Abstract

The actors involved in the energy network have benefited much from its expansion in recent decades, but the network’s management has become more difficult as a result of the sources’ variability and unpredictability. Thus, it is essential to create models that can manage the current energy resources, which are becoming more and more dispersed. This study provides a new optimization model for participating in local energy markets based on peer-to-peer energy trading, using the twin-delayed deep deterministic policy gradient method and the double-auction trading mechanism. The model is integrated into an ecosystem based on agents, which enables the modeling of energy communities to produce a more plausible implementation scenario. The concept was used in a case study with 30 players in an energy community, and the findings revealed that each member saved an average of 1.54 EUR per week.

Abstract

Battery storage is one of the key technologies in the transition toward net zero. Technology is quickly developing toward making energy grids digital. It is important to observe the condition of the battery in real time. A digital twin of battery storage is a virtual replica of a physical battery, which estimates and analyses battery operation and its state in real time. A battery digital twin consists of data collection, pre-processing, parameter estimation, modelling, and forecasting of the state of charge and state of health of the battery. This paper presents the concept and development of a digital twin for a lithium titanium oxide battery using a physics-based and data-driven model. A physics based Thevenin equivalent circuit model was developed. Experimental data from a lithium titanium oxide battery was collected from a robot application to analyze. Experimental data was used in a model-based state estimation approach of the Kalman filter for the state of charge estimation of battery storage. The state of health of the battery was estimated by the estimation of a decrease in total cell capacity and an increase in equivalent series resistance. The least square method was used to estimate total cell capacity. Equivalent series resistance was estimated using experimental voltage and current data. The output terminal voltage of the model is found to be well compared with experimental data.

Abstract

Most mechanism-based battery models rely on the pseudo-two-dimensional (P2D) model, which simplifies electrode particles as homogenously distributed spheres, lacking the capability to represent the intricate microstructure. To develop a modeling approach aligned with authentic electrode microstructure, a mesoscopic image dataset of a NCM523 cathode is obtained through FIB-SEM, and, subsequently, the 3D microstructure model is reconstructed. Within this volume, we utilize thresholding methods to segment the active material (AM), the carbon-binder domain (CBD), and the pores. Furthermore, critical microstructural parameters, i.e., volume fractions, layer-by-layer area fractions of the three phases, equivalent diameter and sphericity distributions of AM particles, are quantified, Additionally, the pores phase undergoes connectivity analysis, and the tortuosity is also calculated. The results show significant heterogeneity in the electrode microstructure. In particular, only 33% of the particles can be regarded as spheres. This work demonstrates the limitations of the P2D model and can be used as the basis for microstructure-based battery modeling.

Abstract

There is a large amount of residual oil in the high part of the reservoir and the recovery rate is low when the high dip angle and low permeability reservoir is developed by water flooding. The use of CO2 assisted gravity flooding can make full use of the geological characteristics of high dip angle and effectively improve the recovery degree of this kind of reservoir. However, because the influence of CO2 on the physical properties of crude oil is much greater than the influence of water on the physical properties of crude oil, the seepage process of fluid in the process of CO2 flooding is more complex, so it is necessary to study the reasonable production allocation optimization of CO2 gravity flooding in low permeability reservoirs. Firstly, according to the relationship between the pressure at any point in the reservoir and the minimum miscible pressure (MMP), the miscible and immiscible models of CO2 gravity flooding in low permeability reservoirs considering the pressure sensitivity effect are established respectively. Secondly, Python is used to solve the model, and the influence of pressure sensitivity coefficient, formation dip angle, injection- production well spacing and gas injection speed on reservoir allocation is analyzed. Finally, combined with the existing work system method and the production increase multiple method, compared with the calculation results of this model, the tNavigtor software is used to predict the recovery rate of the three methods. The results show that this method has a good development effect on the reasonable production allocation of the reservoir, and the recovery rate of the reservoir is relatively high. The research results have a good reference for the development of this kind of low permeability reservoir.

Abstract

Working temperature is a key issue that affects the performance of proton exchange membrane fuel cells (PEMFCs). Proper thermal management can improve the PEMFC output performance and longevity. To deal with the problems of poor robustness and slow
response time of traditional temperature control curves, this paper establishes a dynamic temperature model of PEMFC. It proposes a particle swarm optimized fuzzy proportional-integral-derivative (PSO-Fuzzy-PID)-based temperature control strategy to achieve dynamic control of the electric reactor temperature. The performance of PSO-Fuzzy-PID temperature control is verified for step load, dynamic load, and variable target conditions, and its effectiveness is compared with that of an ordinary PID controller. The results show that the proposed method has the advantages of fast convergence speed, good dynamic performance, and strong disturbance immunity.
The PSO-Fuzzy-PID temperature controller ensures temperature fluctuations within 0.5°C of dynamic perturbations and is capable of strong tracking control of variable targets.

