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Natural gas is one of the most common forms of energy in our daily life, and it is composed of multicomponent hydrocarbon gas mixtures (mainly of methane, ethane and propane). It is of great significant to reveal the condensation mechanism of multicomponent mixtures for the development and utilization of natural gas. A numerical model was adopted to analyze the heat and mass transfer characteristics of propane condensation in binary and ternary gas mixtures on a vertical cold plate. Multicomponent diffusion equations and the volume of fluid method (VOF) are used to describe the in-phase and inter-phase transportation. The conditions of different wall sub-cooled temperatures (temperature difference between the wall and saturated gas mixture) and the inlet molar fraction of methane/ethane are discussed. The numerical results show that ethane gas is more likely to accumulate near the wall compared with the lighter methane gas. The thermal resistance in the gas boundary layer is one hundred times higher than that of the liquid film, revealing the importance of diffusion resistance. The heat transfer coefficients increased about 11% (at ΔT = 10 K) and 7% (at ΔT = 40 K), as the molar fraction of ethane increased from 0 to 40%. Meanwhile, the condensation heat transfer coefficient decreased by 53~56% as the wall sub-cooled temperature increased from 10 K to 40 K.

Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

The study examines the implications of educating prosumers regarding Internet of Things (IoT) use and monitoring to reduce power consumption in the home and encourage energy conservation, sustainable living, and behavior change. Over 15 months, 125 households and household owners received training regarding IoT plug equipment, usage monitoring, and energy reduction. A face to face survey was then conducted regarding power consumption reductions, frequency of monitoring, and user satisfaction compared to the previous year. The study found that participating households used around 5% less energy compared to average households. The reduction rate was found to have increased when more appliances were connected to smart plugs and their power usage was monitored more frequently. Power usage also fell in a greater level when participants were more satisfied with being given smart plugs and related education. Moreover, energy reduction rates increase when smart plugs were used for cooling and heating appliances as well as video, audio, and related devices. The results suggest that this program can be used to reduce energy use, which can be beneficial for smart homes and smart cities. The study demonstrates the importance of education from the perspective of energy conservation and related policies.

Open circuit voltage (OCV) is an important characteristic parameter of lithium-ion batteries, which is used to analyze the changes of electronic energy in electrode materials, and to estimate battery state of charge (SOC) and manage the battery pack. Therefore, accurate OCV modeling is a great significance for lithium-ion battery management. In this paper, the characteristics of high-capacity lithium-ion batteries at different temperatures were considered, and the OCV-SOC characteristic curves at different temperatures were studied by modeling, exponential, polynomial, sum of sin functions, and Gaussian model fitting method with pulse test data. The parameters of fitting OCV-SOC curves by exponential model (n = 2), polynomial model (n = 3~7), sum of sin functions model (n = 3), and Gaussian model (n = 4) at temperatures of 45 °C, 25 °C, 0 °C, and −20°C are obtained, and the errors are analyzed. The experimental results show that the operating temperature of the battery influences the OCV-SOC characteristic significantly. Therefore, these factors need to be considered in order to increase the accuracy of the model and improve the accuracy of battery state estimation.

Increasing wind capacity and capacity factors (CF) are essential for achieving the goals set by the Paris Climate Agreement. From 2010–2012 to 2018–2020, the 3-year mean CF of the global onshore wind turbine fleet rose from 0.22 to 0.25. Wind turbine siting, wind turbine technology, hub height, and curtailed wind energy are well-known CF drivers. However, the extent of these drivers for CF is unknown. Thus, the goal is to quantify the shares of the four drivers in CF development in Germany as a case. Newly developed national power curves from high-resolution wind speed models and hourly energy market data are the basis for the study. We created four scenarios, each with one driver kept constant at the 2010–2012 level, in order to quantify the share of a driver for CF change between 2010–2012 and 2019–2021. The results indicated that rising hub heights increased CF by 10.4%. Improved wind turbine technology caused 7.3% higher CF. However, the absolute CF increase amounted to only 11.9%. It is because less favorable wind turbine sites and curtailment in the later period moderated the CF increase by 2.1% and 3.6%, respectively. The drivers are mainly responsible for perennial CF development. In contrast, variations in wind resource availability drive the enormous CF inter-annual variability. No multi-year wind resource change was detected.

