scholarly journals Optimal Capacity Configuration of a Hybrid Energy Storage System for an Isolated Microgrid Using Quantum-Behaved Particle Swarm Optimization

Energies ◽  
2018 ◽  
Vol 11 (2) ◽  
pp. 454 ◽  
Author(s):  
Hui Wang ◽  
Tengxin Wang ◽  
Xiaohan Xie ◽  
Zhixiang Ling ◽  
Guoliang Gao ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Storage System ◽  
Particle Swarm ◽  
Energy Storage System ◽  
Swarm Optimization ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System ◽  
Optimal Capacity

scholarly journals Particle Swarm Optimization of a Hybrid Energy Storage System

2021 ◽  
Vol 13 (1) ◽  
pp. 82-100
Author(s):  
János Ferencz ◽  
András Kelemen
Keyword(s):  
Particle Swarm Optimization ◽  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Power Sharing ◽  
Loss Minimization ◽  
Swarm Optimization ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System

Abstract The paper presents the energy loss minimization of a hybrid energy storage system used in an electric vehicle, composed by a battery and a supercapacitor. The optimization is carried out by searching the optimal power sharing between the energy storage devices. The power sharing factor is defined as a discrete time variable, with constant values during each subdivision of the driving cycle. The elements of the optimal solution vector are the power sharing factors and the time instants that define the subdivisions. The particle swarm optimization algorithms have been validated using the Rastrigin test function, and three versions of the boundary behaviour have been compared in case of the constrained optimization. The algorithms have been tested for the energy loss minimization in case of a simple driving cycle, and their performance has been assessed by statistical analysis for different swarm sizes.

scholarly journals A Capacity Configuration Control Strategy to Alleviate Power Fluctuation of Hybrid Energy Storage System Based on Improved Particle Swarm Optimization

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 642 ◽  
Author(s):  
Tiezhou Wu ◽  
Xiao Shi ◽  
Li Liao ◽  
Chuanjian Zhou ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Energy Storage ◽  
Objective Function ◽  
Control Strategy ◽  
Storage System ◽  
Particle Swarm ◽  
Energy Storage System ◽  
Power Fluctuation ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System

In view of optimizing the configuration of each unit’s capacity for energy storage in the microgrid system, in order to ensure that the planned energy storage capacity can meet the reasonable operation of the microgrid’s control strategy, the power fluctuations during the grid-connected operation of the microgrid are considered in the planning and The economic benefit of hybrid energy storage is quantified. A multi-objective function aiming at minimizing the power fluctuation on the DC bus in the microgrid and optimizing the capacity ratio of each energy storage system in the hybrid energy storage system (HESS) is established. The improved particle swarm algorithm (PSO) is used to solve the objective function, and the solution is applied to the microgrid experimental platform. By comparing the power fluctuations of the battery and the supercapacitor in the HESS, the power distribution is directly reflected. Comparing with the traditional mixed energy storage control strategy, it shows that the optimized hybrid energy storage control strategy can save 4.3% of the cost compared with the traditional hybrid energy storage control strategy, and the performance of the power fluctuation of the renewable energy is also improved. It proves that the proposed capacity configuration of the HESS has certain theoretical significance and practical application value.

Optimal Control of Hybrid Energy Storage System of New Energy Power Generation System Based on Improved Particle Swarm Algorithm

2022 ◽  
Author(s):  
Shangzhou Zhang
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Storage System ◽  
Particle Swarm ◽  
Energy Storage System ◽  
Particle Swarm Algorithm ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System ◽  
Swarm Algorithm

In order to ensure the stability and reliability of power supply and realize day and night power generation, wind and solar complementary power generation systems are built in areas with abundant solar and wind energy resources. However, the system investment cost is too high. Because of this, there are wind, light intermittent, and non-intermittent power generation systems. For issues such as stability, an energy storage system needs to be configured to stabilize power fluctuations. This paper aims to study the optimization control of hybrid energy storage system of new energy power generation system based on improved particle swarm algorithm. In this paper, the application of particle swarm algorithm to power system reactive power optimization has been researched in two aspects. Through optimization methods, reasonable adjustment of control variables, full use of equipment resources of the power grid, to improve voltage quality and reduce system operation network to ensure the stability of the voltage system. In addition, this paper selects the IEEE30 node test system and simulation data analysis, takes the hybrid energy storage system as the optimization object, and optimizes the reactive power of the newly improved particle swarm algorithm. The experiments in this paper show that the improved algorithm has a good effect in reactive power optimization, increasing the performance of the hybrid energy storage system by 27.02%. MPSO algorithm is also better than basic PSO algorithm. It can be seen from the figure that in the PSO algorithm, the algorithm basically tends to be stable after more than 40 iterations, and finally the algorithm converges to 0.089.

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