Charging Cost Research Articles

Reducing Electric Vehicles Charging Costs Utilizing Optimal Control Method Based on Diverse Charging Patterns

Plug-in electric vehicles (PEVs) present a promising solution for decreasing greenhouse gas emissions and conserving natural oil reserves. However, the growing number of PEVs connected to the electric grid raises concerns regarding the reliable and safe operation of the network. This concern stems from the fact that the charging operation of electric vehicles (EVs) involves a significantly high level of electricity consumption due to the size of EVs' battery charging period. Unscheduled charging activities can lead to increased electricity consumption, increase in PEV charging costs and overload on the distribution grid. To address this issue, this paper proposes a coordinated charging schedule for PEVs, utilizing an optimized EV battery charging model with an optimal control method that deals with finding the best possible control signal for a dynamic system over period of time aiming to optimize a particular performance index, while satisfying various constraints such as boundary condition, state, and control path. The results demonstrate that the proposed optimized charging schedule reduces charging costs by up to 21% compared to an unscheduled charging pattern. Index Terms— Charging cost, coordinated charging, optimal control, plug-in electric vehicle, penetration level, unscheduled charging.

Smart City Charging Station allocation for electric vehicles using analytic hierarchy process and multiobjective goal-programming

In view of recent developments in urban transport systems, there has been a significant increase in the presence of electric vehicles on the market. One of the major challenges faced is the effective allocation of these electric vehicles to appropriate charging stations. The aim of this study is to tackle this challenge by leveraging various input parameter models within a smart city context. The primary objective is to assign electric vehicles to the most suitable charging stations, taking into account four objectives: maximizing energy requested by electric vehicles, minimizing total response time, reducing charging costs, and minimizing battery degradation. Several factors, such as travel distance, state of charge, grid-to-vehicle energy trading, time, road traffic, driver priorities, and charging station capacity, are taken into account to address this problem. In addition, we propose a novel methodology that combines the analytic hierarchy process with a multi-objective goal programming model to aid decision-making regarding the allocation of electric vehicles–charging stations. The aim of this methodology is to assist electric vehicle owners in selecting effective charging stations within a complex decision-making environment. In addition, practical examples of how these methodologies can be used to solve a real-life problem have been highlighted. We validated the effectiveness of the proposed method in terms of enhancing the satisfaction factor of electric vehicles by updating their energy levels and reducing range anxiety through simulation results using SUMO and GAMS with the BARON solver. These results confirm the efficacy of the proposed method.

A game-theoretic approach to mitigate charging anxiety for electric vehicle users through multi-parameter dynamic pricing and real-time traffic flow

While price-based demand response is universally recognized as crucial for managing electric vehicle charging load, the academic literature has explored diverse mechanisms for its implementation. Prior research has revealed that applying load management schemes based on price-based demand response programs results in higher scheduling costs for low or constant-energy consumers. In addition, the inherent uncertainties involved in arrival, service requirements, market pricing and user behaviours have posed serious challenges in managing real-time charging operations. To handle these challenges, this work has addressed the charging problem at the scheduling level by analysing arrival, waiting, service and departure requests. This work proposed a mathematical model to coordinate charging to minimize the total charging cost and time for a given number of vehicles. We have developed an advanced charging algorithm and introduced a multi-leader multi-follower Stackelberg game. This strategic model ensures a Nash equilibrium, aligning the interests of consumers (vehicles) and providers (charging stations), ensuring a fair and balanced charging system. Where, charging load demand, real-time pricing, charging location, and vehicle types are used as input parameters. In the first stage, the real-time pricing obtained from the Spanish Electricity network is used to schedule the charging load, whereas the second stage is dedicated to using the proposed multi-parameter pricing for customer satisfaction and reduced cost. The results demonstrate the superior performance of our proposed scheme compared to existing approaches. Finally, we present a real-life case study to illustrate the practical effectiveness of the proposed techniques. The study underscores the potential of our approach in real-world scenarios, offering a promising avenue for enhancing efficiency and user experience in charging systems.

