Charging Strategy Research Articles

Demand Response Approach for Coordinated Scheduling of EV Charging in a Micro-Grid

Compared to traditional fuel vehicles, electric vehicles (EVs) can use energy efficiently without polluting the environment, which is why environmental experts are pushing the switch to EVs. When a large number of EVs are connected to the present power grid, negative impacts to the power system dynamics may happen. Implantation of demand response (DR) programming may avoid unplanned demand peaks, and support grid voltage. The objective of this article is to minimize the cost of charging the EVs while also utilizing their battery storage capacities to support grid voltage. Price-based DR programming is used to control the EVs’ charging. A study of an 8-bus distribution system with three EVs connected during charging/discharging periods, and different scenarios are simulated and compared with the grid response without any EV. New distribution tariffs have been developed to encourage small electricity users toward peak load restriction. The proposed scheduling method is valuable for the utility to estimate the capability of different control strategies for EV charging. It can also be extended to more complicated systems with large number of EVs and hence applied to a real network. MATLAB environment is used to test the suggested control scheme’s robustness and efficacy.

Hybridized Triboelectric-Electromagnetic Aeolian Vibration Generator as a Self-Powered System for Efficient Vibration Energy Harvesting and Vibration Online Monitoring of Transmission Lines.

In this work, based on the triboelectric-electromagnetic working principle, a comprehensive strategy appropriately hybridizes a multilayered elastic structure TENG (ME-TENG) and a double-electromagnetic generator (EMG) for efficient aeolian vibration energy harvesting and vibration state monitoring. The ME-TENG with the feature of elasticity is integrated with a movable plate embedded with a magnet as the counterweight, which acts as a spring-like mass system in response to external vibration excitation, making the inseparable integrity of the TENG and EMG. The basic hybridized triboelectric-electromagnetic aeolian vibration generator (HAVG) consisting of ME-TENG and double-EMGs in terms of structural parameters and response characteristics is first optimized and discussed, thereby the efficient vibration energy harvesting and effective vibration state response can be further improved through the mutual complementarity of TENG and EMG. Furthermore, the self-powered capacity of the HAVG in terms of LED arrays and a wireless ambient temperature and humidity monitoring system is verified through the hybrid charging strategy of TENG and EMG modules and the combination of HVAG and energy management circuits, benefiting from the sophisticated-designed structure and excellent output performance of the HAVG. Importantly, a self-powered aeolian vibration monitoring system is established and demonstrated for vibration-state sensing and abnormal vibration alarm. This work demonstrates a novel strategy for energy harvesting and state sensing of overhead transmission line aeolian vibration, which not only reveals TENG-EMG promising potential for energy harvesting for aeolian vibration, but also provides valuable guidance for the construction of a self-powered online-monitoring system for transmission lines.

Charging and discharging optimization strategy for electric vehicles considering elasticity demand response

The electrification of urban transportation systems is a critical step toward achieving low-carbon transportation and meeting climate commitments. With the support of the Chinese government for the electric vehicle industry, the penetration rate of electric vehicles has continued to increase. In the context of large-scale electric vehicles connected to the grid, a coordinated charging-discharging system is particularly vital studied to avoid grid overload caused by customers' random charging. In this paper, a two-stage optimization strategy for electric vehicle charging and discharging that considers elasticity demand response based on particle swarm optimization was proposed, allowing the user to respond autonomously according to the reference value of the charge and discharge demand response and select the optimization weight independently to meet their travel and charging needs. To facilitate the user to balance the charging cost and the charging energy, we have introduced the virtual SOC to calculate the optimization result in advance. The results show that the optimized scheme can reduce the charging cost by 40%∼110%, and the load variance of the distribution network can be reduced by 19%∼100%, realizing the "win-win" benefit of the grid side and the user side. In addition, our research found that under the proposed strategy, the cost of battery loss caused by cyclic charging and discharging is negligible compared to the discharge benefit.

How do the affective and symbolic factors of private car driving influence car users’ travel behavior in a car restriction policy scenario?

