Plug-in Electric Vehicles Charging Research Articles

Enhanced Economic Load Dispatch by Teaching–Learning-Based Optimization (TLBO) on Thermal Units: A Comparative Study with Different Plug-in Electric Vehicle (PEV) Charging Strategies

This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching–Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging scenarios. The TLBO algorithm was utilized to optimize the ELD problem, considering the complexities associated with thermal units. The integration of PEVs in the load dispatch optimization was investigated, and different charging profiles and probability distributions were defined for PEVs in various scenarios, including overall charging profile, off-peak charging, peak charging, and stochastic charging. These tables allow for the modeling and analysis of PEV charging behavior and power requirements within the power system. By incorporating PEVs, additional controllable resources were introduced, enabling more effective load management and grid stability. The comparative analysis showcases the advantages of the TLBO-based ELD model with PEVs, demonstrating the potential of coordinated dispatch strategies leveraging PEV storage and controllability. This paper emphasizes the importance of integrating PEVs into the load dispatch optimization process, utilizing the TLBO algorithm, to achieve economic and reliable power system operation while considering different PEV charging scenarios.

Optimal charging of plug-in electric vehicles and renewable energy sources integration considering PV/WT/BT based on cost and emission reduction

The integration of Electric Vehicles (EVs) on the power system with Renewable Energy Sources (RESs) can have both positive and negative impacts. The positive impact could provide a comfort on the generation system and the EV, on the other hand, the negative impact increases the Greenhouse Gas (GHG) along with the cost and emission. Proper management and coordination will be needed to ensure that the integration is optimized for maximum benefit. The purpose of this article is to investigate the EV integration impact from the perspective of cost and reliability. The integration of EVs can also impact renewable energy generation. For example, if EVs are charged during periods of high renewable energy generation, it could lead to curtailment of RESs if the electricity demand from EVs is not balanced with the supply of renewable energy.

Asynchronous distributed charging protocol for plug-in electric vehicles

The proliferation of plug-in electric vehicles (PEVs) advocates a distributed paradigm for coordinating PEV charging. Unlike existing primal-dual decomposition or consensus methods, this paper proposes a cutting-plane-based distributed algorithm considering a peer-to-peer (P2P) communication network, which enables asynchronous coordination among PEVs. First, an equivalent surrogate model is established by exploiting the duality of the original optimization problem, which masks the private information of individual users by equivalent transformation. Then, a cutting-plane based algorithm is derived to solve the surrogate problem in a distributed manner with intrinsic superiority to cope with various asynchrony. Critical implementation issues, including distributed initialization, distributed cutting-plane generation, and localized stopping criteria, are discussed in detail. Numerical tests on IEEE 37- and 123-node feeders with actual data illustrate that the proposed method is resilient to several asynchronies and admits the plug-and-play operation mode. The proposed methodology is expected to provide an alternative path toward the robust protocol for PEV charging.

An improved differential evolution using self-adaptable cosine similarity for economic emission dispatch

As increasing demand of electric energy and deterioration of environment pollution in global, economic emission dispatch (EED) problem has become one of the most important problems in power systems. In order to improve the quality of life and further reduce emissions, plug-in electric vehicle (PEV) has become a popular transportation. But the frequent and random charging of PEVs will bring potential risk that undermines the stability of the power grid. Therefore, dynamic economic emission dispatch considering plug-in electric vehicles (DEED-PEV) as a new hot-spot issue has gradually become more and more important in the power system. In this paper, an improved differential evolution using self-adaptable cosine similarity (DE-SCS) is proposed for EED and DEED-PEV problems. First, a dynamic cosine similarity calculated replaces the F of the differential evolution algorithm (DE) to scale the perturbation by self-adaptability. Second, a result-driven selection operation for multiple mutation strategies is implemented in each iteration. Third, a modified environment selection changes the usual way to ranking in the vectors pool during the update on Pareto front. Last, an evaluation mechanism that involves two series of solution sets and three types of comparing approaches is conducted on our proposed algorithms in the experiment. DE-SCS improves the equilibrium between exploration and exploration of the DE. The cosine similarity makes the convergence more self-adaptive. The modified environment selection maximizes the worth of previous generation vectors and brings the randomness into the next iteration. DE-SCS matching various environmental selection are tested on twelve unconstrained multi-objectives problems, six EED cases and six DEED-PEV cases containing charging load, discharging power, valley filling, and peak shaving. Experimental results confirm that DE-SCS is capable of obtaining such excellent and feasible solutions that it has good potential to deal with EED and DEED-PEV problems.

