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Special Issue "Smart Energy Management for Smart Grids 2019"

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

Deadline for manuscript submissions: 30 April 2019

Special Issue Editors

Guest Editor
Prof. Dr. José L. Bernal-Agustín

Electrical Engineering. Department, University of Zaragoza. Calle María de Luna, 3. 50018 Zaragoza, Spain
Website | E-Mail
Phone: +34 976 76 19 21
Fax: +34 976 76 22 26
Interests: evolutionary computation applications to engineering; renewable energy; distribution power system; energy management; electric markets
Guest Editor
Prof. Dr. Rodolfo Dufo-López

Electrical Engineering. Department, University of Zaragoza. Calle María de Luna, 3. 50018 Zaragoza, Spain
Website | E-Mail
Interests: renewable energy; distribution power systems; electricity storage; net metering; energy management; optimization algorithms

Special Issue Information

Dear Colleagues,

Electricity networks have evolved over the last few years, improving their reliability and increasing their functionality. These changes have led to the development of smart grids.

Smart grids allow communication between the different agents of the electrical system, and the interaction of these agents with the equipment, devices and software that are part of the smart grids. One notable, but not the only, feature of smart grids is their application in energy management. The possibility of transmitting information from metering equipment to electricity companies and consumers allows for proper demand management, both from the point of view of the companies and the consumer. Energy management through smart grids improves safety and quality by adequately monitoring the different elements of the grid, and facilitates the use of new storage technologies, the renewable energies integration, the implementation of electric vehicles and self-consumption systems.

Many aspects must be taken into account to ensure that smart grids function properly, including the development of control and measurement devices, communication systems and the software required to manage all devices, reducing costs and maximizing reliability.

Taking into account all the above, this Special Issue is dedicated to topics related to “Smart Energy Management for Smart Grids”, including both technical and economic topics.

Prof. Dr. José L. Bernal-Agustín
Prof. Dr. Rodolfo Dufo-López
Guest Editors

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 bimonthly 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 metering
  • Load management
  • Energy management
  • Energy storage
  • Communication systems
  • Electrical grid protection
  • Grid connected renewable generation
  • Distributed generation
  • Self-consumption
  • Vehicle-to-Grid (V2G)
  • Load forecasting

Published Papers (4 papers)

