Presence Of Electric Vehicles Research Articles

Optimizing load frequency control in microgrid with vehicle-to-grid integration in Australia: Based on an enhanced control approach

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency.

Optimization of Heat Transfer Performance Using Response Surface Methodology-Central Composite Design (RSM-CCD) for Nano-Coolant (Al2O3+EG/W) in Electric Vehicle Battery

The presence of electric vehicles (EVs) must be supported by batteries that have good-quality energy storage. Battery power is critical to the development of electric cars. Temperature affects battery strength, so operating within the optimum temperature range must be ensured. During the charge and discharge processes, the electrochemical reaction generates hot energy, causing an increase in battery temperature. In this research, the solution to the problem is to make a cooling system with a mini channel cold plate and Al2O3 1%vol+EG/W (50:50) nano coolant. Optimization of heat transfer enhancement using Response Surface Methodology-Central Composite Design (RSM-CCD) and experimental tests with various flow rate variations. The research findings revealed that the RSM-CCD results and the outcomes of studies employing test equipment agreed that the highest cooling fluid flow rate was the most optimal condition, the highest T2 temperature drop of 17.63% occurred at a flow rate of 1.7 LPM, and the lowest T2 temperature was 13.13% at a flow rate of 1 LPM.

The Impact of the Presence of a High Number of Electric Vehicles in Future Smart Cities – A Review

Attention to the increasing growth of electric vehicles (8.3% in light type and 39% in heavy type) in smart grid structures, it is essential to consider bottlenecks like proper energy management, charging infrastructure, ICT and cybersecurity problems, and economic and environmental challenges. Tariffs for the production of distributors and charging stations for electric vehicles in smart cities are definitely in a state of smart changes. The correct response to the consumption of electric vehicles will make energy distribution completely intelligent and structured with a new framework. In this paper, the structures of future smart cities will be discussed in the presence of a significant number of electric vehicles. The benefits that smart energy grids bring to future smart cities are explored and discussed in detail. A complete review of past research in the field of potential and capacities and challenges faced by future smart cities in the presence of electric vehicles has been demonstrated and the latest achievements and methods in this field have been reviewed. In the end, the bottlenecks and basic challenges that microgrids face in future smart cities are described.

Charging management of electric vehicles with the presence of renewable resources

Considering the increasing use of electric vehicles, the establishment of charging stations to exchange power between the grid and electric devices, and the integration of charging stations with solar power generation sources, the optimal use of electric vehicle charging stations in the power system. The purpose of cost reduction in the presence of the intelligent environment is a challenge that must be investigated so that this platform is suitable for predicting the behaviour of vehicles and, as a result, optimizing their presence in the power network. This research presents a relatively complete radial distribution network development planning model in two scenarios. In the first scenario, the effects of electric vehicles are not considered, and only the effects of distributed production (renewable and dispatchable) are considered. Studies have been done on a sample 54-bus network, a common system in most Distribution expansion planning (DEP) articles for distribution networks. In addition, the real data of American highways have been used to create raw input data. Also, due to the distance limit, the information on vehicles under 100 miles has been received as electric vehicle information. The clustering method and Capiola multivariate probability distribution functions have created suitable vehicle scenarios during different planning years. Capiola’s method increases the accuracy of vehicle load forecasting according to a predetermined growth rate. The DEP problem in this research is modeled as an optimization problem based on scenario, dynamic, and in 5 one-year time frames (5-year time horizon and one-year accuracy). The results indicate that, in the presence of electric vehicles and distributed production sources, the technical characteristics of the network are improved.Similarly, the use of DGs, in addition to reducing the cost of equipment, has reduced undistributed energy in the system. But 10,000 vehicles, which have been applied to the network as an uncontrolled load, have caused an increase in undistributed energy. The cost of equipment required for the network development is almost as much as 5%.

