Electric Vehicle Aggregator Research Articles

A Two-Stage Scheme for Both Power Allocation and EV Charging Coordination in a Grid-Tied PV–Battery Charging Station

Charging station that incorporates renewable energy resource and energy storage is a promising solution to meet the growing charging demand of electric vehicles (EVs) without the need to expand the distribution network. The aggregation of multiple energy resources and EVs requires an efficient and flexible energy management strategy. This article presents a two-stage scheme to solve the power allocation and charging coordination of plugged-in EVs. Game-theory-based control is utilized to address the interaction among different components for respecting their individual preferences. The first stage determines the power allocation of photovoltaic, battery, and the grid as well as total charging power for EVs. In the second stage, charging power dispatch among individual EVs is coordinated based on the available total charging power determined in the first stage. As a result, the two energy management problems of charging station are addressed sequentially. The proposed solution is validated via simulation and experiment, and the comparisons with benchmarks show its advantages.

Modified Salp Swarm Algorithm-Optimized Fractional-Order Adaptive Fuzzy PID Controller for Frequency Regulation of Hybrid Power System with Electric Vehicle

A considerable no. of intermittent renewable sources such as PV generation and wind energy when integrated to the conventional grid technology causes serious issues in the power systems like frequency instability. So a more balancing controller is desired for a stable and reliable operation of the power system. Bidirectional power control of the EV aggregator is making itself a wise choice for distributed energy storage to scale down the frequency and power fluctuation. In this work, an intelligent load frequency controller using a fractional-order adaptive fuzzy PID controller with filter (FOAFPIDF) for hybrid power system with electric vehicle (EV) based on modified salp swarm algorithm (MSSA) technique is proposed. The effectiveness of MSSA technique is compared with original salp swarm algorithm as well as moth flame optimization , grey wolf optimization , particle swarm optimization and sine cosine algorithm techniques for benchmark test functions using statistical analysis. The effectiveness of the suggested load frequency control strategy by the use of electric vehicle as well as with other energy storing elements such as the superconducting magnetic energy storage component, flywheel energy storage system and ultra-capacitor along with their inherent rate constraint nonlinearity is validated by numerical simulations conducted on the studied test system. It is demonstrated that the proposed controller provides a better control action to suppress the frequency fluctuations as compared to PID controller. The robustness of the controller is also investigated against variation of system parameters and random load changes.

Coordination of Multiple Electric Vehicle Aggregators for Peak Shaving and Valley Filling in Distribution Feeders

In this paper, a coordination method of multiple electric vehicle (EV) aggregators has been devised to flatten the system load profile. The proposed scheme tends to reduce the peak demand by discharging EVs and fills the valley gap through EV charging in the off-peak period. Upper level fair proportional power distribution to the EV aggregators is exercised by the system operator which provides coordination among the aggregators based on their aggregated energy demand or capacity. The lower level min max objective function is implemented at each aggregator to distribute power to the EVs. Each aggregator ensures that the EV customers’ driving requirements are not relinquished in spite of their employment to support the grid. The scheme has been tested on IEEE 13-node distribution system and an actual distribution system situated in Seoul, Republic of Korea whilst utilizing actual EV mobility data. The results show that the system load profile is smoothed by the coordination of aggregators under peak shaving and valley filling goals. Also, the EVs are fully charged before departure while maintaining a minimum energy for emergency travel.

Research on Cooperation Between Wind Farm and Electric Vehicle Aggregator Based on A3C Algorithm

As renewable energy sources such as wind are connected to the grid on a large scale, the safe and stable operation of the power system is facing challenges and the demand for flexibility is becoming increasingly prominent. In recent years, with the advancement of Vehicle-to-Grid (V2G) technology, electric vehicles (EVs) have become a non-negligible flexibility resource for the power system and an emerging path to solve the renewable energy consumption problem. To address the problem of wind farms’ difficulty in making profits in the power market, this paper considers the cooperation between wind farms and EV aggregators and uses the levelable characteristics of EVs charging load to ease the anti-peak characteristics of wind power. Given this, this paper proposes a cooperation mode between the wind farm and the Electric Vehicle (EV) aggregator, constructs a cooperation income and income distribution model, and solves the model using the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning algorithm. Finally, based on the simulation analysis of historical data, the following conclusions are drawn: (1) the cooperation between the wind farm and the EV aggregator can effectively mitigate the negative impact of the anti-peak characteristics of wind power on profitability and achieve an increase in overall economic benefits; (2) the income distribution based on the Shapley value method ensures that the respective income of the wind farm and the EV aggregator increase after cooperation, which is conducive to the promotion of the willingness of both parties to cooperate; (3) the A3C reinforcement learning algorithm is applied to solve the model with good convergence to achieve fast and continuous intelligent pricing decisions for EV aggregators, thus optimizing the charging schedule of EVs promptly.