Abstract

The increasing electrification of energy demand and connection of distributed energy resources pose a high burden on electrical power systems. Future power distribution networks are increasingly vulnerable to disruptions and extreme events with less redundancy of network capacity. This paper proposes a novel coordinated operation scheme to improve power distribution network resilience, assessing the value of operating mobile emergency generators (MEG) in coordination with other flexible resources. Three forms of flexibilities are considered in this research: flexibility from networks, local distributed energy resources, and mobile emergency generators. An optimization model is formulated and demonstrated on a European representative distribution network. Results show the value of mobile emergency generators to provide emergency services through coordinating with existing energy networks and distributed energy resources, thereby contributing significantly to power distribution network resilience.

Abstract

In order to handle the rising global energy demand and lower carbon emissions, hydrogen, a clean and sustainable energy source, is essential. The creation of hydrogen is significant because it has the potential to transform the energy industry by providing a sustainable
alternative to conventional fossil fuels. Deep learning has been a potent tool in recent years, exhibiting outstanding performance and dependability in a variety of domains, including the prediction of hydrogen generation. The optimization of hydrogen production methods to increase their effectiveness and reduce costs has shown promise. The susceptibility of deep learning models to adversarial attacks, which can reduce the precision and dependability of their predictions, is a growing worry. Adversarial attacks entail the purposeful
alteration of input data to trick machine learning algorithms and provide false results. Such attacks may have far-reaching effects on hydrogen production prediction, thereby compromising the efficiency, economic feasibility, and safety of processes. To address these concerns, we conducted an extensive investigation into the susceptibility of deep learning models used for hydrogen production prediction to adversarial attacks using the co-gasification of biomass and plastics datasets. In the co-gasification of biomass and plastics dataset, the dependent variable was the quantity of hydrogen generated, and the independent variables included the gasification temperature, high-density polyethylene (HDPE) and rubber seed shell (RSS) particle size, and the quantity of plastic in the final product. The implemented adversarial attacks include the limitedmemory broyden-fletcher-goldfarb-shanno (L-BFGS), fast gradient sign method (FGSM), basic iterative method, and projected gradient descent method (PGD). This study employed 4 machine learning regression models and a novel deep learning model based on Keras API to analyze the effect of the adversarial attack models under several perturbations including 0.1, 0.2, 0.4, 0.6 and 0.8. From the yielded result, it was evident that the FGSM and PGD adversarial attack has a significant influence on the employed model prediction results while the L-BFGS and the basic iterative method yielded results that will be addressed in our future works. Our research highlights the potential risks of relying on these models for decision-making in hydrogen production processes while also revealing the vulnerabilities of deep learning models in this crucial domain. We also highlight the significance of developing defense mechanisms and security protocols to protect the integrity of deep learning-based predictions in this crucial sector.

Abstract

The term “carbon capture and storage” (CCS) pertains to the technological process of capturing and subsequently storing carbon dioxide (CO2). Our research team is currently engaged in the development of a “metal-air fuel cell” with the objective of effectively capturing CO2 from the atmosphere, regardless of its concentration. In this study, we present the utilization of liquidambar Formosana fruit (LFF) as a potential gas diffusion electrode (GDE) for battery applications. Our findings reveal that this natural GDE exhibits a much higher battery discharge specific capacity density of 1441.4 wh/kg compared to conventional electrodes. Furthermore, the evaluated battery life using the LFF-based GDE is seen to be 7.77 times longer than that of alkaline AA batteries. In addition, the utilization of the advanced adsorption material known as active charcoal resorcinol-formaldehyde (ACRF) has demonstrated a notable enhancement in the adsorption capability of CO2, reaching a value of 4.52 mmol/g at standard conditions of 1.00 bar and 298.15 K. This innovative technology is designed to effectively purify the air by simultaneously absorbing oxygen and CO2, while converting them into direct current (DC) power and valuable chemical compounds. Not only is it capable of capturing CO2, but it also possesses the ability to store and subsequently utilize it. The development of a cost-effective and highly efficient optimal CCS technology solution is a promising pathway towards achieving economic, environmental, and sustainability objectives.