Discharging Of Electric Vehicles Research Articles

Advanced intelligent optimization for electric vehicle charging stations integrated with photovoltaic system and battery storage in commercial buildings

This paper presents a sophisticated four-stage optimization and intelligent control algorithm tailored for two-way electric vehicle charging (EVC) stations integrated with advanced photovoltaic systems and fixed battery energy storage in commercial buildings. The primary objective is to minimize operating costs while prioritizing customer satisfaction within a dynamic and uncertain energy landscape. Our algorithm optimizes the scheduled charging and discharging of electric vehicles (EVs), local battery storage (BS) units, grid power supply, and deferred loads to balance instantaneous supply and demand. The first stage focuses on developing optimal energy management plans for the day ahead, considering factors such as projected energy production, anticipated EVC demand, and building energy consumption patterns. Building on this foundation, the second stage introduces multilayer EV charging price structures and optimizes participation rewards for discharging, dynamically addressing EV charging patterns and price sensitivities. Approaching the commissioning timeline, the third stage refines energy management plans for the upcoming hours using real-time data and forecasts, adapting to evolving conditions for optimal resource allocation. The final stage involves real-time control and the implementation of optimized programs, dynamically adjusting charge/discharge processes, grid interactions, and load deferral to maintain supply-demand balance and minimize operating costs. Our algorithm enhances system resilience in unpredictable conditions, providing compelling incentives for active EV user participation. Coordinating the integrated system efficiently, including the commercial building’s energy load, ensures reliable service to customers while reducing costs. Extensive case studies and a comparative analysis validate the algorithm’s efficiency in significantly reducing operating costs and enhancing resilience to uncertainty. The paper concludes by highlighting the algorithm’s pioneering role in intelligent EV charging station (CHS) management, offering a cost-effective, customer-oriented, and dynamic energy control strategy for advancing global energy practices.

A Distributed Coordination Approach for the Charge and Discharge of Electric Vehicles in Unbalanced Distribution Grids

A Distributed Coordination Approach for the Charge and Discharge of Electric Vehicles in Unbalanced Distribution Grids

A study of home energy management considering carbon quota

The household energy management system (HEMS) has become an important system for energy conservation and emission reduction. In this study, home energy management considering carbon quota has been established. Firstly, the household photovoltaic output model, load model of various electrical appliances, battery load model, and charging and discharging of electric vehicles (EVs) model are established. Secondly, the carbon emission and carbon quota of household appliances and EVs are considered in these models. Thirdly, the energy optimization model of minimum the household user’s total comprehensive operation cost with the minimum total electricity consumption, carbon trading cost, battery degradation cost, and carbon quota income are proposed, taking into account constraints such as the comfort of users’ energy use time. Subsequently, the improved particle swarm optimization (IPSO) algorithm is used to tackle the problem. Compared to the standard particle swarm optimization (PSO), the IPSO has significantly improved the optimization effect. By comparing the optimization results in different scenarios, the effectiveness of the strategy is verified, and the influence of different carbon trading prices on optimal energy scheduling has been analyzed. The result shows that the comprehensive consideration of carbon trading cost and total electricity cost can reduce the household carbon emissions and the total electricity cost of the household user. By increasing the carbon trading price, the user’s carbon trading income and the EV carbon quota income increase, and the overall operating cost decreases; the guidance and regulation of carbon trading price can make a valuable contribution to HEMS optimization. Compared to the original situation, the household carbon emissions are reduced by 14.58 kg, a decrease of over 21.47%, while the total comprehensive operation cost are reduced by 14.12%. Carbon quota trading can guide household users to use electricity reasonably, reducing household carbon emissions and the total cost of household electricity.

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.