Optimizing Electric Vehicle Charging Costs Using Machine Learning

Optimizing Electric Vehicle Charging Costs Using Machine Learning

Hierarchical control for collaborative electric vehicle charging to alleviate network congestion and enhance EV hosting in constrained distribution networks

This paper introduces a novel hierarchical architecture aimed at enhancing coordination between distribution system operators and electric vehicle aggregators in order to minimize Electric Vehicle (EV) charging costs for users while optimizing EV hosting capacity to alleviate network congestion. Real-world distribution networks are employed to evaluate EV charging strategies and their impact on medium and low-voltage networks. Two distinct EV charging optimization strategies are proposed to ensure fair power allocation among EV Aggregators (EVAs), alleviating congestion while managing EV charging power efficiently. Results demonstrate that the proposed collaborative EV charging effectively flattens the load curve, reducing peak power and avoiding grid congestion. The main findings underscore the importance of incentivizing EV flexibility to support Distribution System Operator (DSO) objectives beyond static tariffs. Furthermore, a battery degradation model is introduced into the optimization problem, reducing high currents and capacity decay. Despite capturing a higher mean electricity price, the total cost of EV charging is reduced.

Sustainable location-routing problem for medical waste management using electric vehicles

The sustainable management of medical waste is essential for environmental protection and public health safety, contributing to urban development and societal well-being. This study focuses on the medical waste Location-Routing Problem with Electric Vehicles (EVs), aiming to mitigate infection risk while providing cost-effective and energy-efficient logistics solutions. It encompasses diverse elements such as disposal location selection, vehicle scheduling, recharge strategies, time windows, working duration, and load-dependent energy consumption. To tackle this complex problem, we introduce an Evolutionary Decomposition-based Adapted Large Neighborhood Search algorithm (E-ALNS/D), which surpasses existing methods such as MOEA/D-HGS, MOEA/D-LNS, MPGA, and NSGAII-LNS across various urban settings. Empirical analysis and a case study reveal opportunities for enhancing medical waste management with EVs. Key findings show that optimizing service times, adopting fast-charging infrastructure, and strategically placing temporary disposal facilities in response to unforeseen demand spikes significantly reduce energy consumption, lower infection risk, and cut charging cost. Furthermore, selecting peripheral over central disposal locations better minimizes infection risks. This research offers valuable insights for decision-makers in EV-based medical waste management, emphasizing the need to balance public health, environmental sustainability, and economic efficiency. Our methodology paves the way for future research in sustainable medical waste management with EVs.

Study on charge and discharge control strategy of improved PSO for EV

In view of the negative influence of electric vehicle (EV) random charging on power grid load stability and charging cost of users, on the premise of guaranteeing users' travel demand, in this paper, an improved PSO model is proposed with the objective function of optimizing the daily load variance of the power grid and minimizing the charge cost of the vehicle owner. In this model, chaotic mapping is used to initialize the position of particles so that the particles are uniformly distributed in space and the diversity of particle solutions is increased, based on the global search ability and local search ability of the improved algorithm, combined with the analysis of a numerical example, the results show that the improved multi-objective PSO algorithm converges quickly and can jump out of the local optimum, better multi-objective optimization to reduce the peak-valley load difference and charging costs.

Planning renewables in distribution system with electric vehicles to improve hosting capacity and energy losses: Two-stage stochastic optimization framework

Excessive penetration of renewables in distribution system can create several operational issues. One of the technologies that can maximize accommodation of renewables is electric vehicles (EVs). However, along with EVs additional measures are required while maximizing hosting capacity (HC) to avoid negative impact on system and account for uncertain parameters. In this regard, this paper proposes a two-stage stochastic optimization approach for planning of renewables in distribution system integrated with EVs. In the first stage, capacity of renewables is determined while maximizing HC whereas in the second stage operating strategy of EVs, renewables and grid power is evaluated to minimize expected energy losses. The proposed framework includes uncertainties in load demand, renewables and EVs characteristics through stochastic programming approach. The optimization problem is formulated as second order cone programming problem. The proposed model is tested on modified IEEE-33 bus and real-life 108-bus Indian distribution systems with different charging/discharging patterns and penetration rates of EVs. Moreover, an economic based cost analysis to compare planning cost and EV charging cost is also provided. Simulation results reveal that in 33-bus system with 500 EVs, the HC with vehicle-to-grid capability (V2G) is higher as compared to uncoordinated and coordinated charging by 29.86% and 6.36% respectively, while in 108-bus system with 1000 EVs, it is higher by 18.51% and 5.69% respectively. Further, the energy losses with V2G are 27.54% and 1.36% less in 33-bus system and 7.31% and 3.22% less in 108-bus system as compared to uncoordinated and coordinated charging respectively.

