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

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.

2020 ◽  
Vol 185 ◽  
pp. 01023
Author(s):  
Yuan An ◽  
Jianing Li ◽  
Cenyue Chen

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.


2019 ◽  
Vol 11 (19) ◽  
pp. 5441 ◽  
Author(s):  
Chao Ma ◽  
Sen Dong ◽  
Jijian Lian ◽  
Xiulan Pang

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%.


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