High conversion ratio dc-dc converters have received significant attention in renewable energy systems, primarily due to their necessary high-gain characteristics. This research proposes a high step-up ratio full-bridge resonant cascaded (FBRC) dc-dc converter designed for use in photovoltaics (PV), fuel cells (FC), electric vehicles (EV), and other low-voltage output energy sectors to achieve high voltage gain. This converter contains a full-bridge cell with a boost input inductor, a diode-capacitor cascaded stage that replaces the transformer as a voltage multiplier and an inductor-capacitor (LC) parallel-series resonant network across the FB terminal. One of the strategic features of the converter is its high voltage step-up characteristic combined with lower duty cycle operation that limits the maximum current through the active devices, making it particularly suitable for systems that generate low output voltage. In addition, zero-voltage switching (ZVS) is achieved during the turn-off and turn-on operation of the FB switches from 25% to full load, thereby lessening the switching losses. Moreover, the diminished necessity for passive components and the decreased voltage stress on both active and passive devices lead to the use of smaller and more cost-effective components. The theoretical analysis of the proposed converter is validated using a 500 W laboratory-scale prototype wherein high-performance SiC-based MOSFETs have been utilized as switching devices. It offers reduced ripples, with input current ripple at 5% and output voltage ripple at 0.76%. When the load is 400 W and 60 V as the input voltage, the maximum efficiency is found 95.8% at 400 V output voltage. The proposed dc-dc converter, with its high voltage gain and reduced component stress, shows significant promise for application in renewable energy systems.

This study presents a novel approach to integrating combined cooling, heating, and power (CCHP) systems with water desalination for enhanced energy and water management in educational buildings. Two distinct layouts for CCHP and desalination systems are introduced: one prioritizing efficient power generation to meet electricity demands while providing waste heat for desalination, and the other focusing on balancing cooling and heating loads alongside water desalination. Both layouts are tailored to meet the building\'s energy and water demands while considering operational efficiency. Optimization of these layouts against traditional systems using the bat search algorithm emphasizes economic viability and the gas engine\'s operational flexibility, which are crucial for partial load operation. In addition, an environmental assessment compares the proposed CCHP-desalination systems with conventional setups, assessing CO2 emission reductions and overall sustainability. The evaluation encompasses key environmental metrics, such as resource consumption and the integration of renewable energy sources. Results highlight significant CO2 emission reductions across various gas engine capacities, with notable enhancements in economic and environmental performance achieved by selecting a 3,250 kW gas engine within the CCHP-desalination system. This choice not only maximizes the annual profit but also reduces CO2 emissions by 57% compared to conventional systems, underscoring the system\'s sustainability benefits.

Assessing and analyzing the complementary characteristics of renewable energy (RE) is crucial for designing, operating, and optimizing multi-energy complementary systems (MECSs). However, unified and precise quantitative descriptions of the complementary and stability characteristics among various energy outputs in MECSs have lacked attention and research. Here, this study innovatively proposed a mathematical model for the multi-energy complementarity index (MECI), which considers the complementarity rates of multiple energy outputs during zero and non-zero output periods, and a mathematical model for the multi-energy volatility index (MEVI), which accounts for fluctuation thresholds and the overall volatility of output processes. An evaluation system for multi-energy complementarity characteristics qualitative analysis has been established. The natural output processes of RE at three MECSs in China were applied in the case calculations and verification. Results show that the hydropower rated discharge (Qrating) has a significant negative correlation with MECI, with the MECI decreasing by an average of 0.0046 for every 5 m³/s increase in Qrating. The relationship between the Qrating and MEVI shows an overall negative correlation with local fluctuations. Notably, The MECI of the BeiPan River MECSs exhibits significant seasonal characteristics, with the MEVI in summer (0.378) and autumn (0.395) higher than those in spring (0.132) and winter (0.160), closely related to the natural seasonal variations of the three energy sources: water, wind, and solar. We believe that the study can assist in evaluating and making decisions on the multi-energy complementarity characteristics of RE bases in the future, making a significant contribution to achieving dual carbon goals.

