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Optimal Planning, Integration, and Control of Energy in Smart Cities

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G1: Smart Cities and Urban Management".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 16286

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Special Issue Editors

Prof. Dr. Pedro Ponce-Cruz

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Guest Editor

Tecnologico de Monterrey, Mexico City Campus, Mexico City 14380, Mexico
Interests: smart grids; micro-grids; automation of industrial systems; electrical machines; electric drives; electric vehicles; power electronics; control systems; energy and buildings; renewable energy; machine learning; evolutionary systems; mechatronics; human–machine interaction; smart cities; robotics; rapid product design
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Prof. Dr. Ricardo A. Ramirez-Mendoza

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Guest Editor

School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico
Interests: vehicle dynamic control; automotive control; shock and vibrations; active control
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Prof. Dr. Arturo Molina Gutiérrez

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Guest Editor

Tecnologico de Monterrey, School of Engineering and Sciences, Mexico City 02860, Mexico
Interests: product innovation; enterprise integration engineering; concurrent engineering; rapid product development
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Dr. Luis Ibarra

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Guest Editor

Tecnologico de Monterrey, Mexico City Campus, Mexico City 14380, Mexico
Interests: control systems; power systems; smart grids; micro grids; power electronics; artificial intelligence; electric machines; renebable energy; electromobility; mechatronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world is changing dramatically by rapid urbanization and population growth, so cities' energy conditions and needs are essential elements to consider in urban centers. The smart city could be defined as a sustainable and efficient urban center that provides its citizens with a high quality of life. Hence, optimizing energy resources is mandatory. When conventional cities transform into smart cities, they require optimal planning, integration, and control energy.

The energy system in smart cities is one of the most changing systems due to the rising demand, the intermittent nature of renewable energy, the need for an efficient energy transportation system, among others. Furthermore, smart cities comprise smart homes, smart buildings, and smart factories that are usually interconnected with microgrids to create smart grids. The smart grid concept is the most advanced topology in electric grids that could help to transform conventional cities into smart cities swiftly. Nevertheless, generally, deploying a smart grid topology is not always possible. Thus, partial solutions of each energy segment of the electric grid are proposed to improve the electric grid performance. As a result, optimal planning, integration, and energy control in smart cities are critical components to improve citizens' quality of life in smart cities.

This Special Issue aims to present and disseminate the most recent advances related to the optimal planning, integration, and energy control in smart cities.

Topics of interest for publication include, but are not limited to:

All aspects of electromobility;
Integration of renewable energy systems;
Microgrids and smart grids studies;
Novel strategies of control systems applied in energy systems for smart cities;
Electric devices for saving energy in smart houses or buildings;
Planning generation and distribution of energy;
Quality of energy in smart cities;
Energy Storage;
Frameworks for deploying renewable energy.

Prof. Dr. Pedro Ponce-Cruz
Prof. Dr. Ricardo A. Ramirez-Mendoza
Prof. Dr. Arturo Molina Gutiérrez
Dr. Luis Ibarra
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. 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

microgrids, smart grids
electromobility
energy planning in Smart cites
smart houses
smart buildings
renewable energy

Published Papers (8 papers)

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Research

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22 pages, 10166 KiB

Open AccessArticle

Energy Savings in Buildings Based on Image Depth Sensors for Human Activity Recognition

by Omar Mata, Juana Isabel Méndez, Pedro Ponce, Therese Peffer, Alan Meier and Arturo Molina

Energies 2023, 16(3), 1078; https://doi.org/10.3390/en16031078 - 18 Jan 2023

Cited by 12 | Viewed by 2177

Abstract

A smart city is a city that binds together technology, society, and government to enable the existence of a smart economy, smart mobility, smart environment, smart living, smart people, and smart governance in order to reduce the environmental impact of cities and improve life quality. The first step to achieve a fully connected smart city is to start with smaller modules such as smart homes and smart buildings with energy management systems. Buildings are responsible for a third of the total energy consumption; moreover, heating, ventilation, and air conditioning (HVAC) systems account for more than half of the residential energy consumption in the United States. Even though connected thermostats are widely available, they are not used as intended since most people do not have the expertise to control this device to reduce energy consumption. It is commonly set according to their thermal comfort needs; therefore, unnecessary energy consumption is often caused by wasteful behaviors and the estimated energy saving is not reached. Most studies in the thermal comfort domain to date have relied on simple activity diaries to estimate metabolic rate and fixed values of clothing parameters for strategies to set the connected thermostat’s setpoints because of the difficulty in tracking those variables. Therefore, this paper proposes a strategy to save energy by dynamically changing the setpoint of a connected thermostat by human activity recognition based on computer vision preserving the occupant’s thermal comfort. With the use of a depth sensor in conjunction with an RGB (Red–Green–Blue) camera, a methodology is proposed to eliminate the most common challenges in computer vision: background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance on human detection. Moreover, a Recurrent Neural Network (RNN) is implemented for human activity recognition (HAR) because of its data’s sequential characteristics, in combination with physiological parameters identification to estimate a dynamic metabolic rate. Finally, a strategy for dynamic setpoints based on the metabolic rate, predicted mean vote (PMV) parameter and the air temperature is simulated using EnergyPlus™ to evaluate the energy consumption in comparison with the expected energy consumption with fixed value setpoints. This work contributes with a strategy to reduce energy consumption up to 15% in buildings with connected thermostats from the successful implementation of the proposed method. Full article

