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Smart Electric Power Systems and Smart Grids
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Smart Electric Power Systems and Smart Grids

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (12 October 2020) | Viewed by 16995

Special Issue Editors

Dr. Miltiadis (Miltos) Alamaniotis

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Texas at San Antonio (UTSA), San Antonio, TX 78249, USA
Interests: AI in radiation detection and nuclear security; AI in radiation sensor networks
Special Issues, Collections and Topics in MDPI journals
Dr. Antonio Martinez-Molina

E-Mail Website
Guest Editor
College of Architecture, Construction and Planning (CACP) | University of Texas at San Antonio (UTSA), Room 3.380G - Monterey Bldg. | 501 W. Cesar E. Chavez Blvd., San Antonio, TX 78207, USA
Interests: Smart buildings; Renewable Energy Generation; Architectural Technology; Sustainability and Healthy Architecture

Special Issue Information

Dear Colleagues,

As world energy shifts from demand-led to supply-constrained, advancements in information and communication technologies (ICT) are brought to bear on shaping demand at the nodal level and in ways that intrinsic volatility does not threaten the security of an energy system. Towards that end, coupling power systems with information technologies have converted traditional electric energy delivery infrastructures into a hybrid energy-data system (smart grid) that controls power flow via information signals. Hence, grid entities are exposed to multiple heterogeneous data streams coming from various sources, while being engaged to make real-time decisions regarding their load demand and consumption behavior. Dynamic data-driven control is identified as a means of affecting power behavior through incentive signals found in information vectors that include anticipations of future demand (referred to as smart power systems).

The focus of this issue will be on intelligent methods to allow management of power systems and promote real-time decision making for load demand in a dynamically data-driven and varying environment. Specifically, the interest lies in methods that take into consideration the utilization of artificial intelligence tools such as neural networks, kernel machines, clustering and deep learners, either as independent tools or in novel synergistic frameworks for building advanced data analytics.

The issue welcomes submissions in any methods that promote intelligent decision making and data analytics that apparently promote the notion of smart power systems and smart grids.

Dr. Miltiadis Alamaniotis
Dr. Antonio Martinez-Molina
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 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. Sustainability 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 2400 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

  • Artificial Intelligence in power systems
  • data analytics
  • decision making
  • intelligent systems
  • machine learning
  • uncertainty quantification in decision

Published Papers (6 papers)

