Electric Vehicles In Distribution Networks Research Articles

Electrical vehicles impact on unbalanced distribution networks considering different loads and energy prices

The expansion of distributed generation and erratic loads create many challenges for electricity distribution networks (DN), like grid congestion and load unbalance. Technological advances in recent years have made the use of electric vehicles (EVs) more economical and justified for network connection. The use of smart EVs in the network scale, in addition to the peak shedding, if it is used in a single phase, it can also lead to the improvement of load unbalance. This paper proposes an energy and cost management model for the appropriate and distributed deployment of single phase EVs in distribution networks to provide network support for a DN including renewable resources. By connecting or disconnecting single-phase smart EVs to the DN, unbalanced loads in this network will be balanced as much as possible. To evaluate the proposed model, a network of 13 buses has been used, and the calculations of these energy costs have been measured during 24 h by two types of household and industrial loads. These loads are unbalanced in the DN and single-phase EVs have the ability to improve the unbalanced load of the DN. Also, the energy price is variable during the day and depends on the peak load. From the sensitivity analysis and with the relative increase or decrease of load, load change effects on the cost of each DN per-unit power have been measured and compared with the presence or absence of single-phase smart EVs to obtain more practical results. According to this paper, the use of EVs, in addition to improving the load of the DN, greatly reduces the costs of DN.

Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides.

Stochastic two-stage coordination of electric vehicles in distribution networks: A multi-follower bi-level approach

As the number of electric vehicles (EVs) continues to rise, it is essential to consider appropriate management strategies for coordinating EVs connected to different buses in the power networks. In light of this, this paper proposes a stochastic two-stage bi-level model for coordinating EVs in a distribution network with charging stations under alternating current optimal power flow (ACOPF) constraints. The scheduling problem is considered to independently minimize the costs of the distribution system operator (DSO) and EVs parked at different charging stations located at various buses of the network. In the proposed model, the DSO as the leader, and all EVs as independent followers, are individual entities who try to follow their priorities and objectives. The amount and price of exchanged power between the DSO and EVs are optimally determined in the proposed model. The proposed bi-level model has been converted to a single-level model using the Karush–Kuhn–Tucker (KKT) conditions. Afterwards, the Big M method is used to convert the non-linear equations that appear due to utilizing the KKT approach. The scenario-based uncertainty modeling is used to model the uncertainty in input data such as day-ahead and real-time market prices, EVs’ initial state of charge (SOC), and arrival/departure time. The centralized unilateral form of the model has also been developed to validate the proposed model. The results indicate that the bi-level model can lead to cost reduction for the EVs.

Increasing the Penetration of Electric Vehicles in Distribution Networks Using Optimal Charging/Discharging Control and Reactive Power Support in the Presence of Nonlinear Loads

Electrical vehicles (EVs) are among the fastest-growing electrical loads that change both temporally and spatially on distribution networks. The large-scale integration of EVs equipped with power electronic-based chargers into distribution networks, to meet new electrical load demands, can cause instability and power quality issues. Moreover, the absence of control strategies for the smart charging and discharging of EVs at their plug-in intervals poses serious challenges to them. Accordingly, the implementation of controlled charging/discharging scheduling of EV batteries along with the use of charger capabilities, such as reactive power support, is a must. Against this background, this paper introduces an integrated model to solve the problem of simultaneous active and reactive power management in distribution networks subject to network operation constraints imposed by EV batteries and chargers. To this end, this problem is modeled as an optimization problem. In this respect, minimization of the costs associated with power generation and losses and improvement of the total harmonic distortion of voltage (THDv) on network buses are two terms of the objective function. The problem is solved by a hybrid technique named the “ PSO-GA algorithm” that takes advantage of both the genetic algorithm (GA) and the particle swarm optimization (PSO) method. Accordingly, the effectiveness of the proposed model is examined in a standard IEEE 33-bus distribution network populated with EVs and nonlinear devices (NLDs). The results obtained show that the maximum possible penetration rate of EVs into the network is facilitated, while technical and financial goals of the network and parking lots are ensured.

Coordinated Management and Control Strategy in the Low-Voltage Distribution Network Based on the Cloud-Edge Collaborative Mechanism

With the gradual increase in the deployment of distributed power sources and electric vehicles, the coordinated dispatch of clean power is of great significance to improve the economics of electricity consumption by prosumers and promote the consumption of renewable energy. On the basis of considering photovoltaic as a shared power resource, a low-voltage distribution network electric vehicle and distributed photovoltaic coordinated management and control strategy was proposed, and a day-ahead dispatch model of photovoltaic and electric vehicles for prosumers was established, which also considered the total cost of electricity consumption and two targets for photovoltaic consumption. The NSGA-2 algorithm is used to solve the model to obtain the Pareto optimal solution set, and the satisfaction evaluation method is used to select the optimal compromise solution. Based on the cloud-edge collaborative mechanism, the aforementioned technologies are deployed in the intelligent perception terminal device to execute downward computing, storage, and resource management strategies of the cloud station. A 24-period simulation calculation was performed on a low-voltage distribution network with 20 households. The results show that the proposed collaborative management and control strategy is beneficial to improve the economic efficiency of users’ electricity consumption and promote the consumption of clean energy.

Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network

In order to manage electric vehicles (EVs) connected to charging grids, this paper presents an orderly charging approach based on the EVs’ optimal time-of-use pricing (OTOUP) demand response. Firstly, the Monte Carlo approach is employed to anticipate charging power by developing a probability distribution model of the charging behavior of EVs. Secondly, a scientific classification of the load period is performed using the fuzzy clustering approach. Then, a matrix of demand price elasticity is developed to measure the link between EV charging demand and charging price. Finally, the charging scheme is optimized by an adaptive genetic algorithm from the distribution network and EV user viewpoints. This paper describes how to implement the method presented in this paper in an IEEE-33-bus distribution network. The simulation results reveal that, when compared to fixed price and common time-of-use pricing (CTOUP), the OTOUP charging strategy bears a stronger impact on reducing peak–valley disparities, boosting operating voltage, and decreasing charging cost. Additionally, this paper studies the effect of varied degrees of responsiveness on charging strategies for EVs. The data imply that increased responsiveness enhances the likelihood of new load peak, and that additional countermeasures are required.

State of Charge Influence on the Harmonic Distortion From Electric Vehicle Charging

There is a general interest from manufacturers, users, regulation institutions, and research around electric vehicles development and its relation with the grid infrastructure. The penetration of electric vehicles in distribution networks brings considerable challenges in terms of power quality, particularly harmonic emissions. Such challenges are more evident in scenarios with high penetration of electric vehicles which consider the clustering effect in the charging process. Methodologies such as hosting capacity model electric vehicles as a constant power load with constant harmonic distortion along the entire charging process. This article presents the relationship between the charging algorithms of the electric vehicle chargers, the state of charge of the batteries, and the harmonic current distortion generated during the charging process. This model proposes that the harmonic current distortion depends on the state of charge of the battery and the charging algorithm of the vehicle. As this study would change meaningfully, the current studies of power quality related to electric vehicles, practical measurements in two charging stations, and offline simulations of the identified on-board chargers are shown to support the content of this article.

A Customized Voltage Control Strategy for Electric Vehicles in Distribution Networks With Reinforcement Learning Method

The increasing electric vehicles (EVs) at charging stations will impose great challenges on the conventional voltage control in distribution networks. In this article, a two-stage voltage control strategy based on deep reinforcement learning is proposed to mitigate voltage violations caused by the uncertainty of EVs and load. In the first stage, the charging demand of EVs is predicted based on trip chain theory and simulated by Monte Carlo simulation. The optimal power flow is then performed to determine the day-ahead dispatch of on-load tap changer and capacitor banks. In the second stage, the real-time voltage control problem is formulated as a Markov Game considering both reactive power control and vehicle to grid modes of EVs. The problem is solved by the deep deterministic policy gradient algorithm to develop a well-trained control strategy that can be implemented online. Moreover, a novel customized charging criterion is proposed to conduct the charging behavior of EVs and guarantee full charging at the departure time. The proposed approach is tested on the IEEE 33-bus and 123-bus distribution systems and comparative simulation results show the effectiveness in addressing voltage problems.

Integration of Electric Vehicles in Distribution Network Considering Dynamic Power Imbalance Issue

The continuous growth of the number of electric vehicles (EVs) poses great challenges to distribution networks. Intermittent and stochastic EV access detrimentally affects the security of the power supply. In terms of unplanned events, EV users might dispersedly plug in or out at any available spot instead of ensuring identical distribution across feeders. This uneven deployment may lead to imbalanced loading conditions among the adjacent feeders. The situation may become worse with increased penetration of the distributed photovoltaic (PV) generation due to the intermittent generation characteristic. This article proposes a novel control scheme to manage the imbalanced power among feeders. In the proposed dynamic power balance system, unbalanced power can be transferred from the heavily loaded feeder to the neighboring feeder that is lightly loaded through tie-line voltage-source converters (VSCs). The tie-line VSCs are designed to connect adjacent feeders at the low-voltage side of distribution transformers via an 800 V dc link. In this way, the power variance caused by either EV or PV can be split into joint feeders and transformers so as to mitigate fluctuations. The stochastic variations on EV charging loads are addressed using a Monte–Carlo simulation technique. The effectiveness of the proposed scheme is demonstrated on a typical distribution network with the integration of EVs and PV. It could be used as a supplementary measure to obtain a fast demand balance response without restraining EV users and to considerably curtail the risk of overloading the power distribution equipment.

