, and scalable approach for an electric vehicle fleet under realistic operating conditions.

Performance Analysis of Lithium-Ion Battery Based on State of Charge and State of Health for EV Applications

The focus of this article is to make a comparison between temperature-regulated and uncontrolled charging methodologies in an attempt to decide which of these is appropriate for enhancing battery performance while significantly reducing charging time. Temperature-controlled pulse charging (TRPC) and constant current (CC) charging techniques have been evaluated. The temperature of the battery increases in proportion to the magnitude of the current injected during CC charging and cannot be governed without external cooling. The performance measures are the effect on the State of Health (SOH) as well as the expected lifespan of the battery. The computation employs 12.8 V, 40 Ah Lithium-Ion (LiFePO4) batteries to demonstrate that increasing the temperature relation of control leads to greater charge submission at the same charging rate.

Simultaneous Distributed Generation and Electric Vehicles Hosting Capacity Assessment in Electric Distribution Systems

This work presents a strategy, from the perspective of the distribution system operator (DSO), that aims at simultaneously estimating the maximum penetration levels of renewable-based distributed generation (DG) and electric vehicles (EVs) that can be accommodated into an electric distribution system (EDS). To estimate such capacity, operational resources such as generation curtailment and controllable features of EVs can be managed to ensure the safe operation of the EDS and avoid infeasible operational conditions. Through a multi-period representation, the proposed strategy models the variability in demand consumption and DG power production. In addition, driving patterns of EV owners and energy requirements of EVs, obtained through probability density functions, are incorporated in this representation. Inherently, the problem is represented as an optimization model, and to determine its solution, an algorithm based on the metaheuristics greedy randomized adaptive search and tabu search (GRASP-TS) is developed. The applicability of the planning strategy is assessed on a 33-bus EDS under different test conditions and the numerical results show that higher penetrations of EVs and renewable-based DG can be accommodated without impacting the safe operation of the EDS. The results also demonstrate that by controlling the power draw by EV aggregators, an increase of 9% can be obtained in the DG installed capacity compared to the case of uncontrolled charging of EVs. In addition, the scalability of the proposed approach is studied using two distribution systems, the 83-bus system and the 135-bus system, where the results show that the convergence of the algorithm is achieved in a few iterations.

НЕСТАЦИОНАРНОЕ ПОВЕДЕНИЕ ЗАРЯДА ТТ БЕССОПЛОВОГО РДТТ ПОД ДЕЙСТВИЕМ ГАЗОДИНАМИЧЕСКОЙ НАГРУЗКИ

The numerical solution to a conjugate problem of an unsteady flow of combustion products in a flow path of the nozzleless solid rocket motor (SRM) and the oscillation of a solid propellant charge under the action of the forces directed from combustion products is considered. The Navier-Stokes equations for a compressible viscous gas are used to mathematically describe the flow of the combustion products. To model the charge oscillations, the equations of solid mechanics are applied, which take into account the propellant hyperelasticity. Pressure distributions and the propellant burning rate along the charge channel are presented for different models of the propellant burning rate. It is revealed that at the stage of SRM design, the use of the burning rate law, determined by pressure in the head of the combustion chamber, is more preferable in order to assess the internal ballistic characteristics. The solution to the conjugate problem shows that in the nozzleless SRM with the propellant having low Young's modulus, resonance can occur, which causes uncontrolled charge oscillations.

Renewable Energy Tracking and Optimization in a Hybrid Electric Vehicle Charging Station

The increasing electric vehicle fleet requires an upgrade and expansion of the available charging infrastructure. The uncontrolled charging cycles greatly affect the electric grid, and for this reason, renewable energy sources and battery storage are getting incorporated into a hybrid charging station solution. Adding a renewable source and a battery to the charging station can help to “buffer” the power required from the grid, thus avoiding peaks and related grid constraints. To date, the origin of the energy coming from the battery has not been traced. In this paper, a solution of the hybrid electric vehicle charging station coupled with the small-scale photovoltaic system and battery energy storage is proposed to eliminate the adverse effects of uncontrolled electric vehicle charging, with the exact calculation of renewable energy share coming from energy stored in the battery. The methodology for the multicriteria optimization of the charging/discharging schedule of a battery and electric vehicle charging level is based on multi-attribute utility theory. The optimization criteria include the minimization of charging costs, maximization of renewable energy (from both the solar plant and the battery), and the minimization of battery degradation. The problem is solved using a genetic algorithm optimization procedure adapted to the multicriteria optimization function. The methodology is tested on an illustrative example, and it is proven that the decision-maker’s preferences greatly affects the choice of the optimal strategy and the optimal battery capacity.