The number of private car trips a person makes is usually linked to external factors such as economic incentives or disincentives, legislation, infrastructure, and the quality of transport systems, among others. The impact of these options are typically analyzed to evaluate urban transport policies and projects. However, internal factors related to an individuals' attitudes and perceptions also play an essential role in the amount of private car driving they do and on urban mobility as well. Therefore, insight into private car driving's affective and symbolic aspects is important in order to formulate appropriate transport policies and strategies to reduce car-use dependency and encourage private car drivers to use public transport. Congestion charging schemes make drivers pay the full social cost of driving, they achieve congestion reduction, pollution reduction, they make cities more attractive for pedestrians and cyclists, and help improve quality of life. Congestion charging schemes are often difficult to implement and expensive to administer. As a result of these reasons, several cities worldwide have adopted a variety of car restriction schemes that try to reduce air pollution and congestion as well, instead of establishing congestion charges. The effects of driving restrictions in the short term are positive, but in the medium and long term, there are unwanted consequences. However, by giving private car drivers the option to pay a toll so that their cars are exempted from the restriction, these perverse incentives could be eliminated. This paper analyzes the impacts of internal factors related to individuals' attitudes and perceptions about the travel behavior of car users affected by car restriction policies in urban areas. We designed a stated preference survey conducted among car owners in Cali, Colombia, where a License Plate Restriction Charging (LPRC) policy has been in place since January 2017. Through hybrid discrete choice modeling, we demonstrated that latent variables, such as the feelings of being in control, independence, and higher social status, positively influence the decision to use cars for daily trips, thus impacting the urban modal split. The heterogeneity captured through these latent variables allowed us to understand more deeply how individuals deal with the LPRC policy in order to travel to their destination.

Multi-objective optimal scheduling of an integrated energy system under the multi-time scale ladder-type carbon trading mechanism

Given the ladder-type carbon tax in the carbon quota market is a relatively macroscopic large-time scale concept, and the volatility and uncertainty of new energy generation output and the load, it is currently difficult to accurately predict and control carbon trading cost in the multi-time scale scheduling of integrated energy system (IES), there is a lack of in-depth research on dynamic adjustment and ground implementation in small time scale. To address the challenges of these problems, this paper proposes a multi-objective optimal scheduling model for IES considering the multi-timescale ladder-type carbon trading mechanism. Optimization models and algorithms were developed for day-ahead, intraday and real-time scheduling time scales, enhancing scheduling accuracy and usability. Day-ahead scheduling minimizes comprehensive cost and pollutant emissions while considering the ladder-type carbon trading mechanism; intraday scheduling dynamically adjusts carbon trading cost functions based on day-ahead results and equipment output; and real-time scheduling establishes new carbon trading ladder-type cost functions according to intraday results and emissions. A novel particle swarm optimization algorithm was introduced, integrating uncertainty models for wind, photovoltaic power, and load, along with electric vehicle charging strategies. The multi-time scale model in ladder-type carbon trading exhibits approximately 5% more economic advantage and 2% more environmental benefit.

A safe reinforcement learning-based charging strategy for electric vehicles in residential microgrid

With the growing popularity of electric vehicles (EVs), it is a new challenge for the residential microgrid system to conduct charging scheduling to meet the charging demands of EVs while maximizing its profit. In this work, a safe reinforcement learning (RL)-based charging scheduling strategy is proposed to meet this challenge. We construct a complete microgrid system equipped with a large charging station and consider different types of EVs, as well as the vehicle-to-grid (V2G) mode and nonlinear charging characteristics of EVs. Subsequently, the charging scheduling problem is formulated as a constrained Markov decision process (CMDP) due to the various limitations of power and demands. To effectively capture the uncertainties of the supply side and demand side of the microgrid, a model-free RL framework is employed. However, the curse of dimensionality of the action space is inevitable as EVs increase. To solve this problem, a charging and discharging strategy based on a general ladder electricity pricing scheme is designed. Different EVs are divided into different sets according to their states under this strategy, and the agent gives control signals of different sets instead of controlling each EV individually, which effectively reduces the dimension of the action space. Subsequently, a constrained soft actor-critic (CSAC) algorithm is designed to solve the established CMDP, and a safety filter is introduced to ensure safety. In the end, a numerical case is conducted to verify the effectiveness of the proposed method.

