energy management system
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Reda Jabeur ◽  
Youness Boujoudar ◽  
Mohamed Azeroual ◽  
Ayman Aljarbouh ◽  
Najat Ouaaline

This paper proposes a multi-agent system for energy management in a microgrid for smart home applications, the microgrid comprises a photovoltaic source, battery energy storage, electrical loads, and an energy management system (EMS) based on smart agents. The microgrid can be connected to the grid or operating in island mode. All distributed sources are implemented using MATLAB/Simulink to simulate a dynamic model of each electrical component. The agent proposed can interact with each other to find the best strategy for energy management using the java agent development framework (JADE) simulator. Furthermore, the proposed agent framework is also validated through a different case study, the efficiency of the proposed approach to schedule local resources and energy management for microgrid is analyzed. The simulation results verify the efficacy of the proposed approach using Simulink/JADE co-simulation.

2022 ◽  
Vol 8 ◽  
pp. 560-566
Ejaz Ul Haq ◽  
Cheng Lyu ◽  
Peng Xie ◽  
Shuo Yan ◽  
Fiaz Ahmad ◽  

2022 ◽  
Vol 8 ◽  
pp. 722-734
Yan Cao ◽  
Ardashir Mohammadzadeh ◽  
Jafar Tavoosi ◽  
Saleh Mobayen ◽  
Rabia Safdar ◽  

Nishi Singh ◽  
M.P.S. Chawla ◽  
Sandeep Bhongade ◽  

HEMS (home energy management systems) are controllers that manage and coordinate a home's generation, storage, and loads. These controllers are becoming increasingly important. To ensure that distributed energy penetration continues to grow resources are appropriately utilized and the process is not disrupted within the grid[1]. An approach to hems design based on behavioural control approaches is discussed in this paper which do not require accurate models or forecasts and are particularly responsive to changing situations, in this study. In this study, the role of the customer as well as the micro grid in intelligent demand management is demonstrated using MATLAB 2018 Fuzzy tool.[3]

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Tzu-Chia Chen ◽  
Fouad Jameel Ibrahim Alazzawi ◽  
John William Grimaldo Guerrero ◽  
Paitoon Chetthamrongchai ◽  
Aleksei Dorofeev ◽  

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.

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 574
Muhammad Hilal Khan ◽  
Azzam Ul Asar ◽  
Nasim Ullah ◽  
Fahad R. Albogamy ◽  
Muhammad Kashif Rafique

Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.

2022 ◽  
Vol 12 (2) ◽  
pp. 812
Claudio Maino ◽  
Antonio Mastropietro ◽  
Luca Sorrentino ◽  
Enrico Busto ◽  
Daniela Misul ◽  

Hybrid electric vehicles are, nowadays, considered as one of the most promising technologies for reducing on-road greenhouse gases and pollutant emissions. Such a goal can be accomplished by developing an intelligent energy management system which could lead the powertrain to exploit its maximum energetic performances under real-world driving conditions. According to the latest research in the field of control algorithms for hybrid electric vehicles, Reinforcement Learning has emerged between several Artificial Intelligence approaches as it has proved to retain the capability of producing near-optimal solutions to the control problem even in real-time conditions. Nevertheless, an accurate design of both agent and environment is needed for this class of algorithms. Within this paper, a detailed plan for the complete project and development of an energy management system based on Q-learning for hybrid powertrains is discussed. An integrated modular software framework for co-simulation has been developed and it is thoroughly described. Finally, results have been presented about a massive testing of the agent aimed at assessing for the change in its performance when different training parameters are considered.

2022 ◽  
Vol 12 (1) ◽  
pp. 530
Yu-Sheng Yang ◽  
Shih-Hsiung Lee ◽  
Wei-Che Chen ◽  
Chu-Sing Yang ◽  
Yuen-Min Huang ◽  

The advanced connection requirements of industrial automation and control systems have sparked a new revolution in the Industrial Internet of Things (IIoT), and the Supervisory Control and Data Acquisition (SCADA) network has evolved into an open and highly interconnected network. In addition, the equipment of industrial electronic devices has experienced complete systemic integration by connecting with the SCADA network, and due to the control and monitoring advantages of SCADA, the interconnectivity and working efficiency among systems have been tremendously improved. However, it is inevitable that the SCADA system cannot be separated from the public network, which indicates that there are concerns over cyber-attacks and cyber-threats, as well as information security breaches, in the SCADA network system. According to this context, this paper proposes a module based on the token authentication service to deter attackers from performing distributed denial-of-service (DDoS) attacks. Moreover, a simulated experiment has been conducted in an energy management system in the actual field, and the experimental results have suggested that the security defense architecture proposed by this paper can effectively improve security and is compatible with real field systems.

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