Electric Vehicle Cluster Research Articles

Electric Vehicle Cluster Scheduling Model for Distribution Systems Considering Reactive-Power Compensation of Charging Piles

With the increasing number of electric vehicles (EVs), their uncoordinated charging poses a great challenge to the safe operation of the power grid. In addition, traditional individual-EV scheduling models may be difficult to solve due to the increasing number of constraints. Therefore, this paper proposes a cluster-based EV scheduling model. Firstly, electric vehicle clusters (EVCs) are formed based on the charging and discharging preferences of EV users and the expected time for EVs to leave. Secondly, the EVC energy and power boundary aggregation method based on the Minkowski addition algorithm is proposed. Finally, for the sake of reducing user charging cost and distribution network energy loss, and smoothing the daily load curve, an EVC scheduling model for EV participation in grid auxiliary services is proposed. The optimization model includes the reactive-power compensation of EV charging piles. The simulation results show that the proposed EVC scheduling model can greatly reduce the solution time compared to traditional individual-EV scheduling model. The model has high potential to be applied to large-scale EV scheduling. The reactive-power compensation provided by EV charging piles improves the voltage quality of the grid and enables more EVs to be connected to the grid.

Optimization scheduling method for multiple flexibility resources considering electric vehicle cluster

To unleash the scheduling potential of flexible resources, an enhanced flexibility approach is proposed, which takes into account various flexible resources including electric vehicle (EV) clusters. Initially, the dispatch potential of individual EV users is predicted based on historical data regarding travel habits and charging station parameters. Subsequently, the power and energy-feasible regions are constructed to quantify the flexibility of the EV clusters. To enhance the overall system’s operational efficiency and flexibility, various constraints related to flexibility resources are taken into consideration, including the source-grid-load-storage aspects. As a result, a multidimensional flexibility resource supply-demand model is established and the analysis target cascading method is applied to solve this sophisticated model. Finally, the modified system is used to test and analyze the proposed model and method. The results demonstrate the proposed model and method can enhance the operational flexibility of high-proportion new energy distribution networks.

A semi-distributed charging strategy for electric vehicle clusters

Uncoordinated charging of electric vehicle (EV) clusters can lead to unanticipated surges in demand that strain the grid, hence reducing its reliability. To address this challenge, this paper proposes a semi-distributed control method for EV clusters charging where the decision-making process takes place locally, rather than relying on an external controller. The method ensures that charging demand can be fulfilled while achieving several goals, including following local renewable energy generation, pausing charging for transformer protection, and continuing charging progress in the absence of external information. The proposed method is validated through simulations and experimental tests during a public demonstration. The results demonstrate the functionality of the control method and show that it can effectively manage the charging of a cluster of EVs while reducing strain on the power grid.

Clustering-based optimal operation of charging stations under high penetration of electric vehicles

With the rapid growth of technologies related to electric vehicles (EVs), the market penetration of EVs has increased significantly in recent years and has resulted in an increase in the charging load demand for charging stations. In addition, the uncertainty in EVs’ schedules introduces various difficulties for optimal operation of charging stations. Operation of a charging station with consideration of individual EV behaviors may result in computational complexity and lead to infeasibility. Therefore, this study proposes an operational strategy to reduce the computational burden while maintaining EV schedules for charging stations under high penetration of EVs. The proposed method consists of two major stages, i.e., clustering of EVs and optimizing the operation of each cluster. In the first stage, EVs with similar attributes and behaviors are grouped into different clusters, where EVs in the same cluster share similar schedules and operational constraints. This significantly reduces the computational burden as well as the complexity of optimization modeling. In the second stage, instead of considering the behavior of hundreds of EVs, the optimal operation of each EV cluster is considered. In addition, the proposed method allows cooperative operation among EV clusters via vehicle-to-vehicle (V2V) services. The proposed cooperative operation strategy not only reduces the imported power from the utility grid during peak price intervals, but also significantly reduces the amount of energy storage required in the charging stations. This results in the minimization of the investment and operation costs of the entire system. Finally, the effectiveness of the proposed method is verified for an EV charging station with over 180 EVs.