AN EFFECTIVE METHOD OF ELECTRICAL VEHICLE CHARGING BY WIRELESS POWER TRANSFER

Electric vehicles are a viable solution for lowering greenhouse gas emissions. Electric vehicles not only reduce fossil fuel dependency, but they also minimise ozone damaging compounds and support large-scale renewable deployment. Despite extensive research on the qualities and attributes of electric vehicles, as well as the nature of their charging infrastructure, electric vehicle manufacturing and network modelling continue to evolve and be constrained. Various non-contact Plug-In Electric Vehicle charging methods are covered in this paper. They're either in the works or are already being used as flexible aftermarket choices in the light-duty vehicle industry. Wireless power transfer (WPT) is a commonly used name for wireless charging that includes inductive power transfer and magnetic resonance coupling. Wireless technology is still in its infancy, and there is a lack of standardisation, particularly in terms of interoperability, centre frequency selection, magnetic fringe field suppression, and power flow regulation methods.

Energy management optimization in smart railway stations with the ability to charge plug-in hybrid electric vehicles

The increasing development of traffic in the world has led to an increase in the consumption of electrical energy in electric vehicles (EVs). One of the most widely used electric vehicles is the electric train. Thus, energy optimization becomes more crucial in electrical railways to reduce consumption and total electricity bill as well as environmental issues. Consequently, electrical railway energy management must be technically and economically efficient and effective. This paper proposes an energy efficiency optimization framework for intelligent railway stations that provide plug-in electric vehicle charging parking capacity use, renewable energy sources (REs), and regenerative braking energy (RBE). The structure of the proposed convex programming (CP) model is used for fast and efficient optimization of decision variables, Equipment size, and the cost function of the station for reducing the power purchased from the Grid. Furthermore, by presenting meta-heuristic algorithms, the state variables of the energy storage system (ESS) and Plug-in hybrid electric vehicles (PHEVs) battery have been optimized. The results obtained from the proposed method prove that the operation cost of the station decreases by 61.4 %.

Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation

Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation

Placement and Capacity of EV Charging Stations by Considering Uncertainties With Energy Management Strategies

At the present context, Plug-in electric vehicles (PEVs) are gaining popularity in the automotive industry due to their low CO2 emissions, simple maintenance, and low operating costs. As the number of PEVs on the road increases, the charging demand of PEVs affects distribution network features, such as power loss, voltage profile, and harmonic distortion. Furthermore, one more problem arises due to the high peak power demand from the grid to charge the PEVs at the charging station (CS). In addition, the location of CS also affects the behavior of EV users and CS investors. Hence, this paper applies CS investor, PEV user, and distribution network operator who could approach to CS's optimal location and capacity. Integrating renewable energy sources (RESs) at the charging station is suggested to lower the energy stress on the grid. Moreover, to keep down the peak power demand from the grid and utilize renewable energy more efficiently, energy management strategies (EMS) have been applied through the control of charging and discharging of the battery storage system (BSS). In addition, vehicle to grid (V2G) strategy is also applied to discharge the EV battery at charging station. Furthermore, the uncertainties related to PEV charging demand and PV power generation are addressed by the Monte Carlo Simulation (MCS) method.