Intelligent charging and discharging of electric vehicles in a vehicle-to-grid system using a reinforcement learning-based approach

Recent advances in electric vehicle (EV) technology have increased the importance of vehicle-to-grid (V2G) systems in the smart grid domain. These systems allow bidirectional energy and information flow between consumers and suppliers, enabling the EV to act as an energy storage system that can provide surplus energy to the grid. V2G is particularly useful for reducing the peak demand and load shifting for utilities, acting as a backup system for renewable energy. To optimize the benefits of these systems, the intelligent management of charging and discharging is essential, while considering the electricity prices and user requirements. However, uncertainties such as commuting behavior, charging preferences, and energy requirements, pose challenges in determining the optimal charging/discharging strategy. In this study, individual EV charging/discharging is formulated as a sequential decision-making problem and a model-free reinforcement learning (RL) approach is utilized to learn the optimal sequential charging/discharging decisions until the EV battery reaches its end-of-life. The goal is to minimize the charging cost for the individual user and maximize the use of the EV battery as the vehicle proceeds through various charging and discharging cycles, while also considering the distance traveled by the vehicle. The proposed algorithm is evaluated using real-world data, and the learned charging and discharging strategies are examined to investigate the effectiveness of the proposed method. The experimental scenarios demonstrated that utilizing the RL approach is advantageous compared to the other approaches for reducing the overall cost and maximizing the use of EV batteries.

V2G Strategies to Flatten the Daily Load Curve in Seoul, South Korea

In order to meet the increasing demand for electricity to maintain electric vehicles (EVs) worldwide, this paper aims to improve our understanding of the impact of the load on the power grid generated by the charging and discharging of electric vehicles. The rapid development of the electric vehicle (EV) industry offers new economic and environmental benefits, such as mitigating global warming by reducing carbon dioxide. On the other hand, however, we will face the reality that the emergence of such large-scale EVs will undoubtedly put additional strain on the power grid. In this context, solving the problem of excessive power usage associated with charging large electric vehicles and reducing the impact on the grid is paramount. Accordingly, in order to meet the increasing demand for electricity to maintain electric vehicles (EVs) worldwide, this paper aims to improve our understanding of the impact of the load on the power grid generated by the charging and discharging of electric vehicles. A V2G strategy is presented with the goal of flattening the daily load curve by considering the charge and the discharge positions of EVs. First, in this paper, based on the estimated share of electric vehicles, we set the assumption that EVs travel to work in the morning and leave work in the afternoon. Second, we develop an efficient V2G strategy to equalize the daily load curve due to charging and discharging of electric vehicles in Seoul by applying a system marginal price (SMP) and time-of-use (TOU) rate system. The EV charging/discharging load and existing load using V2G modeling are added up, all daily load curves are calculated and analyzed based on the 2030 and 2040 EV share scenarios for Seoul, and the grid load is leveled. The analysis suggests measures to minimize the impact of EV loads on the power grid according to the V2G strategy based on charging and discharging plans. Overall, this paper aims to smooth the grid’s daily load curve and avoid grid overload by applying appropriate SMP and TOU plans; we also present an efficient V2G strategy, established through charge and discharge modeling and EV charge and discharge management techniques, in order to minimize grid expansion.

Uncertainty compensation with coordinated control of EVs and DER systems in smart grids

The exploitation of wind farms and solar cells have a special place due to their ability to produce more, more general acceptability, and more affordable. The only challenge facing the use of new energies is the uncertainty in their production due to the lack of sunlight and wind blowing continuously at different hours of the day and night. In this article, the problem of planning the flexibility of electric vehicle charging and discharging to coordinate with wind and solar production and to compensate for the uncertainty of these resources has been solved using the CPLEX solver. Also, in this article, the flexibility of charging and discharging of electric vehicles has been used to coordinate with wind and solar production and compensate for the uncertainty of these sources. In addition, the EVs (Electric Vehicles) are planned in such a way that the operating cost of the microgrid is minimized. To consider the uncertainty related to loading, wind, and solar production, different scenarios have been considered and programming in R language has been done. These scenarios have been considered using the time series method of data determination and correlation. Due to the high number of scenarios and the increase in the volume of calculations, the scenario reduction method is used, then several scenarios with the highest probability of occurrence are selected and optimization is done. The simulation is done using GAMS software. The optimization problem is solved by integer linear programming. The results indicate that the cost is reduced by considering the uncertainties, through optimal planning for the EVs, and it is also profitable for the operator.