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Research

Open AccessArticle A Methodology for Determination and Definition of Key Performance Indicators for Smart Grids Development in Island Energy Systems
Energies 2019, 12(2), 242; https://doi.org/10.3390/en12020242
Received: 21 November 2018 / Revised: 20 December 2018 / Accepted: 8 January 2019 / Published: 14 January 2019
PDF Full-text (684 KB)
Abstract
There is a growing interest over the last decades in the field of autonomous island grids that is driven mainly by climate reasons. The common objective among the members of the European Union (EU) is the increase of Renewable Energy Sources (RES) penetration
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There is a growing interest over the last decades in the field of autonomous island grids that is driven mainly by climate reasons. The common objective among the members of the European Union (EU) is the increase of Renewable Energy Sources (RES) penetration in the energy mixture, as well as turning the grid into a smart grid. Consequently, more and more state-of-the-art solutions are being proposed for the electricity generation and the optimization of the energy system management, taking advantage of innovations in all energy related sectors. The evaluation of all available solutions requires quantitative assessment, through the adoption of representative Key Performance Indicators (KPIs) for the projects that are related to smart grid development in isolated energy systems, providing the relevant stakeholders with a useful comparison among the proposed solutions. The evaluation approach that is described in this paper emphasizes the role of the various stakeholder groups who face the proposed solutions by different points of view. Apart from the domains of interest that are also observed in previous approaches, the proposed list also contains a set of legal KPIs, since the regulatory framework can either represent a serious barrier or grant a strong incentive for the implementation of state-of-the-art energy technology and grid management solutions in different countries. Full article
(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)
Open AccessArticle Smart Energy Optimization Using Heuristic Algorithm in Smart Grid with Integration of Solar Energy Sources
Energies 2018, 11(12), 3494; https://doi.org/10.3390/en11123494
Received: 31 October 2018 / Revised: 27 November 2018 / Accepted: 6 December 2018 / Published: 14 December 2018
PDF Full-text (1134 KB) | HTML Full-text | XML Full-text
Abstract
Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the
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Smart grid (SG) vision has come to incorporate various communication technologies, which facilitate residential users to adopt different scheduling schemes in order to manage energy usage with reduced carbon emission. In this work, we have proposed a residential load management mechanism with the incorporation of energy resources (RESs) i.e., solar energy. For this purpose, a real-time electricity price (RTP), energy demand, user preferences and renewable energy parameters are taken as an inputs and genetic algorithm (GA) has been used to manage and schedule residential load with the objective of cost, user discomfort, and peak-to-average ratio (PAR) reduction. Initially, RTP is used to reduce the energy consumption cost. However, to minimize the cost along with reducing the peaks, a combined pricing model, i.e., RTP with inclining block rate (IBR) has been used which incorporates user preferences and RES to optimally schedule load demand. User comfort and cost reduction are contradictory objectives, and difficult to maximize, simultaneously. Considering this trade-off, a combined pricing scheme is modelled in such a way that users are given priority to achieve their objective as per their requirements. To validate and analyze the performance of the proposed algorithm, we first propose mathematical models of all utilized loads, and then multi-objective optimization problem has been formulated. Furthermore, analytical results regarding the objective function and the associated constraints have also been provided to validate simulation results. Simulation results demonstrate a significant reduction in the energy cost along with the achievement of both grid stability in terms of reduced peak and high comfort. Full article
(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)
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Open AccessArticle Adaptive Robust Optimization-Based Optimal Operation of Microgrids Considering Uncertainties in Arrival and Departure Times of Electric Vehicles
Energies 2018, 11(10), 2646; https://doi.org/10.3390/en11102646
Received: 27 August 2018 / Revised: 19 September 2018 / Accepted: 1 October 2018 / Published: 3 October 2018
Cited by 1 | PDF Full-text (2438 KB) | HTML Full-text | XML Full-text
Abstract
The optimal operation of microgrids is challenging due to the presence of various uncertain factors, i.e., renewable energy sources, loads, market price signals, and arrival and departure times of electric vehicles (EVs). In order to incorporate these uncertainties into the operation model of
[...] Read more.
The optimal operation of microgrids is challenging due to the presence of various uncertain factors, i.e., renewable energy sources, loads, market price signals, and arrival and departure times of electric vehicles (EVs). In order to incorporate these uncertainties into the operation model of microgrids, an adaptive robust optimization-based operation method is proposed in this paper. In particular, the focus is on the uncertainties in arrival and departure times of EVs. The optimization problem is divided into inner and outer problems and is solved iteratively by introducing column and constraint cuts. The unit commitment status of dispatchable generators is determined in the outer problem. Then, the worst-case realizations of all the uncertain factors are determined in the inner problem. Based on the values of uncertain factors, the generation amount of dispatchable generators, the amount of power trading with the utility grid, and the charging/discharging amount of storage elements are determined. The performance of the proposed method is evaluated using three different cases, and sensitivity analysis is carried out by varying the number of EVs and the budget of uncertainty. The impact of the budget of uncertainty and number of EVs on the operation cost of the microgrid is also evaluated considering uncertainties in arrival and departure times of EVs. Full article
(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)
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Open AccessArticle QoE-Aware Smart Home Energy Management Considering Renewables and Electric Vehicles
Energies 2018, 11(9), 2304; https://doi.org/10.3390/en11092304
Received: 13 August 2018 / Revised: 29 August 2018 / Accepted: 30 August 2018 / Published: 1 September 2018
Cited by 2 | PDF Full-text (755 KB) | HTML Full-text | XML Full-text
Abstract
To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed
[...] Read more.
To reduce the peak load and electricity bill while preserving the user comfort, a quality of experience (QoE)-aware smart appliance control algorithm for the smart home energy management system (sHEMS) with renewable energy sources (RES) and electric vehicles (EV) was proposed. The proposed algorithm decreases the peak load and electricity bill by deferring starting times of delay-tolerant appliances from peak to off-peak hours, controlling the temperature setting of heating, ventilation, and air conditioning (HVAC), and properly scheduling the discharging and charging periods of an EV. In this paper, the user comfort is evaluated by means of QoE functions. To preserve the user’s QoE, the delay of the starting time of a home appliance and the temperature setting of HVAC are constrained by a QoE threshold. Additionally, to solve the trade-off problem between the peak load/electricity bill reduction and user’s QoE, a fuzzy logic controller for dynamically adjusting the QoE threshold to optimize the user’s QoE was also designed. Simulation results demonstrate that the proposed smart appliance control algorithm with a fuzzy-controlled QoE threshold significantly reduces the peak load and electricity bill while optimally preserving the user’s QoE. Compared with the baseline case, the proposed scheme reduces the electricity bill by 65% under the scenario with RES and EV. Additionally, compared with the method of optimal scheduling of appliances in the literature, the proposed scheme achieves much better peak load reduction performance and user’s QoE. Full article
(This article belongs to the Special Issue Smart Energy Management for Smart Grids 2019)
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