Achieving reliable coordination of residential plug-in electric vehicle charging: A pilot study

Achieving reliable coordination of residential plug-in electric vehicle charging: A pilot study

Optimal allocation of cloud energy storage system in low-voltage distribution network

In low-voltage distribution networks, an uncontrolled charging of plug-in hybrid electric vehicles (PHEVs) brings about intensive peak loads in distribution transformers’ daily load curves. Due to insulation degradations, this issue ends in transformers’ loss of life (LOL) and early aging. The notion of cloud energy storage system (CESS) with larger power and energy capacities enables consumers to have access to cheaper energy storage facilities. Thanks to CESS installation, semi-smart, controlled, and low-cost charging of PHEVs could be realized to relieve the transformer’s peak loads and reduce the peak-to-average (PAR) ratio of load curve. To do so, this paper develops an optimal planning model for siting and sizing of a CESS to accommodate the charge/discharge requirements of consumers who are willing to collaborate with CESS and hence, implementing a controlled charging of PHEVs. The proposed model considers the investment and operation costs of both CESS and distribution network. It also models the transformer LOL costs. The PHEVs, in private garages, are supposed to be connected to the grid through a smart module whose connection time and status is optimally scheduled and controlled through the operator of the CESS. The CESS charging/discharging is regulated such that the previously determined time targets of PHEV owners are satisfied. To assess performance of the proposed model, extensive numerical studies are carried out in terms of techno-economic indices say as PAR reduction, conducting energy arbitrage, power losses reduction, and etc. Results are discussed in depth.

Optimized Deep Reinforcement Learning for Smart Charging Scheduling of Plug-in Electric Vehicles

In recent decades, fossil fuels have been the major source of energy but concerns about price fluctuations, security, and environmental issues. Plug-in electric vehicles (PEVs) should be connected to the power grid for battery charging. With a wide range of cars, the power system performance in the distribution network. The uncontrolled charging of the battery can cause issues such as overvoltage, overload, unbalanced load, instability, harmonics, and increased loss. Hence, efficient charging methodologies need to be developed for the power grid. The objective of this article is to maximize the total revenue of the charging stations and schedule the charging stations for PEV charging with reduced cost. First, the system model with its objectives such as demand, constraints, and fitness values are computed. Next, efficient charging path planning is proposed using the deep Q network, which is a reinforcement learning model. This can assist the PEV user in selecting the charging station and planning its path. Lastly, the particle sailfish optimization from our previous research work has been used to solve the charging station optimal solution. The simulation results analysis shows the efficiency of the proposed model in reducing the charging cost and time and improves the charging scheduling of PEVs.

Power and Energy Management Strategies for a Microgrid with the Presence of Electric Vehicles and CAES Considering the Uncertainty of Resources

We are witnessing the growth of microgrid technology and the development of electric vehicles (EVs) in the world. These microgrids seek demand response (DR) and energy storage for better management of their resources. In this research, microgrids, including wind turbines, photovoltaics, battery charging/discharging, and compressed air energy storage (CAES), are considered. We will consider two scenarios under uncertainty: (a) planning a microgrid and DR without considering CAES, and (b) planning a microgrid and DR considering CAES. The cost of charging the battery in the second study decreased by $0.66 compared to the first study. The battery is charged with a difference of $0.7 compared to the case of the first study. We will also pay for unsupplied energy and excess energy in this microgrid. Then, we test the scheduling of vehicles to the grid (V2G) in the IEEE 33-bus network. The first framework for increasing network flexibility is the use of EVs as active loads. The scheduling of vehicles in the IEEE 33-bus network is simulated. Every hour, plug-in hybrid electric vehicle (PHEV) charging and discharging, active power loss, and cost will be compared with IHS and PSO algorithms. The difference obtained using the IHS algorithm compared to the PSO algorithm is 1.002 MW and the voltage difference is 9.14 pu.

