Special Issue "Data-Intensive Computing in Smart Microgrids"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Smart Grids and Microgrids".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editor

Assist. Prof. Dr. Herodotos Herodotou
Website
Guest Editor
Department of Electrical Engineering, Computer Engineering and Informatics (EECEI), Cyprus University of Technology, Limassol 3036, Cyprus
Interests: microgrids; smart grid; data-intensive analytics and systems; data-driven management

Special Issue Information

Dear Colleagues,

Microgrids have recently emerged as the building blocks of smart grids, combining distributed renewable energy sources, energy storage devices, and load management to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless, and network communication, as well as cloud and fog computing, are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, and consumption data).

The application of big data analysis techniques (e.g., forecasting, classification, and clustering) to such data can optimize power generation and operation in real-time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. The efficient and intelligent analysis of the data will enable smart microgrids to detect and restore from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management.

We are inviting submissions of relevant original research articles or comprehensive reviews to Special Issue of Energies on “Data-Intensive Computing in Smart Microgrids”. The topics of the Special Issue include, but are not limited to, the following:

  • Energy data-intensive analytics in smart microgrids
  • Data-driven management and control of smart microgrids
  • Big data management in smart grids and microgrids
  • Cloud and fog computing in smart microgrids operations and management
  • Applications of network science in modeling and the analysis of smart microgrids
  • Data-driven dynamic pricing mechanisms and strategies in smart grids
  • Intelligent decision-making in smart microgrids
  • Demand-side management in smart microgrids
  • Energy load/demand forecasting for residential, commercial, and/or industrial consumers
  • Electricity price forecasting for residential, commercial, and/or industrial consumers
  • Power forecasting from renewable energy resources (e.g., solar, wind)
  • Management of data for Advanced Metering Infrastructure (AMI) in smart grids
  • Data-driven renewable energy integration in smart grids
  • Blockchain integration in smart grid and microgrids

Assist. Prof. Dr. Herodotos Herodotou
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart grid
  • Smart microgrids
  • Renewable energy
  • Energy analytics
  • Data-driven management
  • Dynamic electricity pricing
  • Energy load forecasting
  • Electricity demand forecasting
  • Advanced metering infrastructure
  • Blockchain in smart grid/microgrids

Published Papers (3 papers)

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Research

Open AccessArticle
Cost Efficient Real Time Electricity Management Services for Green Community Using Fog
Energies 2020, 13(12), 3164; https://doi.org/10.3390/en13123164 - 18 Jun 2020
Abstract
The computing devices in data centers of cloud and fog remain in continues running cycle to provide services. The long execution state of large number of computing devices consumes a significant amount of power, which emits an equivalent amount of heat in the [...] Read more.
The computing devices in data centers of cloud and fog remain in continues running cycle to provide services. The long execution state of large number of computing devices consumes a significant amount of power, which emits an equivalent amount of heat in the environment. The performance of the devices is compromised in heating environment. The high powered cooling systems are installed to cool the data centers. Accordingly, data centers demand high electricity for computing devices and cooling systems. Moreover, in Smart Grid (SG) managing energy consumption to reduce the electricity cost for consumers and minimum rely on fossil fuel based power supply (utility) is an interesting domain for researchers. The SG applications are time-sensitive. In this paper, fog based model is proposed for a community to ensure real-time energy management service provision. Three scenarios are implemented to analyze cost efficient energy management for power-users. In first scenario, community’s and fog’s power demand is fulfilled from the utility. In second scenario, community’s Renewable Energy Resources (RES) based Microgrid (MG) is integrated with the utility to meet the demand. In third scenario, the demand is fulfilled by integrating fog’s MG, community’s MG and the utility. In the scenarios, the energy demand of fog is evaluated with proposed mechanism. The required amount of energy to run computing devices against number of requests and amount of power require cooling down the devices are calculated to find energy demand by fog’s data center. The simulations of case studies show that the energy cost to meet the demand of the community and fog’s data center in third scenario is 15.09% and 1.2% more efficient as compared to first and second scenarios, respectively. In this paper, an energy contract is also proposed that ensures the participation of all power generating stakeholders. The results advocate the cost efficiency of proposed contract as compared to third scenario. The integration of RES reduce the energy cost and reduce emission of CO 2 . The simulations for energy management and plots of results are performed in Matlab. The simulation for fog’s resource management, measuring processing, and response time are performed in CloudAnalyst. Full article
(This article belongs to the Special Issue Data-Intensive Computing in Smart Microgrids)
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Open AccessArticle
Investigation of Deterministic, Statistical and Parametric NB-PLC Channel Modeling Techniques for Advanced Metering Infrastructure
Energies 2020, 13(12), 3098; https://doi.org/10.3390/en13123098 - 15 Jun 2020
Abstract
This paper is focused on the channel modeling techniques for implementation of narrowband power line communication (NB-PLC) over medium voltage (MV) network for the purpose of advanced metering infrastructure (AMI). Three different types of models, based on deterministic method, statistical method, and network [...] Read more.
This paper is focused on the channel modeling techniques for implementation of narrowband power line communication (NB-PLC) over medium voltage (MV) network for the purpose of advanced metering infrastructure (AMI). Three different types of models, based on deterministic method, statistical method, and network parameters based method are investigated in detail. Transmission line (TL) theory model is used to express the MV network as a two-port network to examine characteristics of sending and receiving NB-PLC signals. Multipath signal propagation model is used to incorporate the effect of multipath signals to determine the NB-PLC transfer function. A Simulink model is proposed which considers the values of MV network to examine the characteristics of NB-PLC signals. Frequency selectivity is also introduced in the impedances to compare variations and characteristics with constant impedances based MV network. A state-of-the-art mechanism for the modeling of capacitive coupling device, and impedances of low voltage (LV) and MV networks is developed. Moreover, a comparative analysis of TL theory and multipath signal propagation models with the proposed Simulink model is presented to validate the performance and accuracy of proposed model. This research work will pave the way to improve the efficiency of next-generation NB-PLC technologies. Full article
(This article belongs to the Special Issue Data-Intensive Computing in Smart Microgrids)
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Open AccessArticle
Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine
Energies 2020, 13(11), 2907; https://doi.org/10.3390/en13112907 - 05 Jun 2020
Cited by 2
Abstract
Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter [...] Read more.
Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier’s hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques. Full article
(This article belongs to the Special Issue Data-Intensive Computing in Smart Microgrids)
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