A social-economic-technical framework for reinforcing the automated distribution systems considering optimal switching and plug-in hybrid electric vehicles

This paper proposes a hybrid efficient stochastic framework for optimal operation of automated distribution grids considering the social, economic and technical priorities. To this end, optimal feeder reconfiguration is regarded as a key solution for altering the network topology and improving the above targets. Considering the wide popularity and high penetration of the plug-in hybrid electric vehicles in the future grids, three different charging schemes including controlled charging, uncontrolled charging and smart charging are introduced and compared with each other. In addition, different types of non-dispatchable renewable power sources including wind turbines and solar panels are considered to let the analysis be more practical. In order to capture the uncertainties existing in the nature of the problem, a hybrid data driven approach based on machine learning and unscented transform is proposed. The machine learning based approach, i.e. support vector machine, will help to estimate the standard deviation of the uncertain parameters. In order to make a global search in the multi-objective space, a new optimization method based on flower pollination algorithm and a new three-stage modification method are introduced to solve the problem. The quality and capability of the proposed framework are examined on an IEEE test system.

Energy cost minimization in an electric vehicle solar charging station via dynamic programming

Environmental crisis and shortage of fossil fuels make Electric Vehicles (EVs) alternatives for conventional vehicles. With growing numbers of EVs, the coordinated charging is necessary to prevent problems such as large peaks and power losses for grid and to minimize charging costs of EVs for EV owners. Therefore, this paper proposes an optimal charging schedule based on Dynamic Programming (DP) to minimize the overall cost of charging EVs for consumers in a solar Charging Station (CS). The large state space that makes the use of general DP inefficient is handled by using modified DP. Also, due to the stochastic behavior of the PV production, four different cases accounting for four different weather conditions are considered. Simulations are done for each weather condition and potential cost savings for customers and benefits for the grid are investigated in comparison to uncontrolled charging in each case. Simulation results demonstrated a significant decrease in the total CS purchased power cost, indicating reduced costs for consumers. Also, the optimal charging schedule shifts the charging sequence of EVs from high demand hours to low demand hours, to keep a smooth load shape for the distribution grid.

Mechanical removal of surface residues on graphene for TEM characterizations

Contamination on two-dimensional (2D) crystal surfaces poses serious limitations on fundamental studies and applications of 2D crystals. Surface residues induce uncontrolled doping and charge carrier scattering in 2D crystals, and trapped residues in mechanically assembled 2D vertical heterostructures often hinder coupling between stacked layers. Developing a process that can reduce the surface residues on 2D crystals is important. In this study, we explored the use of atomic force microscopy (AFM) to remove surface residues from 2D crystals. Using various transmission electron microscopy (TEM) investigations, we confirmed that surface residues on graphene samples can be effectively removed via contact-mode AFM scanning. The mechanical cleaning process dramatically increases the residue-free areas, where high-resolution imaging of graphene layers can be obtained. We believe that our mechanical cleaning process can be utilized to prepare high-quality 2D crystal samples with minimum surface residues.

