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Optimal Photovoltaic/Battery Energy Storage/Electric Vehicle Charging Station Design Based on Multi-Agent Particle Swarm Optimization Algorithm April 2019 Sustainability 11(7):1973
In this paper, a particle swarm optimization algorithm is presented for improving the energy storage density to optimize the structure of the CFRP/Al hybrid co‐cured high‐speed flywheel.
DOI: 10.3390/APP8091520 Corpus ID: 117188763 Energy Storage Coordination in Energy Internet Based on Multi-Agent Particle Swarm Optimization @article{Liu2018EnergySC, title={Energy Storage Coordination in Energy Internet Based on Multi-Agent Particle Swarm Optimization}, author={Jicheng Liu and Dandan He and Qiushuang Wei and Suli
A novel state of health estimation of lithium-ion battery energy storage system based on linear decreasing weight-particle swarm optimization algorithm and
An improved multi-objective particle swarm optimization algorithm is proposed to solve the model.Sensitivity method is widely used in distributed energy storage site selection [7] [7,8], a
The installation of BESS at a random size or non-optimum size can increase in cost, system losses and larger BESS capacity. Thus, this paper proposes the
To enhance power supply reliability of wind-PV power system and improve utilization of wind power and PV, it is necessary to configure the capacity of wind turbine generators, PV modules and energy storage devices reasonably. Based on the feature of joint-operation of wind-PV generation system with energy storage device and considering dynamic
Particle swarm adaptation is an optimization paradigm that simulates the ability of human societies to The implementation of energy storage system (ESS) has proven successful in tackling these
The optimization technique Constriction Coefficient Particle Swarm Optimization (CPSO) is utilized to reduce the total energy loss, which is subjected to equality and inequality constraints. Different DG parameters are considered and evaluated to reduce energy losses in electricity DN.
In conclusion, a particle swarm-optimized fuzzy logic energy management of a LIB-UC hybrid storage system for an EV was investigated. First, the GM EV1 power demand based on the UDDS driving cycle was extracted from the single LIB energy source.
With the optimization of EE and EUE, Zhu et al. [ 24] applied particle swarm optimization (PSO) to solve the problem of energy storage coordination (ESC) in EI. On the other
A hybrid energy storage system controlled by a smart energy management strategy can play a key role in the design and development of multisource electric vehicles. In this study, an optimal energy management strategy based on particle swarm optimization incorporating the Nelder-Mead simplex method is proposed.
Energy transformation is a severe challenge and major demand faced by China''s sustainable development, and new energy development has become a key driving force for energy transformation. The issue of system stability is brought to light by the steadily rising share of renewable energy sources like wind and solar, which in turn
On this basis, the improved particle swarm optimization (IPSO) is used to solve the model, so that the optimal allocation scheme is obtained. Finally, in MATLAB
DOI: 10.1016/J.IJEPES.2016.02.006 Corpus ID: 111615251 Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids @article{Kerdphol2016OptimizationOA, title={Optimization of a
Research on operation optimization problem of energy storage station in microgrid based on improved particle swarm optimization Yikun Fan 1 Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series, Volume 2580, 3rd International Conference on Signal Processing and Machine Learning (CONF-SPML
：. An optimization model for energy storage locating and sizing was established. It was based on a fully consideration of the voltage fluctuations of system node, load
Optimal Battery Energy Storage Size Using Particle Swarm Optimization for Microgrid System April 2015 International Review of Electrical Engineering (IREE) 10(2):277
Optimal allocation of energy storage participating in peak shaving based on improved hybrid particle swarm optimization Abstract: With the increasing number of photovoltaic
An improved particle swarm optimizer (IPSO) is proposed. In the algorithm, the value of inertia weight was directed by the distance between the particle and the global optimal
For most electric vehicles, Li-ion batteries are adopted as the energy source owing to their advantages in specific energy density, safety and reliability [1], [2]. During the operation of Li-ion batteries, it is essential to achieve the high-precision estimation of state of charge (SOC) in order to manage the charging and discharging activities as well as avoid
However, the battery energy storage system (BESS) is an equipment that can be used to smooth PV fluctuation and enhance the flexibility of the microgrid. In this paper, an improved particle swarm optimization (I-PSO) is developed to mitigate the voltage fluctuation by optimizing both BESS active and reactive power.
It can be obtained that the differential particle swarm algorithm outperforms the standard particle swarm algorithm in the energy storage siting and capacity determination problem. Energy storage access nodes and capacities of 18 (0.7650) and 33 (0.6001), the charging and discharging power of energy storage for 24
Trams with energy storage are popular for their energy efficiency and reduced operational risk.An effective energy management strategy is optimized to enable a reasonable distribution of demand power among the storage elements, efficient use of energy as well as enhance the service life of the hybrid energy storage system (HESS).
The objective function of DC bus power fluctuation is established, and the optimized particle swarm algorithm (PSO) is used to obtain the output power coefficient of each energy storage unit.
The proposed improved hybrid particle swarm optimization algorithm benefits in fast convergence speed and strong global optimization ability and is implemented in MATLAB software. With the increasing number of photovoltaic grid-connected in recent years, severe challenges are faced in the peak-shaving process of the power grid. Consequently, a
e-mail: newzeanland@163 Distributed Energy Storage Scheduling Optimization of Micro Grid Based on Particle Swarm Optimization Algorithm Zinan Liu Chongqing Airport Group CO., Ltd, Yubei District Chongqing, 401120 Abstract. Distributed generation is
Preliminary research in this area has been conducted. (1) In terms of EV peak dispatching, Lu et al. (2017) treated the batteries of the accessed electric vehicles as a kind of mobile distributed energy-storage device and used an improved particle swarm
DOI: 10.1109/ICCPEIC.2016.7557326 Corpus ID: 16789007 Economic dispatch of microgrid with smart energy storage systems using Particle Swarm Optimization @article{Karthikeyan2016EconomicDO, title={Economic dispatch
distribution system energy storage multi-objective particle swarm optimizer adaptive inertia weight Pareto solution DOI ： 10.13335/j.1000-3673.pst.2014.12.021 ： 36 ： 2014
In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES
A lithium-ion battery–ultracapacitor hybrid energy storage system (HESS) has been recognized as a viable solution to address the limitations of single battery energy sources in electric vehicles (EVs), especially in urban driving conditions, owing to its complementary energy features. However, an energy management strategy (EMS) is
Optimization of a battery energy storage system using particle swarm optimization for stand-alone microgrids October 2016 International Journal of Electrical Power & Energy Systems 81:32-39 DOI:10
Firstly, we establish a wind-solar complementary power generation system with a hybrid energy storage comprising lithium-ion batteries and supercapacitors. The system configuration is illustrated in Fig. 1, consisting of a photovoltaic (PV) generation system, wind turbine generators, lithium-ion battery packs, supercapacitors, inverters,
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