The development of efficient non-precious metal electrocatalysts through more economical and safe methods is consistent with the goals of sustainable development and accelerating the achievement of “carbon neutrality” in the 21st century but remains potentially challenging. Mott–Schottky heterojunction interfaces generated from metal/semiconductor have been a hot topic of recent research because of the unique built-in electric field effect which allows the preparation of more superior catalysts for water electrolysis. Herein, a glutinous rice potpourri-like Mott–Schottky two-dimensional (2D) nanosheet (abbreviated as Ni/CeO2 HJ-NSs) electrocatalyst composed of metal nickel (Ni) and cerium oxide (CeO2) hetero-nanoparticles was synthesized by a simple and scalable self-assembly and thermal reduction strategy. The experimental results and mechanistic analysis show that the Mott–Schottky heterojunction interface composed of metallic Ni and n-type semiconductor CeO2 with built-in electric field induces the electron redistribution at the interface to accelerate the dissociation of water and the binding of reaction intermediates, thus achieving lower water dissociation energy and more thermoneutral ΔGH* value to expedite the kinetics of the hydrogen evolution reaction (HER). Thus, the prepared Ni/CeO2 HJ-NSs exhibit excellent HER catalytic performance in 1 M KOH electrolyte with an overpotential of only 72 mV at 10 mA cm−2, as well as a moderate Tafel slope of 65 mV dec−1 and an extraordinary long-term stability over 50 h, laying a solid foundation for further in-depth investigation. The synthesis of splendid electrocatalysts by exploiting the metal/semiconductor interface effect provides an innovative way for the future generation of Mott–Schottky-based heterostructures with three or more heterocompositions with two or more heterojunction interfaces.

This paper presents an approach to the assessment of the Mexican energy system’s evolution under the climate and energy objectives set by the National Climate Change Strategy using an energy optimization model. Some strategic indicators have been chosen to analyze the performance of three integration elements: sustainability, efficiency, and energy security. Two scenarios have been defined in the medium and long-term: the business as usual scenario, with no energy or climate targets, and the National Climate Change Strategy scenario, where clean energy technologies and CO2 emissions objectives are considered. The aim of this work is the analysis of some of those strategic indicators’ evolution using the EUROfusion Times Model. Results show that reaching the strategy targets leads to improvements in the integration elements in the medium and long term. Besides, meeting the CO2 emission limits is achievable in terms of technologies and resources availability but at a high cost, while clean technologies targets are met with no extra costs even in the business as usual scenario.

In order to improve the functionality and efficiency of a unified power quality conditioner (UPQC), a DC-link bus integrated photovoltaic charging module is proposed in a UPQC. It can generate power for essential loads apart from providing energy to a DC-link bus. A conventional proportional integral (PI) controller fails to run smoothly in dynamic conditions of the micro-grid, since it has poor capabilities in determining suitable values of proportional gain and integral gain. So, the optimization algorithm for a PI controller based on chaos particle swarm optimization based on a multi-agent system (CPSO-MAS) algorithm was developed in this paper to overcome properties such as intermittent instability in the micro-grid. Through verification by simulation and experiment of UPQC harmonic compensation, it showed that the proposed DC link bus voltage control strategy can be effectively applied to UPQC towards various conditions related to voltage and current distortion. In addition, it proved that the proposed strategy has faster convergence than other algorithms, which enhances the stability of DC-link bus voltage. Hence, the contribution presented in this paper is to provide a novel approach for the power quality improvement of UPQC.