A cost analysis of decarbonizing the heavy-duty road transport sector

We introduce a static cost model that simulates the cost of decarbonizing the heavy-duty road transport sector. This time-variant model aims to refine existing estimations of investment needs under different scenarios. The paper advances sustainability transition’s cost analysis in three ways: 1) providing a novel methodological tool, 2) including the financing cost in the analysis, and 2) testing the cost variance under different charging strategies for battery-electric vehicles and financing interest rates. A plausible scenario is applied to test the model. The paper discusses charging opportunities reliant on charging place, time, power effect, and electricity self-consumption within the charging strategies. Our results highlight the vital contribution of charging strategies to fuel and capital expenses for the system. Moreover, system cost analysis reacts to the change in financing policies, stressing the criticality of sustainable financing in the sustainability transition’s advancement.

Impact of electric vehicles fast frequency regulation and charging strategies on grid frequency stability

With the gradual reduction of grid inertia, system sensitivity to load unbalances is increasing, compromising frequency stability. To face this problem, faster frequency regulation resources are required. In this context, vehicle-to-grid (V2G) is one of the concepts that is suggested for this purpose. When electric vehicles (EVs) are parked, their batteries can be used as distributed energy storage systems (EESs), injecting and absorbing power faster than conventional generation units (CGUs). The goal of this paper is to analyse the impact of fast frequency regulation of EVs in a small-scale grid, under sudden load variations. In this paper, the effect of charging strategies (CSs) is addressed, as they have a direct impact on the magnitude of the EV fleet regulation. In this way, different CSs can be used to achieve symmetrical up and down regulation, or to maximize one of them. The effect of the EV fleet dynamics in the frequency transient is also studied. The speed of response, and hence, the frequency transient, are influenced by these dynamics. The focus of the analysis will be the communication delay between the EV fleet and the central controller.

Infrastructure planning for airport microgrid integrated with electric aircraft and parking lot electric vehicles

To achieve net-zero emissions in aviation with defined CO2 mitigation objectives in “Flightpath 2050″, electric aircraft (EA) becomes an attractive technology. Many aircraft prototypes with a fully electrically powered propulsion system for short-haul commuting air transport have been designed in recent years. Planning and designing the ground power systems and associated electric aircraft charging operations are also essential for realising electrified aviation. In this work, a bi-objective infrastructure planning framework for airport microgrid to accommodate parking lot electric vehicles (EVs) and EA is developed, and the impact of V2G on the airport microgrid is assessed as well. The dispatching problem of the airport microgrid is formulated as a heuristic optimisation problem, and the NSGA-II algorithm is adopted to find the Pareto Fronts and optimal solutions. There are two different scheduling strategies for charging EA: plug-in charge and battery swap. The economic and technological assessments for both strategies are conducted and compared. The results show that adopting V2G strategy can indeed improve the airport microgrid economical performance. In general, the EA plug-in charge strategy performs better than the EA battery strategy in terms of resilience, while the EA battery swap strategy can reduce the operation costs of the airport microgrid.

Sensitivity analysis of electrochemical model parameters for lithium-ion batteries on terminal voltages and anode lithium plating criterion

The design of optimized charging strategies based on electrochemical models is essential for the broader use of electric vehicles. Lithium plating on the anode surface is strongly linked to charging lifetime and safety of batteries; thus, it cannot be ignored in the design of charging strategies. The use of electrochemical models in combination with the lithium plating criterion allows the identification of the maximum charging current that prevents lithium plating, a key prerequisite for the accurate identification of model parameters. However, most current studies have not yet considered the influence of electrochemical parameters on the characterizing variables for the lithium plating criterion. To improve the simulation accuracy of the electrochemical model for the lithium plating reaction, we have not only carried out a parameter sensitivity analysis using the cell terminal voltage as the target, but also have selected the anode potential and the anode surface lithium-ion concentration as sensitivity analysis targets, enabling the comprehensive evaluation of crucial characterizing variables for lithium plating criterion. The sensitivity of electrochemical parameters on three model outputs was investigated, not only at 25 °C but also at 5 °C and 40 °C. The results show that the parameters sensitive to the terminal voltage are mainly cathode-related; while the parameters sensitive to the characterizing variables for lithium plating criterion are mostly anode-related. The temperature has a significant effect on the parameter sensitivity, with low temperatures having a larger effect. Finally, positive and negative correlations between the sensitivity parameters and the anode potential were analyzed, providing crucial support for the determination of parameter values.

