Sustainable Energy Technologies and Assessments最新文献

Chunkwood fuels have a particle size larger than normal chips which enables good drying and storage properties and are therefore appreciated by small-scale users. However, small-scale boilers optimized for chunkwood are not commercially available and the research question is if modern wood chip stokers, selected for having a robust fuel feeding system could feed and combust the fuel. Chunkwood fuel feeding, and combustion tests are performed in a 27-kW and a 240-kW wood chip stoker. Both boilers fulfill Ecodesign emission requirements for carbon monoxide (CO) at nominal load, but further optimization is required to fulfil requirements for dust. Partial load combustion needs to be further studied. There were problems with high stress on the fuel feeding system in both stokers, traced to when excessively large fuel pieces passed the outlet of the fuel bin and when fuel discs became trapped between the auger screw and the lid of the conveyor. Suggestions to solve the fuel feeding problems includes redesign of the fuel bin auger screw to cut oversized pieces, alternatively use of previously developed prototype conveyors that worked. Further studies are required to optimize the fuel feeding system and the combustion performance including a solution for partial load operation.

Achieving food self-sufficiency in hot desert climates requires year-round farming, which is challenging due to extreme weather, water scarcity, and limited arable land. Indoor soil-less farming can mitigate these issues by reducing land and water use but increases operational complexity and electricity needs for cooling, impacting economic sustainability. This paper presents a resource management system using Artificial Intelligence of Things (AIoT) to simplify operations and optimize resources, alongside techno-economic analysis for economic viability. A case study on hydroponic tomato farming in hot deserts demonstrates that beyond a crop yield threshold (24.022 kg/m${}^2$), significantly more energy is required for marginal yield increases (e.g., 18% more electricity for a 0.35% yield increase). Despite higher energy use, the techno-economic analysis shows a net present value increase even with unsubsidized electricity. Thus, optimizing energy alongside water and nutrients is crucial for economic sustainability in indoor farming.

The state of charge (SOC) is a critical state quantity that must be determined in real-time for a battery energy storage system (BESS). It is a prerequisite for the operation of a BESS. However, obtaining the precise value of SOC is challenging due to it being a hidden state quantity. Existing neural network models commonly employ an end-to-end prediction paradigm for SOC estimation, which fails to fully exploit the rich information present in the time-series battery data. Unlike most studies available in the literature, we propose a novel SOC prediction method named CLDMM. This method is the first to apply contrastive learning techniques from the image field to the SOC prediction of lithium batteries. The method utilizes data augmentation, a multi-scale encoder, and multi-layer perceptrons to learn latent representations and mix these with raw data proportionally for downstream predictive tasks. The performance of the proposed method is evaluated using the Panasonic NCR18650PF dataset, and ablation study were conducted. Experimental results show that CLDMM outperforms baseline methods, achieving an average mean absolute error (MAE) of 0.64% and an average maximum error (MAX) of 2.66%.

Leading-edge tubercles, inspired by the flippers of humpback whales, are widely adopted passive flow control devices to enhance the aerodynamic performance of various lifting surfaces. This experimental study investigates the implementation of sinusoidal and triangular tubercles on H-type Vertical Axis Wind Turbine blades to analyze their effects on dynamic stall characteristics. Experimental tests were conducted using a specially designed oscillating rig to replicate blade motion at different reduced frequencies. The results reveal that tubercle blades exhibit a lower stall angle and maximum normal force compared to the baseline configuration. Moreover, the dynamic stall characteristics of tubercle blades are notably smoother, leading to reduced hysteresis losses. A variation in the tubercle amplitude-wavelength ratio further decreases hysteresis, albeit at the cost of reduced normal force generation. At the highest tested reduced frequency of 0.065, tubercles reduce hysteresis by up to 38%. Despite the reduction in normal force, tubercles effectively mitigate the effects of dynamic stall vortices, resulting in smoother stall behavior. The observed reduction in hysteresis can contribute to enhancing the turbine’s lifespan and increasing power production efficiency. This experimental approach provides a cost-effective alternative to more expensive methods for studying dynamic stall characteristics.