Strategy of Large-Scale Electric Vehicles Absorbing Renewable Energy Abandoned Electricity Based on Master-Slave Game

The rapid development of renewable energy power has improved global energy and environmental problems. However, with the high volatility of renewable energy, it is an important challenge to guarantee the consumption of renewable energy and the reliable operation of high percentage renewable energy power systems. To solve this problem, this paper proposes a tracking absorption strategy for renewable energy based on the interaction between the supply side and the demand side, which adjusts the charging process of electric vehicles (EVs) through electric vehicle aggregator (EVA) to realize the tracking absorption of renewable energy abandoned electricity. In view of this process, we analyze the interaction among power grid, EVA and renewable energy generation (REG) as well as their market characteristics. The master-slave game model of EVA and REG was constructed considering the charging behavior characteristics of EVs and the output characteristics of REGs. Then the model solving strategy based on soft actor-critic (SAC) algorithm is proposed, and the REG pricing strategy and EVA scheduling strategy are calculated to optimize the mutual benefits. The case analysis shows that, under the same scale of electric vehicles, the proposed method can promote about 93.89% of the power abandonment consumption of wind power system, 96.00% of the photovoltaic system, and 97.41% of the wind-solar system. This strategy reduces the electricity purchase cost of EVA, promotes the interaction among renewable energy, vehicles and power grid, and improves the utilization efficiency of renewable energy.

Model Predictive Control for EV Aggregators Participating in System Frequency Regulation Market

The penetration rate of renewable energy source generation in power systems continues to increase in recent years. However, due to the intermittent nature of renewable energy source (RES) generation, more frequency regulation resources with faster response time and larger capacities are required in the system in order to maintain the system frequency stability. Electric vehicles (EVs) can provide frequency regulation capacities to the system with their batteries when idle, but first they need to be aggregated to enter the ancillary service market for frequency regulation. In this paper, a model predictive control scheme is proposed for the EV aggregators. With the proposed control scheme, an EV aggregator can receive more payment through participation in system frequency regulation while not violating the EV users' convenience. A prediction method based on a seasonal-autoregressive-integral-moving-average (SARIMA) model on the regulation capacity price is also implemented to further boost the EV aggregator's payment. Simulation based on actual frequency regulation market price is conducted to examine the performance of the proposed control scheme. Compared with the simple prediction method on the regulation capacity price used in the existing literature, the proposed MPC scheme with SARIMA prediction increases the payments from the ancillary service market by 4.3% for the EV aggregator.

Dynamic Pricing Strategy of Electric Vehicle Aggregators Based on DDPG Reinforcement Learning Algorithm

The fixed service charge pricing model adopted by traditional electric vehicle aggregators (EVAs) is difficult to effectively guide the demand side resources to respond to the power market price signal. At the same time, real-time pricing strategy can flexibly reflect the situation of market supply and demand, shift the charging load of electric vehicles (EVs), reduce the negative impact of disorderly charging on the stable operation of power systems, and fully tap the economic potential of EVA participating in the power market. Based on the historical behavior data of EVs, this paper considers various market factors such as peak-valley time-of-use tariff, demand-side response mode and deviation balance of spot market to formulate the objective function of EVA comprehensive revenue maximization and establishes a quarter-hourly vehicle-to-grid (V2G) dynamic time-sharing pricing model based on deep deterministic policy gradient (DDPG) reinforcement learning algorithm. The EVA yield difference between peak-valley time-of-use tariff and hourly pricing strategy under the same algorithm is compared through the case studies. The results show that the scheme with higher pricing frequency can guide the charging behavior of users more effectively, tap the economic potential of power market to a greater extent, and calm the load fluctuation of power grid.

Impact of Electric Vehicle Aggregator with Communication Time Delay on Stability Regions and Stability Delay Margins in Load Frequency Control System

This paper investigates the impact of electric vehicle (EV) aggregator with communication time delay on stability regions and stability delay margins of a single-area load frequency control (LFC) system. Primarily, a graphical method characterizing stability boundary locus is implemented. For a given time delay, the method computes all the stabilizing pro-portional-integral (PI) controller gains, which constitutes a stability region in the parameter space of PI controller. Secondly, in order to complement the stability regions, a frequency-domain exact method is used to calculate stability delay margins for various values of PI controller gains. The qualitative impact of EV aggregator on both stability regions and stability delay margins is thoroughly analyzed and the results are authenticated by time-domain simulations and quasi-polynomial mapping-based root finder (QPmR) algorithm.