Bi-Directional Optimization of V2G Strategy Based on Multi-Objective Optimization: Balancing Grid Load and Reducing Electric Vehicle Charging Costs

Bi-Directional Optimization of V2G Strategy Based on Multi-Objective Optimization: Balancing Grid Load and Reducing Electric Vehicle Charging Costs

Enhancing electric vehicle performance through buck‐boost converters with renewable energy integration using hybrid approach

AbstractThe electrification of vehicles has emerged as a pivotal technique for addressing environmental concerns and reducing reliance on conventional fuel sources. Conversely, the best way to incorporate renewable energy into electric vehicles (EVs) is still a challenging task, particularly in enhancing the performance of EVs through efficient energy management. The transition to EVs has gained momentum as part of global efforts to mitigate environmental impacts and reduce dependence on fossil fuels. This paper proposes a hybrid method for enhancing EV performance through buck‐boost converters with renewable energy integration. The proposed technique is the joined execution of Flying Foxes Optimization (FFO) and Viscoelastic Constitutive Artificial Neural Networks (vCANNs) techniques. The proposed method's goal is to enhance the energy efficiency, minimize EV charging cost, and mitigating environmental impacts. The renewable energy sources: solar panels, fuel cells, and wind turbines, are integrated into the EV power system through buck‐boost converters. The buck‐boost converter's control signal is optimized through the FFO method. vCANNs are used to predict these control parameters. The proposed strategy is executed in MATLAB software and is compared with existing strategies. In comparison with other current approaches like particle swarm optimization, heap based optimizer, and wild horse optimize, the proposed method achieves a high efficiency of 99% and low cost of 0.05 €/KWh.

Optimal hybrid power plants for electric vehicle charging demand

Transmission constraints, increasing motivations to decarbonize, and concerns over peak electric vehicle (EV) load impacts on local grids have driven electric customers to consider behind-the-meter, hybrid power plant generation and storage at the distributed-grid level for EV charging. In this study, we develop capabilities to optimize hybrid power plant component capacities for EV charging. We then demonstrate these capabilities in a case study for Boulder, Colorado, using public EV charging data as well as wind and solar resource data.Our results show system designs that balance the cost of energy with load-meeting and peak shaving performance. Within the case study, systems designed for wind, solar photovoltaic (PV), and storage resulted in lower cost of energy than those optimized for PV and storage only. This indicates that in areas where wind resource exists, hybrid power plants that include wind, PV, and battery assets can better meet EV charging loads (including peak loads that are prone to overloading local grids) than PV and battery assets alone. Future work to address limitations in this paper include extending cost modeling to include performance losses (e.g., based on operations or weather) and charging station costs to estimate levelized cost of charging, and quantifying uncertainty and error in our aggregation methods for estimating EV charging loads at the hourly timescale.

Comparing costs and climate impacts of various electric vehicle charging systems across the United States

The seamless adoption of electric vehicles (EVs) in the United States necessitates the development of extensive and effective charging infrastructure. Various charging systems have been proposed, including Direct Current Fast Charging, Battery Swapping, and Dynamic Wireless Power Transfer. While many studies have evaluated the charging costs and greenhouse gas (GHG) intensity of EVs, a comprehensive analysis comparing these systems and their implications across vehicle categories remains unexplored. This study compares the total cost of ownership (TCO) and GHG-intensity of EVs using these charging systems. Based on nationwide infrastructure deployment simulations, the change to TCO from adopting EVs varies by scenario, vehicle category, and location, with local fuel prices, electricity prices, and traffic volumes dramatically impacting results. Further, EV GHG-intensity depends on local electricity mixes and infrastructure utilizations. This research highlights the responsiveness of EV benefits resulting from technology advancements, deployment decisions, and policymaking.

