Fast Charging Load Research Articles

A Stochastic Methodology for EV Fast-Charging Load Curve Estimation Considering the Highway Traffic and User Behavior

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations’ load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations’ operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs.

Reconfigurable Power Circuits to Series or Parallel for Energy-Balanced Multicell Battery Pack

Multicell battery pack has the cells connected in series and parallel for fast charging and heavy load with low conduction loss. Thus, cell balancing control is required to maximize the utilization of the battery pack. The previous studies on cell balancing have used dedicated cell balancing circuits, including magnetic components and multiple capacitors. Instead of using additional passive components, this article proposes reconfigurable power circuits that only consist of power switches. With the merits of being reconfigurable into series or parallel in a multicell battery pack, the proposed circuits perform active cell balancing with a load capacitor and a load current for low cost and high system density. These features are essential for low-power applications with multiple cells, such as drones, wireless speakers, electronic point-of-sale, and mobile devices, which require compact system size. The operational principle, balancing features, and loss models of the proposed circuits are shown with a comprehensive analysis. A prototype board has been built to evaluate the performance of the proposed circuits with the experimental results. It is shown that the proposed circuits have a peak efficiency of 95.3% with 12 V, 1 A charging and 3.7 V, 0.4 A load.

A robust optimal dispatching strategy of distribution networks considering fast charging stations integrated with photovoltaic and energy storage

With the increasing number of electric vehicles, a large number of charging loads connected to the power system will have an impact on the economic and safe operation of the power system. In this paper a day-ahead optimal dispatching method for distribution network (DN) with fast charging station (FCS) integrated with photovoltaic (PV) and energy storage (ES) is proposed to deal with the negative impact of FCS on DN. By adjusting the load distribution of DN through the optimization decision of ES and soft open points (SOP), the operation level of DN is improved. Firstly, based on the historical vehicle travel data, Monte Carlo simulation method (MCSM) is applied to realize the simulation of fast charging load. Secondly, the uncertainties of PV power is addressed via a robust optimization model of the economic operation level of DN. Based on the second order cone relaxation and duality theory, a two-stage optimal dispatching model of DN is proposed. The optimization model is divided into main problem (MP) and sub problem (SP). For MP, the access position of FCS is adjusted based on SOP. And the charging and discharging power of ES is adjusted. The load distribution is optimized. For SP, based on the uncertainty of PV, the worst scenario of DN is calculated. The robustness of the proposed strategy is guaranteed. Finally, the proposed is verified based on the IEEE 33 bus system and a road network with 34 nodes. The simulation results show that the proposed method can effectively relieve line congestion of DN. The operating range of the voltage is better optimized. And the operation cost of DN is reduced significantly.

Optimal planning of photovoltaic-storage fast charging station considering electric vehicle charging demand response

The charging demand response of electric vehicle(EV) users will affect the social and economic benefits of fast charging services, so it is an important factor in EV charging station planning. In this paper, a photovoltaic-storage fast charging station(PSFCS) planning method considering charging demand response is proposed. Firstly, the EV fast charging behaviors and the factors influencing the admitting ability of PSFCS to the fast charging load are discussed, and the interaction characters between them are analyzed. Based on the above analysis, a three-stage dual-objective optimization model of PSFCS planning considering charging demand response is proposed and solved by the elitist non-dominated sorting genetic algorithm(NSGA-II). Finally, the case study is formulated based on the 21 power nodes-12 traffic nodes network, simulation results show that the proposed method can effectively improve the profit of charging service providers and reduce the total charging time of EVs.

Fast charging load guidance strategy based on adjustable charging service fee

Fast charging load guidance strategy based on adjustable charging service fee

Fast charging demand forecasting based on the intelligent sensing system of dynamic vehicle under EVs-traffic-distribution coupling

In order to promote the efficient utilization of clean energy, in this paper, a fast charging demand prediction model based on the intelligent sensing system of dynamic electric vehicles (EVs) under EVs-Traffic-Distribution coupling is proposed. The established model consists of two parts: electric vehicle dynamic driving model and fast charging demand prediction model based on state transition intelligent sensing. The dynamic driving model of electric vehicle reflects the impact of traffic conditions on driving speed and power consumption, as well as the impact of vehicle driving on the road network under various states such as driving, parking and charging. In the fast charging demand prediction model based on state transition intelligent sensing, the intelligent sensing system can recommend the optimal path to users according to real-time road conditions and vehicle driving conditions. Taking a planning area as an example, the Monte Carlo simulation method is used for fast charging load forecasting. The simulation results verify the correctness and effectiveness of the proposed model.

