Energy Management Algorithms in Smart Grids : State of the Art and Emerging Trends

Klaimi, Joelle; Rahim-Amoud, Rana; Merghem-Boulahia, Leïla; Jrad, Akil

doi:10.5121/ijaia.2016.7403

Cited by 7 publications

(7 citation statements)

References 71 publications

(87 reference statements)

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“…Computer and software technology are also closely related to energy management systems. Intelligent systems with bidirectional communication capability, which provide the energy supply and the coordination between the demanders, are developing every day [8]. One of the most important features of the smart grid is to provide energy efficiency and management of existing resources [9].…”

Section: A Cognitive Approach To Electrical Power Engineeringmentioning

confidence: 99%

“…The integration of these elements ensures the flexibility of the energy systems. Energy security is another of the elements that are considered in terms of energy security both by supply and demand [8][9][10][11]. In this context, smart grid systems enable the communication of information between consumers and producers.…”

Section: A Cognitive Approach To Electrical Power Engineeringmentioning

confidence: 99%

“…The main role of DR in the electricity market is that it economically optimizes supplydemand balance during peak hours. Without changing the energy consumption during the high price hours, it shifts some of the high demand processes to non-peak periods and makes optimum adjustments [8,13,14].…”

Section: Demand Response For Electric Power Distributionmentioning

confidence: 99%

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Cognitive Based Electric Power Management System

Akıncı

2022

Balkan Journal of Electrical and Computer Engineering

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An electric power network can be evolved into smart grids, which are measured by providing energy efficiency and improving the available resources. With the development of software and hardware elements, the decision-making mechanism of existing smart grids is transformed into more robust uninterrupted and economical energy management systems. In this study, a cognitive-based algorithm using dynamic energy management flexibility, storage and energy management algorithm and cloud computing architecture is proposed. Using this approach, an uninterrupted and economical energy management system can be planned. In addition, the proposed approach provides the optimization of supply and demand sides.

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Section: A Cognitive Approach To Electrical Power Engineeringmentioning

confidence: 99%

Section: A Cognitive Approach To Electrical Power Engineeringmentioning

confidence: 99%

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Cognitive Based Electric Power Management System

Akıncı

2022

Balkan Journal of Electrical and Computer Engineering

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“…In addition, it may not be easy to obtain an accurate demand forecast based on parameters such as change in temperature, humidity, and other factors that influence consumption. The utility may suffer losses if they do not understand and decide on an acceptable margin of error in load forecasting [95], [96].…”

Section: B Load Forecasting Techniquesmentioning

confidence: 99%

A Qualitative Study on the United States Internet of Energy: A Step Towards Computational Sustainability

2020

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The burgeoning growth of Big Data not only matures and improves the data management efficiency and useful information extraction techniques, but also motivates the computational science researchers to come up with a new method or solution that can be repurposed for problems across the domain. Computational Sustainability joins this movement for a transferrable computational technique for sustainable development and a better future. Internet-of-energy (IoE)-leveraging IoT to smart grids associated with advanced analytics-is one of the prominent efforts in this regard. This paper presents a qualitative analysis on the elements of the energy and power management ecosystem in the United States. This qualitative study includes the Grid Overview of the United States; Weather and Climate and its impact on the entire energy generation and consumption dynamics; Peak Load Forecasting and its techniques and burgeoning challenges; Variable Renewable Energy, its reliability challenges and how we can take advantage of this variability; Commodity Prices and its criticality; Energy Disaggregation and its impact on consumptionawareness; and Generation Expansion and Decision Analysis. Besides, IoE integration, associated tradeoffs, challenges, research opportunities and transferable computational techniques are addressed in this communication. Furthermore, schematics and quantitative analysis are presented in support of this study.INDEX TERMS Computational sustainability, Internet of Things, smart grid, Internet of Energy, big data, transferable computational techniques.

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“…A game theoretic-based efficient energy management strategy in which the cloud computing services as cyber support have been utilized. 34…”

mentioning

confidence: 99%

A survey on cloud computing in energy management of the smart grids

Allahvirdizadeh

Moghaddam

Shayanfar

2019

Int Trans Electr Energ Syst

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Summary Smart grid mitigates the environmental concerns associated with the global warming and climate shifting and reduces the dependence of the power generation on conventional fossil fuels. This is done by energy efficiency improvement and renewable energy sources exploitation at large scales in the form of centralized systems as well as small‐scale systems in the form of microgrids and nanogrids. However, smart grid technology brings new challenges to the big data management of the new power system construction. Additionally, due to the large‐scale intermittent distributed renewable resources contribution and stochastic electric vehicles integration, the current energy management systems must undergo some improvements to handle the smart grid requirements. Cloud computing, as an intermittent based on‐demand computing model, serves an emerging solution to the aforementioned challenges. As a result, this paper presents a survey study on the smart grid and state of art energy management methods and the necessitation of the cloud computing incorporation in smart grid energy management. Then, the application of the cloud computing in energy management, demand side management programs, building energy management systems, energy hubs, and power dispatching systems have been discussed and associated models are addressed.

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Energy Management Algorithms in Smart Grids : State of the Art and Emerging Trends

Cited by 7 publications

References 71 publications

Cognitive Based Electric Power Management System

Cognitive Based Electric Power Management System

A Qualitative Study on the United States Internet of Energy: A Step Towards Computational Sustainability

A survey on cloud computing in energy management of the smart grids

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