Plug-in Hybrid Electric Vehicles Charging Research Articles

Multi-objective energy management using a smart charging technique of a microgrid with the charging impact of plug-in hybrid electric vehicles

The Microgrid (MG) concept is being developed to better integrate renewable energy sources and automate distribution networks. Microgrids combine distributed generating units (DGs) and energy storage systems to achieve this. This research paper aims to simultaneously minimize the daily operational cost and net environmental pollution of a small MG system, factoring in the charging demand from Plug-in-Hybrid Electric Vehicles (PHEVs) and consumer load demands. The proposed energy management process not only minimizes operational costs and emissions, but also determines the optimal battery size for the energy storage system. The analysis also explores the importance of two critical variables - the operation and maintenance costs of the DGs, and the total daily cost of the battery energy storage system. The demand for PHEV charging is managed using an intelligent charging approach. Given the complexity of the optimization, a recently developed metaheuristic algorithm, Slime Mould Algorithm (SMA), is applied. The performance of SMA is compared against the Grasshopper Optimization Algorithm and Sine Cosine Algorithm. To solve the multi-objective problem, a weighted sum method maintaining non-dominance and a fuzzy decision-maker technique are employed alongside the suggested algorithms. Three different scenarios verify the proposed method's effectiveness.

Coordinated Charging Scheduling Approach for Plug-In Hybrid Electric Vehicles Considering Multi-Objective Weighting Control in a Large-Scale Future Smart Grid

The growing popularity of plug-in hybrid electric vehicles (PHEVs) is due to their environmental advantages. But uncoordinated charging of a large number of PHEVs can lead to a significant surge in peak loads and higher charging costs for PHEV owners. To end this, this paper introduces an innovative approach to address the issue by proposing a multi-objective weighting control for coordinated charging of PHEVs in a future smart grid, which aims to find an economically optimal solution while also considering load stabilization with large-scale PHEV penetration. Technical constraints related to the owner’s demand and power limitations are considered. In the proposed approach, the charging behavior of PHEV owners is modeled by a normal distribution. It is observed that owners typically start charging their vehicles when they arrive home and stop charging when they go to their workplace. The charging cost is then calculated based on the tiered electricity price and charging power. By adjusting the cost weighting factor and the load stability weighting factor in the multi-objective function, the grid allows for flexible weight selection between the two objectives. This approach effectively encourages owners to actively participate in coordinated charging scheduling, which sets it apart from existing works. The algorithm offers better robustness and adaptability for large-scale PHEV penetration, making it highly relevant for the future smart grid. Finally, numerical simulations are presented to demonstrate the desirable performance of theory and simulation.

Optimal Deployment of DGs, DSTATCOMs and EVCSs in Distribution System using Multi-Objective Artificial Hummingbird Optimization

Distribution systems have a lot of obstacles to deal with, like increasing load demands, environmental issues, operating limits, and infrastructure development limitations. On the other hand, the number of plug-in hybrid electric vehicles (PHEVs) has grown significantly in recent years and is likely to continue due to concerns over the environment and fossil fuel shortages. Due to the increasing use of PHEVs, distribution systems were not built to accept them, requiring planners to create parking lots that support PHEV charging. To address these issues, in this study, optimal planning of distributed generation (DG) and electric vehicle charging stations (EVCS) in radial distribution systems by the maiden application of a novel Pareto-based multi-objective artificial hummingbird optimization (MOAHO) algorithm is addressed. Three technical aspects of the distribution system are improved by optimal planning of various types of DGs and EVCSs: active power loss reduction, total voltage deviation minimization, and voltage stability improvement. The Pareto-based MOAHO is employed to generate the optimal front between the three competing objectives and later TOPSIS method is employed for selecting the most compromised solution from the optimal front. The proposed methodology is tested on IEEE-33, IEEE-69 bus radial distribution test systems. To validate the efficacy of the MOAHO algorithm, the simulation outcomes of the proposed methodology are generated using a multi-objective non-dominated sorting genetic algorithm (NSGA2), particle swarm optimization algorithm (PSO), grey wolf optimization algorithm (GWO) and compared with the outcomes of the MOAHO algorithm.