Integration of Distributed Generation and Plug-in Electric Vehicles on Power Distribution System by Using Queuing Theory

Plug-in electric vehicles (PEVs) and distributed generation (DG) can positively and negatively impact the distribution system. Therefore, this paper presents the modeling and analysis of DG and PEVs’ penetration levels of the three-phase unbalanced radial distribution system. The study aims to optimize the distribution system’s DG sizing and PEV charging to minimize total power loss. The test system is the 4th circuit of the Nonsung service station along Thaharn Road, Muang District, Udon Thani, Thailand. According to objective function and constraints, such control variables as installation buses and rated outputs of DG and the penetration levels of PEVs were obtained to evaluate the total power loss. Significantly, the charging demand of PEVs is an uncertain load estimated by queuing theory integration with the minimization tool called the differential evaluation (DE) method. According to the result comparison of a four case simulation, the total power losses of queuing theory and DE application are minimum. Finally, total power losses conform to the regulation of the Provincial Electricity Authority (PEA), Thailand.

A new decentralized architecture and a fully parallel distributed algorithm for charge scheduling of plug-in electric vehicles

In this paper, a distributed charge scheduling problem for plug-in electric vehicles (PEVs) is considered. It is shown that the problem of charge scheduling of PEVs in the centralized framework can also be recast into a Resource Allocation problem. A differentiation from existing literature is that this paper presents a new configuration of network architecture connecting charging station to PEVs for which a discrete-time fully parallel distributed primal-dual algorithm with fixed step size is proposed. In the proposed configuration, the existence of aggregator is removed, and the interactions are allowed among individual PEVs as well as between PEVs and the smart charging station (SCS) while private information of each PEV and the SCS is preserved. The heterogeneity in PEV charge specifications is considered in the formulated problem. The distributed algorithm can converge to an optimal solution of the problem for any convex cost functions of PEVs/utility with arbitrary initialization over any non-switching connected undirected networks without solving a sub-problem at each time step. Performance of the algorithm does not depend on the structure of the graph for convergence. Finally, a numerical example on load regulation is provided to demonstrate the results.

New optimal planning strategy for plug‐in electric vehicles charging stations in a coupled power and transportation network

AbstractThe use of plug‐in electric vehicles (PEV) and their developing technology can create new challenges to the smart power system. The type, method, and time of charging electric vehicles are also other issues. Allocating and determining the optimal capacity of electric vehicle charging stations (EVCS) is related to the technical requirements of the distribution network. This is economically important for the construction of charging stations. This paper proposes a new approach for optimal siting and sizing of PEV charging stations in a coupled electrical and transportation network. This work presents the problem from a techno‐economic point of view of the electrical network as a multi‐objective problem with the objectives of simultaneously reducing the cost of building EVCSs and active power losses. The Pareto method is used to solve the problem and to display optimal points. In order to carry out the simulation, the proposed method is tested on a case study of the standard IEEE 37‐bus network with a 25‐node transport system and the proposed solution in the subject environment. The Floyd–Warshall method is utilized to determine the shortest travel routes for PEVs. The obtained results confirm the effectiveness of the optimal planning of PEV charging stations.

Multi-Objective-Based Charging and Discharging Coordination of Plug-in Electric Vehicle Integrating Capacitor and OLTC

The integration of plug-in electric vehicles (PEVs) in residential distribution networks demands a significant amount of electrical load where random and uncoordinated charging affects the quality and performance of the distribution network. Random and uncoordinated charging may increase the peak demand and can increase stress on critical network assets such as line, transformer, and switching devices. Moreover, the charging of PEVs in a low network reduces the voltage of the system below the lower limit. On the other hand, using PEVs as storage in the V2G mode can improve the network condition. Therefore, it is critical to properly manage the charging and discharging operation of PEVs. This paper proposes a multi-objective-based charging and discharging coordination of PEVs with the operation of the capacitor and on-load tap changer (OLTC). With the proposed strategy, the distribution network is operated safely, and charging is ensured for all PEVs connected to the network. The main consideration of this research is to reduce the daily power loss, operational cost, and voltage deviation of the system. The metaheuristic optimization binary firefly algorithm (BFA) has been applied to coordinate PEV charging and discharging as well as capacitor and OLTC operation in the system. A modified IEEE 31 bus 23 kV distribution system is used to implement the proposed strategy. From the obtained results, it is found that the combined PEV charging and discharging coordination with capacitor and OLTC operation reduces the power loss and cost by 34.16% and 12.68%, respectively, with respect to uncoordinated charging and enhances the voltage condition of the network.