In today\'s world, where the dramatic effects of climate change continue to increase, it is critical to turn from fossil fuels to renewable energy sources to achieve the CO2 emission reduction targets that countries have committed at the Paris Climate Agreement and COP 27 conference. This study analyzes the effects of macroeconomic factors, including economic growth, investments, and unemployment, on the transition to renewable energy in OECD countries. From 1996 to 2020, long-run relationships between variables were examined using advanced econometric methodologies for empirical analysis. For this purpose, panel data analysis, second-generation panel unit root tests, cross-sectional dependence tests, and panel cointegration tests were applied. Economically, in the long run, according to panel CCEMG and AMG estimator, while economic growth enhances the renewable energy transitions, investment does not statistically promote an impact on the renewable energy transitions. Renewable energy transition increases with unemployment. Moreover, the role of the considered variables in the renewable energy transition varies among country-specific. Within the framework of the results obtained, it has been proven that before determining policies for renewable energy transformation, it is necessary to do the necessary groundwork in the economy to increase economic growth and investments and reduce unemployment.

暂无摘要。

The Royal Society and UK Department for International Development supported a consortium of three universities across sub-Saharan Africa and Imperial College London with the aim of developing new knowledge on direct-steam-generation concentrated solar power (CSP) plants and supporting relevant capacity building across the Universities of Lagos, Mauritius and Pretoria. Key research findings from the programme include an improved flow-classification scheme for two-phase, liquid-liquid flows; testing of advanced surfaces with much-improved steady-state heat transfer performance-the commercial nanoFLUX surface showed up to 200% higher heat-transfer coefficients (HTCs) in pool boiling compared with other surfaces with R-134a/R-245fa; first-of-a-kind measurements of transient flow boiling HTCs, which were up to 30% lower in step perturbations than quasi-steady-state expectations in horizontal pipes with R-245fa; error estimation and corrections for laser-induced fluorescence (LIF) measurements, leading to the development of an adapted planar LIF technique with uncertainty <10% for local, instantaneous film thickness measurements in annular flows, and the application of such diagnostic methods to pool, falling-film and flow boiling in pipes; and predictions of an ~80% increase in the net present value of a case-study CSP plant when integrated with solid storage media.

The economic management of lignocellulosic biomass from semi-natural grasslands is now a challenge across Europe. The abandonment of mowing these grasslands leads to the gradual degradation of these ecosystems. This study investigates how chemical and biological factors affect the suitability of biomass from abandoned grasslands for biogas production. We sampled 30 mown and 30 abandoned grassland sites in the Sudetes Mountains (Poland and Czechia). The cover contribution of short herbs was found to be significantly higher in mown grasslands (p < 0.001), while that of tall herbs was more prevalent in abandoned grasslands (p < 0.01). The specific biogas yield (SBY, NL kg-1 volatile solids) is negatively affected by an increased percentage of herbs in the biomass of mown and abandoned grasslands. This is due to the inhibitory effect of herbs on biodegradation, the increase in lignin content and the decrease in cellulose. This study highlights the importance of individual plant species in assessing grassland biomass for area biogas yield (ABY, m3 ha-1) and provides new insights into a field that has not yet been extensively investigated. In mown grasslands, ABY was most positively correlated with grass species (Arrhenatherum elatius, Trisetum flavescens and Festuca pratensis). In abandoned grasslands, the ABY was most correlated with herbaceous species (Galium aparine, Urtica dioica and Chaerophyllum aromaticum) and grasses (A. elatius and Elymus repens). Mown grasslands had significantly higher species richness (p < 0.001) compared to abandoned grasslands, but the number of species sampled did not correlate with SBY and ABY. This study contributes to the development of a sustainable bio-economy by highlighting the need for efficient use of grassland biomass. This approach helps protect semi-natural ecosystems and facilitates sustainable management of renewable resources.

