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Energies
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Electrical Energy Optimization and Cost Saving in Smart Environments
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Electrical Energy Optimization and Cost Saving in Smart Environments

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 9174

Special Issue Editor

Prof. Dr. Juan Francisco De Paz Santana

E-Mail Website
Guest Editor
Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
Interests: ambient intelligence; artificial intelligence; multi-agent systems; wireless sensor networks; big data; edge computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advances in sensor networks and devices provides new opportunities to create smart environments. The use of smart devices and sensor networks allow us to obtain information in different environments (homes, cities, factories, industry, etc). This information can be used to monitor the activity of users in order to detect different patterns that can be used in several cases studies such as reducing consumption, monitoring activities or knowledge extraction. This Special Issue will focus on the use of smart devices and sensors in order to obtain information and use it to act on intelligent behaviors. We invite the submission of contributions on software/hardware developments, reviews and cases studies with relevant contributions and new trends in energy optimization and monitoring in smart environments.

Topics of interest include, but are not limited to:

  • Smart meters
  • Internet of Things
  • Blockchain
  • Energy optimization
  • Smart textile

Prof. Dr. Juan Francisco De Paz Santana
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 submissions that pass pre-check are 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 2600 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

  • Energy optimization
  • Smart environment
  • Knowledge extraction
  • Smart devices

Published Papers (4 papers)

