Charging Cost Research Articles

A customer satisfaction-based optimization model for the charging and discharging path and battery swapping stations’ site selection of electric vehicles

This paper studies the problem of considering customer satisfaction in the no-battery-swap mode and in the power-swap mode. First, with the goal of maximizing customer satisfaction, the total cost of charging and discharging and the minimum construction cost of swapping stations, the customer time window, and the load constraints of electric vehicles are considered. A model of electric vehicle charging and discharging route optimization and replacement station location without battery swapping behavior, considering customer satisfaction, is established, and then, a two-stage improved ant colony–genetic algorithm is designed to solve the model, and finally, the comparative analysis considers customer satisfaction. Based on the path optimization results and location decisions considering the cost of charging and discharging, the following conclusions are obtained: 1) electric vehicle route optimization and swap station location planning considering customer satisfaction can not only effectively reduce logistics distribution costs and replacement costs but also improve customer satisfaction levels. 2) Reducing the number of route crossings in the process of logistics distribution routes can save electricity costs for electric vehicles and logistics distribution costs, and help reduce the total cost of the entire logistics distribution network. 3) The gradient setting of the electricity price for electricity exchange will reduce the cost of electricity exchange, improve the utilization efficiency of the battery, reduce the cost of logistics and distribution, and improve the electricity exchange revenue of the electricity exchange station.

DC fast charging stations for electric vehicles: A review

AbstractThe expansion of the DC fast‐charging (DCFC) network is expected to accelerate the transition to sustainable transportation by offering drivers additional charging options for longer journeys. However, DCFC places significant stress on the grid, leading to costly system upgrades and high monthly operational expenses. Incorporating energy storage into DCFC stations can mitigate these challenges. This article conducts a comprehensive review of DCFC station design, optimal sizing, location optimization based on charging/driver behaviour, electric vehicle charging time, cost of charging, and the impact of DC power on fast‐charging stations. The review is closely aligned with current state‐of‐the‐art technologies and encompasses academic research contributions. A critical assessment of 146 research articles published from 2000 to 2023 identifies research gaps and explores avenues for future study based on the literature review.

Electric vehicle charging optimization strategy based on the mopso algorithm

Aiming at the problems of electric vehicle disorderly charging on grid load stability and charging cost, this study considers the grid load pressure and presents a multi-faceted optimized model for electric vehicle charging. The model is addressed by utilizing a particle swarm optimization algorithm designed for multiple objectives. The outcomes demonstrate that the charging framework built upon the multi-objective particle swarm algorithm has a fast convergence speed and can avoid the limitation of the local optimal solutions. Under the premise of reducing by managing the grid load fluctuation, the model effectively curbs the expense of vehicle charging while also minimizing peak-to-valley disparities in grid load.

Multi-objective optimization of distributed energy resources based microgrid using random forest model

Microgrids (MG) in integration with distributed energy resources (DERs) are one of the key models for resolving the current energy problem by offering sustainable and clean electricity. This research presents a novel approach to address the complex challenges of optimizing a DERs based microgrid while considering multiple objectives. In this paper, the utilization of a popular machine learning algorithm, random forest (RF) model is proposed to optimize the DERs based MG configuration. The research commences by collecting historical data on energy consumption, renewable energy production, electricity prices, weather conditions, and other relevant factors of Bengaluru City (Karnataka, India) for different seasons. This research covers the conflicting objectives by finding optimal seasonal sizing of the battery, minimum generation cost, and reduction in battery charging cost. The optimization and analysis are done using an ensemble learning-based RF model. The findings from the RF model are compared with meta-heuristics and artificial intelligence (AI) methods such as particle swarm optimization (PSO) and artificial neural networks (ANN) for different seasons, i.e., winter, spring and autumn, summer, and monsoon.

Electric vehicle charging navigation strategy in coupled smart grid and transportation network: A hierarchical reinforcement learning approach

Most of the existing electric vehicle (EV) charging navigation methods do not simultaneously take into account the electric vehicle charging destination optimization and path planning. Moreover, they are unable to provide online real-time decision-making under a variety of uncertain factors. To address these problems, this paper first establishes a bilevel stochastic optimization model for EV charging navigation considering various uncertainties, and then proposes an EV charging navigation method based on the hierarchical enhanced deep Q network (HEDQN) to solve the above stochastic optimization model in real-time. The proposed HEDQN contains two enhanced deep Q networks, which are utilized to optimize the charging destination and charging route path of EVs, respectively. Finally, the proposed method is simulated and validated in two urban transportation networks. The simulation results demonstrate that compared with the Dijkstra shortest path algorithm, single-layer deep reinforcement learning algorithm, and traditional hierarchical deep reinforcement learning algorithm, the proposed HEDQN algorithm can effectively reduce the total charging cost of electric vehicles and realize online real-time charging navigation of electric vehicles, that shows excellent generalization ability and scalability.

