• Energy Research
• 2021-2025close

Electricity is fundamental to modern societies and will become even more so as its use expands through different technologies and population growth. Power generation is currently the largest source of carbon-dioxide (CO2) emissions globally, but it is also the sector that is leading the transition to net zero emissions through the rapid rise of renewables. The impacts of COVID-19 on the electricity sector led to a reduction in the demand for electricity, while at the same time, the current global energy crisis has placed the security and affordability of electricity at the top of the political agenda in many countries. In this way, the decrease in the demand for electricity, as well as its gradual recovery, makes it necessary to carry out energy planning that considers the adverse effects caused by global events with a high socioeconomic impact. In this article, the Low Emission Analysis Platform (LEAP) 2020 software has been used to determine the distribution of energy sources to 2050 for Mexico. The variables that lead to the possible profiles for 2050 are social, economic, and technological. The results correspond to a possible future based on official data from the National Electric System (SEN) of Mexico. The forecast for 2050 indicates that the electricity sector will have almost double the current installed capacity; however, emissions do not correspond to twice as much: they are practically 50% higher.

The integration of theoretical insights and current findings in the articles included in this Special Issue holds the potential to bridge the gap between academic research and real-world challenges in enhancing physical–chemical processes [...]

Thermal energy recovery systems have different candidates to mitigate CO2 emissions as recommended by the UN in its list of SDGs. One of these promising systems is thermal absorption transformers, which generally use lithium-water bromide as the working fluid. A Double Stage Heat Transformer (DSHT) is a thermal machine that allows the recovery of thermal energy at a higher temperature than it is supplied through the effect of steam absorption in a concentrated solution of lithium bromide. There are very precise thermodynamic models which allow us to calculate all the possible operating conditions of the DSHT. To perform the control of these systems, the use of Artificial Intelligence (AI) is proposed with two computational techniques—Fuzzy Logic (FL) and Artificial Neural Network (ANN)—to calculate in real-time the set of variables that maximize the product’s Gross Temperature Lift (GTL) and Coefficient of Performance (COP) in a DSHT. The values for Coefficient of Determination (R2), Mean Square Error Root (MRSE), and Mean Error Bias (MBE) for the two types of computational techniques were analyzed and compared with the purpose of identifying which of them may be more accurate to calculate the operating conditions (temperatures, pressures, concentration and flows) with the highest COP for an interval of the value of the temperature absorption entered by the user. The result of the analysis of the evaluated techniques concluded that the control strategy of a DSHT in real-time will be based on the precise calculation of the refrigerant flow in the second evaporator with a Neural Network of 30 neurons, 300 weights and 40 bias, as it is more accurate than the Fuzzy Logic technique. The goodness-of-fit for two computational techniques was evaluated as having an R2 higher than 0.98 for the provided data. Future AI controllers must be based on evaporator flow values with evaporator power at 3.9−04 kg/KJ.

A reverse osmosis system driven by photovoltaic energy is an eco-friendly and sustainable way to produce freshwater in rural areas without connection to a power grid and with available brackish water sources. This paper describes a project where a photovoltaic-driven low-pressure reverse osmosis system (LPRO-PV) was designed, tested under laboratory conditions, and installed in Samalayuca, Chihuahua, Mexico, to evaluate the technical feasibility and social impact of this technology. The LPRO-PV system was tested with synthetic water with a salinity of 2921 ± 62.3 mg/L; the maximum freshwater volume produced was 1.8 ± 0.06 m3/day with a salinity value of 91 ± 1.9 mg/L. The LPRO-PV system satisfied the basic freshwater requirements for a local family of three members for one year, including the mobility-restriction period due to the COVID-19 pandemic. The social evaluation analysis reflects the importance of considering the technical aspects derived from the experimental tests, as well as the users’ perception of the performance and operation of the system. As a result of the implementation of this technology and the benefits described by the users, they committed to the maintenance activities required for the LPRO-PV system’s operation. This technology has great potential to produce fresh water in arid and isolated regions with high-salinity groundwater sources, thus fulfilling the human right to safe and clean drinking water.

The energy consumption for space cooling is growing faster than for any other end-use in buildings, more than tripling between 1990 and 2016. Energy efficiency is an important topic in the drive to reduce the consumption of electricity, particularly in air conditioning. This paper presents a simulation of an absorption cooling system with a parabolic trough collector under dynamic conditions using TRaNsient SYstem Simulation (TRNSYS) software. The thermal analysis seeks to evaluate a storage tank at three different configurations: (1) sensible heat, (2) latent heat, and (3) latent heat incorporating a tempering valve. The latent heat storage tank is a rectangular heat exchanger using MgCl2·6H2O as the phase change material, programmed in EES software; in addition, water and synthetic organic fluid were analyzed as heating fluids. The process was analyzed while varying the solar collector area from 20 to 40 m2 and the storage tank volume from 0.25 to 0.75 m3. The results showed that the solar collector of configuration 1 is unable to satisfy the energy demand. Configuration 2 can satisfy the demand with water and a storage tank volume above 0.50 m3 and 30 m2, while configuration 3 can satisfy the demand above 0.50 m3 and 20 m2 with water.

The forecast of the generation of electrical energy from the solar resource is associated with its uncertainty due to the meteorological variations that it presents. Solar power generation forecasts are important for the efficient operation of solar plants. This article shows a methodology entailing a multilayer neural network with backpropagation and input data from a model with time lag coordinates for a horizon of 24 h and beyond. The neural network model was compared with statistical and prediction models numerical time, resulting in a MAPE of 0.57% and a MAE of 69.29 W.