Plug-in Electric Vehicles Charging Research Articles

Reliability-driven time-of-use tariffs for efficient plug-in electric vehicle integration

Demand response (DR) plays a crucial role in balancing electricity supply and demand, but existing studies on price-based DR for plug-in electric vehicle (PEV) load management lack comprehensive considerations. Therefore, this study designs reliability-driven time-of-use (TOU) tariffs for the integration of PEVs, considering various factors such as random component failures, the stochastic characteristics of non-dispatchable components, and fluctuations in load patterns and uncertainties. The proposed framework employs advanced stochastic simulation models to assess generation facility reliability amidst extensive PEV deployment in three steps. First, individual components, including conventional generation capacity, renewable energy sources, and demand, as well as PEV load models, are developed. Then, a system reserve margin assessment is carried out via the generation of seasonal synthetic time series data. During this step, innovative techniques, including box-plot methods and expert rules, are used to simplify the representation of the system reserve margin at each hour. Finally, optimized TOU pricing schedules for PEVs are generated. These pricing hours are initially categorized as off-peak, mid-peak, or on-peak, streamlining scenario analysis and aligning PEV charging profiles with predefined pricing schedules for grid reliability evaluation. Case studies have shown that the proposed method can improve the reliability indices by effectively mitigating the impacts.

Cleaning up while Changing Gears: The Role of Battery Design, Fossil Fuel Power Plants, and Vehicle Policy for Reducing Emissions in the Transition to Electric Vehicles.

Plug-in electric vehicles (PEVs) can reduce air emissions when charged with clean power, but prior work estimated that in 2010, PEVs produced 2 to 3 times the consequential air emission externalities of gasoline vehicles in PJM (the largest US regional transmission operator, serving 65 million people) due largely to increased generation from coal-fired power plants to charge the vehicles. We investigate how this situation has changed since 2010, where we are now, and what the largest levers are for reducing PEV consequential life cycle emission externalities in the near future. We estimate that PEV emission externalities have dropped by 17% to 18% in PJM as natural gas replaced coal, but they will remain comparable to gasoline vehicle externalities in base case trajectories through at least 2035. Increased wind and solar power capacity is critical to achieving deep decarbonization in the long run, but through 2035 we estimate that it will primarily shift which fossil generators operate on the margin at times when PEVs charge and can even increase consequential PEV charging emissions in the near term. We find that the largest levers for reducing PEV emissions over the next decade are (1) shifting away from nickel-based batteries to lithium iron phosphate, (2) reducing emissions from fossil generators, and (3) revising vehicle fleet emission standards. While our numerical estimates are regionally specific, key findings apply to most power systems today, in which renewable generators typically produce as much output as possible, regardless of the load, while dispatchable fossil fuel generators respond to the changes in load.

Distributed scheduling for multi-energy synergy system considering renewable energy generations and plug-in electric vehicles: A level-based coupled optimization method

Multi-energy synergy systems integrating high-penetration large-scale plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems have great potential to reduce the reliance of the grid on traditional fossil fuels. However, the random charging characteristics of plug-in electric vehicles and the uncertainty of photovoltaics may impose an additional burden on the grid and affect the supply–demand equilibrium. To address this issue, judicious scheduling optimization offers an effective solution. In this study, considering charge and discharge management of plug-in electric vehicles and intermittent photovoltaics, a novel Multi-energy synergy systems scheduling framework is developed for solving grid instability and unreliability issues. This formulates a large-scale mixed-integer problem, which calls for a powerful and effective optimizer. The new binary level-based learning optimization algorithm is proposed to address nonlinear large-scale high-coupling unit commitment problems. To investigate the feasibility of the proposed scheme, numerical experiments have been carried out considering multiple scales of unit numbers and various scenarios. Finally, the results confirm that the proposed scheduling framework is reasonable and effective in solving unit commitment problems, can achieve 3.3% cost reduction and demonstrates superior performance in handling large-scale energy optimization problems. The integration of plug-in electric vehicles, distributed renewable energy generations, and battery energy storage systems is verified to reduce the output power of 192.72 MW units during peak periods to improve grid stability. Therefore, optimizing energy utilization and distribution will become an indispensable part of future power systems.

An effective strategy for unit commitment of microgrid power systems integrated with renewable energy sources including effects of battery degradation and uncertainties.

The large use of renewable sources and plug-in electric vehicles (PEVs) would play a critical part in achieving a low-carbon energy source and reducing greenhouse gas emissions, which are the primary cause of global warming. On the other hand, predicting the instability and intermittent nature of wind and solar power output poses significant challenges. To reduce the unpredictable and random nature of renewable microgrids (MGs) and additional unreliable energy sources, a battery energy storage system (BESS) is connected to an MG system. The uncoordinated charging of PEVs offers further hurdles to the unit commitment (UC) required in contemporary MG management. The UC problem is an exceptionally difficult optimization problem due to the mixed-integer structure, large scale, and nonlinearity. It is further complicated by the multiple uncertainties associated with renewable sources, PEV charging and discharging, and electricity market pricing, in addition to the BESS degradation factor. Therefore, in this study, a new variant of mixed-integer particle swarm optimizer is introduced as a reliable optimization framework to handle the UC problem. This study considerssix various case studies of UC problems, including uncertainties and battery degradation to validate the reliability and robustness of the proposed algorithm. Out of which, two case studies defined as a multiobjective problem, and it has been transformed into a single-objective model using different weight factors. The simulation findings demonstrate that the proposed approach and improved methodology for the UC problem are effectivethan itspeers. Based on the average results, the economic consequences of numerous scenarios are thoroughly examined and contrasted, and some significant conclusions are presented.

