Search

To address the issues related to the construction of battery swapping stations during the development and popularization process of new energy battery-swapping vehicles, an optimization mathematical model with the goal of minimizing the average annual comprehensive cost over the operational target years of the swapping stations was established. The model comprehensively considered factors such as land price, construction cost, operational cost, maintenance cost, road traffic flow, and service capacity, with battery swapping capacity and distance as constraints. Additionally, an improved quantum particle swarm optimization algorithm was utilized to solve the model. The algorithm introduced an adaptive adjustment of inertia weight to enhance the overall search capability of the particles. It employed Logistic chaotic mapping to initialize population information, improving the population's travers ability. Through Levy flight strategy and Cauchy mutation strategy, the diversity of the population was increased, and the search space in the iteration process was expanded, further enhancing the global search capability of the algorithm and enabling it to quickly escape from local optima. This algorithm is applied to the actual planning of Kuancheng District of Changchun City, and the relevant data of this area is introduced into the established mathematical model. It is determined that the construction of four power conversion stations in this area meets the construction objectives. Meanwhile, the construction location and capacity of each power station are determined, which proves the feasibility and practicability of the method mentioned. [ABSTRACT FROM AUTHOR]

The planning of public charging stations is crucial for the growth of electric vehicles (EVs). To improve the accuracy of predicting EV charging demand in urban areas, we propose a charging decision-making model based on fuzzy logic. Meanwhile, the influence of private charging piles is considered to further enhance the accuracy of predicting public charging demand. To address the issue of optimization algorithms easily getting stuck in local optima due to the vast quantity of variables involved in the location and capacity planning process, this paper introduces a two-layer planning method. In specific, the upper-level location model optimizes the locations of charging stations, while the lower-layer capacity model determines the number of charging piles within each station, leading to a reduction in the number of variables for each layer. Moreover, through iterative exchange results between the upper-layer location model and lower-layer capacity model, the optimal solution can be attained. The simulation results demonstrate that the proposed method can simultaneously consider the perspectives of both EV drivers and charging station investors, while also enhancing the utilization rates of public charging piles.

Purposes In order to meet the expansion planning needs of electric vehicle charging stations under the “double carbon” goal and study the influence of charging station location and capacity on the absorption of distributed power supply. Methods This paper establishes a multi-objective double-layer configuration optimization model considering the uncertainty and correlation of wind-power output, and analyzes the influence of charging behavior change of electric vehicle(EV) users on charging pile location and volume scheme under time of use(TOU) power price incentive. The upper layer comprehensively considers the two objective functions of operator investment and construction, operation and maintenance, network loss cost and the minimum equivalent cost of user charging and queuing. The upper layer also uses the improved multi-objective particle swarm optimization algorithm to generate pareto optimal planning scheme. In order to minimize the wind and light discarding rate, the lower model verifies the Pareto solution set generated by the upper model. Findings Finally, the model is verified by IEEE33 node road network diagram, and the optimal site selection scheme of charging piles under uncertain scenery is obtained.

Deploying edge computing nodes on the demand side can effectively reduce the pressure of data transmission and storage of the power network and improve the quality of electric power service. At present, the deployment location of edge nodes is mostly determined from the dimension of grid topology, and the service scope of nodes is defined by the grid method. The working process among nodes is independent. Therefore, the flexibility of the location and capacitydetermination of edge nodes is low, and it may cause waste of equipment computing resources.Therefore, an edge computing node deployment method considering task migration is proposed in this paper. Firstly, an edge computing architecture considering task migration is proposed based on the characteristics of edge devices and the spatial characteristics of residential areas. Secondly, feature data is formed according to spatial information and load characteristics of residential nodes. Then the number, address and service scope of nodes are determined by using the improved density peak analysis algorithm. Finally, a heuristic algorithm is designed to realize the task migration among the edge nodes to ensure the balanced utilization of computing resources of nodes and improve the stability of the system. A residential area in Nanjing is taken as an example to design simulation experiments. The results show that the proposed edge nodes deployment method can effectively reduce the data transmission costs of residential nodes. Also, the task migration algorithm can effectively balance the computing resources among edge devices and improve execution efficiency of edge computing services.

Copyright of Electric Power Engineering Technology is the property of Editorial Department of Electric Power Engineering Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• A two-layer location and capacity planning method of charging stations is proposed. • A fuzzy logic-based charging decision-making model of electric vehicles is proposed. • The impact of private charging piles is considered when predicting charging demand. The planning of public charging stations is crucial for the growth of electric vehicles (EVs). To improve the accuracy of predicting EV charging demand in urban areas, we propose a charging decision-making model based on fuzzy logic. Meanwhile, the influence of private charging piles is considered to further enhance the accuracy of predicting public charging demand. To address the issue of optimization algorithms easily getting stuck in local optima due to the vast quantity of variables involved in the location and capacity planning process, this paper introduces a two-layer planning method. In specific, the upper-level location model optimizes the locations of charging stations, while the lower-layer capacity model determines the number of charging piles within each station, leading to a reduction in the number of variables for each layer. Moreover, through iterative exchange results between the upper-layer location model and lower-layer capacity model, the optimal solution can be attained. The simulation results demonstrate that the proposed method can simultaneously consider the perspectives of both EV drivers and charging station investors, while also enhancing the utilization rates of public charging piles. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For coyote optimization algorithm's global search ability was insufficient, easily plunged into local optimum, this paper proposed an improved coyote optimization algorithm. In the optimization process of coyote optimization algorithm, this improved algorithm introduced a deformed elite retention strategy and added the environmental impact factor in the growth process of coyote, then brought the grown coyotes into the Kent mapping to traverse the search space to strengthen the mining ability and search performance of the algorithm. Numerical experiments show that the improved algorithm has better perfor-mance. Taking the whole social and economic costs and carbon emissions as the decision-making objectives, this paper constructed a bi-level programming model for location and capacity of electric vehicle charging station, and used the improved coyote optimization algorithm to solve the programming model to verify the feasibility and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The infrastructure construction of charging facilities for electric vehicles (EVs) is one of the key factors influencing the development of electric vehicle industry. A reasonable location and capacity determination scheme is very crucial to this issue. In this study, a probability calculation model that fully analyses the charging behavior of the owners is proposed to predict the charging load of the planned land. Based on the results of electric vehicle load forecasting, a location model with the lowest users travel cost is established. The location of charging station is optimized by genetic algorithm to obtain the location and capacity library of charging station. Especially, the two‐way cost for both the owners and the operators is considered for the location and capacity decision, and the optimal scheme is selected from the location and capacity library as the final planning result. An example analysis shows that the proposed method is effective and feasible for the location and capacity determination of electric vehicles charging stations in cities. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]