Charging and discharging of PHEVs in smart grids with ICA and TLBO algorithms with the approach of simultaneously reducing network peak load and distribution costs

The widespread use of electric vehicles is a suitable solution to deal with the reduction of fossil fuel reserves and the increase in air pollution. Reducing pollution can be realized through optimal planning of charging and discharging of electric vehicles as consumers and storage of electric energy. Also, vehicles connected to the network increase the reliability of the network during power outages. However, the connection of electric vehicles to the grid can also bring limitations. The uncoordinated and unmanaged presence of electric vehicles as an additional load in the network can aggravate problems such as voltage drop, voltage stability and increase in network losses. Consequently, these problems will have economic costs. In this way, the management of charging and discharging of electric vehicles will be of special importance in the future of control and operation of distribution networks. In this article, electric vehicle charging and discharging management has been done in order to minimize the amount of active power loss, bus voltage deviation, increase the stability of the network voltage, and as a result, reduce the costs of the distribution company. For this purpose, a 33-bus distribution network that absorbed its required energy only through the connection point to the network was studied with different scenarios in which the objective function of the problem was different. The proposed algorithm was implemented to calculate the best state of charging and discharging of cars. In choosing these vehicles, it has been tried to examine all charging modes. The simulation results show the effectiveness of the proposed plan for planning the development of electric vehicles in the distribution network.

Designing multi-objective electric and thermal energy management system of microgrid in the presence of controllable loads and electric vehicles

The global electricity network and natural gas have been developed worldwide. So far, various investigations in relation to energy infrastructures have been performed; however, the combination of these systems has not been examined much. For this reason, electric and thermal loads have been interconnected by CHP units, and the combined study of power and heat has helped the operator to propose a more optimal structure for energy management. Therefore, integrating different energy carriers and using the benefits of each one can increase the efficiency and reliability along with optimizing system performance. Such a capability in structure and operation requires a comprehensive framework to describe the effects of combining different energy carriers on the economic and environmental indicators of energy systems. In this paper, the microgrid has been modeled as an energy hub in order to combine different energy carriers and perform conversions in them to provide the optimal electric and thermal load required by the consumer. This structure has been created by managing the incoming carriers of gas, electricity and renewable energy and managing their conversion and storage in order to minimize the cost of energy supply and environmental pollutants. In addition to managing generation units, in order to improve the performance of the microgrid and help the operator of the microgrid, a responsive load management strategy and charging and discharging of electric vehicles have been proposed. Epsilon constraints and fuzzy decision-making methods have been used to solve this issue. The results of the study indicated that in the optimal state with the application of responsive loads, the total cost of operation has been reduced by more than 6% and the cost of environmental pollutants has been mitigated by about 13%.

Status and Development of Research on Orderly Charging and Discharging of Electric Vehicles

As the scale of electric vehicles continues to expand, the charging load of electric vehicles into the network has become an issue that cannot be ignored. This paper introduces the concept and development of ordered charging based on the current background of ordered charging research. The application architecture of ordered charging is summarized, and the advantages and disadvantages of centralized, distributed, and hierarchical control architectures are introduced. The current status of research on orderly charging is analyzed at four levels: steps and methods of load modeling for orderly charging, optimization objectives of orderly charging, optimization methods of orderly charging, and practical projects of orderly charging. The methods of load modeling for orderly charging are summarized, different optimization objectives of grid operation for orderly charging are introduced, and the advantages and disadvantages of different optimization algorithms are compared and analyzed. Practical projects on orderly charging illustrate the great potential of orderly charging. This paper points out four problems of communication, data security, market mechanism, and the number of charging stations that orderly charging is currently facing and proposes feasible solutions. The development prospect of orderly charging being more environmentally friendly, energy-efficient, intelligent, and safe is proposed.