Flexible Charging to Energy Saving—Strategies Assessment with Big Data Analysis for PHEVs Private Cars

In road transport, most vehicles today still rely on internal combustion engines. However, these engines have lower efficiency and generate higher pollution levels compared to electric motors. Consequently, there is a growing interest in the transition from conventional vehicles to electric ones. However, the transition to an electrified road transport system is not without challenges. Among these, the impact that electric vehicle charging will have on the electricity grid is of particular concern. This paper analyzes different charging scenarios for plug-in hybrid electric vehicles (PHEVs) and proposes charging strategies to minimize their impact on the electricity grid. The analysis is based on a large dataset of trips in urban areas in Italy. The study shows that smart charging of PHEVs can be implemented to minimize the impact on the electricity grid. The implementation of optimized charging strategies can contribute to making PHEVs a valid, eco-sustainable alternative to conventional vehicles while also promoting the stability and efficiency of the electricity grid. The study aims to verify the effectiveness and efficiency of the flexible charging strategy by comparing the common charging operation (first in–first out) with other, less impactful charging schemes.

Enhancing microgrid sustainability: Dynamic management of renewable resources and plug-in hybrid electric vehicles

This paper addresses the challenges inherent in Microgrids (MGs), Renewable Energy Sources (RES), and Plug-in Hybrid Electric Vehicles (PHEVs), presenting a pioneering approach for a sustainable energy future. The focal point is a novel Multi-Stage Dynamic Energy Management (EM) framework, strategically designed to optimize the use of non-dispatchable RES and PHEVs while reducing dependency on the Utility Grid (UG). The research integrates Solar Photovoltaic arrays (SPVs), Wind Turbines (WTs), Micro Turbines (MTs), Battery Energy Storage Systems (BESS), PHEVs, and loads, forming a comprehensive MG to enhance overall efficiency and resilience. The core innovation lies in the dynamic EM framework, minimizing UG dependency and adapting to stochastic RES generation and PHEV charging patterns. The study underscores the significance of demand response in enhancing energy efficiency and grid reliability. Demonstrating effective RES utilization, the research showcases the potential for a greener and more resilient energy infrastructure. Through advanced simulations, the proposed framework's performance is rigorously evaluated under various scenarios, including high RES penetration. The results not only affirm the framework's effectiveness but also highlight its potential to revolutionize MG management by reducing grid dependency and maximizing renewable resource utilization.

Efficient Microgrid Management with Meerkat Optimization for Energy Storage, Renewables, Hydrogen Storage, Demand Response, and EV Charging

Within microgrids (MGs), the integration of renewable energy resources (RERs), plug-in hybrid electric vehicles (PHEVs), combined heat and power (CHP) systems, demand response (DR) initiatives, and energy storage solutions poses intricate scheduling challenges. Coordinating these diverse components is pivotal for optimizing MG performance. This study presents an innovative stochastic framework to streamline energy management in MGs, covering proton exchange membrane fuel cell–CHP (PEMFC-CHP) units, RERs, PHEVs, and various storage methods. To tackle uncertainties in PHEV and RER models, we employ the robust Monte Carlo Simulation (MCS) technique. Challenges related to hydrogen storage strategies in PEMFC-CHP units are addressed through a customized mixed-integer nonlinear programming (MINLP) approach. The integration of intelligent charging protocols governing PHEV charging dynamics is emphasized. Our primary goal centers on maximizing market profits, serving as the foundation for our optimization endeavors. At the heart of our approach is the Meerkat Optimization Algorithm (MOA), unraveling optimal MG operation amidst the intermittent nature of uncertain parameters. To amplify its exploratory capabilities and expedite global optima discovery, we enhance the MOA algorithm. The revised summary commences by outlining the overall goal and core algorithm, followed by a detailed explanation of optimization points for each MG component. Rigorous validation is executed using a conventional test system across diverse planning horizons. A comprehensive comparative analysis spanning varied scenarios establishes our proposed method as a benchmark against existing alternatives.

