Abstract

Photovoltaic (PV) systems are being increasingly adopted in buildings, but the installed capacity often fails to account for uncertainties during the usage phase. A novel probabilistic optimization model based on the reliability level is applied to size the PV arrays on a residential building. The minimum total installation area of the PV panels is treated as the objective of the optimization. To consider the impacts of various uncertainties during the installation and continuous operations of PV system, a design variable, installation angle, and multiple environmental variables, including annual solar irradiation, ambient temperature, and attenuation rate on the system efficiency, are integrated into the model. A unique statistic profile of uncertainty is established for each of the variables based on the verifications from the documented literature. In the meantime, to concurrently ensure the sufficient electricity supply provided by the PV system and mitigating the system degradation, constraints on the total power generation, self-use power ratio, and temperature increase were formulated. By establishing different levels of confidential reliability and magnitudes of uncertainties associated with design and random variables, the optimized size and installed angle of PV exhibited significantly different results.

Abstract

With the global de-carbon goals, criticisms regarding the associated “carbon transfer” within The Belt and Road Initiative (BRI) oil and gas cooperation have emerged. These critiques highlight the potential for the BRI to facilitate the long-term transfer of carbon-intensive industries, such as the upstream and midstream sectors of oil and natural gas, from China to signatory countries. This paper aims to investigate and compare the factors influencing the carbon emissions of host countries. Firstly, it examines the effect of external BRI policy on emissions in signatories. Secondly, it explores the impact of host country’s internal sociocultural characteristics that may shape its inclination to emission reduction. Thirdly, the study conducts heterogeneity analysis based on the first two aspects. For example, it examines whether gas cooperation is more effective in emission reduction than oil cooperation. Additionally, it assesses whether downstream trade contributes more to emission reduction compared to midstream & upstream business. Moreover, the study investigates whether different cooperation models in upstream business exert varying impacts on carbon emissions. The findings indicate that the internal sociocultural characteristics of the host country exert a considerably greater influence on GHG emission in the energy sector compared to China’s BRI. Moreover, variations in the effects do varies between oil and gas; downstream and up-midstream business; as well as various contract modes employed in upstream activities.

Abstract

This research work explores the integration of
Peltier module in hybrid source thermal desalination
system for efficient surface temperature measurement.
The study investigates the performance of Peltier
module that can be used to monitor temperature
differentials between the evaporation and condensation
chambers and surface temperature of the condensation
chamber for the developed desalination unit, enhancing
the efficiency of desalination process. An analytical
model is derived to link the effective temperature
difference within the Peltier module to the load current
measured by the true rms multimeter. The module
parameter is estimated for the intrinsic and extrinsic
values of seebeck coefficient and resistance for the
loading condition. The study indicates that the slope of
change of internal temperature difference with respect
to the load current is 0.8 oC/A at the boiling point
temperature. The hardware setup is designed and
developed to verify the analytical value obtained from
the derived analytical expression of parameter of the
Peltier module. The hardware results are being analysed
and will be presented in the future work.

Abstract

Incorporating Peltier heat pump techniques into
existing energy systems and designing new large-scale
applications require an understanding of the modelling
and analysis of the Peltier module at the system level.
Developing a specific application model for the energy
conversion process is crucial for understanding and
predicting the performance of Peltier-based systems.
From the point of view that the thermal domain of
desalination system can be easily translated to electrical
network system for heat transfer analysis. In the present
research work a thermal and electrical submodel of
Peltier module is developed from the fundamental
physics principles with circuit perspective based on the
governing of the operation of the Peltier modules. The
two submodels are interconnected, reflecting the
bidirectional coupling between the thermal and
electrical aspects of the Peltier module. In particular,
the
development and explanation of thermal
capacitance associated with the heat capacity of material
of the Peltier element is also described. Further, the
model is extended for the application in heat pump in
desalination system. The verification of the developed
model simulation is carried out with the Peltier module,
mounted on a heat sink and cold thermal mass, functions
to actively pump the heat produced from the heat source
in cyclic manner. A Proportional-Integral-Derivative (PID)
controller is incorporated to the developed model for the
Peltier module for the further verification of system
temperature control within 1oC range for the set point
temperature. The Experimental arrangement is designed
and developed for the desalination system with the
Peltier module for the validation of model.

Abstract

Using passive solar energy for heating and lighting, greenhouses can be maintained at significantly lower energy consumptions. The design of the geometry of greenhouses, on the other hand, serves as a crucial factor that determines the effectiveness of solar energy absorption and thermal insulation depending on different indoor thermal conditioning requirements. This study conducts an analytical study regarding the impacts of greenhouse geometry on the intake of solar radiation. When altering the slope, curve, symmetry and the transparency of the greenhouse envelope, the light transmittance and the solar accumulation are found to be significantly varied. The overheating issue in summer and evaluated under different greenhouse configurations. The results of this study can be used to optimize the design of traditional greenhouses in different climate regions.