Research on Charging Load Characteristics of EVs Based on Actual Charging Power

With the expansion of the scale of electric vehicles (EVs) and the maturity of fast charging technology and the popularization of fast charging piles, the disorderly fast charging of EVs caused a significant damage on distribution network, the actual charging power of EVs is different from the rated power, the traditional method of using the rated power of the charging pile to forecast the charging load will cause a significant error and affect the dispatching operation of power grid. Based on the actual EV charging curve, this paper establishes a variable power fast charging load model for EVs. The Monte Carlo simulation method is used to simulate the charging load of EV groups. The fast charging loads of EVs with different vehicle types and different battery capacities are compared. The actual variable power fast charging mode of the electric vehicles is compared with the constant power fast charging mode and the slow charging mode, and the charging loads of the three charging modes are significantly different. Finally, the impact of charging load of electric vehicles of different scales on distribution network load is analyzed.

Construction Method of Electrical Vehicle Fast Charging Load Model

Electric vehicle (EV) has been increasing recent years, which brings more challenge to power grids, especially impacting on voltage stability. Building accurate EV model is important to evaluate voltage stability. Firstly, EV charging configuration is analyzed. Secondly, a static load model for EV is developed based on charging configuration. Finally, simulations analyze that Rs and Rl in the rectifier impact on voltage stability. The EV load model is developed and constant power, current and impedance (P, Z, I ) load model impacting on voltage stability are compared, and results reveal that loading margin with EV load model is more lower than other load models such as P, Z, I.

A Hierarchical Navigation Strategy of EV Fast Charging Based on Dynamic Scene

With the development of the EV industry, the growing demand for EV fast charging load has brought enormous impact on the power system. Due to the unbalance of charging time distribution and the high power of EV fast charging, the difference between peak and valley of the load curve will widen. In this paper, a hierarchical navigation strategy (HNS) based on dynamic traffic/temperature data is proposed to decrease the EV fast charging load at peak hours and the time and energy cost during the charging process. The upper layer of the HNS is charging time selection. The optimal selections of charging time, which is based on the habits of EV users, is proposed in this layer. It aims at providing efficient time slots for charging, which can decentralize the fast charging demand and decrease the EV users’ time cost. The underlayer is the route selection layer, which is based on the priority coding genetic algorithm. It proposes the optimal charging routes to decrease EV users’ energy cost and time cost. At the same time, the peak charging load can also be shaved due to the decline in energy cost. The case study under the scene with realistic traffic, temperature, and power grid information shows that the proposed HNS can shave the peak load of the power grid and decrease the energy/time cost during the EV fast charging process. Therefore, the effectiveness of the HNS is proved.

A charging pricing strategy of electric vehicle fast charging stations for the voltage control of electricity distribution networks

With the increasing number of electric vehicles (EVs), the EV fast charging load will significantly affect the voltage quality of electricity distribution networks. On the other hand, EVs have potentials to change the choices of charging locations due to the incentives from the variations of charging prices, which can be considered as a flexible response resource for electricity distribution networks. In this paper, a charging pricing strategy of EV fast charging stations (FCSs) was developed to determine the pricing scheme for the voltage control of electricity distribution networks, which consisted of a simulation model of EV mobility and a double-layer optimization model. Considering the travel characteristics of users, the simulation model of EV mobility was developed to accurately determine the fast charging demand. Taking the total income of FCSs and the users’ response to the pricing scheme into account, the double-layer optimization model was developed to optimize the charging pricing scheme and minimize the total voltage magnitude deviation of distribution networks. A test case was used to verify the proposed strategy. The results show that the spatial distribution of EV fast charging loads was reallocated by the proposed charging pricing scheme. It can also be seen that the proposed strategy can make full use of the response capacity from EVs to improve the voltage profiles without decreasing the income of the FCSs.

An Elastic Charging Service Fee-Based Load Guiding Strategy for Fast Charging Stations

Compared with the traditional slow charging loads, random integration of large scale fast charging loads will exert more serious impacts on the security of power network operation. Besides, to maximize social benefits, effective scheduling strategies guiding fast charging behaviors should be formulated rather than simply increasing infrastructure construction investments on the power grid. This paper first analyzes the charging users’ various responses to an elastic charging service fee, and introduces the index of charging balance degree to a target region by considering the influence of fast charging loads on the power grid. Then, a multi-objective optimization model of the fast charging service fee is constructed, whose service fee can be further optimized by employing a fuzzy programming method. Therefore, both users’ satisfaction degree and the equilibrium of charging loads can be maintained simultaneously by reasonably guiding electric vehicles (EVs) to different fast charging stations. The simulation results demonstrate the effectiveness of the proposed dynamic charging service pricing and the corresponding fast charging load guidance strategy.

Optimization of Spot Power Price in Coordinated Fast Charging Model of Electric Vehicles

A real-time mechanism as a level of electricity price is proposed to dispatch electric vehicle fast charging load. Considering the peak load shifting effect of grid, the benefit of charging station and the desire of customer, the mathematics model is built. And a genetic algorithm is used to solve this problem. Finally, a simulation based on an areas predicted data of year 2020 is made to show that the proposed method can lower the peak-valley difference, promise charging station benefit as well as meet customers desire, and achieve a win-win result.