• Energy Research

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.

Universities need to explore drastic changes regarding their facilities to be more inclusive and sustainable. Those changes must move forward to a Smart university campus. A Smart University Campus involves Smart City's concept because different socio-cultural aspects in the community and services like health care, public safety, mobility, education, transportation, and energy are integrated into the Campus. For instance, 25% of the total electric consumption comes from the cooling in an educational facility. Besides, energy awareness campaigns reach 5% to 15% energy cost savings. Nevertheless, the end-users do not engage in those applications due to multiple platform requirements and the generalization of a conventional user; thus, energy awareness is not reached. Knowing the type of user enhances gamification strategies that engage them in energy reduction activities without being obtrusive. Through collaborative networks, the university members socially interact with others, strengths their relationships, and improve their skills toward saving energy. Hence, this paper proposes a case study located at the Tecnologico de Monterrey, Mexico City Campus and analyzes six different scenarios to understand the energy impacts of using different cooling values on thermostats during classes. Dividing these services into a three-level gamified interface: Smart building, Smart Community, and Smart Campus to promote energy awareness to reduce electrical consumption. Besides, if the reference temperature is set correctly, a terminal comfort is achieved, the students and professors could increment their work performance. This gamified interface runs a feed-forward Artificial Neural Network (ANN) as a decision-making system that supports the proposed structure.

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.

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.

Nowadays in modern industrial applications, where the power supply efficiency is more important than the output noise performance, DC-DC converters are widely used in order to fulfill the requirements. Yet, component selection and precise estimation of parameters can improve the converter’s performance, leading to smaller and more efficient designs. Hence, metaheuristic optimization algorithms can be applied using the mathematical model of DC-DC converters, in order to optimize their performance through an optimal inductance selection. Therefore, this work presents a novel design methodology for DC-DC converters, where the inductance selection is optimized, in order to achieve an optimal relation between the inductance size and the required energy. Moreover, a multi-objective metaheuristic optimization is presented through the Earthquake Algorithm, for parameter estimation and component selection, using the inductance of a buck DC-DC converter as a case study. The experimental results validate the design methodology, showing ripple improvement and operating power range extension, which are key features to have an efficient performance in DC-DC converters. Results also confirm the Small-Signal Model of the circuit, as a correct objective function for the parameter optimization, achieving more than 90% of accuracy on the presented behavior.

The interconnection of new generating and storing devices to the power grid imposes the necessity of synchronizing, so the power flow can be manipulated and distributed. In the presence of an increasingly perturbed electric grid, many proposals of novel and modified synchronization techniques attained enough robustness to deal with known perturbations. However, such proposals exhibit drawbacks on their own, leaving open enhancement opportunities, mostly over their discrete implementation—e.g., sampling issues and not-considered inter/harmonics—and their inherent complexity—e.g., the need for frequency adaptability. In this work, three traditional synchronous reference frame (SRF) phase-locked loops (PLL) are modified to implement discrete filtering, such as the well-known proposals based on moving average filters (MAFs), to avoid the problems mentioned above, known for affecting the MAF’s performance. This proposal makes use of discrete, efficient units modularly assembled to yield a signal’s average, based on elliptic half-band filters. The proposed PLLs were tested and exhibited clear advantages—robustness against frequency disturbances—over MAF-based equivalents at standardized tests over a typical simulation environment, setting through this work an initial milestone for its verification and further incorporation in more complex synchronization topologies.

A microgrid including distributed generators can operate connected to the main electrical network or in an isolated manner, referred to as island operation. The transition between both states can occur voluntarily, but a disconnection can also happen unexpectedly. The associated transients can be harmful to the grid, and compensating actions must be triggered to avoid service interruption, preserve power quality, and minimize the possibility of faults; island detection methods are essential to this end. Such techniques typically depend on communication networks or on the introduction of minor electrical disturbances to identify and broadcast unexpected islanding events. However, local energy resources are distributed, variable, and are expected to be integrated in a plug-and-play manner; then, conventional island detection strategies can be ineffective as they rely on specific infrastructure. To overcome those problems, this work proposes a straightforward, distributed island detection technique only relying on local electrical measurements, available at the output of each generating unit. The proposed method is based on the estimated power-frequency ratio, associated with the stiffness of the grid. A “stiffness change” effectively reveals island operating conditions, discards heavy load variations, and enables independent (distributed) operation. The proposal was validated through digital simulations and an experimental test-bed. Results showed that the proposed technique can effectively detect island operation at each generating unit interacting in the microgrid. Moreover, it was about three times faster than other reported techniques.

Nowadays, owing to the growing interest in renewable energy, Photovoltaic systems (PV) are responsible of supplying more than 500,000 GW of the electrical energy consumed around the world. Therefore, different converters topologies, control algorithms, and techniques have been studied and developed in order to maximize the energy harvested by PV sources. Maximum Power Point Tracking (MPPT) methods are usually employed with DC/DC converters, which together are responsible for varying the impedance at the output of photovoltaic arrays, leading to a change in the current and voltage supplied in order to achieve a dynamic optimization of the transferred energy. MPPT algorithms such as, Perturb and Observe (P&O) guarantee correct tracking behavior with low calibration parameter dependence, but with a compromised relation between the settling time and steady-state oscillations, leading to a trade off between them. Nevertheless, proposed methods like Particle Swarm Optimization- (PSO) based techniques have improved the settling time with the addition of lower steady-state oscillations. Yet, such a proposal performance is highly susceptible and dependent to correct and precise parameter calibration, which may not always ensure the expected behavior. Therefore, this work presents a novel alternative for MPPT, based on the Earthquake Optimization Algorithm (EA) that enables a solution with an easy parameters calibration and an improved dynamic behavior. Hence, a boost converter case study is proposed to verify the suitability of the proposed technique through Simscape Power Systems™ simulations, regarding the dynamic model fidelity capabilities of the software. Results show that the proposed structure can easily be suited into different power applications. The proposed solution, reduced between 12% and 36% the energy wasted in the simulation compared to the P&O and PSO based proposals.