Pricing Design for EV Platoon Charging Network With Hybrid Traffic Flows

A distributed charging pricing strategy for highway microgrid considering carbon trading: A Stackelberg evolutionary joint game approach

Electric vehicles (EVs) are becoming a popular mode of transportation. However, one of the main concerns for EV and CNG owners is the availability and accessibility of charging stations. In this project, we present the development and implementation of an EV and CNG charging station finder web application. The app uses the Google Maps API to display the location of nearby pay stations and provide details such as connection type, availability and price. The app also allows users to filter charging and CNG stations according to their preferences and report charging stations as false or unserviceable. User testing is done to evaluate the usability and performance of the application. The results show that the app is easy to use and provides accurate and up-to date information on payment facilities In this system, the User is able to manage All his EVs inside the app plus he has search/book as lot in advance in the charging station. The User can also search an EV station based on nearby, city or kilometers. The System also provides a Roadmap if you enter source & destination with the charging stations on the way according to the kilometers entered. Admin will manage all the stations and slots. The need for developing infrastructure such as charging stations is undeniable. This EV Charging Station web application has been developed to help EV drivers locate available charging stations near them. After locating a charging station, users can also book a slot at the station to charge their vehicle. EV owners can also use this system to plan their trips more efficiently. Users simply need to specify the source and destination. Based on these two parameters, this system prepares a roadmap with all available charging stations along the journey.

The crowded urban environment and busy traffic lead to heavy roadside pollutions in high-density cities, thereby causing health damages to city pedestrians. Electric vehicle (EV) is considered as a promising solution to such street-level air pollutions. Currently, in high-density cities, the number of public charging stations is limited, and they are far from enough to form a complete charging network with a high coverage ratio that can provide easy and convenient charging services for EV users. Concerns and worries on being unable to find a charging port when needed become a major hurdle to EV practical applications. Meanwhile, greener and cheaper renewable energy is recommended to replace fossil fuel-based grid energy that is commonly used in existing charging stations. Thus, this study proposes a novel geographic information system (GIS)-assisted optimal design method for renewable-powered EV charging stations in high-density cities. By selecting the optimal locations and optimal number of the renewable-powered charging stations with the considerations of the existing charging stations and renewable potentials, the proposed method is able to minimize the life cycle cost of the charging stations while satisfying a user-defined area coverage ratio. Using Hong Kong as an example, case studies have been conducted to verify the proposed design method. The design method can be used in practice to help high-density cities build their public charging networks with cost-effectiveness, which will promote EV practical applications and thus alleviate the roadside air pollutions in high-density cities.

The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator.

Peak reduction in decentralised electricity systems : Markets and prices for flexible planning

Electric Vehicle Charging Pricing Design for Agent-Based Traffic Microsimulation

As an important part of transportation electrifycation, the increasing electric vehicles (EVs) have drawn a growing concern about their impacts on power systems. Charging stations (CSs), which aggregate the EVs' charging demand and provide charging services to them, are suitable for the study of the impacts of the moving EV loads. In this paper, a probabilistic model based on the spatial-temporal dynamics of moving EVs is applied to model the EV charging demand at various locations and time. A network calculus based QoS model, where the CSs' charging services are modeled as the arrival and departure of energy flows, is proposed and used to evaluate the QoS performance of the CSs. Simulations are conducted within a proposed integrated traffic-power system based on IEEE-30 bus test system and the results are presented to study the impacts of the moving EV loads on each bus voltage and the QoS performance of the CSs' charging services under different service policies.

Indonesia has significant solar energy potential, ranging from 4.88-5.78 kWh/m2 /day with a production of 444 GWh by 2022, which is supported by various government initiatives and research projects focused on renewable energy-based two-wheeled electric vehicles. The increasing prevalence of electric vehicles in Indonesia necessitates the adaptation of photovoltaic (PV)-based renewable energy infrastructure to provide power sources for battery-operated two-wheeled electric vehicle charging stations. This paper presents proposals for the implementation of two-wheeled electric vehicles, utilizing various battery technologies combined with PV-based renewable energy systems, incorporated into various plans and schemes. The research methodology was developed by evaluating the electric vehicle battery requirements and system component capacities, while the charging cycle describes the relationship between the battery capacity and the capacity of 200 Wp, 400 Wp, and 500 Wp photovoltaic panels. The calculation results show that the charging system uses various PV panel capacities. The 200 Wp PV panel requires the longest charging duration. The 400 Wp photovoltaic panel serves as an intermediate charging station. The 500 Wp photovoltaic panel shows the fastest charging duration among the three types of panels, ranging from 1 to 3.7 hours, depending on the battery load rating of the electric wheeled vehicle. In addition, the proposed PV charging system provides various battery capacity options with consideration of autonomous time (1-4 days) where the charging time of 4 days for electric vehicle battery capacity 2000 Wh will be in line with the battery demand in the charging station.