Efficient Load Management for BEV Charging Infrastructure in Multi-Apartment Buildings

Interest in and demand for battery electric vehicles (BEVs) is growing strongly due to the increasing awareness of climate change and specific decarbonization goals. One of the largest challenges remains the provision of large-scale, efficient charging infrastructure in multi-apartment buildings. Successful load management (LM) for BEV charging directly influences the technical requirements and the economic and environmental aspects of charging infrastructure and can prevent costly distribution grid expansion. The main objective of this paper is to evaluate potential LM approaches in multi-apartment buildings to avoid an increase in existing electricity demand peaks with BEV diffusion. Using our model parameters, off-peak charging achieved a 40% reduction in the building’s demand peak at 100% BEV diffusion compared to uncontrolled charging and reduced the correlation between BEV charging and the national share of thermal power generation. The most efficient charging capacity in the private network was achieved at 0.44 kW/BEV. A verification of the model results with the demonstration phase of the “Urcharge” project supports our overall findings. Our results outline the advantages of LM across a large-scale BEV charging network to control the impact on the electricity system along with the diffusion of e-mobility.

Optimised controlled charging of electric vehicles under peak power‐based electricity pricing

This study presents a practical control method for electric vehicle (EV) charging optimisation for detached and attached houses. The developed EV charging control method utilises real-time measurements to minimise charging costs of up to two EVs in a single household. Since some Finnish distribution system operators have already launched peak power-based distribution tariffs for small-scale customers and because there is a lot of discussion on this kind of tariff development, the control method considers peak power-based charges. Additionally, the proposed smart charging control method utilises charging current measurements as feedback to reallocate unused charging capacity if an EV does not utilise the whole capacity allocated for it. The control method is implemented and tested with commercial EVs. The conducted hardware-in-the-loop simulations and measurements confirm that the control method works as intended. The proposed smart charging control reduces EV charging electricity distribution costs around 60% when compared to the uncontrolled EV charging.

Optimal EV Charging Scheduling by Considering the Limited Number of Chargers

Lack of charging facilities is still a significant barrier to the electrification of the logistic system. How to effectively schedule the electric vehicle (EV) charging power to reduce the charging station operating cost when the number of chargers was limited becomes an important issue. To tackle this issue, an optimal charging scheduling method by responding to the time-of-used (TOU) electricity price is proposed. First, the uncontrolled charging model to fully charge EVs as fast as possible is established. Then, an optimal charging scheduling model by considering the limited number of chargers is proposed to both reduce the charging cost and guarantee the charging demand of each EVs. The proposed model is formulated as a bilevel programming (BP) model. The charger index and available charging duration for each EV are determined at the upper level, while the charging power of each EV at each time slot is determined at the lower level. After that, a solving approach is introduced for the proposed BP model. The efficacy and performance of the proposed charging scheduling method are verified by simulation results.

Hybrid Charging System Management With Automatic Switching Method

Abstract— Hybrid energy generation is a renewable energy power plant with more efficiency and advantages than a stand-alone generator. Using 2 generators for battry storage can speed up the charging of energy to the battery but in the battery charging system, there is still wasted energy and an uncontrolled Charging and Discharging system on the battery resulting in a short battery life. Therefore we need a tool to control the Charging and Discharging performance of the battery in order to optimize battery charging thereby extending battery life.Automatic Switching System is an innovation of Charging and Discharging activity management so that it does not occur simultaneously on the battery, this process is carried out by a microcontroller. The use of 2 DC power sources as a charging source helps speed up battery charging for 30 minutes faster than 1 source. Through several modes provided, this tool can select sources and batteries that can be used as a condition for Charging and Discharging.

Agent-based bidirectional charging algorithms for battery electric vehicles in renewable energy communities

This paper introduces a framework for agent based autonomous charging and discharging of Battery Electric Vehicle (BEV) at local energy communities. Agents are programmed to control the bidirectional charging according to green energy utilisation incentives, based on load and generation forecasts. The optimization is achieved within a group of independent prosumers following a fully distributed approach using multiple self-organising agents. No central instance is needed for communication, billing or decision making. To demonstrate the ecological benefits of the system, simulations for a car pool, with a Photovoltaic (PV) plant, in a residential neighborhood were performed. The simulations resulted in an increased community PV self-consumption value of 48% compared to 29% in case of uncontrolled charging processes.

