• Energy Research

Abstract Although smart grid technology has been extensively accepted, social factors influence the performance of smart grid systems. This smart technology will enable the automated monitoring and control of the power delivery system, increase the capacity of the power delivery system, and enhance the performance and connectivity of end users. However, the perceptions of end users are a key factor for adoption of this technology. When end users do not fully accept the smart grid, the operation of the smart grid is not satisfactory. Most literature has concentrated on the technological aspects of smart grids; a technological solution may be defined as one that requires a change only in the developed technology, demanding little or no change in human values or ideas of morality about the usage of electrical energy. However, the solutions to the problems of implementing smart grid technology are not to be found only in technological aspects. This paper presents experimental scenarios that use signal detection theory (SDT), a well-known tool in psychology research, to capture the perceptions of end users about smart grid technology. If the perceptions of end users are positive, the performance of the smart grid is improved. End user criteria can be analyzed using SDT. In addition, fuzzy logic type 2 is suggested as a way to increase the descriptive power of fuzzy signal detection theory. To obtain end user perceptions, several experimental scenarios were created using a didactic smart grid system designed by Delorenzo Group Italy. End users were faced with real situations that enabled determination of their perceptions about the smart grid technology. Experimental results of end users׳ perceptions of smart grid technology are shown using SDT, fuzzy detection theory, and fuzzy detection theory type 2. The results show that end users have a conservative criterion because they are not entirely confident in the intelligent technology provided by the smart grid; this conservative criterion limits smart grid performance.

Electric direct-current (DC) drives based on DC motor are extremely important in the manufacturing process, so it must be crucial to increase their performance when they are working on load disturbances or the DC motor’s parameters change. Usually, several load torque suddenly appears when electric drives are operating in a speed closed-loop, so robust controllers are required to keep the speed high-performance. One of the most well-known robust strategies is the sliding mode controller (SMC), which works under discontinue operation. This controller can handle disturbances and variations in the plant’s parameters, so the controller has robust performance. Nevertheless, it has some disadvantages (chattering). Therefore, this paper proposed a fuzzy logic controller (FLC) that includes an artificial organic network for adjusting the command signal of the SMC. The proposed controller gives a smooth signal that decrements the chattering in the SMC. The stability condition that is based on Lyapunov of the DC motor is driven is evaluated; besides, the stability margins are calculated. The proposed controller is designed using co-simulation and a real testbed since co-simulation is an extremely useful tool in academia and industry allows to move from co-simulation to real implementation in short period of time. Moreover, there are several universities and industries that adopt co-simulation as the main step to design prototypes. Thus, engineering students and designers are able to achieve excellent results when they design rapid and functional prototypes. For instance, co-simulation based on Multisim leads to design directly printed circuit boards so engineering students or designers could swiftly get an experimental DC drive. The experimental results using this platform show excellent DC-drive performance when the load torque disturbances are suddenly applied to the system. As a result, the proposed controller based on fuzzy artificial organic and SMC allows for adjusting the command signal that improves the dynamic response in DC drives. The experimental response using the sliding-mode controller with fuzzy artificial organic networks is compared against an auto-tuning, Proportional-Integral-Derivative (PID), which is a conventional controller. The PID controller is the most implemented controller in several industries, so this proposal can contribute to improving manufacturing applications, such as micro-computer numerical control (CNC) machines. Moreover, the proposed robust controller achieves a superior-speed response under the whole tested scenarios. Finally, the presented design methodology based on co-simulation could be used by universities and industry for validating and implementing advanced control systems in DC drives.

