A Guided Particle Swarm Optimizer for Distributed Operation of Electric Vehicle to Building Integration

2017 ◽  
Author(s):  
Yang Chen ◽  
Mengqi Hu
Keyword(s):  
Private Information ◽  
Large Scale ◽  
Particle Swarm ◽  
Commercial Building ◽  
Particle Swarm Optimizer ◽  
Charging Station ◽  
Solution Strategies ◽  
Ev Charging ◽  
Marginal Price ◽  
Distributed Decision

Relevant research has demonstrated that more potential benefits can be achieved when energy and information are transacted and exchanged locally among different energy consumers. With increasing number of electric vehicles (EVs), various models and solution strategies have been developed for collaboration between building and EV charging station to achieve greater energy efficiency. However, most of the existing research employs centralized decision model which is time consuming for large scale problems and cannot protect private information for each participator. To bridge these research gaps, a guided particle swarm optimizer based distributed decision approach is proposed to study the energy transaction between building and EV charging station. In the proposed decision approach, the marginal price signal of transactive energy is collected to guide iterative direction of particle’s velocity and position which can maximally protect private information of building and EV charging station. A study case based on a commercial building and a nearby charging station in Chicago area is designed for illustration. The experimental results demonstrate that our proposed marginal price guided particle swarm optimizer is more stable and efficient comparing with canonical particle swarm optimizer and two state-of-the-art distributed decision algorithms.

scholarly journals An Adaptive Multi-Swarm Competition Particle Swarm Optimizer for Large-Scale Optimization

Mathematics ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 521 ◽  
Author(s):  
Fanrong Kong ◽  
Jianhui Jiang ◽  
Yan Huang
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Particle Swarm ◽  
The Other ◽  
Large Scale Optimization ◽  
Particle Swarm Optimizer ◽  
Competition Mechanism ◽  
Benchmark Suite ◽  
Scale Optimization ◽  
The One

As a powerful tool in optimization, particle swarm optimizers have been widely applied to many different optimization areas and drawn much attention. However, for large-scale optimization problems, the algorithms exhibit poor ability to pursue satisfactory results due to the lack of ability in diversity maintenance. In this paper, an adaptive multi-swarm particle swarm optimizer is proposed, which adaptively divides a swarm into several sub-swarms and a competition mechanism is employed to select exemplars. In this way, on the one hand, the diversity of exemplars increases, which helps the swarm preserve the exploitation ability. On the other hand, the number of sub-swarms adaptively changes from a large value to a small value, which helps the algorithm make a suitable balance between exploitation and exploration. By employing several peer algorithms, we conducted comparisons to validate the proposed algorithm on a large-scale optimization benchmark suite of CEC 2013. The experiments results demonstrate the proposed algorithm is effective and competitive to address large-scale optimization problems.

scholarly journals An Entropy-Assisted Particle Swarm Optimizer for Large-Scale Optimization Problem

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 414 ◽  
Author(s):  
Weian Guo ◽  
Lei Zhu ◽  
Lei Wang ◽  
Qidi Wu ◽  
Fanrong Kong
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Large Scale Optimization ◽  
Particle Swarm Optimizer ◽  
Fitness Evaluation ◽  
Diversity Maintenance ◽  
Entropy Measurement ◽  
Benchmark Suite ◽  
Scale Optimization

Diversity maintenance is crucial for particle swarm optimizer’s (PSO) performance. However, the update mechanism for particles in the conventional PSO is poor in the performance of diversity maintenance, which usually results in a premature convergence or a stagnation of exploration in the searching space. To help particle swarm optimization enhance the ability in diversity maintenance, many works have proposed to adjust the distances among particles. However, such operators will result in a situation where the diversity maintenance and fitness evaluation are conducted in the same distance-based space. Therefore, it also brings a new challenge in trade-off between convergence speed and diversity preserving. In this paper, a novel PSO is proposed that employs competitive strategy and entropy measurement to manage convergence operator and diversity maintenance respectively. The proposed algorithm was applied to the large-scale optimization benchmark suite on CEC 2013 and the results demonstrate the proposed algorithm is feasible and competitive to address large scale optimization problems.