Electric Vehicle Charging Scheduling Optimization Method Based on Improved Ant Colony

The influx of electric vehicles significantly impacts the power grid. In the process of power grid operation, the operation of charging and discharging of electric vehicles can be used to dispatch the load of power grid operation. The optimization method of charging and dispatching electric vehicles based on improved ant colony is studied. Pheromones generated during the charging of the electric vehicle are obtained, and the charging limit range of the battery is delimited. The dynamic window constructs the charging scheduling model improved ant colony algorithm, and the optimal charging path is selected to complete the scheduling method design. The experimental results show that the proposed method can adjust the charging and discharge in the period of valley price and peak price, play the role of peak clipping, and realize the load regulation in different periods, which has application value.

Maximizing solar-powered charging and discharging of electric vehicles in residential and parking areas: A case study in optimal scheduling

The expectation of a huge number of Electric Vehicles (EVs) into the market creates multiple technical problems. The power system is at risk due to the unsecured and unreliable operation resulting from unplanned charging and discharging. The unplanned EV charging and discharging change the load profile of the electric grid. Hence, it is required to develop an effective scheduling scheme for charging and discharging EVs. This research focuses on the minimization of the charging cost of a large number of Electric Vehicles (EVs) in a residential area and parking station. When a huge number of EVs are aggregated in the residential and parking stations, charging and discharging power should be a significant and viable contributor to the power grid. V2G (Vehicle-to-Grid) technology offers additional potential benefits to improve power quality and also system reliability. EVs can also supply G2V (Grid-to-Vehicle) power through the discharge of the battery, which benefits both EV owners and electric utilities. It is very difficult to schedule the configurations of EV charging and discharging in an optimized way so as to decrease the charging cost of EVs. The usage of renewable energy sources like solar Photovoltaic (PV) power, reduces the burden on the grid. The solar PV systems are likely to have significant surplus power during the daytime. In this research, optimal charging and discharging scheduling schemes for EVs are developed and the proposed work is carried out under the MATLAB environment with and without Photovoltaic (PV) systems. The realistic load data is collected from Transmission Corporation of Andhra Pradesh (APTRANSCO), India and the solar data is taken from the Indian Solar Resource Data website. The effectiveness of the scheduling scheme is rated according to its capability to reduce the total cost. Extensive simulation studies have been performed on the distribution system under consideration and the results have been presented. The results demonstrate that the charging cost of EVs can be reduced significantly with the use of solar PV power.

Coordinated Charging and Discharging of Electric Vehicles: A New Class of Switching Attacks

In this work, we investigate that the abundance of Electric Vehicles (EVs) can be exploited to target the stability of the power grid. Through a cyber attack that compromises a lot of available EVs and their charging infrastructure, we present a realistic coordinated switching attack that initiates inter-area oscillations between different areas of the power grid. The threat model as well as linearized state-space representation of the grid are formulated to illustrate possible consequences of the attack. Two variations of switching attack are considered, namely, switching of EV charging and discharging power into the grid. Moreover, two possible attack strategies are also considered (i) using an insider to reveal the accurate system parameters and (ii) using reconnaissance activities in the absence of the grid parameters. In the former strategy, the system equations are used to compute the required knowledge to launch the attack. However, a stealthy system identification technique, which is tailored based on Eigenvalue Realization Algorithm (ERA), is proposed in latter strategy to calculate the required data for attack execution. The two-area Kundur, 39-Bus New England, and the Australian 5-area power grids are used to demonstrate the attack strategies and their consequences. The collected results demonstrate that by manipulation of EV charging stations and launching a coordinated switching attack to those portions of load, inter-area oscillations can be initiated. Finally, to protect the grid from this anticipated attack, a Support Vector Machine (SVM) based framework is proposed to detect and eliminate this attack even before being executed.