(This article belongs to the Special Issue Optimal Planning, Integration, and Control of Energy in Smart Cities)

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19 pages, 1180 KiB

Open AccessArticle

Solar Energy Implementation in Manufacturing Industry Using Multi-Criteria Decision-Making Fuzzy TOPSIS and S4 Framework

by Pedro Ponce, Citlaly Pérez, Aminah Robinson Fayek and Arturo Molina

Energies 2022, 15(23), 8838; https://doi.org/10.3390/en15238838 - 23 Nov 2022

Cited by 8 | Viewed by 1954

Abstract

The demand for electrical energy has increased since the population of and automation in factories have grown. The manufacturing industry has been growing dramatically due to the fast-changing market, so electrical energy for manufacturing processes has increased. As a result, solar energy has been installed to supply electrical energy. Thus, assessing a solar panel company could be a complex task for manufacturing companies that need to assess, install, and operate solar panels when several criteria with different hierarchies from decision-makers are involved. In addition, the stages of a solar panel system could be divided into analysis, installation, operation, and disposal, and all of them must be considered. Thus, the solar panel company must provide a holistic solution for each stage of the solar panel lifespan. This paper provides a fuzzy decision-making approach (Fuzzy TOPSIS) to deal with the assessment of solar companies using the S4 framework in which the sensing, smart, sustainable, and social features are labeled with linguistic values that allow the evaluation of companies using fuzzy values and linguistic labels, instead of using crisp values that are difficult to define when decision-makers are evaluating a solar company for installation of the solar panels. The S4 features are considered the benefits of the evaluation. In the case study presented, three solar panel companies with different alternatives are evaluated on the basis of three decision-makers from manufacturing companies using the S4 framework. This paper considers the benefits of solar companies in the context of decision-makers participating in a multi-decision selection of such a company to install solar panels, so that the selection process is more effective. Thus, the proposed Fuzzy TOPSIS method proved efficient when selecting a solar panel company from among many options that best meets the needs of manufacturing companies. Full article

(This article belongs to the Special Issue Optimal Planning, Integration, and Control of Energy in Smart Cities)

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25 pages, 8464 KiB

Open AccessArticle

A Real-Time Digital Twin and Neural Net Cluster-Based Framework for Faults Identification in Power Converters of Microgrids, Self Organized Map Neural Network

by Juan R. Lopez, Jose de Jesus Camacho, Pedro Ponce, Brian MacCleery and Arturo Molina

Energies 2022, 15(19), 7306; https://doi.org/10.3390/en15197306 - 04 Oct 2022

Cited by 4 | Viewed by 1874

Abstract

In developing distribution networks, the deployment of alternative generation sources is heavily motivated by the growing energy demand, as by environmental and political motives. Consequently, microgrids are implemented to coordinate the operation of these energy generation assets. Microgrids are systems that rely on power conversion technologies based on high-frequency switching devices to generate a stable distribution network. However, disrupting scenarios can occur in deployed systems, causing faults at the sub-component and the system level of microgrids where its identification is an economical and technological challenge. This paradigm can be addressed by having a digital twin of the low-level components to monitor and analyze their response and identify faults to take preventive or corrective actions. Nonetheless, accurate execution of digital twins of low-level components in traditional simulation systems is a difficult task to achieve due to the fast dynamics of the power converter devices, leading to inaccurate results and false identification of system faults. Therefore, this work proposes a fault identification framework for low-level components that includes the combination of Real-Time systems with the Digital Twin concept to guarantee the dynamic consistency of the low-level components. The proposed framework includes an offline trained Self Organized Map Neural Network in a hexagonal topology to identify such faults within a Real-Time system. As a case study, the proposed framework is applied to a three-phase two-level inverter connected to its digital model in a Real-Time simulator for open circuit faults identification. Full article

(This article belongs to the Special Issue Optimal Planning, Integration, and Control of Energy in Smart Cities)

