Electric Vehicle Cluster Research Articles

Multi‐objective optimal provision of fast frequency response from EV clusters

Declining levels of rotational inertia in modern power systems prompt transmission system operators (TSOs) to develop novel ways of maintaining the balance between generation and demand. Services such as fast frequency response (FFR) can help the TSO achieve this balance. The growing penetration of electric vehicles (EVs) promotes the provision of FFR from clusters of EVs. Fast charging stations are more geared towards destination charging, whereas slow charging stations are more attractive as providers of FFR, given the longer connection times. In this study, the provision of FFR from EV clusters is formulated as a multi-objective optimisation problem with network security constraints and two minimisation objectives, i.e. the maximum frequency deviation following a disturbance and the energy provided by public EV charging stations. A methodology was developed to solve the optimisation problem with a variant of the non-dominated sorting genetic algorithm. This methodology allows the decision-maker to consider trade-offs among the objectives, leading to a more informed decision. An enhanced frequency-responsive aggregate model of an EV cluster was developed to study the provision of FFR in a multi-area power system. A reduced model of the Nordic power system was used to illustrate the performance of the proposed methodology.

Game-Theory Based V2G Coordination Strategy for Providing Ramping Flexibility in Power Systems

Large-scale integration of renewable generation into power systems invariably affects the system ramping capability. However, the vehicle-to-grid (V2G) concept that allows for using electric vehicles (EVs) as energy storages with the capability of bidirectional energy transfer between the EVs and the grid, can be employed to mitigate the above adverse effect. This paper proposes a game-theory-based V2G coordination strategy that uses EV clusters to improve ramping flexibility in power systems. In the proposed strategy, the V2G concept, representing the interactions between the distribution system operator (DSO) and EV clusters, is formulated as a Stackelberg game. The DSO acts as a leader who decides the charging prices for the buses to which the EV clusters are connected, while the EV clusters simply serve as followers, scheduling their own charging and discharging. This bi-level model is further reduced to a single-level, mixed-integer second-order cone programming (MISOCP) problem based on the Karush-Kuhn-Tucker (KKT) conditions, the strong duality theorem and second-order cone (SOC) relaxation. The performance of the proposed V2G coordination strategy on a modified IEEE 33-bus system connecting EV clusters and PV generations is investigated through simulations, and the results demonstrate that the largest ramp of the system can be reduced by up to 39% when EV clusters are providing flexibility, while the EV clusters can also have greatly reduced charging costs.

Optimised TSO–DSO interaction in unbalanced networks through frequency‐responsive EV clusters in virtual power plants

The increased penetration of distributed energy resources (DERs) installed in distribution networks via power electronic converters reduces the overall rotating inertia of the power system, causing faster frequency dynamics after a large disturbance. However, these DERs coupled with energy storage systems (ESSs) can be managed to provide valuable grid support functions such as fast frequency response (FFR) and assist the transmission system operator (TSO) in frequency control. In this study, single- and three-phase clusters of electric vehicles (EVs), acting as mobile-ESSs, are grouped together as virtual power plants (VPPs) to centralise the provision of FFR for the distribution system operator (DSO) whilst considering network unbalance. Furthermore, the parameters of the EV clusters within each VPP are optimised to minimise the inertia-weighed maximum frequency deviation following a disturbance whilst adhering to network security and power quality constraints. To this aim, a variant of the metaheuristic optimisation algorithm called improved harmony search is used for the optimisation process. Finally, the merits of the proposed methodology are shown with an illustrative example of an unbalanced three-phase transmission and distribution network modelled in DIgSILENT PowerFactory. The results show that clusters of EVs grouped as VPPs effectively improve frequency support via the TSO–DSO interaction.

Self-Adaptive Secondary Frequency Regulation Strategy of Micro-Grid With Multiple Virtual Synchronous Generators

With the penetration rate of distributed generator and distributed energy storage growing, the frequency stability of microgrid (MG) is severely affected. In this paper, a self-adaptive secondary frequency regulation (FR) strategy based on virtual synchronous generator (VSG) for a microgrid containing wind turbine, photovoltaic array, and electric vehicle (EV) cluster is proposed. First, modified VSG control is applied to renewable energy units and EV cluster, where nonerror frequency regulation is realized by introducing proportional-integral control into power-frequency controller of VSGs. Secondly, the FR reserve capacity of wind turbine, photovoltaic array, and EV are predicted in real time through their respective characteristics. Then, a self-adaptive adjustment strategy for secondary FR parameters of VSGs is designed to control the FR power of VSGs according to real-time predicted FR capacity. Finally, the coordinated FR strategy of VSGs and conventional unit is proposed considering the situation of insufficient FR capacity of VSGs. Through the analysis of simulation results, the reasonability and validity of proposed self-adaptive FR strategy for multiple VSGs have been verified.

Real-time stochastic optimal scheduling of large-scale electric vehicles: A multidimensional approximate dynamic programming approach

This paper studies the real-time charging optimization (RTCO) of large-scale electric vehicles (EVs), which is a multistage and multidimensional stochastic resource allocation problem. In order to handle the complex RTCO of large-scale EVs, a multidimensional approximate dynamic programming (ADP-RTCO) is designed for the sequent optimal decision makings. The hierarchy of ADP-RTCO contains two layers. In the upper layer, RTCO of large-scale EVs is formulated as a multidimensional energy storage problem by classifying EVs into several virtual EV clusters (EVCs). Then, temporal difference learning based policy iteration method is used to obtain the value function approximation for each EVC. In the lower layer, priority based reallocation algorithm is employed to obtain the detailed charging power for connected EVs. ADP-RTCO is designed with on-line learning ability to enhance its adaptability and robustness to ambiguous environment. Comprehensive simulation results demonstrate the optimality and robustness of ADP-RTCO in both cost-saving and load-flattening problems. Furthermore, ADP-RTCO can obtain higher-quality solution compared with other algorithms and is applicable for RTCO of large-scale EVs under uncertainties due to its high computation efficiency.