Amidst pressing global environmental challenges, exacerbated by climate change and the imminent threat of global warming, there is a critical need to assess the efficacy of environmental policies. This study centers its attention on the pivotal role of these policies in addressing environmental concerns. Specifically, our research aims to scrutinize the impact of stringent environmental policies on environmental quality under the theoretical underpinnings of environmental Kuznets curve. To achieve this objective, the study collected data from BRICS-T economies over the period of 1990-2020. This study employed the method of moments quantile regression technique for empirical analysis. Our study validates the presence of the Environmental Kuznets curve (EKC hypothesis). Empirical findings reveal the sustained significance of environmental stringency across all quantiles, demonstrating a positive correlation in lower quantiles and a negative correlation in higher quantiles. At lower quantiles, the impact is insignificant initially, but pronounced due to efficiency improvements induced by stringent policies. The effects became negative at middle quantiles, indicating stringent policies might encounter diminishing returns where policy measures start stabilizing ecological impacts. At higher quantiles, the influence of ESI remains significant, reflecting ongoing adaptations in larger economies with higher ecological footprints. This suggests the potential effectiveness of stringent regulatory measures in mitigating environmental impacts and reducing ecological footprints. The identified inverted U-shaped curve signifies that while stringent policies may not inherently enhance environmental health, beyond a certain threshold, they can indeed contribute to its improvement. Our policy recommendation advocates for the widespread adoption and promotion of such stringent measures to safeguard environmental health.

Third-generation biofuels from microalgae are becoming necessary for sustainable energy. In this context, this study explores the hydrothermal liquefaction (HTL) of microalgae biomass grown in wastewater, consisting of 30% Chlorella vulgaris, 69% Tetradesmus obliquus, and 1% cyanobacteria Limnothrix planctonica, and the subsequent upgrading of the produced bio-oil. The novelty of the work lies in integrating microalgae cultivation in wastewater with HTL in a biorefinery approach, enhanced using a catalyst to upgrade the bio-oil. Different temperatures (300, 325, and 350 °C) and reaction times (15, 30, and 45 min) were tested. The bio-oil upgrading occurred with a Cobalt-Molybdenum (CoMo) catalyst for 1 h at 375 °C. Post-HTL, although the hydrogen-to-carbon (H/C) ratio decreased from 1.70 to 1.38-1.60, the oxygen-to-carbon (O/C) ratio also decreased from 0.39 to 0.079-0.104, and the higher heating value increased from 20.6 to 36.4-38.3 MJ kg-1. Palmitic acid was the main component in all bio-oil samples. The highest bio-oil yield was at 300 °C for 30 min (23.4%). Upgrading increased long-chain hydrocarbons like heptadecane (5%), indicating biofuel potential, though nitrogenous compounds such as hexadecanenitrile suggest a need for further hydrodenitrogenation. Aqueous phase, solid residues, and gas from HTL can be used for applications such as biomass cultivation, bio-hydrogen, valuable chemicals, and materials like carbon composites and cement additives, promoting a circular economy. The study underscores the potential of microalgae-derived bio-oil as sustainable biofuel, although further refinement is needed to meet current fuel standards.

It is challenging to accurately model the overall uncertainty of the power system when it is connected to large-scale intermittent generation sources such as wind and photovoltaic generation due to the inherent volatility, uncertainty, and indivisibility of renewable energy. Deep reinforcement learning (DRL) algorithms are introduced as a solution to avoid modeling the complex uncertainties and to adapt the fluctuation of uncertainty by interacting with the environment and using feedback to continuously improve their strategies. However, the large-scale nature and uncertainty of the system lead to the sparse reward problem and high-dimensional space issue in DRL. A hierarchical deep reinforcement learning (HDRL) scheme is designed to decompose the process of solving this problem into two stages, using the reinforcement learning (RL) agent in the global stage and the heuristic algorithm in the local stage to find optimal dispatching decisions for power systems under uncertainty. Simulation studies have shown that the proposed HDRL scheme is efficient in solving power system economic dispatch problems under both deterministic and uncertain scenarios thanks to its adaptation system uncertainty, and coping with the volatility of uncertain factors while significantly improving the speed of online decision-making.