Multi-objective optimization and scheduling research on electric bicycle charging and discharging in distribution networks

Abstract With the continuous development of urbanization, the rapid growth of electric bicycles has become a prominent trend. The number of battery-swapping stations for convenient battery replacement has also increased. The large-scale and unregulated connection of electric bicycles and battery-swapping stations to the power grid can significantly impact the distribution network, leading to localized overloads and affecting grid stability. Therefore, this paper proposes a multi-objective optimization scheduling strategy for electric bicycle charging and dis-charging based on both the transmission and distribution systems. Firstly, in the transmission system, the objective is to reduce the operating costs of generators and user charging costs. Sub-sequently, in the distribution system, the goal is to minimize network losses, considering system constraints and the spatial migration characteristics of electric bicycles. The study establishes a multi-objective optimization problem. Simulation analyses were conducted on the power system models based on a standard 10-machine transmission network and an IEEE 33-node distribution network to verify the effectiveness of the proposed multi-objective optimization scheduling strategy.

IOT Based- Smart Metering and Charging System for EV

Abstract: Electric vehicles often face concerns regarding overcharging, leading to detrimental outcomes like pollution, reduced hardware lifespan, and safety hazards. Unlike conventional fuels, electric vehicles lack standardized systems to regulate charging parameters and costs. This absence of fail-safe mechanisms can lead to overcharging, causing negative consequences without intervention. To tackle this issue, the project proposes an Arduino-based solution coupled with a relay sensor and dedicated application. This system aims to prevent overcharging, minimizing both financial and electrical wastage. The project serves as a compact prototype, offering a viable resolution to the problem of unregulated charging in electric vehicles.

Smart vehicle-to-grid integration strategy for enhancing distribution system performance and electric vehicle profitability

This study presents a novel Vehicle-to-Grid (V2G) integration strategy. By utilizing the energy stored in electric vehicles (EVs) to inject power into the grid optimally during peak-load/high-tariff periods. This V2G strategy has two main objectives: enhancement of the system performance and offering financial profit to EV owners. The V2G strategy optimizes the energy exchange between EVs and the grid based on load leveling methodology through intelligent scheduling of charging/discharging times to alleviate grid stress during peak demand, leading to enhanced system performance. Also, it enables EV owners to sell their excess energy to the utility at favorable prices, which not only helps EV owners offset their charging costs but also turns their EVs into profitable assets. The contribution over recent similar studies is targeting multiple objectives simultaneously, besides employing a comprehensive set of factors, involving all relevant participants, and rigorous load modelling. The effectiveness of this strategy is demonstrated through comprehensive simulations with two different charging scenarios applied to two networks, RBTS-bus2 and ShC-D8. The results show the enhancement of system performance, as evidenced by improvements in voltage profiles and load curves, besides reductions in losses, operational costs, and feeder and transformer loading. Furthermore, the proposed approach maximizes the profit of EV owners.

Electric Vehicle Cluster Scheduling Model for Distribution Systems Considering Reactive-Power Compensation of Charging Piles

With the increasing number of electric vehicles (EVs), their uncoordinated charging poses a great challenge to the safe operation of the power grid. In addition, traditional individual-EV scheduling models may be difficult to solve due to the increasing number of constraints. Therefore, this paper proposes a cluster-based EV scheduling model. Firstly, electric vehicle clusters (EVCs) are formed based on the charging and discharging preferences of EV users and the expected time for EVs to leave. Secondly, the EVC energy and power boundary aggregation method based on the Minkowski addition algorithm is proposed. Finally, for the sake of reducing user charging cost and distribution network energy loss, and smoothing the daily load curve, an EVC scheduling model for EV participation in grid auxiliary services is proposed. The optimization model includes the reactive-power compensation of EV charging piles. The simulation results show that the proposed EVC scheduling model can greatly reduce the solution time compared to traditional individual-EV scheduling model. The model has high potential to be applied to large-scale EV scheduling. The reactive-power compensation provided by EV charging piles improves the voltage quality of the grid and enables more EVs to be connected to the grid.