An Improved Sine Cosine Algorithm for the Day-ahead Microgrid Management in the Presence of Electric Vehicles

An Improved Sine Cosine Algorithm for the Day-ahead Microgrid Management in the Presence of Electric Vehicles

Fault Detection in a Single-Bus DC Microgrid Connected to EV/PV Systems and Hybrid Energy Storage Using the DMD-IF Method

Variations in fault currents, short times to clear the fault, and a lack of a natural current zero-crossing point are the most important challenges that DC microgrid protection faces. This challenge becomes more complicated with the presence of electric vehicles and energy storage systems due to their uncertainties. For this reason, in this paper, a new method for fault detection in DC microgrids with the presence of electric vehicles and energy storage systems is proposed. The new proposed method uses the combination of dynamic mode decomposition and instantaneous frequency for fault detection. In this method, first, a reference signal is made using the voltage and current signal sampled from the DC microgrid using the dynamic mode decomposition method. Next, in order to detect the fault, the instantaneous frequency value of the reference signal is calculated by the Hilbert transform. The simultaneous use of voltage and current signals reduces the transient effects of the control system on the proposed protection method. In order to measure voltage and current signals, only one intelligent electronic device unit is used in this paper. The proposed new method has been tested on a single-bus DC microgrid with the presence of electric vehicles and energy storage systems in MATLAB 2019b software. The results show that this method can detect all types of faults in DC microgrids, electric vehicles, and photovoltaics. Also, this method is immune to the uncertainties of the generation of distributed generation resources and the existence of noise distortions in the measured signals.

Short-term rescheduling of microgrids with high-penetration of smart electric vehicles using grasshoppers optimisation algorithm

Short-term rescheduling of microgrids is suggested to minimise the operating cost in the presence of electric vehicles and distributed energy resources. The model considered the uncertainties of load, wind, and solar. The problem is modelled as a single-objective problem, which minimises the operating costs of microgrids by considering the technical specifications of the distribution network. The grasshoppers’ optimisation is employed to solve problem. By applying the methodology, the optimal charging/discharging pattern of electric vehicles and DERs generation has been achieved. A significant reduction has been obtained in the microgrids operating costs. A comparison through the various algorithms has been implemented to show the effeteness of the GOA over the other recent algorithms. The efficiency of the approach has been studied on the modified IEEE 85-bus distribution system. The amount of active power loss has been reduced to 5.62% as well as minimum voltage profile has been achieved to 0.9489 p.u.

Risk-based Peer-to-peer Energy Trading with Info-Gap Approach in the Presence of Electric Vehicles

Small-scale smart microgrid is prone to economic consideration of adequate transactive energy sharing and reliable certified power pool hub. Moreover, direct integration of efficient with renewable energy providers in presence of storage such as electric vehicle (EV) aggregation improves the performance of energy systems and guarantees secure trade among the consumers. However, unified modeling of the energy community framework faces the challenge of load management. In this paper, an optimal risk management procedure is proposed for renewable-based prosumers such as photovoltaic (PV), and EV to maximize the horizon of uncertainty parameter. The energy demand in peer-to-peer (P2P) household energy sharing is considered as the uncertain parameter. To this end, in order to study the behavior of a risk-averse and a risk-seeker decision-maker, an information-gap decision theory (IGDT) probability model is applied. In order to demonstrate the performance of the proposed approach, the model is implemented in a test microgrid and the simulation results are presented and discussed.

Analysis of distribution systems in the presence of electric vehicles and optimal allocation of distributed generations considering power loss and voltage stability index

AbstractElectric Vehicles (EV) have the potential to completely transform the transportation industry, reduce carbon emissions significantly, and pave the road for significant climate progress. According to surveys, worries regarding range and charging accessibility are a significant barrier to EV customer adoption. The electricity grid will be significantly impacted by EV charging. A coordinated charging approach that facilitates EV charging while avoiding the power grid from overloading is required to manage EV charging. In this work, the EVs are classified based on their charging capacity. This classification includes car, truck, and bus. With this a charging discharging scheme has been established using Real Time Pricing for each hour for a 20‐min block. The Voltage Stability Index (VSI) for the system is calculated for the system and the strongest bus is identified for locating the EVs to charge/discharge.Augmenting this, in the present work, the power loss is analyzed when Distributed Generation (DG) units are installed after the EVs are located in the system for charging and discharging. The DG units are installed in each feeder, considering the VSI and Penetration Level (PL) and the power losses incurred due to the presence of EVs. The Monetary benefit of such an investment is analyzed for different topologies of IEEE‐33 and 69 bus systems and compared with the existing methods where DGs are installed before the EVs are located in the system for charging and discharging.