Quantifying flexibility of residential electric vehicle charging loads using non-intrusive load extracting algorithm in demand response

The increasing penetration of electric vehicles (EVs) has increased the operational burden on power grids during peak load periods. Identifying the charging patterns of residential EV clusters and quantifying their flexibility in demand response (DR) can help grid operators make effective regulatory decisions for balancing grid supply and load. In this study, statistical models are established for the charging patterns (charging start time, charging end time, and charge duration) of residential EVs. A training-free non-intrusive load extracting (NILE) algorithm is proposed. The algorithm is based on different load signatures and power block extremums, and can extract the EV load charged by different charging power. A price-based DR model is established to optimise the charging strategy of EVs, and the flexibility of EVs in DR is quantified according to the change of charging behaviour before and after optimisation. In addition, the validity of the NILE algorithm is verified with a real data set, and the flexibility of the EV cluster in different situations (seasons, weekdays, and weekends) is quantified based on the separated charging data.

Training-Free Non-Intrusive Load Extracting of Residential Electric Vehicle Charging Loads

Extracting the charging loads of residential electric vehicle (EV) clusters and identifying their charging patterns can help grid operators develop effective regulation strategies. The duration of the power consumption event (PCE) and the interval between adjacent events are used to characterize the difference in the stochastic behavior of the load pattern between the EV cluster and the air conditioner (AC) cluster. An event detection method based on skipping power difference is proposed, which can effectively identify changing edges of the PCE. A training-free non-intrusive load extracting (NILE) algorithm based on bounding-box fitting and load signatures is proposed, which can automatically identify the start time, the end time and the power amplitude of the charging event. The validity of the NILE algorithm is verified by multiple performance metrics on the real data set.

Optimal Charging Strategy Based on Model Predictive Control in Electric Vehicle Parking Lots Considering Voltage Stability

Recently, electric vehicles (EVs) using energy storage have gained attention over conventional vehicles using fossil fuels owing to their advantages such as being eco-friendly and reducing the operation cost. In a power system, an EV, which operates through the energy stored in the battery, can be used as a type of load or energy source; hence, an optimal operation of EV clusters in power systems is being extensively studied. This paper proposes an optimal strategy for charging EVs in parking lots. This strategy is based on the model predictive control (MPC) framework due to the uncertainty of loads, renewable energy sources, and EVs, and considers the voltage stability of the distribution systems. EV chargers in the parking lot charge EVs to minimize the charging cost, which results in a sudden increase in charge load at a certain time. As a result, an excessive voltage drop may occur in the power system at that time. Therefore, we need to minimize the charging cost of EVs while preventing an excessive voltage drop in the power system. The parking lot is stochastically modeled to consider EV uncertainty under the MPC framework. In the MPC framework, the charging schedule of an EV charger in the parking lot is optimized by considering both voltage stability and charging cost minimization in real time. The charging constraints on voltage stability are updated through parameters that change in real time, and thus, errors caused by uncertainty can be reduced. Subsequently, this charging strategy is applied to multiple chargers through Monte Carlo simulation. The proposed charging strategy is verified based on MATLAB/Simulink.

Planning of Fast EV Charging Stations on a Round Freeway

A novel planning method of fast electric vehicle (EV) charging stations on a round freeway was developed, considering the spatial and temporal transportation behaviors. A spatial and temporal model based on the origin-destination (OD) analysis was developed to obtain all the EV charging points (the location on the round freeway that an EV needs recharging due to the low battery capacity). Based on a shared nearest neighbor (SNN) clustering algorithm, a location determination model was developed to obtain the specific locations for EV charging stations with their service EV clusters. A capacity determination model based on the queuing theory was proposed to determine the capacity of each EV charging station. The round-island freeway in Hainan Island of China was employed as a test system to illustrate the planning method. Simulation results show that the developed planning method can not only accurately determine the most suitable locations for EV fast charging stations considering the travelling convenience of EV users, but also minimize the sum of waiting cost and charger cost.

Dynamic frequency response from electric vehicles considering travelling behavior in the Great Britain power system

In order to pursue the low-carbon development around the world, a large scale of renewable generation will be connected to the power systems. Take the Great Britain (GB) as an example, the GB power system has a large wind energy integration potential. The intermittency of wind generation will have great impact on the system frequency stability. Electric vehicles (EVs) have a crucial role in decarbonizing the transport sector. To increase the utilization of wind energy, EVs are suggested to provide frequency response service to the power system due to their quick power reaction characteristic. This paper proposes a general dynamic EV frequency control strategy considering the travelling behavior of the EV users. A droop control method is used to regulate the EV charging/discharging power according to the frequency signal. A Forced-Charge Boundary (FCB) and a Forced-Charge Area (FCA) are proposed to guarantee sufficient energy in the EV battery for user’s travel at the plug-out time. A dynamic Virtual Energy Storage System (VESS) is developed to evaluate the frequency response capacity of the EV clusters. In the case study, the model of the GB power system is used to investigate the frequency control effect of the control strategy. The simulation results show that the proposed strategy provides effective EV frequency response to the power system and thus is able to facilitate the integration of wind energy.