Operational cost minimization of a microgrid with optimum battery energy storage system and plug-in-hybrid electric vehicle charging impact using slime mould algorithm

Microgrid (MG) with battery energy storage system (BESS) is the best for distribution system automation and hosting renewable energies. The proliferation of plug-in hybrid electric vehicles (PHEV) in distribution networks without energy management (EM) puts additional pressure on the utility and creates challenges for MG. This research article proposes a stochastic expert method to minimize the total operational cost through proper EM of a grid-connected low-voltage MG by considering the charging impact of PHEVs with the optimal size of BESS. Three strategies are used to control the PHEV charging demand. Economically improved performance of MG is obtained as compared to previous research without considering the daily cost of the BESS (fBESS) and operation and maintenance cost of different distributed generation sources (OMcost). Then, the study is extended by incorporating these two parameters into the objective function of operational cost. Finally, this article analyzes to what extent the fBESS and OMcost factors raise the microgrid's operational cost. Due to non-linear optimization issues, the slime mould algorithm (SMA) is proposed, which performed better EM with a lower operational cost of MG than other methods.

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.

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.

Reinforcement Learning-Enabled Electric Vehicle Load Forecasting for Grid Energy Management

Electric vehicles are anticipated to be essential components of future energy systems, as they possess the capability to assimilate surplus energy generated by renewable sources. With the increasing popularity of plug-in hybrid electric vehicles (PHEVs), conventional internal combustion engine (ICE)-based vehicles are expected to be gradually phased out, thereby decreasing greenhouse gases and reliance on foreign oil. Intensive research and development efforts across the globe are currently concentrated on developing effective PHEV charging solutions that can efficiently cater to the charging needs of PHEVs, while simultaneously minimizing their detrimental effects on the power infrastructure. Efficient PHEV charging strategies and technologies are necessary to overcome the obstacles presented. Forecasting PHEV charging loads provides a solution by enabling energy delivery to power systems based on anticipated future loads. We have developed a novel approach, utilizing machine learning methods, for accurately forecasting PHEV charging loads at charging stations across three phases of powering (smart, non-cooperative, and cooperative). The proposed Q-learning method outperforms conventional AI techniques, such as recurrent neural and artificial neural networks, in accurately forecasting PHEV loads for various charging scenarios. The findings indicate that the Q-learning method effectively predicts PHEV loads in three scenarios: smart, non-cooperative, and cooperative. Compared to the ANN and RNN models, the forecast precision of the QL model is higher by 31.2% and 40.7%, respectively. The Keras open-source set was utilized to simulate three different approaches and evaluate the efficacy and worth of the suggested Q-learning technique.

Probabilistic optimal power dispatch in a droop controlled islanded microgrid in presence of renewable energy sources and PHEV load demand

This paper proposes a stochastic optimal power allocation strategy for a droop controlled islanded microgrid (DCIMG) with a medium X/R ratio in presence of combined heat and power-based distributed generators (DGs), renewable generators, switched capacitor banks, plug-in hybrid electric vehicle (PHEV) loads, voltage dependent electric loads and heat loads. Optimal values of stochastic power demand due to PHEV charging and discharging load are determined based on the goals of achieving target state-of-charge of PHEV batteries and effective peak shaving during peak load periods, respectively. Active power of dispatchable DGs is optimally allocated depending on the simultaneous fulfillment of an economic objective, an environmental objective along with three network related objectives. Hong's 2m+1 point estimate method (PEM) and Hong's 2m+1 PEM coupled with Nataf transformation (NT) are employed to handle the uncorrelated uncertainties accompanied with PHEV load demand and the correlated uncertainties associated with spatially correlated renewable generation and load demand. Active and reactive power static droop coefficients, and frequency and voltage reference settings of the dispatchable DG units are considered as the control variables for optimal dispatching of active power. A modified hybrid particle swarm and grey wolf optimizer embedded in fuzzy framework is used to solve the constrained multi-objective optimization problem. The efficacy of the proposed framework is validated on a 33-node droop controlled islanded microgrid test system.

Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles

The energy crises and environmental problem can be solved by using plug-in hybrid electric vehicles (PHEVs), the integration of large number of PHEVs with high control capabilities and storage can improve the distribution network flexibility. However, the optimal management of these vehicles in the presence of renewable energy resources (RESs) represents a big challenge that must be adopted, this can be accomplished in the form of microgrid (MG). Therefore, this paper proposes a new energy management strategy (EMS) incorporated bald eagle search (BES) optimizer for MG with RESs and PHEVs to regulate the generation of each unit. The considered devices installed in the MG are wind turbine (WT), photovoltaic (PV), micro turbine (MT), fuel cell (FC), storage battery, PHEVs, and grid. The problem is formulated as an optimization problem that aims at minimizing the total operating cost and mitigating the environmental pollutant emission. Two scenarios related to the operation of RESs are considered in addition to three charging modes of EVs which are uncoordinated, coordinated, and smart. The proposed BES-EMS is validated via conducting comparison to literature works of Fuzzy self-adaptive particle swarm optimizer (FSAPSO) and gravitational search and pattern search (GSA-PS) algorithm in addition to new programmed optimizers of Runge Kutta optimization (RUN), mountain gazelle optimizer (MGO), chef-based optimization algorithm (CBOA), beluga whale optimization (BWO), and dandelion optimizer (DO). Moreover, statistical tests of Friedman rank, ANOVA table, Wilcoxon rank, and Kruskal Wallis are conducted to assess the proposed BES-EMS. In scenario (1), the proposed BES outperformed all optimizers achieving the best operating cost and emission of 111.2518 €ct and 182.0741 kg respectively, the cost is saved by 57.66 % compared to GSA-PS while the emission is mitigated by 56.86 % compared to FSAPSO. In scenario (2), the proposed BES-EMS mitigated the cost and emission by 70.68 % and 51.78 % rather than GSA-PS and FSAPSO respectively. Moreover, in scenrio (3) the proposed approache saved the cost by 61.49 % and 67.29 % compared to GSA-PS during smart charging of PHEVs in normal and rated operations of RESs respectively. Furthermore, according to the Friedman rank test the proposed BES-EMS achieved the first rank with p-value of 2.1. The fetched results proved the robustness and competence of the proposed BES as an efficient energy management strategy for MG.

Battery sizing of 48 V plug-in hybrids considering calendar and cycle degradation

Plug-in hybrid electric vehicles (PHEVs) with battery packs tailored to the driving use case can help to reduce the environmental footprint of the transportation sector. Compared to common high-voltage systems, PHEVs based on a low-voltage level show a higher fuel consumption, but in return benefit from lower component costs and allow the utilization of cheaper high-energy cells. In this paper, the battery size of a 48 V PHEV concept is optimized to minimize the operational costs while taking battery degradation into account and ensure a lifetime-robust system layout. To investigate the applicability of high-energy batteries, 31 automotive-grade cells were investigated experimentally in a calendar and cycle aging study. The results show that calendar aging has a significant contribution of 17.5 % to the overall capacity loss and should be considered during the battery design process. The cycle degradation model is integrated in a Dynamic Programming simulation environment with various real-driving speed and slope profiles, which are extracted from a measured year-round driving profile. The simulation results show, that considering the degradation in the energy management strategy reduces the capacity loss but results in higher operational costs throughout the vehicle lifetime. The extension of a mild hybrid vehicle to a PHEV can reduce the operational costs by 18.5 %. If the vehicle is not charged, the costs increase by 6 % highlighting the need for frequent charging of PHEVs.