Increasing penetration of electric vehicles (EVs) gives rise to the challenges in the secure operation of power systems. The EV charging loads should be distributed among charging stations in a fair and incentive-compatible manner while ensuring that power transmission and transformation facilities are not overloaded. This paper first proposes a charging right (or charging power ration) trading mechanism and model based on blockchain. Considering all kinds of random factors of charging station loads, we use Monte Carlo modeling to determine the charging demand of charging stations in the future. Based on the charging demand of charging stations, a charging station needs to submit the charging demand for a future period. The blockchain first distributes initial charging right in a just manner and ensures the security of facilities. Given that the charging urgency and elasticity differences vary by charging stations, all charging stations then proceed with double auction and peer-to-peer (P2P) transaction of charging right. Bids and offers are cleared via double auctions if bids are higher than offers. The remaining bids and offers are cleared via the P2P market. Then, this paper designs the charging right allocation and trading platform and smart contract based on the Ethernet blockchain to ensure the safety of the distribution network (DN) and the transparency and efficiency of charging right trading. Simulation results based on the Ethereum private blockchain show the fairness and efficiency of the proposed mechanism and the effectiveness of the method and the mechanism.

This research reviewed challenges in electric vehicles (EVs) charging during trips and proposes a novel powertrain architecture that is cost-effective and time-efficient for EVs to instantly increase their range without deviating from their route. The main objective is to study the feasibility and economic analysis of an additional battery for EV applications without disturbing the traction battery under unknown future charging requirement information about EVs, which helps reduce waiting time at charging stations, mitigate the problem of battery swapping, charging 100% through renewables, reduce the negative impact of EVs on the power grid. This study considers an add-on battery of 15kWh capacity offered as battery-as-a-service (BaaS), available at charging stations or delivered at remote locations for users. We conducted an economic analysis, comparing EVs with and without add-on batteries alongside traditional petrol and diesel vehicles. Based on the results obtained, it can be inferred that adopting this framework reduces the travel time by 55 minutes but increases the cost by US$ 0.176 compared to fast charging for a 400 km trip. We can conclude that EVs with main traction and add-on batteries provide a tradeoff between cost and time and are beneficial for both EV user and add-on battery charging and swapping station (ABCSS) operators.

Learning-based demand-supply-coupled charging station location problem for electric vehicle demand management

Operations of electric taxis to serve advance reservations by trip chaining: Sensitivity analysis on network size, customer demand and number of charging stations

What abates ecological footprint in BRICS-T region? Exploring the influence of renewable energy, non-renewable energy, agriculture, forest area and financial development

To improve the computation efficiency of optimally dispatching large-scale cluster electric vehicles (EVs) and to enhance the profit of a charging station (CS) for EVs, this study investigates the optimal dispatch of the CS based on a decentralized optimization method and a time-of-use (TOU) price strategy. With the application of the Lagrange relaxation method (LRM), a decentralized optimization model with its solution is proposed that converts the traditional centralized optimization model into certain sub-problems. The optimization model aims to maximize the profit of CS, but it comprehensively considers the charging preference of EV users, the operation constraints of the distribution network, and the TOU strategy adopted by the CS. To validate the proposed decentralized optimal dispatching method, a series of numerical simulations were conducted to demonstrate its effect on the computation efficiency and stability, the profit of the CS, and the peak-load shifting. The result indicates that the TOU strategy markedly increases the profit of the CS in comparison with the fixed electricity price mechanism, and the computation efficiency and stability are much better than those of the centralized optimization method. Although it does not compensate the load fluctuation completely, the proposed method with the TOU strategy is helpful for filling the valley of power use.

The transport sector is a prominent source of increasing fuel consumption and greenhouse gas (GHG) emissions. Electric vehicle (EV) is deemed an appealing solution for those problems. However, due to the variation in charging demands, the high penetration of electric vehicles may cause different problems to the distribution network and its reliability. Therefore, several approaches are employed to predict the EVs charging demand and avoid the corresponding challenges. This paper compares the performance of four well-known deep learning models, namely artificial neural networks (ANN), recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs), in predicting charging demand for EV users after a charging session begins. We use a dataset consisting of 2000 observations of charging events collected from two public charging stations in Morocco. According to numerical data results, the first layer of the GRU regression approach marginally beats the other three methods in estimating power charging needs. Specifically, the GRU regression model has an RMSE and MAPE of 1.39% and 0.50% in the training stage and 2.90% and 0.76% in the testing stage, respectively. These findings can assist the National Office of Electricity and Water in Morocco in ensuring the reliability of grid utility in the short run and guiding them to construct additional charging stations in the long run.