scholarly journals Multiobjective Optimization of Large-Scale EVs Charging Path Planning and Charging Pricing Strategy for Charging Station

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Weicheng Hou ◽  
Qingsong Luo ◽  
Xiangdong Wu ◽  
Yimin Zhou ◽  
Gangquan Si
Keyword(s):  
Energy Consumption ◽  
Path Planning ◽  
Large Scale ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Pricing Strategy ◽  
Queuing Model ◽  
Charging Station ◽  
New Research ◽  
Charging Stations

With the increasing number of electric vehicles (EVs), the charging demand of EVs has brought many new research hotspots, i.e., charging path planning and charging pricing strategy of the charging stations. In this paper, an integrated framework is proposed for multiobjective EV path planning with varied charging pricing strategies, considering the driving distance, total time consumption, energy consumption, charging fee such factors, while the charging pricing strategy is designed based on the objectives of maximizing the total revenues of the charging stations and balancing the profits of the charging stations. First, the energy consumption model of EVs, the M/M/S queuing model of charging stations, and the charging model of charging piles are established. A novel charging path planning algorithm is proposed based on bidirectional Martins’ algorithm, which can assist EV users to select charging stations and plan charging paths. Then, a particle swarm optimization (PSO) algorithm is applied to solve the optimal solution of charging station pricing designation. Finally, the method proposed in the paper is simulated on the street map of Shenzhen to verify the efficacy of the multiobjective charging path planning for EVs and the feasibility of the charging pricing strategy.

Multiagent Coordination Optimization Based Model Predictive Control Strategy With Application to Balanced Resource Allocation

2014 ◽  
Author(s):  
Haopeng Zhang ◽  
Qing Hui
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Nonlinear Problems ◽  
Pso Algorithm ◽  
Network Resource ◽  
Multiagent Coordination

Model predictive control (MPC) is a heuristic control strategy to find a consequence of best controllers during each finite-horizon regarding to certain performance functions of a dynamic system. MPC involves two main operations: estimation and optimization. Due to high complexity of the performance functions, such as, nonlinear, non-convex, large-scale objective functions, the optimization algorithms for MPC must be capable of handling those problems with both computational efficiency and accuracy. Multiagent coordination optimization (MCO) is a recently developed heuristic algorithm by embedding multiagent coordination into swarm intelligence to accelerate the searching process for the optimal solution in the particle swarm optimization (PSO) algorithm. With only some elementary operations, the MCO algorithm can obtain the best solution extremely fast, which is especially necessary to solve the online optimization problems in MPC. Therefore, in this paper, we propose an MCO based MPC strategy to enhance the performance of the MPC controllers when addressing non-convex large-scale nonlinear problems. Moreover, as an application, the network resource balanced allocation problem is numerically illustrated by the MCO based MPC strategy.

scholarly journals A smart management system of electric vehicles charging plans on the highway charging stations

2021 ◽  
Vol 23 (2) ◽  
pp. 752
Author(s):  
Ibrahim El-Fedany ◽  
Driss Kiouach ◽  
Rachid Alaoui
Keyword(s):  
Electric Vehicles ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Long Distance ◽  
Smart Transportation ◽  
Charging Station ◽  
Charging Time ◽  
Trip Time ◽  
Automatic Mechanism ◽  
Charging Stations

Electric vehicles (EVs) are seen as one of the principal pillars of smart transportation to relieve the airborne pollution induced by fossil CO2 emissions. However, the battery limit, especially where the journey is with a long-distance road remains the most formidable obstacle to the large-scale use of EVs. To overcome the issue of prolonged waiting charging time due to the large number of EVs may have a charging plan at the same charging station (CS) along the highway, we propose a communication system to manage the EVs charging demands. The architecture system contains a smart scheduling algorithm to minimize trip time including waiting time, previous reservations and energyare needed to reach the destination. Moreover, an automatic mechanism for updating reservation is integrated to adjust the EVs charging plans. The results of the evaluation under the Moroccan highway scenario connecting Rabat and Agadir show the effectiveness of our proposal system.<br /><div> </div>

scholarly journals A Novel, Low Computational Complexity, Parallel Swarm Algorithm for Application in Low-Energy Devices

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8449
Author(s):  
Zofia Długosz ◽  
Michał Rajewski ◽  
Rafał Długosz ◽  
Tomasz Talaśka
Keyword(s):  
Energy Consumption ◽  
Computational Complexity ◽  
High Speed ◽  
Hardware Implementation ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Low Energy ◽  
Simultaneous Occurrence ◽  
Hardware Complexity ◽  
Energy Devices