Optimal Operation Strategy of Virtual Power Plant Considering Real-Time Dispatch Uncertainty of Distributed Energy Resource Aggregation

Virtual power plant (VPP) technologies continue to develop to embrace various types of distributed energy resources (DERs) that have inherent real-time uncertainty. To prevent side effects on the power system owing to the uncertainty, the VPP should manage its internal resources’ uncertainty as a whole. This paper proposes an optimal operation strategy for a VPP participating in day-ahead and real-time energy market so that a distributed energy resource aggregation (DERA) can cope with real-time fluctuation due to uncertainties while achieving its maximum profit. The proposed approach has bidding models of the DERAs including microgrid, electric vehicle aggregation, and demand response aggregation, as well as the VPP. The VPP determines internal prices applied to the DERA by evaluating its real-time responses to the day-ahead schedule and updating proposed pricing function parameters, and the DERA adjusts its energy reserves. By repeating this coordination process, the VPP can establish an optimal operation strategy to manage real-time uncertainty on the DERA’s own. The effectiveness of the proposed strategy is verified by identifying a capability of the DERA to cope with real-time fluctuation through scenario-based simulations. The result shows that the VPP can reduce 1.6% of cost while the internal price applied to the DERA is close to the maximum.

Grid Integration of Electric Vehicles for Economic Benefits: A Review

Emissions from the internal combustion engine (ICE) vehicles are one of the primary cause of air pollution and climate change. In recent years, electric vehicles (EVs) are becoming a more sensible alternative to these ICE vehicles. With the recent breakthroughs in battery technology and large-scale production, EVs are becoming cheaper. In the near future, mass deployment of EVs will put severe stress on the existing electrical power system (EPS). Optimal scheduling of EVs can reduce the stress on the existing network while accommodating large-scale integration of EVs. The integration of these EVs can provide several economic benefits to different players in the energy market. In this paper, recent works related to the integration of EV with EPS are classified based on their relevance to different players in the electricity market. This classification refers to four players: generation company (GENCO), distribution system operator (DSO), EV aggregator, and end user. Further classification is done based on scheduling or charging strategies used for the grid integration of EVs. This paper provides a comprehensive review of technical challenges in the grid integration of EVs along with their solution based on optimal scheduling and controlled charging strategies.

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.

Optimal Energy Storage Allocation Strategy by Coordinating Electric Vehicles Participating in Auxiliary Service Market

The further liberalization of China's electricity market encourages demand-side entities to participate in electricity market transactions. Electric vehicles (EVs) are developing rapidly and have high regulating potential, and are the main force for demand-side participation in the auxiliary service market. Aiming at the problems of dispatching accuracy and economy in EV participation in auxiliary service market, this paper analyzes the bidding strategy and dispatching scheme of EV-storage participation in auxiliary service market, and proposes EV-storage optimal allocation strategy with the goal of economic optimum. In the process of optimal allocation, based on the market rules of third-party subject participation in auxiliary services, the bidding strategy of EV-storage coordinated EV participation in auxiliary services market considering daily load scale changes is designed, while the conditional value at risk (CVaR) method is used to determine the short-term coordinated energy storage capacity and efficiency storage capacity considering the uncertainty of EV response situation; then, based on the annual EV load scale change, the benefit calculation function is constructed by considering various factors such as auxiliary service market revenue, spread revenue, investment cost and market opportunity cost of EV-storage participation in the auxiliary market. Finally, taking EV aggregation participation in the valley-filling ancillary service market as an example, it is verified that the strategy proposed in this paper can effectively improve the responsiveness of EV participation in the ancillary service market and increase the revenue of electric vehicle aggregator (EVA).

A Distributed Electric Vehicle Charging Scheduling Platform Considering Aggregators Coordination

In this paper, a two-layer distributed optimization platform employing the alternating direction method of multipliers (ADMM) method is developed as an exchange problem to solve the electric vehicle charging management problem (EVCMP). The proposed model establishes a coordination layer between the EV aggregators, which increases the optimization’s overall efficiency while preserving aggregators’ independence. Numerical tests validate that the proposed coordinated distributed platform (CDP) enhances the load profile’s smoothness compared to the locally coordinated and uncoordinated charging platforms. Moreover, CDP also decreases the total EV charging costs by 35%, compared to the uncoordinated charging approach.

Participation of Electric Vehicle Fleets in Local Flexibility Tenders: Analyzing Barriers to Entry and Workable Solutions

Distribution system operators (DSO) are starting to implement market-based mechanisms to use the flexibility offered by distributed energy resources (DER) such as electric vehicles (EV). Several European countries are trialing a range of real-life tests and market designs, and local flexibility tenders that allow DSOs to procure medium- to long-term flexibility have found early success. Here we set out to: i) identify the remaining barriers to entry for DER aggregators in these new flexibility schemes, and ii) quantify the participation of EV fleets in long-term flexibility tenders. We built a model to evaluate the potential EV aggregator gains on local flexibility tenders considering market rules, definitions of flexibility product, and different EV fleet compositions. Our model shows that the main parameters affecting EV fleet aggregator participation and remuneration are bidirectional capability (V2G), fleet reliability, and the right match-up between availability profiles and tender requirements. In best-case scenarios, EV fleet aggregators can expect revenues of over €1400/EV/year providing services for only a few hours or months per year. The paper concludes with policy recommendations based on best practices to boost DER participation in local flexibility markets.