Coordination of Plug-In Electric Vehicle Charging in a Stochastic Framework: A Decentralized Tax/Incentive-Based Mechanism to Reach Global Optimality

We address the problem of charging plug-in electric vehicles (PEVs) in a decentralized way and under stochastic dynamics affecting the real-time electricity tariff. The model is formulated as a Nash equilibrium seeking problem, where players wish to minimize the costs for charging their own PEVs. For finite PEVs populations, the Nash equilibrium does not correspond to the social optimum, i.e., to a control strategy minimizing the total electricity costs at the aggregate level. We accordingly introduce some taxes/incentives on the price of electricity for charging PEVs and show that it is possible to tune them so that (a) the social optimum is reached as a Nash equilibrium, (b) in correspondence with this equilibrium, players do not pay any net total tax, nor receive any net total incentive.

Aggregative Game for Distributed Charging Strategy of PEVs in a Smart Charging Station

This paper proposes a charging strategy for plug-in electric vehicles (PEVs) in a smart charging station (SCS) that considers load constraints and time anxieties. Due to the rapidly growing load demand of PEVs and the load capacity investments in infrastructure, PEV charging needs to be subject to overload limits, beyond which failures can occur. The time anxiety is presented to address some of the uncertainties that may arise while charging PEVs. Under an aggregative game framework, this paper constructs a price-driven charging model to minimize costs by choosing the optimal charging strategy. Meanwhile, since the driver information is an aggregated item in the PEV cost function, the drivers’ privacy can be protected. Then, a distributed reflected forward–backward (RFB) splitting method is developed to search for the generalized Nash equilibria (GNE) of the game. The convergence of the proposed algorithm and the effectiveness of the charging strategy are verified by the detailed simulation and results.

A priority-based seven-layer strategy for energy management cooperation in a smart city integrated green technology

The notion of smart city is based on using both bidirectional power and data flows. For that, designing a persuasive control model for power flow becomes a big and crucial part in a smart city to optimize the power balance between production and consumption. So, a seven-layer smart city energy management strategy (SLSCEMS) for multiple home energy cooperation is presented in this paper. The optimised aims of this strategy are smoothing the smart homes power demand profiles, reducing the electricity bills (E.B), and gaining a total free charging of Plug-in Electric Vehicles (PEVs). This approach has been designed as hierarchic local and global layers. The first one is divided into three layers that aim to transfer the energy between each smart home and its own Renewable Energy sources (RES) and PEVs. The second one is split into four layers that aim to transfer the energy between each smart home and its PEVs, the neighboring homes and their RES, and the smart grid. All optimised layers are based on Particle Swarm Optimization (PSO) algorithm. The proposed strategy is evaluated in a city that contains one hundred homes classified into five categories, each category is designated by its power profile and its flexible number of RESs and PEVs. Simulation results show a decrease in the daily E.B of 26.24%, 2.42%, 60.33%, 29.51%, and 2.38% respectively of the five categories. So, these numerical results prove that the proposed SLSCEMS has considerable efficiency.

A Dual-Transformer-Based Hybrid Dual Active Bridge Converter for Plug-in Electric Vehicle Charging to Cope With Wide Load Voltages

A dual-transformer-based hybrid dual active bridge (DAB) converter for plug-in electric vehicle charging to cope with wide load voltages is introduced in this article. The control degrees of freedom are extended by splitting the leg and transformer of the conventional DAB converter (CDABC) in two parts. Based on the numerical analysis, the full-domain four-variable closed-form optimal trajectories targeting at the minimum root mean square value of the leakage inductor current is attainted, and the unified-segmental control strategy is proposed to handle the working modes shifting without the current inrush. As a result, the conduction losses and switching losses can be significantly reduced in boost mode compared to CDABC. The discussed converter and control strategy are verified by a 1.0-kW prototype.