Abstract

In recent years particular attention has been emphasized to different diversified means of energy production for the security of supply, availability, reliability, and robustness of electrical energy systems. The attention rests on the most effective preventive organization at the cost of an economic investment which will be all the more profitable as the consequences of the breakdown are significant. Given the random nature of the failures of the existing electricity distribution networks and the intermittency of production, the decision to invest preventively in the electricity system is similar to exposure to risk. Will the network manager then take the risk of not investing in a preventive policy, saving investment, but under the threat of a failure requiring a more costly corrective intervention? An expected utility function models the taste and/or aversion to risk. We use the model of von Neumann and Morgenstern, indicating that rational choice amounts to maximizing the expected utility. In this paper, a new standard methodology of uncertainty modeling techniques for decision making process is proposed. The paper provides a decision support tool to the decision maker that allows him to choose a corrective or preventive policy that best suits the electrical system and his preferences. A decision support tool is provided and allows choosing a corrective or preventive policy that best suits the electrical system and the preferences of the decision maker. The objective is to model risk aversion to the choice of a policy leading to the integration of renewable energies into the electricity system. We take into account the probabilities of the occurrence of failures within the framework of a defined policy, the associated costs, and the degree of risk aversion of the decision-maker. Based on these elements, we provide a policy proposal that is the best compromise for the decision-maker. Several examples are treated and allow one to become familiar with the integration of risk aversion modeling to define a preventive policy for the power supply system.

Abstract

As solar energy gains prominence, the demand of photovoltaic (PV) panels has increased. To assess photovoltaic power capacity, it is vital to derive accurate distribution information of PV panels. Common cost- effective approach involves deep learning technique such as semantic segmentation. However, available datasets remain scarce and expensive. Fortunately, Generative Artificial Intelligence (Generative AI), specifically text-conditioned diffusion models, exhibits the potential to automatically generate high-resolution synthetic images paired with annotations created from cross-attention maps, serving as training datasets for photovoltaic panel semantic segmentation. In this study, we employ the off-the-shelf Stable Diffusion model to explore the power of Generative AI to address dataset limitations and curtail data collection and annotation expenses. From the outcomings, we believe that Generative AI will play a revolutionary role in renewable energy systems.

Abstract

Accurate power load forecasting can significantly reduce the operating costs of the power grid and is an important guarantee for the stable and efficient operation of the power system. However, the randomness and volatility of short-term power loads are strong, and traditional load forecasting methods are difficult to grasp the patterns of short-term load changes. In order to predict short-term power load more accurately, this paper proposes a short-term power load prediction method based on convolutional neural networks and short-term memory networks (CNN- LSTM), and combines down-sampling processing and time features to extract features from the dataset. The prediction results are compared to improve prediction accuracy. By comparing and analyzing the prediction accuracy of the model based on measured data of public buildings, the reliability of the proposed model was verified, and it was confirmed that its application effect in the field of short-term power load forecasting is good, which can provide theoretical basis and technical support for power planning in the power supply department.

Abstract

Given the escalating carbon emission crisis, there is an urgent need for large-scale adoption of renewable energy generation to replace traditional fossil fuel-based energy generation for a smooth energy transition. In this regard, distributed photovoltaic power generation plays a crucial role. Predicting the GHI in advance to predict the power of photovoltaic power generation has become one of the methods to solve the grid-connected stability in recent years, which enables the grid staff to dispatch and plan in advance through the forecast results, reduce fluctuations, and maintain grid stability. In this study, we present a deep learning-based method to assess photovoltaic output potential by solar irradiance forecasting and rooftop segmentation. First, we utilize a multivariate input Transformer model that incorporates various data to predict GHI; Second, using remote sensing images to train Swin-Transformer to identify the potential area of rooftop photovoltaic panel; Finally, the potential assessment was achieved by calculating the array output through the GHI and area data we generated in the first two parts. Our evaluation methodology and results provide technical support for the transition of energy structure.

Abstract

This research paper presents the review of two renewable energy strategies, Feed In Tariff (FIT) and Renewable Portfolio Standards (RPS), in China and other countries. The two FIT schemes, which are Erneuerbare Energien Gesetz (EEG) in Germany and Benchmark Grid Electricity Prices (BGEP) in China, are compared. On the other hand, three RPS schemes, which are California’s RPS Program, Australia’s Renewable Energy Target (RET), and China’s Weighting of Responsibility for Renewable Energy Electricity Consumption (WRREEC) framework are reviewed and discussed. Based on the review and findings, recommendations on the future development of renewable energy strategies in China are presented.