Load Management Strategies to Increase Electric Vehicle Penetration—Case Study on a Local Distribution Network in Stockholm

As part of decarbonization efforts, countries are adapting their energy policies accordingly. Sweden has established ambitious energy goals, which include CO2 emissions reduction in the transport sector and high integration of renewables in the electricity sector. Coupling the two can be an enabling force towards fossil freedom. An increased share of electric vehicles is therefore a promising solution in this regard. However, there are challenges concerning the impact that a surge of electric vehicles would have on the electric infrastructure. Moreover, in Stockholm there is a shortage of power capacity due to limitations in the national transmission infrastructure, which further aggravates the situation. This paper develops a scenario-based simulation study to evaluate the impact of electric vehicle loads on the distribution grid of a Stockholm neighborhood. In this process, limiting factors and bottlenecks in the network were identified as being related to the peak power and transformer capacities for the years of 2025 and 2031. Two load management strategies and their potential to mitigate the power peaks generated from uncontrolled charging were investigated for the critical years.

Review of Positive and Negative Impacts of Electric Vehicles Charging on Electric Power Systems

There is a continuous and fast increase in electric vehicles (EVs) adoption in many countries due to the reduction of EVs prices, governments’ incentives and subsidies on EVs, the need for energy independence, and environmental issues. It is expected that EVs will dominate the private cars market in the coming years. These EVs charge their batteries from the power grid and may cause severe effects if not managed properly. On the other hand, they can provide many benefits to the power grid and get revenues for EV owners if managed properly. The main contribution of the article is to provide a review of potential negative impacts of EVs charging on electric power systems mainly due to uncontrolled charging and how through controlled charging and discharging those impacts can be reduced and become even positive impacts. The impacts of uncontrolled EVs charging on the increase of peak demand, voltage deviation from the acceptable limits, phase unbalance due to the single-phase chargers, harmonics distortion, overloading of the power system equipment, and increase of power losses are presented. Furthermore, a review of the positive impacts of controlled EVs charging and discharging, and the electrical services that it can provide like frequency regulation, voltage regulation and reactive power compensation, congestion management, and improving power quality are presented. Moreover, a few promising research topics that need more investigation in future research are briefly discussed. Furthermore, the concepts and general background of EVs, EVs market, EV charging technology, the charging methods are presented.

Leveraging owners’ flexibility in smart charge/discharge scheduling of electric vehicles to support renewable energy integration

High integration of intermittent renewable energy sources (RES), predominantly wind power, has created complexities in power system operations. In addition, large fleets of Electric Vehicles (EVs) increase the uncertainty on electricity consumption significantly. Thus, an uncoordinated charging process will further complicate the grid scheduling. In this work, we propose a coordinated scheduling algorithm for charge/discharge of aggregated EV fleets to maximize the integration of wind generation and minimize the charging cost for EV owners in a vehicle-to-grid (V2G) setup. Challenges for people participation in V2G, such as battery degradation and insecurity about unexpected events, are also addressed.The real-time charge/discharge scheduling problem is formulated as a multi-objective mixed-integer quadratic programming problem that the aggregator takes advantage of real-time communication in smart grid to frequently update the EV schedule with updated real-time information about EV availabilities, wind generation forecast, and electricity price. Simulations demonstrate significant increase in wind utilization and reduction in charging cost and battery degradation compared to the uncontrolled charging scenario.

Probabilistic assessment of voltage quality on solar-powered electric vehicle charging station

This paper evaluates the potential benefits the connection of photovoltaic generation can bring to a commercial building with electric vehicles charging stations considering voltage quality issues. Monte Carlo simulation is applied to model system uncertainties, and uncontrolled charging Level 2 is considered. Simulations are performed in the modified IEEE 37 node test feeder using OpenDSS software, using real load data and meteorological measurements, considering the seasonality effect of photovoltaic generation. Different scenarios are analyzed, varying the size of photovoltaic generation and electric vehicles connection time. The obtained results show voltage unbalance is not a major issue when charging at Level 2. Also, the use of photovoltaic generation can potentially reduce the probability of occurrence of undervoltage problems, transformer overload and losses, according to the penetration level of photovoltaic generation and daily electric vehicles charging demand.