Optimal scheduling of electric vehicle aggregators for frequency regulation and cost efficiency in renewable-powered grids

The growing integration of intermittent renewable energy sources (RESs), such as wind and solar, into power grids presents significant operational challenges, particularly in maintaining grid stability and managing generation forecasting errors. This study underscores the critical role of Electric Vehicles (EVs) as a flexible load in providing ancillary services, especially for secondary frequency regulation, to address these challenges. Moreover, a novel scheduling model for EV aggregators is introduced that aims to optimize frequency regulation and cost efficiency. This model strategically manages the State of Charge (SOC) to ensure EV owner satisfaction while minimizing operational costs. Employing Mixed-Integer Linear Programming (MILP), solved via CPLEX-GAMS in both deterministic and stochastic scenarios, the proposed approach evaluates various EV integration strategies, demonstrating potential reductions in charging costs and enhancing owner participation by considering parking constraints. This research highlights the importance of innovative grid management solutions in the era of renewable energy proliferation, offering significant insights into cost-effective and reliable energy practices. The results in the deterministic show that by considering the EV parking constraint, the total charging cost will be increased by about 20%. Also, by applying the proposed strategy to the down-regulation value of 18%, the total cost of EV charging is decreased to 11,400 $ which shows about a 5% decrement in comparison with the down-regulation value of 32%.

Multi-Regional Integrated Energy Economic Dispatch Considering Renewable Energy Uncertainty and Electric Vehicle Charging Demand Based on Dynamic Robust Optimization

Aiming at the problem of source-load uncertainty caused by the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs) into modern power system, a robust optimal operation scheduling algorithm for regional integrated energy systems (RIESs) with such uncertain situations is urgently needed. Based on this background, aiming at the problem of the irregular charging demand of EV, this paper first proposes an EV charging demand model based on the trip chain theory. Secondly, a multi-RIES optimization operation model including a shared energy storage station (SESS) and integrated demand response (IDR) is established. Aiming at the uncertainty problem of renewable energy, this paper transforms this kind of problem into a dynamic robust optimization with time-varying parameters and proposes an improved robust optimization over time (ROOT) algorithm based on the scenario method and establishes an optimal scheduling mode with the minimum daily operation cost of a multi-regional integrated energy system. Finally, the proposed uncertainty analysis method is verified by an example of multi-RIES. The simulation results show that in the case of the improved ROOT proposed in this paper to solve the robust solution of renewable energy, compared with the traditional charging load demand that regards the EVs as a whole, the EV charging load demand based on the trip chain can reduce the cost of EV charging by 3.5% and the operating cost of the multi-RIES by 11.7%. With the increasing number of EVs, the choice of the starting point of the future EV trip chain is more variable, and the choice of charging methods is more abundant. Therefore, modeling the charging demand of EVs under more complex trip chains is the work that needs to be studied in the future.

Design of a Charging Station for Electric Vehicles Based on a Photovoltaic-Biodiesel Hybrid Renewable Energy System Combined with Battery Storage

The rapid deployment of electric vehicles (EVs) in Jordan requires massive efforts to prepare the infrastructures that serve the transportation sector. The lack of EV charging stations is the major obstacle that faces EV drivers. Utilizing renewable energy in EV charging stations contributes to the spread of these stations. Renewable energy technologies are environmentally friendly as they mitigate greenhouse gases that cause the global warming phenomenon. In this paper, an EV charging station, based on a PV-biodiesel-battery hybrid system, is investigated. The importance of this article is to discuss the hybrid system of PV and waste vegetable oil (WVO) along with storage that applies the maximum reliability supply, having an environmentally friendly supply and achieving the lowest energy cost of charging. This station is designed based on renewable and WVO utilization. In fact, WVO, coming from restaurants, is exploited to produce electricity by a diesel generator through direct burning after converting it into biodiesel. The capacity of each station is 14 cars/day with a medium-speed charger of 7 kW. The system is simulated and optimized using iHOGA software where multiobjective optimization is applied to achieve the minimum net present cost (NPC) and CO2 emissions considering three cases, PV-diesel, PV-biodiesel, and PV-WVO, all with battery-hybrid system. These values for the EV charging station that uses the PV-biodiesel-battery hybrid system are 624408 € and 15.4 tons/year, respectively. The corresponding values are 781473 € and 15.14 tons/year and 615310 € and 18.84 tons/year for the systems that work with diesel and WVO, respectively, and energy cost is achieved in best solution to be 0.11 Euro/kWh. Simulation results show that the proposed technique led to enhanced operational efficiency in terms of both NPC and annual CO2 emissions.

Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling

Parallel Algorithm on Multicore Processor and Graphics Processing Unit for the Optimization of Electric Vehicle Recharge Scheduling