Aggregation and Scheduling Models for Electric Vehicles in Distribution Networks Considering Power Fluctuations and Load Rebound

With electrical performance similar to that of energy storage systems, electric vehicles (EVs) show great potential for demand response to facilitate higher penetration of distributed renewable energy. Therefore, this article presents a look-ahead scheduling model for EVs to offer a load curtailment service in a distribution network, with the aim of smoothing out the power fluctuations caused by distributed renewable energy sources. An equivalent aggregation method that is not sensitive to heterogeneous private parameters is used to extract the overall dynamic power regulation characteristics of the EV population, while a decomposition model ensures the completion of the regulation task. Besides, in the scheduling model, the variable baselines are dynamically refreshed to capture the load rebound effects caused by load curtailment, allowing more accurate regulation plans to be obtained. A two-stage iterative solution strategy is also adopted to obtain the approximate optimal solution. Simulations demonstrate both the accuracy of the aggregation model and the effectiveness of the scheduling model.

Optimal location of battery swap stations for electric vehicles

In this paper, a methodology that allows to determine the location and optimal sizing of battery swap stations for electric vehicles in distribution networks is proposed, which objective is to minimize investment costs and technical losses of the network. The set of constraints is associated with technical and operational characteristics of the system. To solve the mathematical model, an evolutionary algorithm is used. To verify the efficiency of the methodology, a Colombian distribution system is used, where the obtained results validate what is proposed in this work.

Charging Coordination of Plug-In Electric Vehicles in Distribution Networks With Capacity Constrained Feeder Lines

We study the charging control of plug-in electric vehicles in distribution networks with capacity constrained feeder lines. It is usually challenging to design an effective decentralized method to implement the optimal solution for the formulated constrained optimization problems due to the coupling relationship of charging behaviors on the constrained distribution network system. Alternatively, in this brief, in order to avoid possible overloading on the feeder lines, we propose a gradient projection-based decentralized method such that the step size is properly adjusted. We show that by applying the proposed method, the system converges and the implemented solution satisfies the capacity constraints of feeder lines. Simulation results show that the proposed method can effectively avoid possible overloads on the feeder lines in distribution networks.

Research on the Economic Allocation Method of High-Penetrated Distributed Generations for Smoothing the Power Fluctuation of Active Distribution Network

With the increasing penetration of DG and electric vehicles in distribution network, ADN gradually takes the place of traditional distribution network, which also brings higher requirements to the configuration of distributed ESS; In this paper, we mainly discuss the problem of how to optimize the capacity of wind/photovoltaic/energy storage in ADN with high penetration of DG when the total power connected to the grid of DG is fixed, in order to realize the harmonization of the economic and stable operation in power grid. Firstly, the hybrid system is analysed, which demonstrated the feasibility and rationality of proposed scheme. Secondly, being in distributed network investors’ shoes and considering the constraints of distributed network, DG and ESS, the wind/photovoltaic/energy storage capacity configuration bi-level optimization model is established with the objective function of maximum annual income and minimum fluctuation of DG output power. Then the genetic algorithm is used to solve this model. Finally, the experimental results of 33-bus system verify the effectiveness of the proposed method, and the influence of different penetration scenarios on the annual income of distributed network is also analysed to provide a new idea for the economic operation of ADN.

Noncooperative Game-Based Distributed Charging Control for Plug-In Electric Vehicles in Distribution Networks

Increasing penetration of plug-in electric vehicles (PEVs) has a substantial impact on the operation of power distribution networks. Given the fast-growing load demands from PEVs and unmatched infrastructure investment in transformer and feeder capacity, the PEV charging is subjected to both spatially and temporally security constraints beyond which the network failure may occur. This paper proposes a game-theory-based distributed charging control method to coordinate large-scale PEVs without compromising the security of the distribution network. Under a noncooperative game framework, a price-driven charging model is designed to minimize the cost of each individual PEV customer while satisfying the network loading constraints. Then, a Newton-type method is developed to find a better Nash equilibrium of the game model at a superlinear convergence rate. Furthermore, an accelerated gradient method is proposed to tackle the subproblem for each user's best response. The update of the user's best response is implemented in a distributed way in order to protect user's privacy. The convergence rate of the proposed algorithms is rigorously proved. The effectiveness and efficiency of the proposed methods are tested on the IEEE 13-bus system.