Optimal energy management in smart buildings with electric vehicles based on economic and risk aspects using developed whale optimization algorithm

With the development of smart electricity consumption technology, consumers can benefit from an economically optimized home energy management system, considering their well-being and utilizing electrical appliances. The multi-objective modelling of energy management in smart buildings with the presence of electric vehicles is the main topic of this paper, which also takes risk and economic considerations into account. To make the problem more realistic, various practical and nonlinear constraints are taken into account, such as production limits, flexible loads, and cost-risk limitations and indicators. Since solving this problem with classical methods is challenging and prone to suboptimal solutions, a developed whale optimization algorithm is suggested. In the proposed developed approach, efforts are made to enhance its capabilities and prevent it from converging to local points using local search. Finally, the proposed method and model are discussed and examined under different scenarios. The results demonstrate that when consumers have a positive attitude towards energy consumption, the net cost significantly decreases compared to a conservative attitude. The battery is used during times of favourable prices when users perceive more solar energy availability. Moreover, when users adopt different price risk indicators, these indicators have varying effects on the electricity purchasing strategy of the user.

Stochastic scheduling of power system in the presence of electric vehicle and renewable sources considering security and economic indexes using an improved mutation particle swarm optimization (M-PSO) algorithm

Increased air pollution and global temperature as well as motor vehicle fuel consumption have depleted fossil fuel resources and increased environmental problems caused by the consumption of such fuels. In addition to methods such as combined heat and power (CHP) technology and distributed generation (DG) of energy at the consumption site, renewable energy sources and EVs are considered suitable methods for achieving this goal, which is prepared by the grid or battery electric energy. Generation uncertainty due to the lack of solar radiation and constant wind blow at different hours of the day is the only challenge for using renewable energies. Moreover, system reliability is a concept that refers to the safe and reliable operation of the system. In general, the wider and more important the system, the more attention that is paid to calculating its reliability in planning and decision making. This study aims to examine the problem of probabilistic power system planning by calculating the power system reliability, evaluating the effect of the presence of these vehicles on security and economic indicators and renewable energy sources, and modeling uncertainties using a Least Squares Generative Adversarial Network (LSGANs) method with generating various scenarios for solar irradiance and wind speed. Furthermore, the Kantorovich distance matrix (KDM) is used to reduce the number of generated scenarios. In the proposed model, the conditional value-at-risk (CVaR) method is implemented to assess and control the risk caused by uncertainties of the proposed problem. Using the power stored in the EV battery is evaluated to cover wind and solar energy source uncertainties.

Power and Energy Management Strategies for a Microgrid with the Presence of Electric Vehicles and CAES Considering the Uncertainty of Resources

We are witnessing the growth of microgrid technology and the development of electric vehicles (EVs) in the world. These microgrids seek demand response (DR) and energy storage for better management of their resources. In this research, microgrids, including wind turbines, photovoltaics, battery charging/discharging, and compressed air energy storage (CAES), are considered. We will consider two scenarios under uncertainty: (a) planning a microgrid and DR without considering CAES, and (b) planning a microgrid and DR considering CAES. The cost of charging the battery in the second study decreased by $0.66 compared to the first study. The battery is charged with a difference of $0.7 compared to the case of the first study. We will also pay for unsupplied energy and excess energy in this microgrid. Then, we test the scheduling of vehicles to the grid (V2G) in the IEEE 33-bus network. The first framework for increasing network flexibility is the use of EVs as active loads. The scheduling of vehicles in the IEEE 33-bus network is simulated. Every hour, plug-in hybrid electric vehicle (PHEV) charging and discharging, active power loss, and cost will be compared with IHS and PSO algorithms. The difference obtained using the IHS algorithm compared to the PSO algorithm is 1.002 MW and the voltage difference is 9.14 pu.

