At present, the Charge Station Operator (CSO) has invested a lot of money in the construction of charging stations, but it is difficult to make profits due to problems such as competitive pressure and single business mode. Therefore, a charging pricing strategy of CSOs considering dual business mode based on non-cooperative game is proposed. Firstly, the dynamic road network and Floyd algorithm are combined to simulate the travel of electric vehicles (EVs) and predict the spatial-temporal distribution of the charging demand. Secondly, an EV charging station selection and charging quantity decision model considering charging preference is established. Then, considering the business mode of Behavior-Based Pricing (BBP) and service level difference, a CSO charging pricing model based on non-cooperative game is constructed with the goal of maximizing the profits of CSOs. Finally, the effectiveness of the proposed strategy is verified by the simulation based on a regional road network and the IEEE33 node distribution system. The results demonstrate that the proposed strategy can alleviate direct price competition and increase profitability. And the proposed strategy will improve investment enthusiasm among CSOs and meet the diverse charging demand of EV users.
The effects of Al foil morphology,powder on the Al foil surface,thickness of Al2O3 film,lead quality,control of vivet joint on contact resistance were studied by the means of metallographical analysis and comparisons of contact resistances after rivet joint for Al foils.Some suggests were advanced: ① multistage frequency conversion composite etching technology is used to obtain “arbon-from” etching holes.② treatment technology is improved to reduce powder on the Al foil surface.③ control conditions of technology is regulated to improve etching morphology.
In the context of carbon neutrality, the electric vehicles (EVs) are increasingly used in city logistics service. To utilize EVs in the logistics delivery service in a cost-effective manner, this paper proposes a coordinated operational planning method for EVs considering the real road networks and battery swapping for EVs. Specifically, utilizing the EVs parked in the depot to discharge to power the depot or feed to the grid for an arbitrage, this paper is to minimize the sum of transportation cost and energy cost by determining the optimal number of EVs assigned to delivery goods and their driving route, as well as the charge–discharge operation decisions of all EVs including those without a delivery assignment and those returning to the depot after completing delivery. In simulation study, the effectiveness of the proposed methods is finally validated.
A novel planning method of fast electric vehicle (EV) charging stations on a round freeway was developed, considering the spatial and temporal transportation behaviors. A spatial and temporal model based on the origin-destination (OD) analysis was developed to obtain all the EV charging points (the location on the round freeway that an EV needs recharging due to the low battery capacity). Based on a shared nearest neighbor (SNN) clustering algorithm, a location determination model was developed to obtain the specific locations for EV charging stations with their service EV clusters. A capacity determination model based on the queuing theory was proposed to determine the capacity of each EV charging station. The round-island freeway in Hainan Island of China was employed as a test system to illustrate the planning method. Simulation results show that the developed planning method can not only accurately determine the most suitable locations for EV fast charging stations considering the travelling convenience of EV users, but also minimize the sum of waiting cost and charger cost.
This paper presents a bilevel planning framework to coordinate truck mobile chargers (TMCs) and fixed chargers (FCs) on highways to promote charging flexibility and provide more choices for electric vehicle (EV) users. A collaborative location optimization (CLO) approach is developed at the upper level to optimize the location of charging stations. Furthermore, a collaborative capacity optimization (CCO) approach is formulated at the lower level to optimize the capacity of TMC and FC at candidate stations. In the proposed framework, origin-destination (OD) analysis, Floyd algorithm and Monte Carlo simulation (MCS) are employed to generate the spatial-temporal distribution of charging demand based on historical data. An improved income approach (IIA) is then developed to well capture the heterogeneity of EV users' charging behavior. The waiting cost of EV users is estimated by their value of time (VOT), which helps them to make a better choice between TMC and FC. To solve the optimal model easily, the big-M method is applied to linearize and convert the nonlinear problem into a mixed-integer linear programming (MILP) model. Meanwhile, the analytical target cascading (ATC) technique is employed to realize the data exchange process between the upper and lower layers. Finally, numerical study demonstrates the effectiveness of the proposed framework and method.
The number and penetration of electric vehicles(EVs) are increasing.Electric vehicle charging load has the two characteristics of power load and energy storage because electric vehicles are becoming a new flexible resource to participate in the auxiliary services of power systems, which can improve the operation of power systems.As the basis of electric vehicle flexibility application, the flexibility characterization of electric vehicles has become the primary problem to be solved.Therefore, an electric vehicle flexibility characterization method based on the behavior data of users is proposed.Firstly, the original data is cleaned and reconstructed, and the behavior data set of electric vehicle users is extracted.Then, based on the electric vehicle user behavior data set, an electric vehicle user flexibility potential evaluation index system is proposed, which characterizes the electric vehicle flexibility potential from the three dimensions of capacity, charging time, and charging power.Secondly, an electric vehicle flexibility controllable region construction method based on an evaluation index is proposed to describe the flexibility of electric vehicle users with different charging habits.Finally, using real user data for verification, the results show that the proposed method can accurately describe the flexibility of different electric vehicle users.The results can provide a basis for electric vehicle aggregators (EVA) to participate in power grid auxiliary services.
The meaning of the action on the roadmap of heat treatment technology development in USA was discussed.According to its revelation,the present state of Guangdong heat treatment industry is plainly analyzed,and the process of area new-style of industrialization which Guangdong heat treatment industry confronts and some methods that can be adopted are discussed.
With the intensification of energy crisis and environmental crisis, countries have accelerated the development of new energy sources.Lithium-ion energy systems occupy an important position in the energy storage market because of their excellent performance, but temperature-related issues still hinder their further development.In order to solve this problem, researchers are committed to more accurate prediction of the temperature of lithium-ion energy system.Long and short-term memory network (LSTM) has always been considered to be able to process time series well.The emerging temporal convolution network (TCN), as a special convolutional network, has also been proven to be able to handle sequential tasks well.In this paper, a new allied temporal convolution-recurrent diagnosis network (TCRDN) is constructed by combining LSTM and TCN using an adaptive boosting algorithm.The proposed model is experimentally demonstrated to be able to predict the change of surface temperature of lithium-ion energy system more accurately.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