Sufficient energy is demonstrated overwhelming superiority in both vehicles and aircrafts. Limited by the energy density, energy storage represented by Lithium-ion battery cannot meet the increasing energy requirements of diverse payloads on solar-powered stratospheric airship for months or years. In this paper, the hybrid fuel cell/battery system for stratospheric airship is presented. The relationship between the real wind field and the demand power is illustrated. Based on the reanalysis of historical wind data, the probabilistic model of demand propulsion power is established and integrated with the training environment. The deep reinforcement learning method is adopted to solve the energy management problem. The prioritized experience replay with extra identifier, which encourages the utilization of high-value samples without identifier, is proposed. Comparative analysis shows the proposed method is effective in determining the management strategy with promising convergence speed. The results demonstrate that changing the SOC reference of the proposed method from 0.4 to 0.7 can result in 5.9% increment in energy consumption. Furthermore, the potential decline of regulation capability of the hybrid system and the corresponding influence on the nighttime energy balance is investigated. The proposed method has reference value for advance alarm of power supply failure during long term flight.

This article introduces an innovative method to foster energy efficiency in the wine industry, focussing on the benchmarking of Energy Performance Indicators (EnPIs). It facilitates the evaluation and monitoring of wineries’ performances over time, allowing for comparison with similar entities, through the categorization of wineries into eleven distinct reference-models based on their process types, enhancing the understanding of energy use. Additionally, three “outsourcing” indices are introduced to identify significant energy consumption in key production stages. The methodology is designed for simplicity, requiring only basic input and product output data, readily available to companies. To validate this approach, a specially-developed data collection form was proposed to 20 Italian wineries, ranging from small producers to large-scale operations. The results illustrate some important limitations in methods that solely rely on EnPIs for energy performance benchmarking, which may lead to inaccurate conclusions. The proposed categorization and outsourcing indices allow for a more comprehensive energy consumption analysis related to the actual production process. Interestingly, some companies, initially perceived as efficient, exhibit instead critical performances, which entails the need for further analysis. Correlation analyses confirm the efficacy of these methodological choices, underscoring the robustness of the proposed approach and proving its potential as an asset for companies, decision-makers, and stakeholders aiming at sustainability improvement, including all those boards involved with certification standards.

In pursuit of more efficient power generation, this study explores a novel hybrid system with a solid oxide fuel cell (SOFC) electrochemically generating electricity from the exhaust gases of a diesel engine operating fuel-rich. The investigation delves into the composition of exhaust gases using a chemical kinetics model, particularly focusing on H₂ and CO generated in the diesel engine at equivalence ratios ranging from 1.0 to 5.0. A model of the SOFC system predicts the highest electrical efficiency of 36.1 % occurs at an equivalence ratio of 2.8, considering 90 % fuel utilization and operating voltage of 0.7 V per SOFC. Notably the combined system’s efficiency exhibits a marked increase as equivalence ratio increases until 2.6, subsequently decreasing primarily due to the reduced concentration of H₂ at higher equivalence ratios. A comprehensive sensitivity analysis is conducted, emphasizing that higher fuel utilization in the SOFC results in higher combined efficiency of the hybrid system. This study also explores the potential of dual fuel combustion within the combined system, showcasing consistent efficiency improvements, especially near an equivalence ratio of 3.2 when utilizing H₂/diesel fuel blends.

This study compares half-cell and full-cell photovoltaic (PV) modules under partial shading conditions relevant to sustainable electric vehicle charging stations. Market-available PV modules are evaluated outdoors at SolarTech${}^{LAB}$ in Milan, Italy, with specific consideration given to partial shading caused by a chimney. Two shading scenarios were implemented: one affecting only the upper half of the PV modules and another affecting both halves. This paper comprehensively analyzes and compares the power-voltage curve, global maximum power point, shading losses, and energy yield. The results show that PV modules with Half-Cell technology perform better in partial shading conditions, with an increased energy yield ranging from 11.3% to 20.7%. This significant improvement highlights the potential of Half-Cell technology in optimizing energy production in environments prone to shading, such as electric vehicle charging stations.

This study assesses existing literature on radiative cooling through bibliometric and keyword analyses, shedding light on both quantitative and qualitative aspects of the subject. The research adheres to a systematic methodology, encompassing query formulation, data extraction, data curation, and analysis, accompanied by author interpretations and discussions. The evaluation encompasses the scrutiny of radiative cooling patents and scientific publications. For patents, trends, and geographic distribution are analyzed, while for scientific publications, a comprehensive overview of subtopics, subject areas, top journals of publication, distinctive trends, geographical distribution, affiliations, document types, and the central focus of previous studies are examined. From the results, the main questions on research diversity, dimensions, dominance, methodological approach, evolution, trends, and commercial relevance among others are discussed.

From this investigation, it was found out that, although research on radiative cooling dates to the 1880 s, it is in the last decade when substantial growth was experienced across multiple disciplines. China and the United States of America emerged as the top contributors in this research domain.