scholarly journals Multi-Objective Sizing of Hybrid Energy Storage System for Large-Scale Photovoltaic Power Generation System

2019 ◽  
Vol 11 (19) ◽  
pp. 5441 ◽  
Author(s):  
Chao Ma ◽  
Sen Dong ◽  
Jijian Lian ◽  
Xiulan Pang
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Large Scale ◽  
Yellow River ◽  
Storage System ◽  
Weather Conditions ◽  
Energy Storage System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Power Stations

Hybrid energy storage systems (HESS) are an effective way to improve the output stability for a large-scale photovoltaic (PV) power generation systems. This paper presents a sizing method for HESS-equipped large-scale centralized PV power stations. The method consists of two parts: determining the power capacity by a statistical method considering the effects of multiple weather conditions and calculating the optimal energy capacity by employing a mathematical model. The method fully considers the characteristics of PV output and multiple kinds of energy storage combinations. Additionally, a pre-storage strategy that can further improve stability of output is proposed. All of the above methods were verified through a case study application to an 850 MW centralized PV power station in the upstream of the Yellow river. The optimal hybrid energy storage combination and its optimization results were obtained by this method. The results show that the optimal capacity configuration can significantly improve the stability of PV output and the pre-storage strategy can further improve the target output satisfaction rate by 8.28%.

scholarly journals Research on capacity optimization of micro-grid hybrid energy storage system based on simulated annealing artificial fish swarm algorith with memory function

2020 ◽  
Vol 185 ◽  
pp. 01023
Author(s):  
Yuan An ◽  
Jianing Li ◽  
Cenyue Chen
Keyword(s):  
Simulated Annealing ◽  
Energy Storage ◽  
Large Scale ◽  
Memory Function ◽  
Storage System ◽  
Energy Storage System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System ◽  
With Memory

The intermittence and uncertainty of wind power and photovoltaic power have hindered the large-scale development of both. Therefore, it is very necessary to properly configure energy storage devices in the wind-solar complementary power grid. For the hybrid energy storage system composed of storage battery and supercapacitor, the optimization model of hybrid energy storage capacity is established with the minimum comprehensive cost as the objective function and the energy saving and charging state as the constraints. A simulated annealing artificial fish school algorithm with memory function is proposed to solve the model. The results show that the hybrid energy storage system can greatly save costs and improve system economy.

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.

scholarly journals An Energy Storage Performance Improvement Model for Grid-Connected Wind-Solar Hybrid Energy Storage System

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Rui Zhu ◽  
An-lei Zhao ◽  
Guang-chao Wang ◽  
Xin Xia ◽  
Yaopan Yang
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Generation System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System ◽  
Hybrid Power ◽  
Power Generation System

This study introduces a supercapacitor hybrid energy storage system in a wind-solar hybrid power generation system, which can remarkably increase the energy storage capacity and output power of the system. In the specific solution, this study combines the distributed power generation system and the hybrid energy storage system, while using the static reactive power compensation system and the conductance-fuzzy dual-mode control method to increase output power in stages. At the same time, the optimal configuration model of the wind-solar hybrid power generation system is established using MATLAB/Simulink software. The output power of the microgrid to the wind-photovoltaic hybrid power generation system is calculated by simulation, and the optimization process of each component of the system is simulated. This study mainly uses the static reactive power compensation system and the conductance-fuzzy dual-mode control method to optimize the wind-solar hybrid power generation system. Using MATLAB software simulation verifies the feasibility and rationality of the optimal configuration of the system.

System cost minimisation in hybrid energy storage systems connected to microgrids: A comparative approach

2021 ◽  
pp. 1-16
Author(s):  
P. Jeyaprakash ◽  
C. Agees Kumar ◽  
A. Ravi
Keyword(s):  
Energy Source ◽  
Energy Storage ◽  
Large Scale ◽  
Storage System ◽  
Energy Storage System ◽  
Comparative Approach ◽  
System Cost ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Continuous Power

Electricity is the most critical facility for humans. All traditional energy supplies are rapidly depleting. As a result, the energy resources are moved from traditional to non-conventional. In this research, mixture of two energy tools, namely wind and solar energy are used. Using a Hybrid Energy Storage System (HESS), continuous power can be provided. Electricity can be produced at a cost that is affordable. The integration of solar and wind in a hybrid system cause an increase in the system’s stability, which is the key benefit of this research. The system’s power transmission efficiency and reliability can be greatly enhanced by integrating these two intermittent sources. When one of the energy source is unavailable or inadequate to meet load demands, the other energy source will supply the power. The major contribution in this research is that, the proposed bidirectional single-inductor multiple-port (BSIMP) converter significantly lowers the component count, smaller circuit size and lower cost, allowing HESS to be integrated into DC microgrid. Minimum number of components are used for the same number of ESs in HESS in the proposed BSIMP converter. The hybridization of battery and supercapacitor (SC) for storage purpose is more cost effective, as compared to the battery energy storage system, thus improving the battery stress and hence used for large scale grid energy storage. SC’s are accepted as backup and found very useful in delivering high power, not possible with batteries. The use of SC in addition to batteries can be one solution for achieving the low life cycle economy. The Single Objective Adaptive Firefly Algorithm (SOAFA) is introduced for optimising the Proportional-Integral (PI) controller parameters. The system cost is reduced by about 32%, with the constraints on wind turbine swept area, PV area, total battery and SC capacity with the proposed optimisation algorithm.

scholarly journals An Experimental and Simulation Study on Optimisation of the Operation of a Distributed Power Generation System with Energy Storage—Meeting Dynamic Household Electricity Demand

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 1091 ◽  
Author(s):  
Jie Ji ◽  
Xin Xia ◽  
Wei Ni ◽  
Kailiang Teng ◽  
Chunqiong Miao ◽  
...  
Keyword(s):  
Power Generation ◽  
Energy Storage ◽  
Storage System ◽  
Energy Storage System ◽  
Distributed Power ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Distributed Power Generation ◽  
Simulation Results ◽  
Set Up

There are few experimental reports combining hybrid energy storage and diesel engine generators as the power source of distributed power generation systems. In this article, a distributed power generation with energy storage system (DG-ES) which contains a diesel engine generator and an energy storage unit is set up and tested in the laboratory to satisfy the dynamic changing load. The hybrid energy storage system is composed of a lead-acid battery and a supercapacitor. The DG-ES supplied power to meet the domestic load demand successfully under different seasons. A simulation model of this system was also set up in MATLAB software used to guide to the experimental operation of this system. The simulation results has 2.69% and 2.35% error. The results of the experimental tests show the DG-ES can provide enough power to meet the dynamic loads of the selected house, especially at peak time in both winter and summer time. Computational simulation results show that the performance of DG-ES is improved by 3.61% in summer and 1.86% in winter when the system’s operations are optimised.

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