A review of key technologies in relation to large-scale clusters of electric vehicles supporting a new power system

The construction of a new power system based on new energy sources can support the successful transition of energy consumption toward a low-carbon environment. Large-scale clusters of electric vehicles (EVs) are an important reserve measure supporting the flexibility of the new power system. To summarize the roles of EVs as a reserve measure in practical engineering applications, in this study we analyze three essential elements of EV clusters: control of dispatching, participant behavior, and information communication. First, the characteristics of clusters of EVs that participate in power system operations were analyzed in terms of their physical resources, aggregation and control structure, dispatching strategies, and market mechanisms. Second, the importance of social factors was emphasized, and the limitations of computational and experimental simulation methods used in decision-making behavior research were confirmed. Next, the evolution of hybrid simulation methods integrating human participants, multi-agents, and mathematical models was analyzed. Finally, given the conflicting challenges of maintaining both the security and the economy of the communication network, we discussed the problems and potential solutions relating to full-domain communication coverage, spectrum resource interference, communication security, and customized priority design enabling large-scale clusters of EVs to gain access to the power grid from the perspective of the fusion of the public communication network and the dedicated communication network. The results of our study provide technical and economic reference points and a feasibility guarantee that enable more distributed reserve resources for power systems in various countries and regions.

Secondary Frequency Regulation Control Strategy with Electric Vehicles Considering User Travel Uncertainty

The premise of electric vehicles (EVs) participating in the frequency regulation (FR) of power systems is to satisfy the charging demands of users. In view of problems such as the uncertainty of EV users’ departure time and the increase in power supply pressure due to disordered charging in the frequency regulation process of EV clusters, a secondary frequency regulation control strategy with EVs considering user travel uncertainty is proposed. Firstly, EV charging history was analyzed, a reliability parameter was introduced to describe the user travel uncertainty, and an individual EV controllable domain model based on reliability correction was constructed. Then, EV clusters were grouped according to charging urgency and state of charge (SOC), and the controllable capacity of EV clusters was determined. Finally, EV frequency regulation capability parameters and charging urgency parameters were defined to determine the EV frequency regulation priority list, combined with the EV state grouping and priority list, and the EV cluster frequency control strategy was proposed. The simulation results show that the proposed strategy can satisfy the charging demands of users under uncertain travel conditions, reduce the power supply pressure of the power system caused by EVs entering the forced charging state, and effectively suppress frequency deviation.

A Case of Interdisciplinary Fusion under Dual Carbon Goal: Coordinated Carbon Reduction with Greenhouse Photovoltaics and Electric Vehicles

Building a new type of power system is an important guarantee to support China’s “dual carbon” goal. Due to the inseparable relationship between industrial and agricultural production and electric energy utilization, there must be interdisciplinary integration to achieve the goal of “dual carbon”. The disciplines of horticulture and electric power are taken as examples in this paper to analyze the feasibility of carbon emission reduction through coordinating agricultural photovoltaic (PV) greenhouse and electric vehicle (EV) energy storage. Firstly, the mechanism of carbon emission difference caused by electric energy supplementing during EV charging is analyzed. Secondly, in the context of the contradiction between the reduction of battery life caused by discharging (increasing carbon emission) and the increase in PV output consumption by orderly charging and discharging (reducing carbon emission), an optimization model for the synergistic operation of EV clusters and greenhouse PVs (with the objective of minimizing carbon emissions) is proposed. Finally, the effectiveness of the proposed model is verified through simulation cases. The energy storage characteristics of EVs is capable of realizing the transfer of PV power generation in the time dimension, and the coordinated operation of greenhouse PVs and EVs’ charging and discharging can effectively reduce carbon emission during the EV operation period. In a typical summer scenario of PV output, the carbon emission of EVs in V2G (vehicle to grid) mode was reduced by 69.13% compared to disorderly charging. It is shown that the adequacy of PV generation and the orderly dispatching of the charging and discharging of EVs are the key factors in reducing carbon emission throughout the life cycle of EVs.

Optimal Scheduling Strategy of Electric Vehicle Cluster Based on Index Evaluation System

Aiming at the randomness of current electric vehicle (EV) users’ participation in the vehicle-to-grid (V2G) scheduling, this paper establishes an electric vehicle indicators evaluation model and proposes an optimal scheduling strategy to achieve optimal EV cluster scheduling. By analyzing the influence of various information declared by electric vehicle users on the dispatching plan made by aggregators, and by taking the declared dispatching power, users’ credit, battery loss and users’ participation as evaluation indicators, the evaluation index model of electric vehicle cluster is established. The weight of each index is determined based on the optimal combination weight method of accelerated genetic algorithm. At the same time, this paper compares and analyzes various scenarios under different weights to obtain the scheduling order of each electric vehicle in the cluster. Combined with the requirements of power system scheduling plan, this paper further determines the schedulable ability of each aggregator in each period. The example simulation shows that the comprehensive weights adopted in this paper not only can exclude the fleets with poor credit, but can also eliminate the fleets with low declared power. The proposed strategy can reasonably and effectively dispatch electric vehicle cluster to participate in the scheduling plan by comprehensively considering the influence of multiple indicators.