Abstract

Fluidized bed combustion (FBC) presents numerous advantages over conventional combustion technologies, making it a superior choice for various applications. The application of Computational Fluid Dynamics (CFD) is crucial in understanding and optimizing the performance of fluidized bed combustors. CFD techniques allow for the detailed simulation of complex combustion processes and the hydrodynamics within the fluidized bed, including interactions between solid particles and gas phases. The Eulerian-Eulerian model, which treats gas and solid phases as interpenetrating continua and incorporates the kinetic theory of granular flow, is particularly effective in capturing these dynamics. In this study, we investigated the co-combustion of poultry litter with natural gas in an advanced swirling fluidized bed combustor, focusing on the pressure drop associated with poultry waste combustion. Key parameters examined included the chamber’s primary air flow rate, poultry litter flow rate, and sand mass. The results indicated that increased primary air mass flow rate leads to a higher pressure drop across the combustion chamber. Excessive airflow can potentially force sand out of the chamber, highlighting the need for optimal airflow regulation. Similarly, a higher sand mass within the chamber also results in a greater pressure drop, suggesting the necessity of balancing the bed material mass to maintain efficient combustion without excessive pressure drop. The study underscores the importance of understanding the interplay between primary air flow rate, fuel feed rate, and bed material mass. Proper control and optimization of these variables are crucial for maintaining stable combustion and preventing operational issues such as bed material ejection. By accurately modeling and analyzing the effects of various operational parameters using CFD, it is possible to enhance fluidized bed combustors’ efficiency, stability, and safety. This research demonstrates explicitly that managing the primary air flow rate and sand mass is essential for controlling the pressure drop and ensuring the effective co-combustion of poultry litter with natural gas.

Abstract

Because natural gas emits less carbon than other fossil fuels, it holds promise as a green energy transition fuel. However, the overall carbon footprint of natural gas is significantly elevated by methane emissions that occur during its production and transmission (Cusworth et al. 2022). Methane “super-emitters,” while comprising only about 1% of sites, are responsible for the majority of oil- and gas-sourced methane emissions, making their detection and mitigation critical in reducing the climate impact of natural gas and in meeting national and global sustainability goals (Sherwin et al. 2024). Yet, despite advancements in detection, significant uncertainties remain regarding the size, frequency, and duration distributions of methane emissions (e.g., Frankenberg et al. 2016, Cusworth et al. 2022, Chen, Sherwin et al. 2022, Conrad et al. 2023, Johnson et al. 2023, Sherwin et al. 2024) underscoring the need for comprehensive emissions inventories segmented by basin across the US. Airborne surveys are well-suited for collecting data to build these comprehensive, basin-level inventories because they allow for extensive spatial coverage, and have the spatial resolution, and the sensitivity to pinpoint individual methane sources. As remote sensing technologies enable rapid basin-scale surveys, it is imperative to establish scientifically and statistically robust standards to generate reliable and actionable emissions inventories.

Recent work has shown that differences in airborne sampling strategies, detection technologies, and analysis can lead to large differences between survey conclusions if not correctly accounted for (Chen et al. 2024). This elevates the importance of incorporating proper sampling and analysis techniques when designing a methane emissions monitoring campaign to produce accurate results and facilitate cross-study comparisons. In this paper, we describe a survey strategy designed using the latest conclusions from the literature to align results from different aerial surveys. We identify several sampling and analysis principles, including large sample sizes, balanced sampling across oil and gas production, careful survey area definition, and a unified protocol for analysis, to be vital to producing an unbiased estimate of basin-scale emissions. We present results from a Department of Energy-funded project that deployed this survey strategy in two understudied oil and gas- producing regions in the United States: the Haynesville Basin in Texas and Louisiana, and the Woodford Shale in the Anadarko Basin in Oklahoma.

Abstract

Large scale grid expansion planning studies are essential to rapidly and efficiently decarbonizing the electricity sector.
These studies help policy makers and grid participants understand which renewable generation, storage, and transmission assets should be built and where they will be most cost effective or have the highest emissions impact. However, these studies are often either too computationally expensive to run repeatedly or too coarsely modeled to give actionable decision information. In this study, we present an implicit gradient descent algorithm to solve expansion planning studies at scale, i.e., problems with many scenarios and large network models. Our algorithm is also interactive: given a base plan, planners can modify assumptions and data then quickly receive an updated plan. This allows the planner to study expansion outcomes for a wide variety of technology cost, weather, and electrification assumptions. We demonstrate the scalability of our tool, solving a case with over a hundred million variables. Then, we show that using warm starts can speed up subsequent runs by as much as 100x. We highlight how this can be used to quickly conduct storage cost uncertainty analysis.