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28 pages, 3671 KiB

Open AccessArticle

S4 Framework for the Integration of Solar Energy Systems in Small and Medium-Sized Manufacturing Companies in Mexico

by Citlaly Pérez, Pedro Ponce, Alan Meier, Lourdes Dorantes, Jorge Omar Sandoval, Javier Palma and Arturo Molina

Energies 2022, 15(19), 6882; https://doi.org/10.3390/en15196882 - 20 Sep 2022

Cited by 7 | Viewed by 2915

Abstract

Currently, the industrial sector consumes more than 60% of the energy produced in Mexico, mainly from fossil fuels, causing negative impacts on the environment and human beings. Solar energy helps companies diversify their energy sources, generate savings, and reduce dependence on fossil fuels. Moreover, the environmental impact can be reduced when CO₂ emissions are reduced. Nevertheless, in Mexico, less than 3.5% of the electricity comes from solar energy, and along with a lack of information about the technical and social aspects involved in photovoltaic (PV) systems, it is difficult for companies to analyze and evaluate relevant data, and thus make effective decisions based on their needs. As such, companies cannot understand the complete lifecycle of PV systems, and, usually, the economic, environmental, and technical decisions are made only using the installation analysis, which is only one stage in the lifespan of PV systems. This paper proposes an S4 framework with the sensing, smart, sustainable, and social features that small and medium-sized companies must consider to install, operate, and dispose of PV systems, considering the Mexican context. The current literature does not show a complete classification to cover the essential S4 features to describe PV systems, so companies only have partial information when deciding about the installation of PV systems. This framework considers all the needs that may exist during the PV systems’ lifecycle, making a detailed evaluation of each of its elements in each lifecycle stage. Consequently, this S4 framework gives a complete guideline allowing companies to decide on PV systems. Finally, this paper presents a case study about a Mexican company that uses the proposed S4 framework to analyze the PV’s lifespan. Full article

(This article belongs to the Special Issue Optimal Planning, Integration, and Control of Energy in Smart Cities)

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16 pages, 66090 KiB

Open AccessArticle

Embedded Real-Time Clothing Classifier Using One-Stage Methods for Saving Energy in Thermostats

by Adán Medina, Juana Isabel Méndez, Pedro Ponce, Therese Peffer and Arturo Molina

Energies 2022, 15(17), 6117; https://doi.org/10.3390/en15176117 - 23 Aug 2022

Cited by 9 | Viewed by 1474

Abstract

Energy-saving is a mandatory research topic since the growing population demands additional energy yearly. Moreover, climate change requires more attention to reduce the impact of generating more CO₂. As a result, some new research areas need to be explored to create innovative energy-saving alternatives in electrical devices that have high energy consumption. One research area of interest is the computer visual classification for reducing energy consumption and keeping thermal comfort in thermostats. Usually, connected thermostats obrtain information from sensors for detecting persons and scheduling autonomous operations to save energy. However, there is a lack of knowledge of how computer vision can be deployed in embedded digital systems to analyze clothing insulation in connected thermostats to reduce energy consumption and keep thermal comfort. The clothing classification algorithm embedded in a digital system for saving energy could be a companion device in connected thermostats to obtain the clothing insulation. Currently, there is no connected thermostat in the market using complementary computer visual classification systems to analyze the clothing insulation factor. Hence, this proposal aims to develop and evaluate an embedded real-time clothing classifier that could help to improve the efficiency of heating and ventilation air conditioning systems in homes or buildings. This paper compares six different one-stage object detection and classification algorithms trained with a small custom dataset in two embedded systems and a personal computer to compare the models. In addition, the paper describes how the classifier could interact with the thermostat to tune the temperature set point to save energy and keep thermal comfort. The results confirm that the proposed real-time clothing classifier could be implemented as a companion device in connected thermostats to provide additional information to end-users about making decisions on saving energy. Full article

(This article belongs to the Special Issue Optimal Planning, Integration, and Control of Energy in Smart Cities)

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29 pages, 15489 KiB

Open AccessArticle

Evolving Gamified Smart Communities in Mexico to Save Energy in Communities through Intelligent Interfaces

by Juana Isabel Méndez, Adán Medina, Pedro Ponce, Therese Peffer, Alan Meier and Arturo Molina