Using an Intelligent Control Method for Electric Vehicle Charging in Microgrids

Recently, electric vehicles (EVs) that use energy storage have attracted much attention due to their many advantages, such as environmental compatibility and lower operating costs compared to conventional vehicles (which use fossil fuels). In a microgrid, an EV that works through the energy stored in its battery can be used as a load or energy source; therefore, the optimal utilization of EV clusters in power systems has been intensively studied. This paper aims to present an application of an intelligent control method to a bidirectional DC fast charging station with a new control structure to solve the problems of voltage drops and rises. In this switching strategy, the power converter is modeled as a DC fast charging station, which controls the fast charging of vehicles with a new constant current or reduced constant current method and considers the microgrid voltage stability. The proposed method is not complicated because simple direct voltage control realizes the reactive power compensation, which can provide sufficient injected reactive power to the network. As a result, the test is presented on a fast charging system of electrical outlets with a proposed two-way reactive power compensation control strategy, in which AC/DC converters are used to exchange two-way reactive power to maintain the DC link voltage as well as the network bus voltage in the range of the basis. This charging strategy is carried out through the simulation of fast charge control, DC link voltage control, and reactive power compensation control to adjust the voltage and modify the power factor in the MATLAB software environment and is then verified. Finally, the results indicate that the proposed method can charge with high safety without increasing the battery’s maximum voltage. It can also significantly reduce the charging time compared to the common CV mode.

A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm

A wind power curtailment consumption strategy using electric vehicles (EVs) based on individual differential evolution quantum particle swarm optimization algorithm (IDE-QPSO) is proposed, with the objective of reducing the system’s wind curtailment in order to further improve the wind power consumption rate while effectively reducing wind power output fluctuation and amplitude. EV aggregators act as charging tariff setters, releasing dynamic time-of-use tariffs (DTOU) for EV clusters to respond to based on wind curtailment data accounted for by the dispatch center. This method first establishes an electric vehicle charging load model based on the travel chain theory and residents’ travel rules, then establishes an EV users autonomous response model based on the sensitivity of electric vehicle users to the charging prices. Second, a multi-objective optimization function is established based on the aforementioned model, which integrates wind power curtailment consumption and minimizes wind power output fluctuation and amplitude, and it is solved using an improved quantum particle swarm optimization algorithm. Finally, adequate simulation experiments show that this strategy can effectively smooth out the fluctuation of wind power output and improve the wind power consumption rate.

A charging plan generation method for EV clusters considering the operation of regional distribution network

The continuous development of large-scale electric vehicles (EVs) has provided abundant dispatchable resources for the power grid. However, the existing control methods are not enough to support the effective interaction of the EVs and the grid. This paper proposes a method for generating a charging plan of EV clusters considering the stable operation of the regional distribution network, which fully considers the load regulation requirements of the grid and the charging economy of the EV clusters. Combined with the improved Particle Swarm Optimization based on Genetic Algorithm, a day-ahead charging plan for multiple EV clusters can be figured out. It can be proved by the case analysis that, compared with the disordered charging behavior and the participation of demand response with peak and valley electricity price, the charging plan generated by the method proposed can reduce the comprehensive cost of EV charging while optimizing the operation parameters of the distribution network, and it can reduce the calculation dimension in the process of plan generation.

Online Charging Strategy for Electric Vehicle Clusters Based on Multi-Agent Reinforcement Learning and Long–Short Memory Networks

The electric vehicle (EV) cluster charging strategy is a key factor affecting the grid load shifting in vehicle-to-grid (V2G) mode. The conflict between variable tariffs and electric-powered energy demand at different times of the day directly affects the charging cost, and in the worst case, can even lead to the collapse of the whole grid. In this paper, we propose a multi-agent reinforcement learning and long-short memory network (LSTM)-based online charging strategy for community home EV clusters to solve the grid load problem and minimize the charging cost while ensuring benign EV cluster charging loads. In this paper, the accurate prediction of grid prices is achieved through LSTM networks, and the optimal charging strategy is derived from the MADDPG multi-agent reinforcement learning algorithm. The simulation results show that, compared with the DNQ algorithm, the EV cluster online charging strategy algorithm can effectively reduce the overall charging cost by about 5.8% by dynamically adjusting the charging power at each time period while maintaining the grid load balance.

Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility

Aggregated electric vehicles (EVs) integrated to the grid and intermittent wind and solar energy increased the complexity of the economic dispatch of the power grid. Aggregated EVs have a great potential to reduce system operating costs because of their dual attributes of load and energy storage. In this paper, plugged-in EV is refined into three categories: rated power charging, adjustable charging, and flexible charging–discharging, and then control models are established separately; the concept of temporal flexibility for EV clusters is proposed for the adjustable charging and flexible charging–discharging of EV sets; then, the schedule boundary of EV clusters is determined under the flexibility constraints. The interval is used to describe the intermittent nature of renewable energy, and the minimum operating cost of the system is taken as the goal to construct a distributed energy robust optimization model. By decoupling the model, a two-stage efficient solution is achieved. An example analysis verifies the effectiveness and superiority of the proposed strategy. The proposed strategy can minimize the total cost while meeting the demand difference of EV users.

Feasible Dispatch Limits of PV Generation With Uncertain Interconnection of EVs in the Unbalanced Distribution Network

This paper presents a framework to determine the feasible dispatch limits of solar photovoltaic (PV) generation in the unbalanced distribution network considering the interconnection of electric vehicles (EVs) and associated uncertainties. The proposed framework determines the lower and upper dispatch limits of PV generation considering the worst-case realization of a) the minimum and maximum storage capacity of EVs, b) the minimum and maximum power dispatch of EVs, c) the lower and upper bounds for the arrival and departure times, and d) the available energy at arrival and departure times. The unbalanced operation of the distribution network as well as the uncertainty in the maximum PV generation and demand forecasts were considered. The problem formulation and solution approach are validated using the modified IEEE 34-bus and IEEE 123-bus distribution systems. The impacts of the budget of uncertainty and the vehicle-to-grid operation mode of EV clusters were addressed in the case studies. It is shown that integrating EVs with charging capability will increase the lower dispatch limit or increase the upper dispatch limit of the PV generation. Moreover, the increase in the budget of uncertainty will reduce the difference between the upper and lower dispatch limits by increasing the lower dispatch or decreasing the upper dispatch limit of the PV generation. Finally, it is shown that the vehicle-to-grid capability will reduce the total lower dispatch limit or increase the total upper dispatch limit of the PV generation in the operation horizon.

Real-time joint regulating reserve deployment of electric vehicles and coal-fired generators considering EV battery degradation using scalable approximate dynamic programming

This paper proposes a real-time joint regulating reserve deployment (RRD) model of electric vehicles (EVs) and coal-fired generators (CGs) considering EV battery degradation. The superiority of the proposed model lies in the integration of the EVs’ fast regulation speed and CGs’ large regulation capacity. The whole optimization model is formulated under a two-stage Markov Decision Process (MDP) to correspond to the real-time RRD process. The user behavior characteristic and EV battery degradation are taken into account to stimulate the users to participate in RRD. Based on the formulated MDP, a piece-wise linear (PWL) function based scalable approximate dynamic programming (ADP) algorithm for different EV clusters is constructed to solve the real-time RRD model under uncertainties. A separable slope update method is proposed to update the slopes of PLF for different EV clusters with different user behaviors. The proposed ADP based real-time RRD algorithm (ADP-RTRRD) provides the approximate optimal real-time RRD policy with the empirical knowledge embedded through off-line training. Numerical simulation on a modified IEEE-39 bus system and Henan power grid in China verify the superiority of the proposed joint RRD model and the advantages of the proposed ADP-RTRRD algorithm.

Electric vehicle clusters scheduling strategy considering real-time electricity prices based on deep reinforcement learning

With the increasing penetration of electric vehicles (EVs), the orderly charging–discharging (C–D) strategy of EVs can effectively alleviate the impact of large-scale grid-connected EVs. However, the schedule of the C–D system of EV clusters encounters the curse of dimensionality problem. In addition, the performance of the C–D control strategy faces great challenges of environmental uncertainty of user demand and electricity price. This paper proposes an EV cluster scheduling strategy considering real-time electricity prices based on deep reinforcement learning. Firstly, we establish a distributed real-time optimal scheduling structure according to the real-time price signals of distribution system operators (DSO). Furthermore, to alleviate the curse of dimensionality, we propose a C–D model of a single EV according to the C–D characteristics of EV, and we establish the C–D control model of EVs as a Markov decision process (MDP). Finally, to adapt to the uncertainty of the learning environment, we propose a model-based deep reinforcement learning to optimize the C–D behavior of EVs. After day-ahead training and parameter saving of the proposed model, the C–D scheduling strategy is generated for the real-time system state at each moment of the day. The simulation results of the C–D scheduling strategy for cost-oriented EV charging show that the proposed scheduling strategy effectively reduces the user charging cost by 133.7 dollars and the load peak–valley difference, stabilizes the load fluctuation, and achieves the win–win situation between the power grid and EV users.