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Research

30 pages, 9146 KiB  
Article
Active Power Loss Reduction for Radial Distribution Systems by Placing Capacitors and PV Systems with Geography Location Constraints
by Thuan Thanh Nguyen, Bach Hoang Dinh, Thai Dinh Pham and Thang Trung Nguyen
Sustainability 2020, 12(18), 7806; https://doi.org/10.3390/su12187806 - 21 Sep 2020
Cited by 14 | Viewed by 3252
Abstract
This paper presents a highly effective method of installing both capacitors and PV systems in distribution systems for the purpose of reducing total power loss in branches. Three study cases with the installation of one capacitor, two capacitors and three capacitors were implemented [...] Read more.
This paper presents a highly effective method of installing both capacitors and PV systems in distribution systems for the purpose of reducing total power loss in branches. Three study cases with the installation of one capacitor, two capacitors and three capacitors were implemented and then the optimal solutions were used to install one more photovoltaic (PV) system. One PV system with 20% active power of all loads and less than active power of all loads was tested for two different conditions: (1) with geography location constraint and (2) without geography location constraint for PV system placement. The results from two systems consisting of 33 and 69 nodes were obtained by using the Stochastic Fractal Search Optimization Algorithm (SFSOA). Simulation results show that this method can determine the appropriate location and size of capacitors to reduce the total power losses more effectively than other existing methods. Furthermore, the paper also demonstrates the real impact of using both capacitors and PV systems to reduce active power loss as well as improve the voltage profile of distribution systems. This paper also finds that if it is possible to place PV systems in all nodes in distribution systems, the benefit from reducing total loss is highly significant and the investment of PV system placement is highly encouraged. As a result, it is recommended that capacitors and PV systems be used in distribution networks, and we claim that two important factors of the installed components consisting of location and size can be determined effectively by using SFSOA. Full article
(This article belongs to the Special Issue Smart Electric Power Systems and Smart Grids)
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19 pages, 7869 KiB  
Article
Peak-Load Forecasting for Small Industries: A Machine Learning Approach
by Dong-Hoon Kim, Eun-Kyu Lee and Naik Bakht Sania Qureshi
Sustainability 2020, 12(16), 6539; https://doi.org/10.3390/su12166539 - 13 Aug 2020
Cited by 15 | Viewed by 2416
Abstract
Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied. Most of these studies are focused on improving predictive performance by using varying feature information, [...] Read more.
Peak-load forecasting prevents energy waste and helps with environmental issues by establishing plans for the use of renewable energy. For that reason, the subject is still actively studied. Most of these studies are focused on improving predictive performance by using varying feature information, but most small industrial facilities cannot provide such information because of a lack of infrastructure. Therefore, we introduce a series of studies to implement a generalized prediction model that is applicable to these small industrial facilities. On the basis of the pattern of load information of most industrial facilities, new features were selected, and a generalized model was developed through the aggregation of ensemble models. In addition, a new method is proposed to improve prediction performance by providing additional compensation to the prediction results by reflecting the fewest opinions among the prediction results of each model. Actual data from two small industrial facilities were applied to our process, and the results proved the effectiveness of our proposed method. Full article
(This article belongs to the Special Issue Smart Electric Power Systems and Smart Grids)
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14 pages, 2562 KiB  
Article
Enhancing Historic Building Performance with the Use of Fuzzy Inference System to Control the Electric Cooling System
by Antonio Martinez-Molina and Miltiadis Alamaniotis
Sustainability 2020, 12(14), 5848; https://doi.org/10.3390/su12145848 - 21 Jul 2020
Cited by 7 | Viewed by 2785
Abstract
In recent years, the interest in properly conditioning the indoor environment of historic buildings has increased significantly. However, maintaining a suitable environment for building and artwork preservation while keeping comfortable conditions for occupants is a very challenging and multi-layered job that might require [...] Read more.
In recent years, the interest in properly conditioning the indoor environment of historic buildings has increased significantly. However, maintaining a suitable environment for building and artwork preservation while keeping comfortable conditions for occupants is a very challenging and multi-layered job that might require a considerable increase in energy consumption. Most historic structures use traditional on/off heating, ventilation, and air conditioning (HVAC) system controllers with predetermined setpoints. However, these controllers neglect the building sensitivity to occupancy and relative humidity changes. Thus, sophisticated controllers are needed to enhance historic building performance to reduce electric energy consumption and increase sustainability while maintaining the building historic values. This study presents an electric cooling air controller based on a fuzzy inference system (FIS) model to, simultaneously, control air temperature and relative humidity, taking into account building occupancy patterns. The FIS numerically expresses variables via predetermined fuzzy sets and their correlation via 27 fuzzy rules. This intelligent model is compared to the typical thermostat on/off baseline control to evaluate conditions of cooling supply during cooling season. The comparative analysis shows a FIS controller enhancing building performance by improving thermal comfort and optimizing indoor environmental conditions for building and artwork preservation, while reducing the HVAC operation time by 5.7%. Full article
(This article belongs to the Special Issue Smart Electric Power Systems and Smart Grids)
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17 pages, 3079 KiB  
Article
Minutely Active Power Forecasting Models Using Neural Networks
by Dimitrios Kontogiannis, Dimitrios Bargiotas and Aspassia Daskalopulu
Sustainability 2020, 12(8), 3177; https://doi.org/10.3390/su12083177 - 15 Apr 2020
Cited by 18 | Viewed by 2429
Abstract
Power forecasting is an integral part of the Demand Response design philosophy for power systems, enabling utility companies to understand the electricity consumption patterns of their customers and adjust price signals accordingly, in order to handle load demand more effectively. Since there is [...] Read more.