To charge or not to charge? Using Prospect Theory to model the tradeoffs of electric vehicle users

AbstractElectric vehicle (EV) users who aim to become flexibility providers face a tradeoff between staying in control of charging and minimizing their electricity costs. The common practice is to charge immediately after plugging in and use more electricity than necessary. Changing this can increase the EV’s flexibility potential and reduce electricity costs. Our extended electricity cost optimization model systematically examines how different changes to this practice influence electricity costs. Based on the Prospect Theory and substantiated by empirical data, it captures EV users’ tradeoff between relinquishing control and reducing charging costs. Lowering the need to control charging results in disproportionally large savings in electricity costs. This finding incentivizes EV-users to relinquish even more control of charging. We analyzed changes to two charging settings that express the need for control. We found that changing only one setting offsets the other and reduces its positive effect on cost savings. Behavioral aspects, such as rebound effects and inertia that are widely documented in the literature, support this finding and underline the fit of our model extension to capture different charging behaviors. Our findings suggest that service providers should convince EV-users to relinquish control of both settings.

A Generative AI Approach to Pricing Mechanisms and Consumer Behavior in the Electric Vehicle Charging Market

The electrification of transportation is a growing strategy to reduce mobile source emissions and air pollution globally. To encourage adoption of electric vehicles, there is a need for reliable evidence about pricing in pub-lic charging stations that can serve a greater number of communities. However, user-entered pricing information by thousands of charge point operators (CPOs) has created ambiguity for large-scale aggregation, increasing both the cost of analysis for researchers and search costs for consumers. In this paper, we use large language models to address standing challenges with price discovery in distributed digital data. We show that generative AI models can effectively extract pricing mechanisms from unstructured text with high accuracy, and at substantially lower cost of three to four orders of magnitude lower than human curation (USD 0.006 pennies per observation). We exploit the few-shot learning capabilities of GPT-4 with human-in-the-loop feedback—beating prior classification performance benchmarks with fewer training data. The most common pricing models include free, energy-based (per kWh), and time-based (per unit time), with tiered pricing (variable pricing based on usage) being the most prevalent among paid stations. Behavioral insights from a US nationally representative sample of 13,008 stations suggest that EV users are commonly frustrated with the slower than expected charging rates and the total cost of charging. This study uncovers additional consumer barriers to charging services concerning the need for better price standardization.

Learning-based scheduling of integrated charging-storage-discharging station for minimizing electric vehicle users' cost

Charging cost is an important concern for electric vehicle (EV) users. The ordered charging behavior, such as the reasonable selection of charging period and charging power, can greatly decrease users' charging cost. Towards the integrated charging-storage-discharging station (ICSDS), a learning-based method is proposed in this paper to minimize EV users' cost. The physical constraints of ICSDS and the user's demand are first built, and the charging scheduling problem of ICSDS is formulated as a Markov Decision Process (MDP) with unknown transition probability. Second, in order to generate optimal schedule through the learning network, the deep features of the future electricity price are extracted by using a long short-term memory (LSTM) network. Third, a twofold deep deterministic policy gradient (TDDPG) algorithm is proposed to generate the continuous charging actions and avoid the Q value overestimation. In addition, the TDDPG-based scheduling strategy is designed on the basis of the extracted features of electricity price. Finally, the validations through the real-world data demonstrate that the features of electricity price are extracted with a satisfied accuracy. Moreover, compared with many benchmarked methods, the experimental results demonstrate that the charging scheduling by TDDPG possesses excellent performance in minimizing EV users' cost.