Energies 2022, 15(15), 5553; https://doi.org/10.3390/en15155553 - 30 Jul 2022

Cited by 10 | Viewed by 1615

Abstract

In 2021, the residential sector had an electricity consumption of around 39% in México. Householders influence the quantity of energy they manage in a home due to their preferences, culture, and economy. Hence, profiling the householders’ behavior in communities allows designers or engineers to build strategies that promote energy reductions. The household socially connected products ease routine tasks and help profile the householder. Furthermore, gamification strategies model householders’ habits by enhancing services through ludic experiences. Therefore, a gamified smart community concept emerged during this research as an understanding that this type of community does not need a physical location but has similar characteristics. Thus, this paper proposes a three-step framework to tailor interfaces. During the first step, the householder type and consumption level were analyzed using available online databases for Mexico. Then, two artificial neural networks were built, trained, and deployed during the second step to tailor an interactive interface. Thus, the third step deploys an interactive and tailored dashboard. Moreover, the research analysis reflected the predominant personality traits. Besides, some locations have more electricity consumption than others associated with the relative humidity, the outdoor temperature, or the poverty level. The interactive dashboard provides insights about the game elements needed depending on the personality traits, location, and electricity bill. Therefore, this proposal considers all householders (typical and non-typical users) to deploy tailored interfaces designed for smart communities. Currently, the game elements proposed during this research are reported by the literature, so their adoption is assured. Full article

(This article belongs to the Special Issue Optimal Planning, Integration, and Control of Energy in Smart Cities)

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Review

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38 pages, 8116 KiB

Open AccessReview

Systematic Literature Review on Fuzzy Hybrid Methods in Photovoltaic Solar Energy: Opportunities, Challenges, and Guidance for Implementation

by Nebiyu Kedir, Phuong H. D. Nguyen, Citlaly Pérez, Pedro Ponce and Aminah Robinson Fayek

Energies 2023, 16(9), 3795; https://doi.org/10.3390/en16093795 - 28 Apr 2023

Cited by 2 | Viewed by 1514

Abstract

The application of fuzzy hybrid methods has significantly increased in recent years across various sectors. However, the application of fuzzy hybrid methods for modeling systems or processes, such as fuzzy machine learning, fuzzy simulation, and fuzzy decision-making, has been relatively limited in the energy sector. Moreover, compared to standard methods, the benefits of fuzzy-hybrid methods for capturing complex problems are not adequately explored for the solar energy sector, which is one of the most important renewable energy sources in electric grids. This paper investigates the application of fuzzy hybrid systems in the solar energy sector compared to other sectors through a systematic review of journal articles published from 2012 to 2022. Selection criteria for choosing an appropriate method in each investigated fuzzy hybrid method are also presented and discussed. This study contributes to the existing literature in the solar energy domain by providing a state-of-the-art review of existing fuzzy hybrid techniques to (1) demonstrate their capability for capturing complex problems while overcoming limitations inherent in standard modeling methods, (2) recommend criteria for selecting an appropriate fuzzy hybrid technique for applications in solar energy research, and (3) assess the applicability of fuzzy hybrid techniques for solving practical problems in the solar energy sector. Full article

(This article belongs to the Special Issue Optimal Planning, Integration, and Control of Energy in Smart Cities)

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22 pages, 471 KiB

Open AccessReview

The “Smart” Concept from an Electrical Sustainability Viewpoint

by Ignacio Llanez-Caballero, Luis Ibarra, Angel Peña-Quintal, Glendy Catzín-Contreras, Pedro Ponce, Arturo Molina and Ricardo Ramirez-Mendoza

Energies 2023, 16(7), 3072; https://doi.org/10.3390/en16073072 - 28 Mar 2023

Cited by 4 | Viewed by 1308

Abstract

Nowadays, there are many technological-intensive applications that claim to be “smart”. From smartphones to the smart grid, people relate the word smart with technical novelty, automation, enabled communication, and service integration. There is indeed a gap between those smart technologies and their intended “intelligence”; this has arisen an indirect debate between works focusing on automation and mechatronics design and others pursuing a conceptual approach based on fulfilling determinate objectives. One last approach relates the said smartness to deep learning methodologies. In this work, it is attempted to explore both perspectives by providing an overview of recent works around energy usage toward smart cities and the smart grid, pointing out the main conceptual pillars upon which both approaches stand. Certainly, there are enabling technologies supporting the smart concept overall; thus, this work addresses them to characterize “smart” not from technological or conceptual one-sided viewpoints but from their common backbone. Therefore, the interested reader can find in this work an integrative conceptualization of the smart context, a literature review of recent advances, and a deep discussion of how enabling technologies and current technological trends based on energy consumption are shaping the ongoing efforts toward a sustainable future. More importantly, a new approach to define smart in the said context is elaborated far from the typical misunderstanding of technological nesting or mere usage of “advanced” digital technologies. Rather, smartness is addressed by the integrative objectives the application pursues, the objectives set by its users’ intent, and the attained results in terms of public benefit. Full article

(This article belongs to the Special Issue Optimal Planning, Integration, and Control of Energy in Smart Cities)

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