Construction and demolition waste disposal charging scheme design

AbstractConstruction and demolition waste disposal charging is a critical component in construction waste (CW) management. Over the past few decades of implementation around the world, the effectiveness of waste disposal charging in decreasing negative environmental impact is gradually being questioned. This paper identifies two intertwined issues that have not attracted sufficient attention in actual practice. One is the consideration of contractors’ behavior, which is economically driven. Another is the influence of waste transportation, which is also a major stream of environmental pollution. These two aspects significantly affect the effectiveness of the waste disposal charging scheme. Contractors may not transport their generated CW to appropriate disposal facilities. They make decisions based on the disposal and transportation cost. To address this problem, this paper proposes a general construction and demolition waste disposal charging design methodology in which contractors’ behavior and the influence of waste transportation are taken into account. A mixed‐integer programming model is developed for the optimal design of the charging fees, which analytically formulates both of the abovementioned aspects. Furthermore, we developed a model that partially modifies the current charging scheme to better fulfill the environmental protection objective. Extensive numerical experiments demonstrate that the proposed methodology can effectively change contractors’ waste disposal decisions and significantly decrease the negative environmental impact of construction and demolition waste. This study contributes to a new perspective to better design the construction and demolition waste disposal charging scheme and has a wide range of applicability.

Optimization of Electric Bus Scheduling Considering Charging Station Resource Constraints

The electrification of bus systems is an inevitable trend in sustainable urban development because electric buses do not use fossil fuels and, therefore, do not exacerbate energy shortages. However, because of their limited driving range and the restricted charging station resources, it is crucial to optimize the scheduling and charging plan for electric buses jointly to ensure operational efficiency. This paper studies the electric bus scheduling problem considering charging station resource constraints, including the limited number of chargers and total power output, which are generally simplified in the relevant literature. We first formulate a nonlinear model to optimize vehicle scheduling, charging time, and charging power, to minimize the total system costs, including the usage costs of electric buses and charging costs. Then, a genetic algorithm is developed to solve this model. Finally, the proposed method is demonstrated by using the example of a transit network in Nanjing. Compared with the existing scheme based on an uncontrolled charging strategy, the proposed solution reduces the total system costs and charging costs by 7.34% and 36.51%, respectively. The findings in this paper can provide practical guidance on electric bus scheduling and promote the sustainable development of bus systems.

Charging by Quantum Measurement

We propose a quantum charging scheme fueled by measurements on ancillary qubits serving as disposable chargers. A stream of identical qubits are sequentially coupled to a quantum battery of $N+1$ levels and measured by projective operations after joint unitary evolutions of optimized intervals. If charger qubits are prepared in excited state and measured on ground state, then their excitations (energy) can be near perfectly transferred to battery by iteratively updating the optimized measurement intervals. Starting from its ground state, the battery could be constantly charged to an even higher energy level. Starting from a thermal state, the battery could also achieve a near-unit ratio of ergotropy and energy through less than $N$ measurements, when a population inversion is realized by measurements. If charger qubits are prepared in ground state and measured on excited state, useful work extracted by measurements alone could transform the battery from a thermal state to a high-ergotropy state before the success probability vanishes. Our operations in charging are more efficient than those without measurements and do not invoke the initial coherence in both battery and chargers. Particularly, our finding features quantum measurement in shaping nonequilibrium systems.

Charging strategy selection for electric bus systems: A multi-criteria decision-making approach