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Research

19 pages, 29652 KiB  
Article
Optimal Control Strategy for Distributed Energy Resources in a DC Microgrid for Energy Cost Reduction and Voltage Regulation
by Phi-Hai Trinh and Il-Yop Chung
Energies 2021, 14(4), 992; https://doi.org/10.3390/en14040992 - 14 Feb 2021
Cited by 16 | Viewed by 2203
Abstract
Distributed energy resources (DERs), including renewable energy resources (RESs) and electric vehicles (EVs), have a significant impact on distribution systems because they can cause bi-directional power flow in the distribution lines. Thus, the voltage regulation and thermal limits of the distribution system to [...] Read more.
Distributed energy resources (DERs), including renewable energy resources (RESs) and electric vehicles (EVs), have a significant impact on distribution systems because they can cause bi-directional power flow in the distribution lines. Thus, the voltage regulation and thermal limits of the distribution system to mitigate from the excessive power generation or consumption should be considered. The focus of this study is on a control strategy for DERs in low-voltage DC microgrids to minimize the operating costs and maintain the distribution voltage within the normal range based on intelligent scheduling of the charging and discharging of EVs, and to take advantage of RESs such as photovoltaic (PV) plants. By considering the time-of-use electricity rates, we also propose a 24-h sliding window to mitigate uncertainties in loads and PV plants in which the output is time-varied and the EV arrival cannot be predicted. After obtaining a request from the EV owner, the proposed optimal DER control method satisfies the state-of-charge level for their next journey. We applied the voltage sensitivity factor obtained from a load-flow analysis to effectively maintain voltage profiles for the overall DC distribution system. The performance of the proposed optimal DER control method was evaluated with case studies and by comparison with conventional methods. Full article
(This article belongs to the Special Issue Electrical Energy Optimization and Cost Saving in Smart Environments)
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11 pages, 2317 KiB  
Article
Research on Multi-Attribute Decision-Making in Condition-Based Maintenance for Power Transformers Based on Cloud and Kernel Vector Space Models
by Renxi Gong, Siqiang Li and Weiyu Peng
Energies 2020, 13(22), 5948; https://doi.org/10.3390/en13225948 - 14 Nov 2020
Cited by 5 | Viewed by 1458
Abstract
Decision-making for the condition-based maintenance (CBM) of power transformers is critical to their sustainable operation. Existing research exhibits significant shortcomings; neither group decision-making nor maintenance intention is considered, which does not satisfy the needs of smart grids. Thus, a multivariate assessment system, which [...] Read more.
Decision-making for the condition-based maintenance (CBM) of power transformers is critical to their sustainable operation. Existing research exhibits significant shortcomings; neither group decision-making nor maintenance intention is considered, which does not satisfy the needs of smart grids. Thus, a multivariate assessment system, which includes the consideration of technology, cost-effectiveness, and security, should be created, taking into account current research findings. In order to address the uncertainty of maintenance strategy selection, this paper proposes a maintenance decision-making model composed of cloud and vector space models. The optimal maintenance strategy is selected in a multivariate assessment system. Cloud models allow for the expression of natural language evaluation information and are used to transform qualitative concepts into quantitative expressions. The subjective and objective weights of the evaluation index are derived from the analytic hierarchy process and the grey relational analysis method, respectively. The kernel vector space model is then used to select the best maintenance strategy through the close degree calculation. Finally, an optimal maintenance strategy is determined. A comparison and analysis of three different representative maintenance strategies resulted in the following findings: The proposed model is effective; it provides a new decision-making method for power transformer maintenance decision-making; it is simple, practical, and easy to combine with the traditional state assessment method, and thus should play a role in transformer fault diagnosis. Full article
(This article belongs to the Special Issue Electrical Energy Optimization and Cost Saving in Smart Environments)
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26 pages, 2637 KiB  
Article
Decompositions for MPC of Linear Dynamic Systems with Activation Constraints
by Pedro Henrique Valderrama Bento da Silva, Eduardo Camponogara, Laio Oriel Seman, Gabriel Villarrubia González and Valderi Reis Quietinho Leithardt
Energies 2020, 13(21), 5744; https://doi.org/10.3390/en13215744 - 02 Nov 2020
Cited by 2 | Viewed by 2016
Abstract
The interconnection of dynamic subsystems that share limited resources are found in many applications, and the control of such systems of subsystems has fueled significant attention from scientists and engineers. For the operation of such systems, model predictive control (MPC) has become a [...] Read more.
The interconnection of dynamic subsystems that share limited resources are found in many applications, and the control of such systems of subsystems has fueled significant attention from scientists and engineers. For the operation of such systems, model predictive control (MPC) has become a popular technique, arguably for its ability to deal with complex dynamics and system constraints. The MPC algorithms found in the literature are mostly centralized, with a single controller receiving the signals and performing the computations of output signals. However, the distributed structure of such interconnected subsystems is not necessarily explored by standard MPC. To this end, this work proposes hierarchical decomposition to split the computations between a master problem (centralized component) and a set of decoupled subproblems (distributed components) with activation constraints, which brings about organizational flexibility and distributed computation. Two general methods are considered for hierarchical control and optimization, namely Benders decomposition and outer approximation. Results are reported from a numerical analysis of the decompositions and a simulated application to energy management, in which a limited source of energy is distributed among batteries of electric vehicles. Full article
(This article belongs to the Special Issue Electrical Energy Optimization and Cost Saving in Smart Environments)
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22 pages, 4189 KiB  
Article
Smart Energy Management System of Environmentally Friendly Microgrid Based on Grasshopper Optimization Technique
by Yehia Gad, Hatem Diab, Mahmoud Abdelsalam and Yasser Galal
Energies 2020, 13(19), 5000; https://doi.org/10.3390/en13195000 - 23 Sep 2020
Cited by 15 | Viewed by 2580
Abstract
A microgrid is a group of distributed energy resources and interconnected loads that may be operated either in isolated mode or connected mode with the main utility within electrical boundaries. Microgrids may consist of different types of renewable energy resources such as photovoltaic [...] Read more.
A microgrid is a group of distributed energy resources and interconnected loads that may be operated either in isolated mode or connected mode with the main utility within electrical boundaries. Microgrids may consist of different types of renewable energy resources such as photovoltaic panels, wind turbines, fuel cells, micro turbines, and storage units. It is highly recommended to manage the dependency on these resources by implementing an energy management unit to optimize the energy exchange so that the minimum cost is achieved. In this paper, an energy management system based on the grasshopper optimization algorithm (GOA) is proposed to determine the optimal power generated by the distributed generators in the microgrid which is required to minimize the total generation cost. The proposed unit is applied to a microgrid that consists of five generating units feeding residential, commercial, and industrial loads, and results are compared to other available research in literature to validate the proposed algorithm. Full article
(This article belongs to the Special Issue Electrical Energy Optimization and Cost Saving in Smart Environments)
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