Detecting and isolating false data injection attacks on electric vehicles of smart grids using distributed functional observers

This paper considers the problem of false data injection attacks (FDIAs) on load frequency control of interconnected smart grids (ISGs) with delayed electric vehicles (EVs) and renewable energies. By intruding incorrect information, unauthorised users can corrupt the system information leading to degradation in the performance and disruptions of ISGs. In this paper, a model of ISGs subject to FDIAs in aggregator of EVs and power plants is first presented. This mathematical representation comprises dynamic interactions of power plants, delayed EVs, renewable energies and FDIAs on both system states and outputs. Based on recent advanced techniques on functional observers and matrix inequalities for time-delay systems, then a new distributed functional observers based scheme is developed to realise the tasks of detecting and isolating FDIAs. Also, an effective procedure presented in tractable linear matrix inequalitiesis build with an optimisation process for the synthesis of the detector. The proposed detector is distributed, of reduced order, avoids the risk of centralised malicious incidents, therefore easy for implementation and monitoring tasks. The stability of ISGs and contribution of EVs subject to FDIAs are also discussed. Comprehensive simulations are given to demonstrate the effectiveness of our proposed method by using three-area ISGs.

Stochastic bidding strategy of electric vehicles and energy storage systems in uncertain reserve market

This paper proposes an Electric Vehicle (EV) aggregator bidding strategy in the reserve market. The EV aggregator determines the charging/discharging operations of EVs in providing reserve service for profits maximization. In the Day-Ahead Market (DAM), the EV aggregator submits a bidding plan to the Independent Systems Operator (ISO) including base-load and reserve up/down capacities plans. In the Real-Time Market (RTM), the EV aggregator should deploy reserve based on the ISO's requirements, and the EV aggregator could receive income by deploying reserve or penalty for reserve shortage. The stochastic programming method is applied to address the uncertain reserve deployment requirements in RTM. In addition, Energy Storage Systems (ESS) are utilized by the EV aggregator to enhance the ability in providing reserve service. The aggregator–owner contract is designed to guarantee EV owners' economic benefits. Case studies show the expected profits of the EV aggregator are maximized and the risk of the reserve shortage is well managed, i.e., penalty is minimized. With the utilization of ESS, the performance of the EV aggregator in making response to the ISO's requirements is improved. That is, the required reserve percentage increases from 5.68% to 7.85%, and the deployed reserve percentage increases from 69.71% to 88.47%.

Economic analysis of auxiliary service by V2G: City comparison cases

Due to the increasing realization of environmental protection and energy structure optimization, the scale of electric vehicles (EV) is gradually expanding. However, the scale-up of EV brings new challenges about the security and economics of power system. The use of vehicle-to-grid (V2G) can not only reduce the electrical load, but also provide additional benefits for EV owners. This paper presents a value analysis model for EV aggregators to participate in ancillary services by using V2G in Shanghai and Chengdu. The potential regulatory capacity and economic benefits of V2G are analyzed based on the various demands and charge prices in different area. The results show that V2G can obviously mitigate the load pressure and save charge cost for EV owners both in Shanghai and Chengdu.

Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators

Increasing distributed generation hosting capacity in distribution systems via optimal coordination of electric vehicle aggregators

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.

An Operation Model for Distribution Companies Using the Flexibility of Electric Vehicle Aggregators

An operation model for distribution companies (DISCOs) is proposed to reduce their operation costs by fully utilizing the flexibility of electric vehicle aggregators (EVAs). In the proposed model, linear decision rules approximation is adopted to achieve mathematical tractability, and distributionally robust optimization is applied to evaluate costs affected by uncertainties in renewable power outputs and EVA charging demands. Case studies are conducted under various settings. With the proposed model, using EVAs to mitigate uncertainties is achieved and is further classified into delaying uncertainties and eliminating uncertainties. As a result, average penalties for DISCO's deviations from its planned energy portfolio are reduced. Besides, EVA charging demands are shifted to hours with lower energy prices to reduce energy costs of DISCO. Using EVAs to mitigate uncertainties and shifting EVA charging demands are properly coordinated under the proposed model. Moreover, power losses in EVA charging and discharging are utilized to reduce the scale of uncertainties, which decreases average penalties for energy deviations of DISCO.