Stochastic Modelling and Integration of Electric Vehicles in the Distribution System Using the Jaya Algorithm

Electric vehicles (EVs) have enormous promise for the development of future transportation systems. The widespread use of EVs could negatively impact how power systems operate, particularly at the distribution level. Therefore, smart charging techniques are essential to increasing EV adoption in general. The connection between the electricity grid and the transportation network is made at electric vehicle charging stations (EVCS), and both networks will be simultaneously impacted by the operational behaviour of EVs. Therefore, EVCS must be placed in a distribution network in the best possible way. In this paper, an efficient and smart charging approach is formulated to schedule the charging of electric vehicles so that adverse effects like an increase in peak demand for the system and the cost of charging electric vehicles are minimized. The EV load model is formulated by considering factors such as the state of charge, trip distance travelled, and the user’s charging behaviour. The proposed electric charging schedule reduces the peak load and optimizes the cost of charging. Different types of EVs are considered based on their usage patterns for making realistic problem formulations. The smart charging technique presented in this work reduces the peak demand by 30% and the cost of charging by 50%. In addition, EVCS placement is implemented alongside distributed generation on IEEE 33 and 69 bus systems to reduce power losses and improve the voltage profile by using a metaheuristic algorithm known as Jaya Algorithm. The effectiveness of the proposed algorithm is established by comparing the results with published work.

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

Cooperative Scheduling for Directional Wireless Charging With Spatial Occupation

Wireless Power Transfer (WPT) technology has been developed rapidly in recent years. The cooperative charging model and corresponding scheduling methods have been proposed to save the charging cost in paid charging service. However, the state-of-the-art methods ignore the spatial occupation issue of rechargeable devices. Moreover, the cooperative charging scheduling in directional wireless charging has not been studied yet. This paper studies the cooperative scheduling for directional wireless charging with spatial occupation. We formulate the Cooperative Charging Scheduling with Spatial occupation (CCSS) problem of Mobile Rechargeable Sensor Devices (MRSDs) for optimizing the total cost of whole charging system. We first investigate the properties of optimal arrangement of MRSDs in charging group and calculate the tight intervals of charging angles of MRSDs. We show that it is sufficient to bound the error by conducting angle discretization for only two MRSDs in each charging group. Then, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\ln n+1)(1+\varepsilon )$</tex-math></inline-formula> -approximation algorithm of the CCSS problem is proposed based on greedy approach, where <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> is the number of MRSDs, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> is the discretization error. The results of extensive simulations and field experiments demonstrate that our algorithm can reduce at most 42.5% total cost comparing with the benchmark algorithms.

Optimal Scheduling of Regional Integrated Energy System Considering the Integration of Electric Vehicles and the Life Cycle Assessment Method

With the increasing scale of renewable energy and electric vehicles (EVs), the optimization scheduling of the regional integrated energy system (RIES) is facing new challenges due to the integration of EVs and new energy into the system. To realize the low-carbon economic operation of the RIES, a bi-level optimization scheduling strategy for low-carbon economic operation of the RIES is proposed, which considers EVs' participation and lifecycle carbon emissions, and integrates the electricity market and carbon trading market. Firstly, there are many factors affecting the carbon emissions of EVs and the difficulties in prediction. From the indirect carbon emissions during the use of EVs, the relationship between the carbon emissions of EVs and the charging mode and carbon emission sources is comprehensively analyzed. A carbon emission model of EVs based on the Life Cycle Assessment (LCA) is established in this paper. Secondly, the charging model of EVs based on demand response is combined with the electricity market; The carbon emission model of EVs based on the LCA is combined with the carbon trading market, and a bi-level scheduling strategy based on the flexible interaction between carbon and electricity is proposed. Then the strategy is solved to minimize system economic costs and carbon transaction costs. Finally, simulation example analysis is conducted to demonstrate that the proposed strategy can effectively reduce the total charging costs of electric vehicle users, improve the utilization rate of renewable energy, and reduce carbon dioxide emissions, with good economic and environmental benefits.

A Game-Theoretic Approach for Electric Vehicle Aggregators Participating in Phase Balancing Considering Network Topology

With the increasing popularity of electric vehicles (EVs), uncontrolled charging of single-phase plug-in EVs will result in severe three-phase unbalance problem in distribution networks. To solve the problem, a phase balancing (PB) scheme performed by EV aggregators (EVAs) based on the linear multiphase power flow model is proposed in this paper. In the scheme, the PB compensations are provided to encourage EVAs to help the distribution system operator reduce the phase unbalance of the entire grid. Considering all EVAs are selfish and rational, the PB scheme is formulated as a game problem, in which each EVA competes with other EVAs to determine its charging strategy to minimize the charging cost minus the PB compensation. The consideration of coupling voltage constraints leads this game model to a challenging generalized Nash equilibrium problem (GNEP). By employing the theory of variational inequality, the properties of solutions for the GNEP are investigated. Furthermore, a two-level distributed algorithm is designed to find the unique variational solution, a fair and stable solution. Finally, comprehensive case studies are conducted on the IEEE-13 and IEEE-123 test systems to corroborate that the proposed PB game model can effectively mitigate voltage unbalance and cut EV users’ costs.

