A Multiagent Energy Management System for a Small Microgrid Equipped with Power Sources and Energy Storage Units

2014 ◽  
pp. 411-417
Author(s):  
Weronika Radziszewska ◽  
Zbigniew Nahorski
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Energy Management System ◽  
Power Sources ◽  
Storage Units

scholarly journals Enhanced Intelligent Energy Management System for a Renewable Energy-Based AC Microgrid

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3268
Author(s):  
Mehdi Dhifli ◽  
Abderezak Lashab ◽  
Josep M. Guerrero ◽  
Abdullah Abusorrah ◽  
Yusuf A. Al-Turki ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Optimal Operation ◽  
Green Energy ◽  
Energy Management System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Ac Microgrid

This paper proposes an enhanced energy management system (EEMS) for a residential AC microgrid. The renewable energy-based AC microgrid with hybrid energy storage is broken down into three distinct parts: a photovoltaic (PV) array as a green energy source, a battery (BT) and a supercapacitor (SC) as a hybrid energy storage system (HESS), and apartments and electric vehicles, given that the system is for residential areas. The developed EEMS ensures the optimal use of the PV arrays’ production, aiming to decrease electricity bills while reducing fast power changes in the battery, which increases the reliability of the system, since the battery undergoes fewer charging/discharging cycles. The proposed EEMS is a hybrid control strategy, which is composed of two stages: a state machine (SM) control to ensure the optimal operation of the battery, and an operating mode (OM) for the best operation of the SC. The obtained results show that the EEMS successfully involves SC during fast load and PV generation changes by decreasing the number of BT charging/discharging cycles, which significantly increases the system’s life span. Moreover, power loss is decreased during passing clouds phases by decreasing the power error between the extracted power by the sources and the required equivalent; the improvement in efficiency reaches 9.5%.

scholarly journals Multi-Objective Fuzzy Logic-Based Energy Management System for Microgrids with Battery and Hydrogen Energy Storage System

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1074 ◽  
Author(s):  
Francisco José Vivas ◽  
Francisca Segura ◽  
José Manuel Andújar ◽  
Adriana Palacio ◽  
Jaime Luis Saenz ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Management System ◽  
Hydrogen Energy ◽  
Economic Point ◽  
Load Demand

This paper proposes a fuzzy logic-based energy management system (EMS) for microgrids with a combined battery and hydrogen energy storage system (ESS), which ensures the power balance according to the load demand at the time that it takes into account the improvement of the microgrid performance from a technical and economic point of view. As is known, renewable energy-based microgrids are receiving increasing interest in the research community, since they play a key role in the challenge of designing the next energy transition model. The integration of ESSs allows the absorption of the energy surplus in the microgrid to ensure power supply if the renewable resource is insufficient and the microgrid is isolated. If the microgrid can be connected to the main power grid, the freedom degrees increase and this allows, among other things, diminishment of the ESS size. Planning the operation of renewable sources-based microgrids requires both an efficient dispatching management between the available and the demanded energy and a reliable forecasting tool. The developed EMS is based on a fuzzy logic controller (FLC), which presents different advantages regarding other controllers: It is not necessary to know the model of the plant, and the linguistic rules that make up its inference engine are easily interpretable. These rules can incorporate expert knowledge, which simplifies the microgrid management, generally complex. The developed EMS has been subjected to a stress test that has demonstrated its excellent behavior. For that, a residential-type profile in an actual microgrid has been used. The developed fuzzy logic-based EMS, in addition to responding to the required load demand, can meet both technical (to prolong the devices’ lifespan) and economic (seeking the highest profitability and efficiency) established criteria, which can be introduced by the expert depending on the microgrid characteristic and profile demand to accomplish.

Digitalization Control and Characteristic Analysis of a Superconducting Inductive Energy Management System

2013 ◽  
Vol 416-417 ◽  
pp. 474-479 ◽  
Author(s):  
Jian Xun Jin ◽  
Wei Xu ◽  
Xin Zhou ◽  
Xiao Yuan Chen
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
High Efficiency ◽  
Control Method ◽  
Magnetic Energy ◽  
Storage Device ◽  
Energy Management System ◽  
Characteristic Analysis ◽  
Energy Control

In past decades, the technique of superconducting magnetic energy storage (SMES) has received substantial attention both by academia and industry with the great improvement of applicable high temperature superconductors and relevant control technologies. A bridge-type inductive energy management system topology is presented for SMES applications by using a concept of digitalization. The inductive power charging, storing and discharging status are modelled, and then digitalized for the advanced control implementation. As a consequence, an inductive energy control method can be realized by the digitalized models to satisfy relative project requirements by high efficiency and control precession. New principles and methodologies provide the theoretical foundation to achieve digital power inductor energy control and superconducting inductive energy storage device operation.

scholarly journals Development of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Tzu-Chia Chen ◽  
Fouad Jameel Ibrahim Alazzawi ◽  
John William Grimaldo Guerrero ◽  
Paitoon Chetthamrongchai ◽  
Aleksei Dorofeev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Storage Systems ◽  
Energy Management System ◽  
Energy Storage Systems ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Machine Learning Methods

The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.

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