A Fuzzy Energy Management Strategy for the Coordination of Electric Vehicle Charging in Low Voltage Distribution Grids

Electric vehicles (EVs) have become widespread during the last decade because of the distinct advantages they offer compared to the conventional ones. However, the increased penetration of EVs in the global transportation market has led increased electricity demands, which is expected to affect the operation of energy distribution systems. In the present paper, a demonstration about the effects of uncontrolled EVs charging in a case study low voltage (LV) network is demonstrated and a fuzzy energy management strategy for the coordination of EV charging in LV networks is presented, by including the distance of the EVs from the transformers in the fuzzy management systems for the first time. The Institute of Electrical and Electronics Engineers (IEEE) European Test Feeder is used as a case study low voltage distribution grid. In particular, the developed system configuration takes into consideration the architecture of the grid, the ampacities of the lines and the voltages at the system’s buses. Moreover, electric vehicles are considered as agent-based models, which are characterized by the model of each EV, the state-of-charge of their batteries and the charging power. In particular, an investigation into the effects of uncontrolled charging is performed, in which two approaches are examined. The first approach investigates the maximum number of chargeable EVs in the case study network and how it is influenced by the grid’s household loads. The second approach examines the number of network undervoltages and lines ampacity violations in a set of simulation scenarios. The results of the first approach show that the distance of the EVs from the networks substation affects the maximum number of chargeable EVs in a significant manner. Based on the observed results of the two approaches, a fuzzy management system is designed for the coordination of EV changing, which takes into account the distance from the EV charging points to the feeder substation, the state-of-charge of the EVs’ batteries and the EVs’ charging delay time.

Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits

With high electric vehicle (EV) adoption, optimization of the charging process of EVs is becoming increasingly important. Although the CO2 emission impact of EVs is heavily dependent on the generation mix at the moment of charging, emission minimization of EV charging receives limited attention. Generally, studies neglect the fact that cost and emission savings potential for EV charging can be constrained by the capacity limits of the low-voltage (LV) grid. Grid reinforcements provide EVs more freedom in minimizing charging costs and/or emissions, but also result in additional costs and emissions due to reinforcement of the grid. The first aim of this study is to present the trade-off between cost and emission minimization of EV charging. Second, to compare the costs and emissions of grid reinforcements with the potential cost and emission benefits of EV charging with grid reinforcements. This study proposes a method for multi-objective optimization of EV charging costs and/or emissions at low computational costs by aggregating individual EV batteries characteristics in a single EV charging model, considering vehicle-to-grid (V2G), EV battery degradation and the transformer capacity. The proposed method is applied to a case study grid in Utrecht, the Netherlands, using highly-detailed EV charging transaction data as input. The results of the analysis indicate that even when considering the current transformer capacity, cost savings up to 32.4% compared to uncontrolled EV charging are possible when using V2G. Emission minimization can reduce emissions by 23.6% while simultaneously reducing EV charging costs by 13.2%. This study also shows that in most cases, the extra cost or emission benefits of EV charging under a higher transformer capacity limit do not outweigh the cost and emissions for upgrading that transformer.

Controlled workplace charging of electric vehicles: The impact of rate schedules on transformer aging

To accelerate adoption of non-residential charging for electric vehicles, sites must maximize utilization of existing electrical infrastructure. In this study we model electric vehicle charging at a workplace using real charging data and evaluate the lifetime of the site’s transformer as the number of charging stations is incrementally increased. We implement and compare a range of control schemes for workplace charging including minimizing the peak load, capping the total load, minimizing bills under different rate structures with time-of-use energy costs and demand charges, and directly minimizing the transformer’s aging. These are compared by the number of vehicles they allow the transformer to support, the transformer’s health, and the operator’s electricity bill. We draw a connection between minimizing the peak load and improving the transformer’s health. We observe that minimizing the electricity bill is the best scheme by both criteria when the bill includes a demand charge; in our experiment it allowed the infrastructure to support over 67% more cars than under uncontrolled charging. To protect the transformer we recommend that demand charges or capacity management be applied to parking lots of charging electric vehicles with high infrastructure utilization, and operators schedule charging to minimize their electricity bills.