Two alternative robust optimization models for flexible power management of electric vehicles in distribution networks

This paper presents a robust optimization problem of the flexible bidirectional power management of a smart distribution network and harmonic compensation of nonlinear loads using electric vehicles (EVs) equipped with bidirectional chargers. The base deterministic model of the proposed problem is as mixed-integer nonlinear programming (MINLP), having the objective function to minimize the economic and technical indices subject to harmonic load flow equations, EVs constraints, system operation and harmonic indices limits. This model is converted to a mixed-integer linear programming (MILP) model in the next step. In the proposed MILP model, the active, reactive and apparent loads, electrical energy, reactive power and harmonic current prices, as well as EVs characteristics, are considered uncertain parameters. Accordingly, two alternative robust optimization approaches have been implemented for the conditions of having both the probability distribution function or the bounded uncertainty in the proposed MILP problem model. The proposed model is tested on distribution networks to demonstrate its efficiency and performance. The results show that the MINLP model can be substituted by the proposed high-speed MILP model. In addition, the capacity of the injecting power of EVs is reduced in the worst-case scenario with respect to the scenario that is used in the deterministic model, while the consumed power of loads and EVs and energy price increases in this scenario. Finally, the payment of EV owners is reduced by considering EVs power and harmonic control.

Reallocating Charging Loads of Electric Vehicles in Distribution Networks

In this paper, the charging loads of electric vehicles were controlled to avoid their impact on distribution networks. A centralized control algorithm was developed using unbalanced optimal power flow calculations with a time resolution of one minute. The charging loads were optimally reallocated using a central controller based on non-linear programming. Electric vehicles were recharged using the proposed control algorithm considering the network constraints of voltage magnitudes, voltage unbalances, and limitations of the network components (transformers and cables). Simulation results showed that network components at the medium voltage level can tolerate high uptakes of uncontrolled recharged electric vehicles. However, at the low voltage level, network components exceeded their limits with these high uptakes of uncontrolled charging loads. Using the proposed centralized control algorithm, these high uptakes of electric vehicles were accommodated in the network under study without the need of upgrading the network components.

A Sufficient Condition on Convex Relaxation of AC Optimal Power Flow in Distribution Networks

This paper proposes a sufficient condition for the convex relaxation of ac optimal power flow (OPF) in radial distribution networks as a second order cone program (SOCP) to be exact. The condition requires that the allowed reverse power flow is only reactive or active, or none. Under the proposed sufficient condition, the feasible sub-injection region (power injections of nodes excluding the root node) of the ac OPF is convex. The exactness of the convex relaxation under the proposed cond $\bar{s}$ ition is proved through constructing a group of monotonic series with limits, which ensures that the optimal solution of the SOCP can be converted to an optimal solution of the original ac OPF. The efficacy of the convex relaxation to solve the ac OPF is demonstrated by case studies of an optimal multi-period planning problem of electric vehicles in distribution networks.

Voltage Sensitivity Analysis of a Laboratory Distribution Grid With Incomplete Data

New voltage control algorithms are necessary to cope with the increasing amount of distributed generation and electric vehicles in distribution networks. Many of the newly proposed voltage control algorithms are based on linearized dependencies between the voltage magnitude, and the active and reactive power consumption. These linearized dependencies are normally obtained by algorithms, which rely on accurate grid topology information. Due to the traditionally passive operation of low voltage (LV) distribution networks, this information is typically missing, incomplete, or inaccurate. Therefore, this paper introduces a method to extract these linear dependencies based on historical smart meter data only. No information about the grid topology is required. The model adapts to the changing load conditions in the network. The algorithm has a low complexity and is applied to an unbalanced LV distribution network. Data of a practical laboratory setup is used to validate the proposed method in real-life conditions. With the obtained voltage sensitivity factors a voltage management strategy was implemented for the laboratory grid.

Power Quality Issues Concerning Photovoltaic Generation and Electrical Vehicle Loads in Distribution Grids

The high utilization level of renewable generation including residential photovoltaic (PV) systems together with the uncontrolled charging of electric vehicles (EVs) can have a significant impact on load characteristics in distribution networks. Harmonic content of PV generation, EV charging loads, and their influence on power quality indicators in residential distribution networks are discussed in this paper. For investigating likely power quality scenarios, PV generation and EV charging measurement results including current harmonic amplitude and phase angle values are used and compared with present load characteristics. Different modelling scenarios are analysed and a simplified model of harmonics in PVs and EVs is offered. The results of the study show moderate additional harmonic distortion in residential load current and voltage distortion at the substation’s busbar when PV generation and EV loading are added. The scenarios presented in this paper can be further used for modelling the actual harmonic loads of the PVs and EVs in distribution networks.

Coordination of the Charging of Electric Vehicles Using a Multi-Agent System

An agent-based control system that coordinates the battery charging of electric vehicles in distribution networks is presented. The objective of the control system is to charge the electric vehicles at times of low electricity prices within distribution network technical constraints. Search techniques and neural networks are used for the decision making of the agents. The ability of the control system to work successfully when the distribution network is operated within its loading limits and when the loading limits are violated is demonstrated through experimental validation .