Abstract

Carbon microspheres (CMSs) derived from corn starch were successfully obtained under hydrothermal carbonization (HTC). A 6-blade Pitched blade turbine (PBT) was employed to introduce the controllable shear rate and F127 as the soft template was ultized to find out their effect on the CMSs. The combination of shear rate and soft template method, introducing a controlled flow pattern based on fluid dynamics, provides an alternative to obtain CMSs with wider distribution of pore size and more mesopores, thereby enhancing their adsorption capability. The products were characterized using Scanning Electron Microscopy (SEM), Thermal Gravimetric (TG), and Brunauer-Emmett-Teller (BET) model. The results revealed that the morphology and structure of CMSs were strongly influenced by the shear rate induced by the rotated PBT during the hydrothermal reaction. At a rotating speed of 60rpm, the CMSs showed the maximum CO2 adsorption rate at 25 °C and 0.15 bar CO2, with a specific surface area of 475 m2/g and an average pore diameter of 2nm. Furthermore, the diameter of CMSs decreased with an increase of rotating speeds. The assembly of F127 resulted in products with low degree of sphericity forming a chain, which significantly increased the BJH Adsorption/Desorption average pore width (4V/A). Hence, the controllable shear rate offers an alternative approach to explore the property of CMSs.

Abstract

The kinetic law of natural gas hydrate (NGH) growth is highly significant for the application of NGH technology. In this work, we observed the process of methane hydrate film thickening at the gas-liquid interface by X-ray Computed Tomography (X-CT). Our results showed that the methane hydrate film growth process at the gas-liquid interface can be divided into three stages. Firstly, the methane hydrate film formation process was controlled by heat transfer; secondly, the thickening process was controlled by mass transfer. Above all, the transport of water below the hydrate film caused fractures in the hydrate film, and new hydrate grows at the fractures. The proposed hydrate film formation and growth mechanism at the gas-liquid interface in this study provided theoretical support and research methodology for future studies of the gas-liquid interface growth process of NGH.

Abstract

The flexible adjustment of the air conditioning system can help smooth the load curve and absorb renewable energy. However, the quantification of building air conditioning flexibility (Air-conditioning Virtual Energy Storage，AVES) is still in its early stages. This study takes the climate and architecture of Shanghai as an example to study the changes in VES characteristics of air conditioning under different fence structures. Based on regression analysis and correlation analysis, the main factors affecting the VES of air conditioning are analyzed and quantified. This study can provide theoretical reference for the study of flexible air conditioning regulation in specific regions.

Abstract

High-pressure environments have a significant impact on anaerobic oxidation of methane (AOM), increase in anaerobic methanotrophic euryarchaeota (ANME) growth rate at higher methane pressures. This study investigated the effect of temperature on the anaerobic methane-oxidizing and sulfate-reducing (AOM-SR) activities by a highly enriched ANME-2c community (high-pressure environments). The ANME-2c-enriched biomass was incubated at different temperature for 75 days. The most favorable condition for AOM-SR activity in the studied communities was set as the in situ temperature (4°C), suggesting that the studied ANME-2c is well adapted to conditions similar to those of its origins. Moreover, the dispersal process of the species is promoted by 4°C, whereas the symbiotic network is decreased.

Abstract

The coal-steam gasification method has been proven to be suitable for power generation. However, the low concentration of CO2 generated in the external coal combustion heating process causes significant CO2 capture energy consumption. As a carbon-neutral fuel, biomass combined with coal-steam gasification could contribute to a reduction in CO2 emissions. In this work, using the typical herbaceous biomass as the heating fuel, the energy efficiency and carbon emissions of the biomass-heating-coal-gasification power generation system (BHCG) are systematically studied. The results indicate that the coupling utilization of biomass and coal can significantly improve the emission reduction characteristics of fossil energy systems. In the no-carbon capture scenario, the power efficiency of BHCG is 50.66%, which is 5.31% higher than GE gasification power generation system. When the carbon capture process is introduced, the power efficiency of BHCG is 46.40% with -133.90 kg CO2/MWh. The biomass heating coal-steam gasification method can utilize the negative carbon utilization of fossil fuels and has high energy efficiency.