Sentiment Analysis of Electric Vehicles using the Naïve Bayes Algorithm

Industrial resolutions that are increasingly developing make many transitions in all fields. The increasing use of fuel for vehicles makes Indonesia's fuel stocks run low. Electric vehicles are one type of vehicle that uses electricity. The presence of electric vehicles is expected to be a solution to save fuel oil. Another advantage of electric vehicles is that electric vehicles produce low emissions so that apart from irritating fuel stocks, air pollution will also be reduced. However, the presence of electric vehicles is still a pro and contra in society. One of the media where people express their opinions is the social media Youtube. Testing was carried out using the K-Fold Cross Validation and the Naïve Bayes algorithm. From this test, an accuracy value of 82% was obtained with the polarity of the sentiment obtained, namely negative sentiment of 82% while positive sentiment of 18%. The purpose of this study is to determine the polarity of sentiment and the accuracy value obtained. It is hoped that this research can be properly conveyed to related parties for future progress Keywords: Electric Vehicle , Nave Bayes , K-Fold Cross Validation

Stochastic Traffic assignment with Electric Vehicles: a convex optimization approach

Electric vehicles and charging stations are increasing worldwide to reduce emissions at the city level. In order to analyze traffic flows and quantify the demand for energy recharge, it is necessary to model users’ choices considering different kinds of traffic flows. In this paper, attention is focused on the formalization of the joint traffic and energy demand assignment conditions over a traffic network in presence of electric vehicles (EVs). Specifically, the so-called stochastic user equilibrium (SUE) conditions are used to define the steady state behaviour of a generic traffic network in which some arcs are equipped with EV charging stations. A convex optimization problem is defined and solved, which is equivalent to the solution of the nonlinear and nonconvex set of SUE conditions under a multinomial Logit user's choice function. A real case study, related to a touristic area in the Liguria Region (from Genova airport to Portofino), is considered.

Risk-Based Peer-to-Peer Energy Trading with Info-Gap Approach in the Presence of Electric Vehicles

Risk-Based Peer-to-Peer Energy Trading with Info-Gap Approach in the Presence of Electric Vehicles

Risk Assessment of Industrial Energy Hubs and Peer-to-Peer Heat and Power Transaction in the Presence of Electric Vehicles

The peer-to-peer (P2P) strategy as a new trading scheme has recently gained attention in local electricity markets. This is a practical framework to enhance the flexibility and reliability of energy hubs, specifically for industrial prosumers dealing with high energy costs. In this paper, a Norwegian industrial site with multi-energy hubs (MEHs) is considered, in which they are equipped with various energy sources, namely wind turbines (WT), photovoltaic (PV) systems, combined heat and power (CHP) units (convex and non-convex types), plug-in electric vehicles (EVs), and load-shifting flexibility. The objective is to evaluate the importance of P2P energy transaction with on-site flexibility resources for the industrial site. Regarding the substantial peak power charge in the case of grid power usage, this study analyzes the effects of P2P energy transaction under uncertain parameters. The uncertainties of electricity price, heat and power demands, and renewable generations (WT and PV) are challenges for industrial MEHs. Thus, a stochastically based optimization approach called downside risk constraint (DRC) is applied for risk assessment under the risk-averse and risk-neutral modes. According to the results, applying the DRC approach increased by 35% the operation cost (risk-averse mode) to achieve a zero-based risk level. However, the conservative behavior of the decision maker secures the system from financial losses despite a growth in the operation cost.