A Mixed-integer Linear Programming Model for the Plug-in Electric Vehicle Charging Problem in Unbalanced Low Voltage Electrical Distribution Systems Considering Neutral Conductor

The increasing penetration of plug-in hybrid electric vehicles (PHEVs) imposes new challenges in low voltage distribution networks (LVDNs), due to worsening load imbalance, power losses, and increased probability of blackouts because of the overloads. To overcome these issues, PHEV charging must be performed in a coordinated fashion. In this context, there are several coordinated charging methods in the literature, none of which considers modeling neutral conductors of LVDN.This paper aims to present a mixed-integer linear programming model (MILP) for the PHEV charging problem in unbalanced LVDNs, considering the neutral conductor. In this regard, the main parameters of unbalanced LVDN, such as voltage profile and power loss, are examined with and without modeling the neutral conductor. The results indicate that it is required to consider the neutral conductor to achieve realistic values of the LVDN's parameters. The proposed model is applied to a 449-nodes smart distribution grid consisting of 31 nodes at a medium voltage (MV) level and 418 nodes at a low voltage (LV) level. To verify the performance of the proposed model, it is compared with two uncoordinated charging methods. The results show the effectiveness of the proposed strategy.

Optimal allocation of renewable energy source and charging station for PHEVs

Renewable energy forms have a comparatively less impact on the environment when compared with non-renewable sources. Renewable energies like wind power, biomass, hydropower, solar power, and geothermal energies are preferred for production as they do not run out quickly. Owing to the stochastic nature of renewable energy sources (RES) and Plug-in hybrid electric vehicles (PHEVs) load demand, large-scale penetration of these resources from the power system affects the network performance like reduce power quality, increase power loss, and voltage deviation. These issues can be addressed by the optimum planning depending upon the variables outcome in RES to satisfy the extra demand affected by PHEV charging. At the same time, designing a proper and effective energy management strategy in PHEV can be considered an optimization problem and can be resolved by the use of metaheuristic algorithms. With this motivation, this paper presents a novel deep learning with metaheuristic optimization based allocation of RES and charging stations for PHEVs. The proposed model depends upon model predictive control (MPC) where the actual battery state of charge (SOC) is concerned with real time energy management. Moreover, the black widow optimization (BWO) algorithm is used for the optimal allocation of RES and charging stations. The BWO algorithm derives objective functions using different parameters of the charging stations. In addition, based on the MPC model, deep stacked autoencoder (DSAE) is applied for the prediction of near-future velocity. The experimental results stated that the presented model has resulted in improved efficiency of the proposed model over the other methods.

The effect of plug-in hybrid electric vehicle charging on fuel consumption and tail-pipe emissions

Plug-in hybrid electric vehicles (PHEV) have an electric motor and an internal combustion engine and can reduce greenhouse gas emissions (GHG) from transport. However, their environmental benefit strongly depends on the charging behaviour. Several studies have analysed the GHG emissions from upstream electricity production, yet the impact of individual charging behaviour on PHEV tail-pipe carbon emissions has not been quantified from empirical data so far. Here, we use daily driving data from 7,491 Chevrolet Volt PHEV with a total 3.4 million driving days in the US and Canada to fill this gap. We quantify the effect of daily charging on the electric driving share and the individual fuel consumption. We find that even a minor deviation from charging every driving day significantly increases fuel consumption and thus tail-pipe emissions. Our results show that reducing charging from every day to 9 out of 10 days, increases fuel consumption on average by 1.85 ± 0.03 l/100 km or 42.7 ± 0.8 gCO2 km−1 tail-pipe emissions (± on standard error). Charging more than once per driving day has less impact in our sample, this must occur during at least 20% of driving days to have a noteworthy effect. Even then, a 10% increase in frequency only has moderate effect of decreasing fuel consumption on average by 0.08 ± 0.02 l/100 km or 1.86 ± 0.46 gCO2 km−1 tail-pipe emissions. Our results illustrate the importance of providing adequate charging infrastructure and incentives for PHEV users to charge their vehicles on a regular basis in order to ensure that their environmental impact is small as even long-range PHEVs can have a noteworthy share of conventional fuel use when not regularly charged.