Selecting the best type of charging strategy, among a variety of options such as overnight (slow) charging and opportunity (fast) charging methods, is a crucial step in electrifying bus networks. This step has gained increased importance with the growing need for sustainable transportation solutions and the widespread adoption of electric buses (EBs) in public transport systems. This study develops a multi-criteria decision-making (MCDM) approach for selecting the best EB charging strategy, considering a comprehensive range of criteria, including economic, environmental, social, operational, and quality-of-service criteria. A thorough literature review and a survey of EB experts are conducted to identify key decision-making factors in this area. A Fuzzy Best-Worst Method (FBWM) is designed to determine the weight of criteria, and a Fuzzy Ranking of Alternatives through Functional mapping of criterion subintervals into a Single Interval (FRAFSI) method is designed to rank available charging strategies for EB systems in Munich, Germany. Results show that the most crucial consideration for decision-makers is the economic aspect, followed by operational and environmental factors. The infrastructure cost is the most crucial factor in the economic category, followed by battery cost and operational cost. The driving range is the most crucial factor in the operational category, followed by charging duration and energy monitoring. The experts’ assessments indicate a preference for overnight charging over opportunity charging. The results of the FRAFSI are in line with other methods (fuzzy TOPSIS and fuzzy EDAS). Our findings offer valuable insights and guidance for transportation authorities and decision-makers in charge of selecting charging strategies for EBs.

Adaptive Optimization Operation of Electric Vehicle Energy Replenishment Stations Considering the Degradation of Energy Storage Batteries

As the construction of supporting infrastructure for electric vehicles (EV) becomes more and more perfect, an energy replenishment station (ERS) involving photovoltaics (PV) that can provide charging and battery swapping services for electric vehicle owners comes into the vision of humanity. The operation optimization of each device in the ERS is conducive to improving the service capacity of the ERS, extending the service life of the energy storage batteries (ESB), and enhancing the economic benefits of the ERS. However, traditional model-based optimization algorithms cannot fully consider the stochastic nature of EV owners’ charging and battery swapping demands, the uncertainty of PV output, and the complex operating characteristics of ESB. Therefore, we propose a deep reinforcement learning-based adaptive optimal operation method for ERS considering ESB’s losses. Firstly, a mathematical model of each device in the ERS is established, and a refined energy storage model is introduced to describe ESB’s capacity degradation and efficiency decay. Secondly, to solve the dimensional disaster problem, the state space and action space selection method, and the charging strategy of batteries in the battery swapping station (BSS) are proposed to apply to the ERS, thus modeling the ERS optimization operation problem as a Markov decision process. Then, the solution is performed using a neural network-based proximal policy optimization (PPO) algorithm, consisting of a recurrent neural network that extracts information about the PV outflow trend and a deep neural network used to generate a control policy. Finally, the effectiveness of the proposed method is verified by simulation calculations, which not only enable adaptive decision-making under different PV output scenarios, but also consider the availability of EV battery swapping services, energy storage losses, and the economic benefits of the ERS.

Techno-economic analysis for the electric vehicle battery aging management of charge point operator

Temperature is a major factor that affects battery aging. Therefore, charging stations exposed to the external environment and charge point operator (CPO) operation strategy can significantly affect battery aging. In particular, when providing power services that may require a high C-rate, electric vehicle (EV) battery aging can be aggravated in certain environments. Therefore, battery aging management according to the provided power service needs to be dealt with from the perspective of the CPO, which establishes the operation strategy, in addition to the EV. This study focuses on EV battery aging management considering the charging station operating environment from the point of view of a CPO. Battery aging model considers the mutual influence between charging station's ambient temperature, EV cabin temperature, battery temperature, and battery temperature management system (BTMS). Based on this, the CPO establishes an optimal smart charging strategy considering the BTMS operating conditions and the power consumption of EVs working in an independent environment. Based on this, the technical and economic effects of battery aging management according to participation in power services were analyzed. Case study results show that indoor stations can reduce management costs for battery aging by up to 13.31% compared to that in outdoor stations.

Self-Consistent Chemical Pressure Analysis: Resolving Atomic Packing Effects through the Iterative Partitioning of Space and Energy.