Power forecasting is an integral part of the Demand Response design philosophy for power systems, enabling utility companies to understand the electricity consumption patterns of their customers and adjust price signals accordingly, in order to handle load demand more effectively. Since there is an increasing interest in real-time automation and more flexible Demand Response programs that monitor changes in the residential load profiles and reflect them according to changes in energy pricing schemes, high granularity time series forecasting is at the forefront of energy and artificial intelligence research, aimed at developing machine learning models that can produce accurate time series predictions. In this study we compared the baseline performance and structure of different types of neural networks on residential energy data by formulating a suitable supervised learning problem, based on real world data. After training and testing long short-term memory (LSTM) network variants, a convolutional neural network (CNN), and a multi-layer perceptron (MLP), we observed that the latter performed better on the given problem, yielding the lowest mean absolute error and achieving the fastest training time. Full article
(This article belongs to the Special Issue Smart Electric Power Systems and Smart Grids)
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19 pages, 3692 KiB  
Article
A Study on the Development Trends of the Energy System with Blockchain Technology Using Patent Analysis
by Lin-Yun Huang, Jian-Feng Cai, Tien-Chen Lee and Min-Hang Weng
Sustainability 2020, 12(5), 2005; https://doi.org/10.3390/su12052005 - 05 Mar 2020
Cited by 17 | Viewed by 3733
Abstract
Recently, the application of blockchain to the setting, management, and trading of the energy system has formed an innovative technology and has attracted a lot of attention from industry, academia, and research. In this study, we use patent analysis technology to explore the [...] Read more.
Recently, the application of blockchain to the setting, management, and trading of the energy system has formed an innovative technology and has attracted a lot of attention from industry, academia, and research. In this study, we use patent analysis technology to explore the development trends of the energy system with blockchain technology. During the patent analysis process, this study makes corresponding analysis charts, such as patent application numbers over time, patent application numbers for main leading countries, applicants, patent citations, international patent classification (IPC), and life cycle. Relative research and design (R&D) capability of the top ten applicants is estimated and the cluster map of the technology is obtained. The technical features of the top five IPC patent applications are related to the cluster map to show the development of energy blockchain technology. Through this paper, first, the basics of the blockchain and patent analysis are illustrated and, moreover, the reason why and how blockchain technology can be combined with the energy system is also briefly described and analyzed. The results of the patent analysis of energy blockchain technology indicate that the United States leads the way, accounting for more than half of the global total. It is also interesting to note that the participants are not from traditional specific fields, but included electric power manufacturers, computer software companies, e-commerce companies, and even many new companies devoted to blockchain technology. Walmart Apollo, LLC and International Business Machines Corporation (IBM) have the highest number of patent applications. However, Walmart Apollo, LLC ranks first with a greater number of inventors of 36, an activity year of 2 years, and a relative R&D capability of 100%. IBM ranks second with an activity year of 3 years and a research and development capability of 91%. Among various applicants, IBM and LO3 energy started earlier in this field, and their patent output is also more prominent. The IPC is mainly concentrated in G06Q 50/06, which belongs to the technical field of the setting and management of the energy system including electricity, gas, or water supply. Currently, most projects are in the early development stages, and research on key areas is still ongoing to improve the required scalability, decentralization, and security. Thus, energy blockchain technology is still in the growth period, and there is still considerable room for development of the patent in the later period. Moreover, it is suggested that the novel communication module such as the combination of the consortium blockchain and the private blockchain cold also provide their own advantages to achieve the purpose of improving system performance and efficiency. Full article
(This article belongs to the Special Issue Smart Electric Power Systems and Smart Grids)
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20 pages, 2194 KiB  
Article
Research on Transmission Network Expansion Planning Considering Splitting Control
by Fei Tang, Chufei Xiao, Xin Gao, Yifan Zhang, Nianchun Du and Benxi Hu
Sustainability 2020, 12(5), 1769; https://doi.org/10.3390/su12051769 - 27 Feb 2020
Cited by 2 | Viewed by 1733
Abstract
A robust and reliable grid is one of the core elements for power network planning. Specifically, splitting is an effective way for power grid out-of-step oscillation. Since the cross-section of system out-of-step is mostly found on the weak connection lines, reducing the number [...] Read more.
A robust and reliable grid is one of the core elements for power network planning. Specifically, splitting is an effective way for power grid out-of-step oscillation. Since the cross-section of system out-of-step is mostly found on the weak connection lines, reducing the number of those lines can be conducive to the system partition, save the finding time of the optimal splitting cross-section, and improve the performance of the splitting control. This paper proposed an enhanced method based on slow coherence theory for weak connection lines’ identification and monitoring. The ratio of the number of weak connection lines to the number of all the lines, called weak connection coefficient, is considered as a crucial factor. A bi-level programming model, which perceives the minimum connection coefficient as the optimization goal, is built for the transmission network. Additionally, a fused algorithm, consisting of Boruvka algorithm and particle swarm optimization with adaptive mutation and inertia weight, is employed to solve the proposed method in the instances of an 18-node IEEE Graver system and a practical power grid in East China. Simulation results in PSD-BPA are conducted to verify the effectiveness of the weak connection monitoring method and transmission network planning model. Full article
(This article belongs to the Special Issue Smart Electric Power Systems and Smart Grids)
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