V2G Multi-Objective Dispatching Optimization Strategy Based on User Behavior Model

V2G (Vehicle to Grid) technology can adjust the grid load through the unified control of the charging and discharging of electric vehicles (EVs), and achieve peak shaving and valley filling to smooth load fluctuations. Aiming at the random and uncertain problem of EV users travel and behavior decision-making, this paper proposes a V2G multi-objective dispatching strategy based on user behavior. First, a V2G behavior model was established based on user behavior questionnaire surveys, and the effective effect of EV load was simulated through Monte Carlo simulation. Then, combined with the regional daily load curve and peak-valley time-of-use electricity prices, with the goal of stabilizing grid load fluctuations and increasing the benefits of EV users, a multi-objective optimal dispatching model for EV clusters charging and discharging is established. Finally, Considering the needs of EV users and the operation constraints of the microgrid, the genetic algorithm is used to obtain the Pareto optimal solution. The results show that when dispatching with the maximum benefit of users, the peak-to-valley ratio of the grid side can be reduced by 2.99%, and the variance can be reduced by 9.52%. The optimization strategy can use peak and valley time-of-use electricity prices to guide the intelligent charging and discharging of EVs while meeting user needs, so as to achieve the optimal multi-objective benefit of V2G participation in power response.

Multi-Objective Coordinated Optimal Allocation of DG and EVCSs Based on the V2G Mode

With the vigorous promotion of new energy sources and the development of vehicle-to-grid (V2G) technology, the influence of the V2G mode should be considered in the joint optimal allocation of Distributed Generation (DG) and electric vehicle charging stations (EVCSs). The timing characteristics of the intermittent output of DG, conventional demand for load, and charging load of the electric vehicle (EV) are considered, as is its participation in grid interaction to examine the construction of typical scenarios and the EV cluster dispatching strategy. From the perspective of comprehensively planning the coordination of the distribution network, a DG-EVCSs bi-level joint planning model is established under the peak and valley price mechanism, with the sub-objectives of obtaining a comprehensive profit and high quality of voltage, curbing system load fluctuations, and satisfactorily charging the EV. An improved harmony particle swarm optimization algorithm is proposed to solve the bi-level model. The proposed method was tested on the IEEE-33 and the PG&E-69 (Pacific Gas and Electric Company) bus distribution systems, and the results show that the optimized configuration model that considers the V2G mode can improve the overall performance of the planning scheme, promote the use of clean energy, smoothen the load fluctuations of the system, and improve the quality of voltage and charging satisfaction of EV users.

A time-of-use pricing strategy for managing electric vehicle clusters

Abstract A demand response program for electric vehicles (EV) is proposed to control the charging decision process in EV clusters. This approach corresponds to a time-of-use solution which is an indirect method, based on prices, for inducing demand modifications on consumers. An aggregator of EV fleet acts as a dealer between an electricity market and consumers. The EV aggregator is a price-taker agent from the wholesale electricity market viewpoint and price designer when selling energy to consumers. A game-theoretical model based on a Stackelberg formulation is proposed to capture the interactions between the fleet operator and electric vehicle owners, avoiding the requirement of a price elasticity model for the EV clusters. The interaction between the agents is formulated as a bi-level optimization problem: At the upper-level, the aggregator maximizes its benefits whereas the lower-level represents the dynamic behaviour of rational drivers as a fleet. The EV operator faces uncertainty in wholesale prices when buying energy and when forecasting consumption behaviour, then random parameters are modelled in a scenario framework. The model performance is evaluated through a case study using historical data from car-sharing services in Italy, comparing the result with a fixed-prices model. It is shown that the proposed price-based scheme allows to increment the aggregator profit with respect to a fixed-price contract, producing also a load shifting effect in the charging profile of the fleet.

Energy price forecasting for optimal managing of electric vehicle fleet

Defining tools and algorithms to support the decision-making process for charging electric vehicles (EVs) is a fundamental theme for the spread of EVs. Utilities can use this approach to incentive or discourage the charge of EVs according to different constraints. In this study, the authors refer the EV clusters or fleets, where there is only one energy buyer for all the clusters. This approach corresponds to an indirect method based on prices to induce behaviours in the management of charging on clusters of EVs. The first actor of the algorithm is an aggregator of EV fleet operators acting as a dealer between the electricity market and consumers. A theoretical game model based on Stackelberg's formulation is proposed to capture the interaction between the fleet operator and the owners/drivers of the EVs. A bi-level optimisation problem arises to represent the game between the agents involved: at the upper level, the aggregator maximises its benefits, while the lower level represents the behaviour of rational drivers as a fleet. The proposed method is applied to actual data obtained observing the behaviour of a car-sharing fleet.