While planewave DFT methods offer capabilities to calculate the relative stabilities and many physical properties exhibited by solid state structures, their detailed numerical output does not easily map onto the often empirical concepts and parameters used by synthetic chemists or materials scientists. The DFT-chemical pressure (CP) method is an approach to bridging this divide by explaining or anticipating a variety of structural phenomena in terms of atomic size and packing effects, but its reliance on adjustable parameters has limited the method's predictive potential. In this article, we present the self-consistent (sc)-DFT-CP analysis, in which the criterion of self-consistency is used to automatically solve these parameterization issues. We begin by illustrating the need for this improved method with the results for a series of CaCu5-type/MgCu2-type intergrowth structures, where unphysical trends emerge that have no clear structural origin. To address these challenges, we derive iterative treatments for assigning ionicity and for the partitioning of the EEwald + Eα terms in the DFT total energy into homogeneous and localized terms. In this method, self-consistency is achieved between the input and output charges from a variation of the Hirshfeld charge scheme, while the partitioning of the EEwald + Eα terms is adjusted to create equilibrium between the net atomic pressures calculated within atomic regions and from the interatomic interactions. The behavior of the sc-DFT-CP method is then tested using electronic structure data for several hundred compounds from the Intermetallic Reactivity Database. Finally, we return to the CaCu5-type/MgCu2-type intergrowth series with the sc-DFT-CP approach, showing that trends in the series are now easily traced to changes in the thicknesses of the CaCu5-type domains and lattice mismatch at the interface. Through this analysis and the complete update to the CP schemes in the IRD, the sc-DFT-CP method is demonstrated as a theoretical tool for the investigation of atomic packing issues across intermetallic chemistry.

Smart charging strategy for electric vehicles based on marginal carbon emission factors and time-of-use price

As the penetration of electric vehicles (EVs) has grown rapidly in recent years, the stability and safety of electric grids are inevitably affected. Uncontrolled EV charging can reduce electric grid efficiency and increase greenhouse gas emissions (GHG). This occurs when EV charging aligns with peak demand, requiring the activation of fossil fuel-powered plants to meet the electricity demand. The time-of-use (TOU) price is a traditional management strategy for smoothing the demand power curve, but it does not consider carbon emissions. This study proposes a smart charging strategy based on an improved local search genetic algorithm (LS-GA) that considers both the TOU price and marginal emission factors (MEF). Over 47,000 charging events from 45 charging stations in Tianjin, a major city in North China, were used to simulate the charging pattern and demand. Four different charging strategies are considered in the study. The results indicate that the proposed smart charging strategy can reduce the cost and carbon emissions by up to 27% and 16% compared with uncontrolled charging, respectively. This study demonstrates the significant potential of using optimization methods in the EV smart charging problem.

Collaborative Energy Optimization of Multiple Chargers Based on Node Collaborative Scheduling

Wireless rechargeable sensor network (WRSN) uses mobile chargers (MCs) to charge sensor nodes wirelessly to solve the energy problems faced by traditional wireless sensor network. In WRSN, mobile charging schemes with multiple MCs supplementing energy are quite common. How to properly plan the MC’s moving path to reduce the charge energy loss and deploy nodes to improve network coverage rate has become a huge research challenge. In this paper, a collaborative energy optimization algorithm (CEOA) is proposed for multiple chargers based on k-mean++ and node collaborative scheduling. The CEOA combines internal energy optimization and external device power supply, effectively prolongs network lifetime, and improves network coverage rate. It uses the k-mean++ to cluster nodes in the network; then, the nodes in the network are scheduled to sleep based on the confident information coverage (CIC) model. Finally, the CEOA uses a main mobile charger to carry multiple auxiliary mobile chargers to charge all the nodes in the cluster. Simulation results show that the proposed algorithm increases the network lifetime by more than 8 times and the coverage rate by about 20%.

Joint optimization of infrastructure deployment and fleet operations for an electric carsharing system by considering multi-type vehicles

Electric carsharing (ECS) is considered an effective option to alleviate resource and environmental issues in urban transportation. In this study, we develop a mixed-integer optimization model to jointly optimize infrastructure deployment (station construction and fleet configuration) and fleet operations (vehicle assignment and charging strategy) for the ECS to maximize profits. The model further considers users’ diversified demands and incorporates multi-type electric vehicles into the problem formulation. The user-based assignment and on-demand charging strategy are also integrated into the model to improve the operational efficiency. A specialized logic-based Benders decomposition algorithm is customized to effectively solve the model. A real-life case in Lanzhou, China, is presented to demonstrate the proposed methods. Results indicate that the model has better performance and can increase profits by 20% compared with conventional methods, as hybrid fleet deployment is more energy-saving and can save up to 10% of the energy costs compared with a homogeneous fleet.