The social benefits resulting from electric vehicle smart charging balancing economy and decarbonization

Smart charging is gaining more attention with the popularity of electric vehicles (EVs). Smart charging which aims only at reducing electricity costs or reducing carbon dioxide (CO2) emissions is efficient in achieving these objectives. However, the times of lower electricity costs and CO2 emissions are not identical in power grids, putting the two objectives in conflict, which may lower the social benefits of EV smart charging. This study investigates the potential social benefit improvement of smart charging by balancing the economy and decarbonization objectives. Considering five regions located in the United States as examples, it is deduced that smart charging balancing economy and decarbonization objectives can reduce the total social costs of EV charging by 14.5%–35.1%. Furthermore, this study analyzes the impacts of three factors on the social benefits change of EV smart charging, namely charging behavior, charger power, and the social costs of CO2. In the context of transport electrification, this study provides important guidance on how to adjust the charging mode of EVs in the future to improve social benefits.

The charge‐discharge compensation pricing strategy of electric vehicle aggregator considering users response willingness from the perspective of Stackelberg game

AbstractWith the rapid increase of electric vehicle (EV) ownership, the impact of EV charging load on the power grid is becoming more and more prominent. To reasonably guide EV charging/discharging to participate in Demand Response (DR) and help the power grid achieve peak cutting and valley filling, the charge‐discharge compensation pricing strategy of EV Aggregator (EVA) considering user response willingness from the perspective of Stackelberg game is proposed. Firstly, EVA, as the leader, provides charge‐discharge compensation price, to maximise its income within a day, taking into account user satisfaction constraints. Secondly, a user response willingness model is established. User engagement is used to describe the change in the number of EV responses with the change of the charge‐discharge compensation price by EVA and select the random EV set that accepts EVA charge‐discharge guidance. Finally, EV, as a follower, conducts charging/discharging behaviour to minimise the charging cost. By using the Karush–Kuhn–Tucker (KKT) condition, strong duality theory and iterative method, the strategy equilibrium solution is solved. The results show that considering the user response willingness can effectively reduce the decision risk when EVA participates in bidding. Although EVA income slightly decreases considering the response willingness, the average user satisfaction increases by 0.1.

Quantifying resilient urban energy systems: Statistical analysis of climate adaptation, renewable integration, and socioeconomic dynamics

Multi-energy systems (MES) become increasingly important to accommodate renewable energy (RE) since fossil fuels diminish and RE is more prevalent. Power systems are challenged by high penetrations of RE, including frequent wind curtailments and uncertain RE resources threatening their security. A 2-step scheme for MES is proposed in the study to generate reliable operational decisions by incorporating integrated heat-electricity demand response (DR). In the initial phase, dynamic programming was applied to determine the optimal solution for previous linkage sessions. Subsequently, machine learning techniques were trained using historical data and the resulting optimal decisions to make optimal real-time decisions without prior knowledge of future power costs or vehicle usage. An accurately trained deep neural network is capable of significantly reducing charging costs, usually approaching the retrospectively calculated optimum charging price. In the suggested scheme, the operating prices are minimized under a base scenario ensuring that no constraint is violated regardless of the scenario. RE curtailment can be reduced significantly with power-to-gas (P2G) devices that convert excess energy from wind and photovoltaic sources into natural gas. In addition, this paper proposes integrated heat-electric DR as a means of reducing operating prices and enhancing security against uncertainties by employing couplings between different systems. The outcomes show that robust optimization enhances the security of systems, and the unified electric-heat DR can boost the use of RE while improving the economic benefit. It should be noted that this paper investigated under the impact of the climate changes and also socioeconomic dynamics that can change consuming energy in urban systems.