In this work, we propose a novel metaheuristic algorithm that evolved from a conventional particle swarm optimization (PSO) algorithm for application in miniaturized devices and systems that require low energy consumption. The modifications allowed us to substantially reduce the computational complexity of the PSO algorithm, translating to reduced energy consumption in hardware implementation. This is a paramount feature in the devices used, for example, in wireless sensor networks (WSNs) or wireless body area sensors (WBANs), in which particular devices have limited access to a power source. Various swarm algorithms are widely used in solving problems that require searching for an optimal solution, with simultaneous occurrence of a different number of sub-optimal solutions. This makes the hardware implementation worthy of consideration. However, hardware implementation of the conventional PSO algorithm is challenging task. One of the issues is an efficient implementation of the randomization function. In this work, we propose novel methods to work around this problem. In the proposed approach, we replaced the block responsible for generating random values using deterministic methods, which differentiate the trajectories of particular particles in the swarm. Comprehensive investigations in the software model of the modified algorithm have shown that its performance is comparable with or even surpasses the conventional PSO algorithm in a multitude of scenarios. The proposed algorithm was tested with numerous fitness functions to verify its flexibility and adaptiveness to different problems. The paper also presents the hardware implementation of the selected blocks that modify the algorithm. In particular, we focused on reducing the hardware complexity, achieving high-speed operation, while reducing energy consumption.

scholarly journals Charging Load Allocation Strategy of EV Charging Station Considering Charging Mode

2019 ◽  
Vol 10 (2) ◽  
pp. 47 ◽  
Author(s):  
Yutong Zhao ◽  
Hong Huang ◽  
Xi Chen ◽  
Baoqun Zhang ◽  
Yiguo Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Allocation Strategy ◽  
Charging Station ◽  
Charging Mode ◽  
Grid Load ◽  
Coordinated Charging ◽  
Charging Stations ◽  
Additional Service ◽  
Ev Charging ◽  
Scale Grid

A charging load allocation strategy for Electric Vehicles (EVs) considering charging mode is proposed in this paper in order to solve the challenge and opportunity of large-scale grid-connected charging under the background of booming EV industry in recent years. Based on the peak-to-valley Time-of-Use (TOU) price, this strategy studies the grid load, charging cost and charging station revenue variation of EVs connected to the grid in different charging modes. In addition, this paper proposes an additional charging mechanism for charging stations to encourage EV owners to participate in the peak and valley reduction of the grid through coordinated charging. According to the example analysis, under the same charging demand conditions, the larger EV charging power will have a greater impact on the grid than the conventional charging power. This article collects additional service fees for car owners who are not involved in the coordinated charging. When the response charging ratio is less, the more total service charges are charged, which can compensate for the decline in the sales revenue of the charging station during the valley period. While having good economy, it can also encourage the majority of car owners to participate in the coordinated charging from the perspective of charging cost.

scholarly journals Planning of the Charging Station for Electric Vehicles Utilizing Cellular Signaling Data

2019 ◽  
Vol 11 (3) ◽  
pp. 643 ◽  
Author(s):  
Jianmin Jia ◽  
Chenhui Liu ◽  
Tao Wan
Keyword(s):  
Electric Vehicles ◽  
Large Scale ◽  
Travel Demand ◽  
Cellular Signaling ◽  
Sustainable Transportation ◽  
Charging Station ◽  
Step Model ◽  
Pollutants Emission ◽  
Effective Component ◽  
Charging Stations

Electric Vehicles (EVs), by reducing the dependency on fossil fuel and minimizing the traffic-related pollutants emission, are considered as an effective component of a sustainable transportation system. However, the massive penetration of EVs brings a big challenge to the establishment of charging infrastructures. This paper presents the approach to locate charging stations utilizing the reconstructed EVs trajectory derived from the Cellular Signaling Data (CSD). Most previous work focused on the commute trips estimated from the number of jobs and households between traffic analysis zones (TAZs). This paper investigated the large-scale CSD and illustrated the method to generate the 24-hour travel demand for each EV. The complete trip in a day for EV was reconstructed through merging the time sequenced trajectory derived from simulation. This paper proposed a two-step model that grouped the charging demand location into clusters and then identified the charging station site through optimization. The proposed approach was applied to investigate the charging behavior of medium-range EVs with Cellular Signaling Data collected from the China Unicom in Tianjin. The results indicate that over 50% of the charging stations are located within the central urban area. The developed approach could contribute to the planning of future charging stations.

GAA-Based Decision Approach for Hospital Building Renovation Management

2011 ◽  
Vol 403-408 ◽  
pp. 5265-5272
Author(s):  
Yi Kai Juan ◽  
Yu Ching Cheng ◽  
Yeng Horng Perng ◽  
Guang Bin Wang
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Energy Use ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Developed Countries ◽  
Sustainable Technologies ◽  
Trade Offs ◽  
Hospital Building

More and more attention has been paid to hospital facilities since modern pandemics have emerged such as SARS and avian influenza. Energy consumption by buildings accounts for 20-40% of energy use in developed countries, so many global organizations make efforts to develop sustainable technologies or materials to create a sustainable environment, and to reduce energy consumption when renovating building. Therefore, maintaining high standards of hygiene and reducing energy consumption has become the major task for hospital buildings. This study develops an integrated decision support system to assess existing hospital building conditions and to recommend an optimal scheme of sustainable renovation actions, considering trade-offs between renovation cost, improved building quality, and environmental impacts. A hybrid approach that combines the A* graph search algorithm with genetic algorithms (GA) is used to analyze all possible renovation actions and their trade-offs to develop the optimal solution. A simulated hospital renovation project is established to demonstrate the system. The result reveals the system can solve complicated and large-scale combinational, discrete and determinate problems such as the hospital renovation project, and also improve traditional building condition assessment to be more effective and efficient.