A predictive two-stage user-centered algorithm for smart charging of plug-in electric vehicles considering the state of health of the battery

A predictive two-stage user-centered algorithm for smart charging of plug-in electric vehicles considering the state of health of the battery

Minimizing electric vehicle charging costs in the microgrid using the BFGS Quasi-Newton Method

Electric vehicles (EVs) offer a compelling solution for mitigating pollution, addressing environmental alterations, and enhancing energy security. This research presents a methodology employing the Broyden Fletcher Gold- farb Shanno quasi-Newton technique to streamline the charging costs associated with plug-in electric vehicles (PEVs). The initial step involves formulating an objective function directed at minimizing the expenses tied to PEV charging. This function takes into account crucial constraints pertaining to charger specifications, state of charge limitations, and voltage levels. Subsequently, we de- tail the application of the BFGS Quasi-Newton algorithm in computing node topology voltages within a microgrid featuring distributed energy resources (DERs). The findings demonstrate that the BFGS-enabled method outperforms alternative approaches in minimizing the cost of charging PEVs

If you build it, will they notice? public charging density, charging infrastructure awareness, and consideration to purchase an electric vehicle

This research tests an implicit assumption about plug-in electric vehicle (PEV) charging infrastructure: people—including people not interested in PEVs—see public PEV charging. Data from a survey of all car-owning households in California are combined with data on public PEV charging and PEV registrations to estimate a structural equation model to test the direct and indirect effects of public charging density on whether participants see charging infrastructure and whether they have considered acquiring a PEV. We also explore the direct and indirect effects of seeing public charging on PEV purchase consideration. Results show the density of public charging is neither directly nor indirectly related to whether people see public charging or their level of PEV purchase consideration. Our results suggests that for public charging to drive PEV sales growth, it may need to be accompanied by strategies to increase consumers’ engagement with PEVs.

Optimal Unit Commitment and Generation Scheduling of Integrated Power System with Plug-In Electric Vehicles and Renewable Energy Sources

The integration of wind energy sources and plug-in electric vehicles is essential for the efficient planning, reliability, and operation of modern electric power systems. Minimizing the overall operational cost of integrated power systems while dealing with wind energy sources and plug-in electric vehicles in integrated power systems using a chaotic zebra optimization algorithm (CZOA) is described. The proposed system deals with a probabilistic forecasting system for wind power generation and a realistic plug-in electric vehicle charging profile based on travel patterns and infrastructure characteristics. The objective is to identify the optimal scheduling and committed status of the generating unit for thermal and wind power generation while considering the system power demand, charging, and discharging of electric vehicles, as well as power available from wind energy sources. The proposed CZOA adeptly tackles the intricacies of the unit commitment problem by seamlessly integrating scheduling and the unit’s committed status, thereby enabling highly effective optimization. The proposed algorithm is tested for 10-, 20-, and 40-generating unit systems. The empirical findings pertaining to the 10-unit system indicate that the amalgamation of a thermal generating unit system with plug-in electric vehicles yields a 0.84% reduction in total generation cost. Furthermore, integrating the same system with a wind energy source results in a substantial 12.71% cost saving. Notably, the integration of the thermal generating system with both plug-in electric vehicles and a wind energy source leads to an even more pronounced overall cost reduction of 13.05%. The outcome of this study reveals competitive test results for 20- and 40-generating unit systems and contributes to the advancement of sustainable and reliable power systems, fostering the transition towards a greener energy future.

Model-free reinforcement learning-based energy management for plug-in electric vehicles in a cooperative multi-agent home microgrid with consideration of travel behavior

The rise in popularity of plug-in electric vehicles (PEVs) and the increasing use of renewable energy sources (RESs) have paved the way for advanced energy management systems (EMSs) that optimize energy usage and distribution in home microgrids (H-MGs). This study focuses on the integration of PEVs in H-MGs using an EMS and analyzes its impact on electrical power grids (EPGs). We examine the effect of PEV charging and discharging patterns on H-MG energy flow, considering various scenarios such as high renewable energy generation, different levels of PEV penetration, and EPG connection status. Our findings indicate that an efficient EMS can significantly enhance H-MGs’ overall efficiency by intelligently scheduling PEV charging and discharging, maximizing the use of locally generated renewable energy, and reducing peak load on the EPG. We demonstrate that a cooperative multi-agent system EMS, driven by a robust continuous and real-time fuzzy Q-learning (FQL) method, can reduce electricity market prices by 15% by increasing the use of renewable energy generation by 25%. To fully realize the benefits of EMS, we address challenges such as reducing dependence on EPG by 30%, improving battery state by 12%, and ensuring EPG stability in the face of uncertainties.

Inverse-Impedance-Based Centralized Predictive Current Controller for $N$-Parallel Grid-Connected Converters for Circulating Current Elimination

A constant switching frequency centralised predictive grid current controller is proposed for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -parallel grid-connected converters. The proposed work solves the circulating current problem even when converters of different power ratings are paralleled. The circulating current flow is eliminated by equalising the converter terminal voltages of the parallel converters. A grid equivalent model of the converter is mathematically derived using Thevenin's method to achieve the objective. The converters operate at the same power factors with their currents shared, inversely w.r.t. their line inductances/impedances. The method is flexible in applying any modulation method, without contributing to low-frequency circulating currents. Hardware experiments and software simulations validated the inverse-impedance-based current sharing among the parallel converters. Experiment results show quick redistribution of the grid current among the converters under dynamic conditions like incoming/outgoing converter scenarios. The computational requirement with the proposed scheme is independent of the number of converters in parallel. The proposed design is easy to implement and can be deployed for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$N$</tex-math></inline-formula> -parallel converter systems for grid applications like interlinking converters in AC/DC grids, and plug-in type electric vehicle charging stations.

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.