Research on Optimization of Valley-Filling Charging for Vehicle Network System Based on Multi-Objective Optimization

Many electric vehicles connected to the grid will lead to problems such as poor stability of power grid generation. The key to solving these problems is to propose an efficient, stable, and economical valley-filling charging scheme for electric vehicles and grid users in the vehicle network system. Firstly, the convex optimization theory is used to make the grid achieve the optimization effect of valley filling. On this basis, the electricity price scheme with a time-varying coefficient as the variable is proposed to meet the single objective optimization of EV charging cost optimization, and its degree of influence on the grid valley-filling effect is analyzed. Secondly, based on the competitive relationship between EV charging cost and battery life, the P2D model is simplified and analyzed, and the attenuation law of battery capacity is quantitatively described. The multi-objective optimization problem is established to express in a Pareto matrix. Finally, the compatibility between the multi-objective optimization and grid valley charging is analyzed. The simulation results show that: (1) The convexity electricity price scheme can satisfy the requirements of various retention rates to achieve the valley-filling effect; (2) The filling effect is satisfied with the electricity price scheme that minimizes the charging cost, and the key factors affecting the filling effect are analyzed; (3) The multi-objective optimization scheme with charging cost and battery life is compatible with the valley-filling effect.

Study on Optimization of Departure Interval of Pure Electric Bus in Airport Area

As a zero-emission, pollution-free and pure green public transport, pure electric bus has received more and more attention from society and enterprises. At present, most urban public transport lines in China have basically realized pure electric bus. With the popularization of pure electric bus, the operation and management of pure electric bus in specific areas are required. How to reasonably set the departure time interval, configure the number of vehicles and reduce the charging cost while meeting the travel needs of passengers and the enterprise cost as low as possible has become a hot issue in the research of pure electric bus operation management. In this paper, from the perspective of system cost, a pure electric bus departure interval optimization model is constructed, which considers the comprehensive cost of passengers and enterprises to be minimum. The load factor constraint and departure interval are considered as the main constraints, and the optimal departure interval is obtained.

Smart Decentralized Electric Vehicle Aggregators for Optimal Dispatch Technologies

The number of electric vehicles (EVs) is growing exponentially, which presents the power grid with new challenges to turn their reliance to renewable energy sources (RESs). Coordination between the available generations from RESs and the charging time should be managed to optimally utilize the available generation from RESs. The dispatch scheduling of EVs can significantly reduce the impact of these challenges on power systems. Three different technologies can be used to manage the dispatch of EV batteries which are unregulated charging (UC), unidirectional grid-to-vehicle (G2V), and bidirectional vehicle-to-grid (V2G) technologies. This study aims to address the primary reason for EV owners’ disbelief in the accuracy of battery wear models, which is impeding their involvement in V2G technology. This paper introduces a novel accurate EV battery wear model considering the instantaneous change in the operation of the EV battery. Moreover, an effective musical chairs algorithm (MCA) is used to reduce everyday expenses and increase revenue for V2G technologies in a short convergence time with accurate determination of optimal power dispatch scheduling. The results obtained from these three strategies are compared and discussed. The salient result from this comparison is that V2G technology increases wear and reduces the battery lifespan in comparison with the UC and G2V. The yearly expenses of G2V are reduced by 33% compared to the one associated with the UC. Moreover, the use of V2G technology provides each EV owner with USD 3244.4 net yearly profit after covering the charging and wear costs. The superior results extracted from the proposed model showed the supremacy of V2G usage, which is advantageous for both EV owners and the power grid.

Cost Minimization for Charging Electric Bus Fleets

Recent attention for reduced carbon emissions has pushed transit authorities to adopt battery electric buses (BEBs). One challenge experienced by BEB users is extended charge times, which create logistical challenges and may force BEBs to charge when energy is more expensive. Furthermore, BEB charging leads to high power demands, which can significantly increase monthly power costs and may push the electrical infrastructure beyond its present capacity, requiring expensive upgrades. This work presents a novel method for minimizing the monthly cost of BEB charging while meeting bus route constraints. This method extends previous work by incorporating a more novel cost model, effects from uncontrolled loads, differences between daytime and overnight charging, and variable rate charging. A graph-based network-flow framework, represented by a mixed-integer linear program, encodes the charging action space, physical bus constraints, and battery state of the charge dynamics. The results for three scenarios are considered: uncontested charging, which uses equal numbers of buses and chargers; contested charging, which has more buses than chargers; and variable charge rates. Among other findings, we show that BEBs can be added to the fleet without raising the peak power demand for only the cost of the energy, suggesting that conversion to electrified transit is possible without upgrading power delivery infrastructure.