Smart Grids and Hybrid Energy Storage Systems

2022 ◽  
pp. 779-804
Author(s):  
Muhammad Asif Rabbani
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Smart Grid ◽  
Storage Systems ◽  
Control Strategies ◽  
Energy Systems ◽  
Energy Storage Systems ◽  
Renewable Energy Systems ◽  
Hybrid Energy ◽  
Hybrid Energy Storage

It is very important that the installed renewable energy system should produce the maximum power outputs with minimum costs, and that can only be achieved with the selection of the best optimization technique applied for the best control strategies along with the introduction of the hybrid energy storage systems (HESS). This chapter presents some optimization techniques applied in control strategies for hybrid energy storage systems in distributed renewable energy systems. The integration of energy production and consumption component through the smart grid concept enables increased demand response and energy efficiency. Hybrid energy storage systems and their applications in the renewable energy systems are extensively discussed besides control strategies involved. The storages systems will play an important role in future related to smart grid.

A new operation strategy of PEV parking and hydro storage in smart grid environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Esmaeil Nazari ◽  
Mahsa Zarrini Farahmand
Keyword(s):  
Energy Storage ◽  
Smart Grid ◽  
Heuristic Algorithm ◽  
Storage Systems ◽  
Optimal Operation ◽  
Operation Strategy ◽  
Energy Storage Systems ◽  
Grid Environment ◽  
Content Type ◽  
Total Cost

Purpose The purpose of this study is to solve the optimal operation strategy problem of plug-in electric vehicles (PEV) parking as a demand response (DR) program and hydro storage as an energy storage system in a smart grid environment using a heuristic algorithm. Design/methodology/approach Studying the smart grid with DR, renewable energy resources and energy storage systems is necessary. To do this, the heuristic optimization algorithm is developed to solve the scheduling problem. This deterministic algorithm benefits from the definition of appropriate fitness functions. Findings For validation, it is shown that reduction of 1.1%–12.5% in pollution and 8.8%–34.8% in total cost are achieved, as compared with literature. Also, the suggested operation strategy of PEVs parking and hydro storage results in reducing the total cost by 6.21%. Originality/value DR programs such as PEV parking play a major role in smart grid developments. Also, energy storage systems such as hydro storage lead to better performance of distributed generations and lower costs and pollution by thermal units. However, based on the literature, the effects of PEV parking and hydro storage on smart grid operation strategy are not considered. Therefore, contributions of this study are: effects of hydro storage on the smart grid are considered, effects of PEV parking on the smart grid are considered, a heuristic algorithm is developed to solve operation strategy problem for PEV parking and hydro storage in a smart grid environment.

scholarly journals Data-Driven Model Based Charging Profile Prediction for Energy Storage Systems

2017 ◽  
Author(s):  
Nandha Kumar Kandasamy ◽  
Rudy Tjandra
Keyword(s):  
Energy Storage ◽  
Smart Grid ◽  
Storage Systems ◽  
Distributed Storage ◽  
Data Driven ◽  
Energy Storage Systems ◽  
Storage Devices ◽  
Capacity Loss ◽  
Load Demand ◽  
Different Types

Energy storage systems (ESS) are penetrating into various sections of power system through different applications. ESS can be used either as a buffer for intermittent renewable energy sources or as a stand-alone distributed storage for load shifting. ESS use different types of storage devices such as lead-acid batteries, lithium ion batteries, flow batteries, and super-capacitors. Hybrid ESS consisting of few types of storage devices are also common in practice. Determining the load demand of such ESSs at various instances (charging profile) accurately is indispensable in most of the cases. Capacity loss is common phenomenon that occurs in all types of storage devices because of ageing. Capacity loss has to be accounted while determining the charging profile of storage devices for better accuracy. Data-driven modeling is an attractive approach for determining the load demand of ESS due to the availability of valuable data from smart grid technologies. In this paper, the application of different types of data-driven models to predict the current charging profile of the ESS based on previous charging profiles is examined. The proposed method can leverage on the existing data from smart grid and is a black box modeling approach.

Smart Grids and Hybrid Energy Storage Systems

2021 ◽  
pp. 170-195
Author(s):  
Muhammad Asif Rabbani
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Smart Grid ◽  
Storage Systems ◽  
Control Strategies ◽  
Energy Systems ◽  
Energy Storage Systems ◽  
Renewable Energy Systems ◽  
Hybrid Energy ◽  
Hybrid Energy Storage

It is very important that the installed renewable energy system should produce the maximum power outputs with minimum costs, and that can only be achieved with the selection of the best optimization technique applied for the best control strategies along with the introduction of the hybrid energy storage systems (HESS). This chapter presents some optimization techniques applied in control strategies for hybrid energy storage systems in distributed renewable energy systems. The integration of energy production and consumption component through the smart grid concept enables increased demand response and energy efficiency. Hybrid energy storage systems and their applications in the renewable energy systems are extensively discussed besides control strategies involved. The storages systems will play an important role in future related to smart grid.

Sign in / Sign up

Export Citation Format

Share Document