scholarly journals Utilizing Local Flexibility Resources to Mitigate Grid Challenges at Electric Vehicle Charging Stations

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3506
Author(s):  
Iliana Ilieva ◽  
Bernt Bremdal
Keyword(s):  
Large Scale ◽  
Vehicle To Grid ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Smart Charging ◽  
Local Flexibility ◽  
Optimal Mix ◽  
Grid Applications ◽  
And Performance ◽  
Charging Stations

Charging of electric vehicles (EVs) on a large scale can cause problems for the grid. Utilizing local flexibility resources, such as smart charging, stationary battery, vehicle-to-grid applications, and local generation can be an efficient way to contain the grid challenges and mitigate the need for grid reinforcement. Focusing on the INSPIRIA charging station located in Norway, this paper investigates the possibility of coping with imminent grid challenges by means of local flexibility. First, the potential grid challenges are estimated with the help of Monte Carlo simulations. Second, cost and performance for the various local flexibility sources are presented. Third, an analysis of the choice of battery, charging process, and battery economy are provided. Finally, the paper discusses the optimal mix of flexibility resources to efficiently mitigate grid challenges at the INSPIRIA charging station.

scholarly journals Self-Sustainable off grid Electric Vehicle (EV) Charging Station with Integration of Renewable Sources

2020 ◽  
Vol 9 (3) ◽  
pp. 2669-2674
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Large Scale ◽  
Transport Systems ◽  
Diesel Generator ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Power Generation Technology ◽  
Charging Stations ◽  
Ev Charging

Electric Vehicles (EV) are the world’s future transport systems. With the rise in pollutions and its effects on the environment, there has been a large scale movetowards electrical vehicles. But the plug point availability for charging is the serious problem faced by the mostof Electric Vehicle consumers. Therefore, there is a definite need to move from the GRID based/connected charging stations to standalone off-grid stations for charging the Electric Vehicles. The objective of this paper is to arrive at the best configuration or mix of the renewable resources and energy storage systems along with conventional Diesel Generator set which together works in offgrid for Electric Vehicle charging. As aconclusion, by utilizing self-sustainable off-grid power generation technology, the availability of EV charging stations in remote localities at affordable price can be made and mainly it reduces burden on the existing electrical infrastructure.

Study on Optimal Scale and Layout of Electric Vehicle Charging Stations Based on Chaotic PSO Algorithm

2013 ◽  
Vol 385-386 ◽  
pp. 1869-1872
Author(s):  
Can Qi ◽  
Ya Feng Wen ◽  
Duo Yang ◽  
Wei Liu ◽  
Guo Liang Wu
Keyword(s):  
Transmission Lines ◽  
Electrical Power ◽  
Pso Algorithm ◽  
Grid Security ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Network Losses ◽  
Electrical Power Consumption ◽  
Charging Stations ◽  
Chaotic Pso

The index of investment cycle cost (ICC) and users convenience of electric vehicles are presented. Based on the aim to minimizing investing unit and user fees, fix a charging station model in scales and layouts. ICC consist initial investments, cost of network losses and investments for new transmission lines. Convenience is measured by electrical power consumption expense from users to the charging station. Adding constraint of grid security and economical efficiency, the optimization aim function was set which comprehensively considers investors and users cost and operation of power grid. Chaos Particle Swarm Optimization Algorithm (CPSO) was used to settle it. By empirical study of certain planning area, the proposed model and algorithm are proved to be scientific and effective.

scholarly journals Research on Quantitative Models of Electric Vehicle Charging Stations Based on Principle of Energy Equivalence

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Zhenpo Wang ◽  
Peng Liu ◽  
Jia Cui ◽  
Yue Xi ◽  
Lei Zhang
Keyword(s):  
Energy Consumption ◽  
Electric Vehicle ◽  
Electricity Consumption ◽  
Quantitative Model ◽  
Charging Station ◽  
Gas Stations ◽  
Electric Vehicle Charging ◽  
Energy Equivalence ◽  
Energy Consuming ◽  
Charging Stations

In order to adapt the matching and planning requirements of charging station in the electric vehicle (EV) marketization application, with related layout theories of the gas stations, a location model of charging stations is established based on electricity consumption along the roads among cities. And a quantitative model of charging stations is presented based on the conversion of oil sales in a certain area. Both are combining the principle based on energy consuming equivalence substitution in process of replacing traditional vehicles with EVs. Defined data are adopted in the example analysis of two numerical case models and analyze the influence on charging station layout and quantity from the factors like the proportion of vehicle types and the EV energy consumption at the same time. The results show that the quantitative model of charging stations is reasonable and feasible. The number of EVs and the energy consumption of EVs bring more significant impact on the number of charging stations than that of vehicle type proportion, which provides a basis for decision making for charging stations construction layout in reality.

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