Optimization of the Heat Dissipation Performance of a Lithium-Ion Battery Thermal Management System with CPCM/Liquid Cooling.

In view of the harsh conditions of rapid charging and discharging of electric vehicles, a hybrid lithium-ion battery thermal management system combining composite phase change material (PCM) with liquid cooling was proposed. Based on the numerical heat transfer model, a simulation experiment for the battery thermal management system was carried out. Taking the maximum temperature and temperature difference of the battery module as the objectives, the effects of PCM thickness, the liquid flow rate and the cross-sectional area of the liquid channel on the temperature of the battery module were analyzed using response surface methodology (RSM). The results show that 31 groups of candidate parameter combinations can be obtained through response surface analysis, and phase change material (PCM) thickness should be minimized in order to improve space utilization in the battery module. The optimal parameter combination is a flow rate of 0.4 m/s and a PCM thickness of 5.58 mm, with the cross-sectional area of the liquid channel as 3.35 mm2.

Research on ordered charge and discharge of Electric Vehicles and their effects

Abstract Based on the control strategy of ordered charge and discharge of electric vehicles, the control strategy of ordered charge and discharge is proposed. The control strategy of the minimum variance minimum differential peak and valley load as the goal, and will be the charge and discharge time of electric vehicles and the charge and discharge requirements as constraint conditions, the optimal solution given by the method of charging control flow diagram and orderly, according to the actual place residential distribution network structure, based on MATLAB/simulink to establish the simulation model of the distribution network. The simulation results show that the proposed orderly charge-discharge strategy can effectively complete the “peak-valley filling”, reduce the daily maximum load increment and reduce the peak-valley difference.

Optimal management of vehicle-to-grid and grid-to-vehicle strategies for load profile improvement in distribution system

This paper develops a penalty factor-based objective function for scheduling the strategy of coordinated charging and discharging of Electric Vehicles (EVs). Firstly, EVs owner behaviors, such as arrival time, departure time, duration of EVs at the parking lots, and the EVs' battery capacity, are modeled and estimated using Monte Carlo Simulation (MCS). The proposed objective function is minimized with the help of the Opposition-based Competitive Swarm Optimizer (OCSO) algorithm to obtain EVs' optimal charging and discharging scheduling with minimum daily load variance or flatten daily load curve. The effectiveness of the proposed method is tested on IEEE 69-bus radial distribution system. It is found that the proposed OCSO based controlled scheduling, in conjunction with MCS, reduces the load variance up to 32.67% compared to uncontrolled scheduling. Also, the parametric sensitivity studies are executed to determine the optimal values of free parameters for the OCSO algorithm. Finally, the results obtained with the proposed technique are compared with the other contemporary optimization algorithms to demonstrate its efficacy. The comparative analysis shows that the optimal load variance for the controlled scheduling, as obtained with the proposed method, is 0.89% to 3.71%, smaller than the existing controlled scheduling techniques. Further, it is found that the proposed work provides a flattened load curve and a faster convergence rate with a 100% success rate.

Applying fuzzy logic control to analyze real-time control for charging and discharging power of electric vehicles

The disorderly charging of large-scale electric vehicles will aggravate the peak-valley difference of the power grid, and affect the power quality and life of the transformer. The fuzzy logic control strategy for charging and discharging optimization of charging vehicles under the framework of fuzzy logic control from the perspective of the group is considered in this article. A real-time control method based on the clustering characteristics of the charging end time is proposed according to the different charging requirements of the connected electric vehicles and fuzzy logic control is adopted to solve the problem of optimal charging and discharging power of the entire cluster and a single electric vehicle. A fuzzy logic control model considering the charging and discharging of electric vehicles is established orienting at minimize daily load fluctuations and control penalties in the upper layer. The charging and discharging cost of electric vehicle owners is considered to solve the optimal control problem of the charging and discharging power of a single electric vehicle. Taking the data of the typical regional distribution network load as an example, it is verified that the real-time charging optimization strategy under fuzzy logic control through simulation can ensure the reliable operation of the power grid while considering the interests of all parties.