Economic Load Spreading by Considering the Presence of Electric Vehicles in Order to Reduce Pollution Particles and To Consider Demand Response

Economic Load Spreading by Considering the Presence of Electric Vehicles in Order to Reduce Pollution Particles and To Consider Demand Response

Energy management programming to reduce distribution network operating costs in the presence of electric vehicles and renewable energy sources

Due to growing environmental and economic constraints, countries are exploring renewable sources such as wind, solar, and fuel cells to save energy, and develop the use of dispersed generations. Thus, the use of electric vehicles (EVs) is on the rise. On a large scale, either of these technologies can have damaging effects on the electricity grid; however, with suitable consumption-side management and programming, technologies and energy storage resources can reduce these effects. Thus, energy management optimization has become an interesting topic of research. Accordingly, the effect of the integrated aggregation of PEVs to the grid for the charge/discharge process and the resulting grid instability, especially at load peak time, is the main challenge to the use of these vehicles. The contribution of this paper includes the presentation of a model for managing the coordinated and uncoordinated charging system of grid-connected EVs with wind power and photovoltaic power units as dispersed generation sources and dividing the EVs into 4 classes by considering the share of each in the grid and considering a random number of vehicles per class using the normal distribution function and implementing the incoordination in wind speed and solar irradiation. The proposed model uses a novel Reinforcement Learning (RL) based onDeep Q Network (DQN) algorithm to solve the multi-objective problem. In this model, the costs of annual energy losses and the operation of dispersed generation units are discussed in an integrated manner as the objective function. The simulation is performed on a 57-bus IEEE grid and the results show the efficiency and improved performance of the model.

RETRACTED: Stochastic scheduling of a power grid in the presence of EVs, RESs, and risk index with a developed lightning search algorithm

- Due to the rise in environmental and economic limitations in today's world, the use of dispersed generation that often applies renewable energies, and electric vehicles is markedly increasing. On a large scale, either of these technologies can have damaging effects on the electricity grid; however, with proper energy management and programming, technologies, and energy storage resources can reduce these effects. Accordingly, the impact of the integrated aggregation of EVs to the grid for the charge/discharge process and the resulting grid instability, especially at the load peak time, is the main challenge usig of these vehicles. A challenge associated with these resources is their intrinsic uncertainty and stochastic scheduling. Herein, the stochastic scheduling of a power system in the presence of electric vehicles (EVs) and renewable energy sources (RESs) is solved via the optimization algorithm developed based on lightning search. The proposed model uses the probabilistic method to generate different scenarios for wind speed and solar irradiation coefficient; then, it modifies them via the scenario reduction method. In the proposed method, the conditional value at risk (CVaR) method is adopted to evaluate and manage the risk of the problem uncertainties. The use of the capacity stored in the EV batteries for overcoming the uncertainty of generation by wind and solar energy sources is also evaluated. Finally, the proposed method is tested on a modified sample system. The simulation results show that the power capacity stored in the vehicle-to-grid (V2G) stations greatly contributes to covering the uncertainty of wind and solar farms. The optimization algorithm is superior to other methods in local and global search.

A stochastic tri-layer optimization framework for day-ahead scheduling of microgrids using cooperative game theory approach in the presence of electric vehicles

This study provides a tri-layer optimization framework in which the microgrid strategy for day-ahead market participation is determined by considering the uncertainties of load, RESs and EVs. Scenario-based method is utilized to deal with uncertainties. Besides, distribution feeder reconfiguration (DFR) and an incentive-based demand response (DR) program are used to enhance scheduling flexibility. In the first layer of the proposed model, the buy/sell bids of the microgrids are determined and sent to the pool market. In the second layer, the market clearing price (MCP) is determined according to the buy/sell bids of microgrids, and in the third layer, the microgrids are scheduled by a cooperative game theory approach. The proposed model is implemented on a 118-bus distribution system consisting of 4 microgrids and the results show that the dynamic topology improves the scheduling flexibility and thus reduces the total operating cost by 9.2%. The simulation results also show that EVs participation in scheduling leads to a reduction in the MCP during peak hours. Finally, the results illustrate that considering energy storage systems (EESs) and DR program leads to a reduction in the MCP and thus a 9.33% reduction in the total operating cost.