Legendre-wavelet embedded NeuroFuzzy feedback linearization based control scheme for PHEVs charging station in a microgrid

The immense emergence of plug-in hybrid electric vehicles (PHEVs) is envisioned in the future. The rapid proliferation of PHEVs and their charging triggers intense surges in the load during load peak hours. A sophisticated controlled charging station is developed for PHEVs to alleviate grid load during peak demand hours. A novel feedback linearization embedded full recurrent adaptive NeuroFuzzy Legendre wavelet control (FBL-FRANF-Leg-WC) technique is employed to control the charging of PHEVs. The antecedent part of the NeuroFuzzy framework is based on recurrent Gaussian membership function while the consequent part comprises of recurrent Legendre wavelet. The charging station is integrated into a grid-connected microgrid hybrid power system. The charging station consists of five different PHEVs with seven different modes of operation. The performance of the control scheme is tested for various power quality and power system stability parameters. The effectiveness of the suggested control scheme is validated through simulation results by comparing with adaptive NeuroFuzzy, adaptive PID, and conventional PID control scheme.

An Optimized PV based Plug-in Hybrid Electric Vehicle Charging Station Using Fuzzy-PI Controller Logic

An Optimized PV based Plug-in Hybrid Electric Vehicle Charging Station Using Fuzzy-PI Controller Logic

Enhancing system reliability by optimally integrating PHEV charging station and renewable distributed generators: A Bi-Level programming approach

A hybrid bi-level programming approach is presented in this paper to enhance the system reliability by optimally integrating the Vehicle Charging Stations (VCS) of Plug-in Hybrid Electric Vehicle (PHEV) and the Renewable Distributed Generation (RDG) simultaneously. A wide spectrum of requirement exists among the customers to have a continuity of supply in the presence of the fluctuating nature of RDS. Thus, a non-linear objective function is formulated to minimize the Energy Not Supplied (ENS) to the customers based on the various contingency analysis. Two notable contributions distinguish this work with existing endeavours. Firstly, Simultaneous selection of optimal place for both RDG and charging station are recognized. Succeeded by the consideration of simultaneous integration of VCS and RDG, a Hybrid Nelder-Mead Cuckoo Search (HNM-CS) algorithm based method is put into operation to minimize the ENS, which seamlessly diminishes the power loss and enhances the voltage magnitude of the system. The distribution systems of standard IEEE 33-bus and real time TamilNadu (TN) 84 bus are considered with different RDGs such as photovoltaic and fuel cell systems. Further, the operational cost of PHEVs scheduling in the VCS is analysed for a 24-h scenario. From the results obtained, the proposed method provides maximum advantage to the vehicle holder by placing and utilizing more RDGs and meanwhile it satisfies their preferences also.

Smart V2G/G2V Charging Strategy for PHEVs in AC Microgrids Based on Maximizing Battery Lifetime and RER/DER Employment

Nowadays, the problem of the proper charging of plug-in hybrid electric vehicles (PHEVs) has become increasingly difficult because there is high uncertainty on both the demand and supply sides of microgrid (MG) systems, which may reduce efficiency and increase the cost of MG systems. Available solutions to these challenges are the application of energy storage systems and energy exchange with the upstream utility grid (UUG). However, uncoordinated PHEV charging could have adverse effects on MGs, such as increasing the amounts of energy exchanged between the MGs and the UUG as well as decreasing the battery energy storage systems (BESSs) lifetime. This article proposes a smart charging scheme for PHEVs in ac MGs that can simultaneously minimize the energy drawn from the UUG to charge PHEVs and maximize the lifetime of BESSs based on a multiobjective optimization algorithm. For proving the validity of the proposed power management strategy (PMS), offline digital time-domain simulation studies are conducted based on the modified version of the IEEE 33-bus test system. The obtained results are compared with the results of other previously reported PMSs, which show that by increasing the output power of DER units (even when the penetration level of PHEVs increases), the proposed PMS can ensure proper charging of PHEVs while improving the lifetimes of BESSs and decreasing the energy drawn from the UUG.