Study on Smart Home Energy Management System Based on Artificial Intelligence

With the increase of household electricity consumption and the introduction of distributed new energy sources, more attention has been paid to the issue of optimizing the cost of electricity purchase for household customers. An effective way to deal with these problems is through home energy management system (HEMS). In this paper, a model of home energy management is presented to optimize the home energy mix. The operation of home electricity consumption devices, distributed generation systems, and energy storage devices, as well as the charging and discharging of electric vehicles, are all considered. HEMS is a self-regulating system that can accommodate fluctuations in tariffs and home electricity consumption. The structure and the optimal scheduling algorithm of HEMS are introduced. The smart grid and demand response, smart home, new energy generation, energy storage, and other related technologies are discussed. Furthermore, the optimal scheduling of power consumption devices and energy sources in the HEMS and future development directions are explained and analyzed. A framework of HEMS is presented on the basis of advanced metering infrastructure (AMI). The framework adopts a local information management terminal as the core of data storage and scheduling in the home. Based on the timely purchase of electricity from the grid and the generation of electricity in combination with PV systems, an optimized simulation model for the scheduling of a new home energy management system is established. In addition, the application prospects of artificial intelligence in the HEMS are overviewed.

V2G Multi-Objective Dispatching Optimization Strategy Based on User Behavior Model

V2G (Vehicle to Grid) technology can adjust the grid load through the unified control of the charging and discharging of electric vehicles (EVs), and achieve peak shaving and valley filling to smooth load fluctuations. Aiming at the random and uncertain problem of EV users travel and behavior decision-making, this paper proposes a V2G multi-objective dispatching strategy based on user behavior. First, a V2G behavior model was established based on user behavior questionnaire surveys, and the effective effect of EV load was simulated through Monte Carlo simulation. Then, combined with the regional daily load curve and peak-valley time-of-use electricity prices, with the goal of stabilizing grid load fluctuations and increasing the benefits of EV users, a multi-objective optimal dispatching model for EV clusters charging and discharging is established. Finally, Considering the needs of EV users and the operation constraints of the microgrid, the genetic algorithm is used to obtain the Pareto optimal solution. The results show that when dispatching with the maximum benefit of users, the peak-to-valley ratio of the grid side can be reduced by 2.99%, and the variance can be reduced by 9.52%. The optimization strategy can use peak and valley time-of-use electricity prices to guide the intelligent charging and discharging of EVs while meeting user needs, so as to achieve the optimal multi-objective benefit of V2G participation in power response.

An Evaluation Framework for the Adjustable Capacity of Electric Vehicles’ Charge and Discharge Load Based on Capacity Reliability

With the development of new power systems with new energy as the main body, the number of electric vehicles is proliferating, and the demand for charging and discharging of electric vehicles is also increasing. However, the disordered charging and discharging load of electric vehicles also bring significant challenges to the power system’s safe and stable operation. To this end, a framework for evaluating the adjustable capacity of electric vehicle charging and discharging load based on capacity credibility is proposed. First, we simulate the charging and discharging load of electric vehicles based on the Sequential Monte Carlo Algorithm; then, we calculate the corresponding system’s reliability based on the expectation of insufficient power supply; finally, we use the dichotomy to calculate the related credible capacity and capacity credibility. The simulation results show that the framework can effectively evaluate electric vehicles’ adjustable capacity under different charging and discharging modes and scales and provide a theoretical basis for the safety assessment and effective dispatch of large-scale electric vehicle grid-connected distribution networks.