Discussion on the Mode of Electric Vehicle Power Supply Infrastructures Construction and Operation in China

2011 ◽  
Vol 347-353 ◽  
pp. 3902-3907
Author(s):  
Liang Liang Chen ◽  
Ming Wu ◽  
Hao Zhang ◽  
Xiao Hua Ding ◽  
Jin Da Zhu
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Power Supply ◽  
Energy Supply ◽  
Large Scale ◽  
Operation Mode ◽  
Charging Station ◽  
Combination Mode ◽  
Ev Charging

The energy supply infrastructures construction is the prerequisite and basis for the large-scale promotion and application of electric vehicles (EVs). The characteristics and current construction situation of several EV power supply infrastructures in China such as AC charging spot, charging station and battery swap station are introduced first, and the characteristics of time combination mode and space combination mode for the construction of EV charging facilities are also discussed. Meanwhile, the features of operation mode for EV power supply infrastructures in different developing stage of are analyzed, and the main bodies for EV power supply infrastructures construction are also introduced.

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.

Super-High Dimension Complex Functions Optimization Using Adaptive Particle Swarm Optimizer

2012 ◽  
Vol 532-533 ◽  
pp. 1830-1835
Author(s):  
Ying Zhang ◽  
Bo Qin Liu ◽  
Han Rong Chen
Keyword(s):  
High Dimension ◽  
Large Scale ◽  
Particle Swarm ◽  
Search Space ◽  
Scale Space ◽  
Global Optimum ◽  
Particle Swarm Optimizer ◽  
Local Optima ◽  
Complex Functions ◽  
Global Optima

Due to the existence of large numbers of local and global optima of super-high dimension complex functions, general Particle Swarm Optimizer (PSO) methods are slow speed on convergence and easy to be trapped in local optima. In this paper, an Adaptive Particle Swarm Optimizer(APSO) is proposed, which employ an adaptive inertia factor and dynamic changes strategy of search space and velocity in each cycle to plan large-scale space global search and refined local search as a whole according to the fitness change of swarm in optimization process of the functions, and to quicken convergence speed, avoid premature problem, economize computational expenses, and obtain global optimum. We test the proposed algorithm and compare it with other published methods on several super-high dimension complex functions, the experimental results demonstrate that this revised algorithm can rapidly converge at high quality solutions.

scholarly journals Multiport Converter based EV Charging Station with PV and Battery

2021 ◽  
Vol 9 (VI) ◽  
pp. 2518-2521
Author(s):  
Priya A. Khobragade
Keyword(s):  
Large Scale ◽  
Power Grid ◽  
Storage System ◽  
Charging Station ◽  
Multiport Converter ◽  
Smart Charging ◽  
Fast Charging ◽  
Electrical Vehicle ◽  
Ev Charging ◽  
Fast Charging Station

: As a ecofriendly electrical vehicle, is vehicles that are used electric motor or traction motor. Are receiving widespread attention around the world due to their improved performance and zero carbon emission . The electric vehicle depend on photovoltaic and battery energy storage system . Electric vehicles include not limited road and railways. It consist of many electric appliances for use in domestic and industrial purposes that is electric car ,electric bike ,electric truck ,electric trolley bus , electric air craft ,electric space craft.The main Moto of this paper is a modelling of proposed system smart charging for electrical vehicle insuring minimum stress on power grid . The large scale development of electrical vehicle we need electric charging station for example fast charging station and super-fast charging station . During a peak demand load , large load on charging station due to the voltage sag , line fault and stress on power grid . At this all problem avoid by multiport converter based EV charging station with PV and BES by using analysis of MATLAB simulation. Result and conclusion of this paper to reduce losses improving efficiency of solar energy , no pollution (reduce) fast charging as possible as without any disturbance.

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.

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