Summary Two electromagnetic power generators were designed and built with the purpose of creating a safe, noncarbon emitting, and long-lasting source of energy. Prototype A has 20 coils and B has 10 coils. Each one has 2 flywheels, a second-hand ferrite magnet that spins over the horizontal plane (lateral axis), and neodymium magnets (NdFeB). A literature review is provided covering the origins of the first direct current generators up to the most widely used contemporary alternating current power generators, comparing them with both prototypes. These were compared with each other to determine which performs better by making mathematical projections and checking them experimentally. The prototypes are based on an innovative configuration for mechanical energy conversion, which supplies power every time the system is in motion. The harvested energy has shown results—in terms of output power, power density, and specific power—which depend on several factors, such as the mechanical force induced and the period of time they remain spinning by the flywheels. The different parameters of the systems, which include connections, structure details, spinning plane, experimental results, and materials, are specified. Advantages and disadvantages are also analyzed, including some immediate real-life applications. The designs have been improved by changing the shape of the coils and magnets as a whole to create enduring power systems.

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.

Nowadays, the growth in the consumption of energy and the need to face pollution resulting from its generation are causing concern for consumers and providers. Energy consumption in residential buildings and houses is about 22% of total energy production. Cutting-edge energy managers aim to optimize electrical devices in homes, taking into account users’ patterns, goals, and needs, by creating energy consumption awareness and helping current change habits. In this way, energy manager systems (EMSs) monitor and manage electrical appliances, automate and schedule actions, and make suggestions regarding electrical consumption. Furthermore, gamification strategies may change energy consumption patterns through energy managers, which are seen as an option to save energy and money. Therefore, this paper proposes a personalized gamification strategy for an EMS through an adaptive neuro-fuzzy inference system (ANFIS) decision-making engine to classify the level of electrical consumption and persuade the end-user to reduce and modify consumption patterns, saving energy and money with gamified motivations. These strategies have proven to be effective in changing consumer behavior with intrinsic and extrinsic motivations. The interfaces consider three cases for summer and winter periods to calculate the saving-energy potentials: (1) for a type of user that is interested in home-improvement efforts while helping to save energy; (2) for a type of user that is advocating to save energy; (3) for a type of user that is not interested in saving energy. Hence, each interface considers the end-user’s current consumption and the possibility to modify their consumption habits using their current electrical devices. Finally, an interface displaying the electrical consumption for each case exemplifies its linkage with EMSs.

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.

The rapid growth of the manufacturing, automotive, robotics, and smart-grid industries will require engineers that posses attributes related to multidisciplinary formation and strong tools to design solutions to complex problems. The engineer’s formation requires to include the development of multidisciplinary abilities into their programs and backing the theory with real-world practice. In this sense, specialized laboratories are necessary in order to carry out this kind of formation and training. However, the lack of specialized laboratories is one of the main obstacles preventing us from reaching this objective. The specialized laboratories require specialized installation, maintenance and cost management as well as a design that can accommodate a large number of students. In contrast, the virtual laboratories are one tool that can be used to deal with both issues which are the lack of specialized engineers and the absence of specialized laboratories. This work proposes a new design of a virtual laboratory in order to specialize engineers in several fields such as power electronics, electric machines, automatic control, and information and computational technologies. The laboratory proposed includes tools such as co-simulation, co-modeling, and co-design in order to face complex problems within the industry.

Thermal comfort is associated with clothing insulation, conveying a level of satisfaction with the thermal surroundings. Besides, clothing insulation is commonly associated with indoor thermal comfort. However, clothing classification in smart homes might save energy when the end-user wears appropriate clothes to save energy and obtain thermal comfort. Furthermore, object detection and classification through Convolutional Neural Networks has increased over the last decade. There are real-time clothing garment classifiers, but these are oriented towards single garment recognition for texture, fabric, shape, or style. Consequently, this paper proposes a CNN model classification for the implementation of these classifiers on cameras. First, the Fashion MNIST was analyzed and compared with the VGG16, Inceptionvv4, TinyYOLOv3, and ResNet18 classification algorithms to determine the best clo classifier. Then, for real-time analysis, a new dataset with 12,000 images was created and analyzed with the YOLOv3 and TinyYOLO. Finally, an Azure Kinect DT was employed to analyze the clo value in real-time. Moreover, real-time analysis can be employed with any other webcam. The model recognizes at least three garments of a clothing ensemble, proving that it identifies more than a single clothing garment. Besides, the model has at least 90% accuracy in the test dataset, ensuring that it can be generalized and is not overfitting.