Abstract

Local energy communities enhance energy self- sufficiency and sustainability by promoting local renewable generation and consumption. However, variations in renewable power generation and consumption are inevitable. Using flexible resources is crucial for ensuring uninterrupted energy supply during interruptions, enhancing local sustainability, and improving emergency response. This paper presents a linear model for scheduling the resources of local energy communities in the presence of energy storage and hydrogen systems. To evaluate the impacts of uncertain demand and renewable power generation, robust optimization is used. The model is formulated as a max- min problem, where the inner sub-problems represent the optimal community operations. Furthermore, the worst-case scenarios of uncertain demand and renewable generation are addressed through outer maximization. The strong duality theorem is employed to solve the max-min problem. Moreover, the big-M method is used to develop a mixed integer linear-based model. Finally, the performance of the proposed model is evaluated by a case study and two scenarios. Simulation results demonstrate that the integration of the hydrogen system improves the flexibility of the community and the total energy supply cost. For example, the cost reduction when the uncertainty budget equals zero is 37.99%.

Abstract

Energy flexibility measures play a crucial role in the achievement of carbon neutrality. Renewable energy sources can only be prioritized if energy demand can adapt to the supply. For the targeted use of such measures, a better understanding of the energy markets and the affected systems is essential.
The availability of sustainable energy sources is highly dependent on fluctuating environmental conditions like solar radiation or wind speed. Combined with changing energy demand, this leads to volatility in energy prices and carbon intensity. To react to these fluctuations at an early stage, trends in electricity prices and carbon intensities are urgently needed in addition to weather forecasts, which are already available across the board. This gap shall be addressed by this publication, which presents a machine learning based tool to forecast electricity prices and carbon intensities beyond the German day-ahead market for the following 48 hours. Publicly available market data from the past five years was used to train the machine learning model, which achieved a mean absolute error (MAE) of 20.6 €/MWh for day-ahead energy prices during the first half of 2023. The tool forecasts carbon intensities with a MAE of 47.7 gCO2eq/kWh for the same period. The presented forecasting tool enables planning of energy-flexible operating strategies at an early stage and their implementation at industrial sites.
An air conditioning system as an exemplary industrial use case is used to demonstrate the relevance of the presented forecasting model in the context of energy flexibility. The utilization of the forecasting tool and the development of energy-flexible operating strategies resulted in potential savings of 12.33 % in operating costs and 9.94 % in carbon emissions.

Abstract

Electric vehicles and charging stations are pivotal tools in the endeavor to decarbonise the transportation system. The connection between the adoption of electric vehicles and the deployment of charging stations is widely recognised as a “chicken-and-egg” dilemma. This term describes a situation where the low adoption of electric vehicles impedes the installation of charging stations, and conversely, the limited availability of charging stations dampens drivers’ enthusiasm to switch to electric vehicles. To gain insights into this intricate relationship between consumers’ choices regarding electric vehicles and infrastructure investors’ decisions on charging station expansion, this study employs an evolutionary game model. This model not only captures the dynamic interactions involved but also considers the internal and external factors that influence these decisions. Leveraging this model, this paper analyses the impact of incentive policies on the adoption rates of electric vehicles and charging stations.

Abstract

In the face of current global energy challenges and the growing significance of energy efficiency and carbon neutrality, the optimization of control strategies for industrial energy supply systems has gained importance. Deep Reinforcement Learning (DRL) presents a promising opportunity for control strategy optimization, leading to substantial reductions in operating costs and carbon emissions. The lack of interpretability of such black box models however is hindering broader application in practice.
This paper introduces an Interpretable Machine Learning approach aiming to reconcile advanced optimization with interpretability. Initially, DRL algorithms are deployed on a simulation model of an industrial energy supply system for control strategy optimization. Training and validation data sets of the DRL based control strategy are then used to train Decision Trees. Being intrinsically interpretable, Decision Trees enable representation of conditional logic and the extraction of rules for both local and global interpretability. Through optimization procedures such as depth limitation and input feature selection, the Decision Trees can either operate as controllers for energy supply systems directly or as tools to adapt the conventional rule-based control strategy.
Tested successfully on a white-box model – the conventional rule-based control strategy for an industrial energy supply system – the proposed approach correctly identifies the underlying rules of the strategy. When applied on a black box model – the DRL based control strategy of the exemplary use case – in various experiments this paper showcases promising results as well as remaining challenges of the approach.

Abstract

Climate change has been a pressing global issue and people are experiencing more frequent and severer extreme weather events. In South Korea, extreme heat has been drawing much attention in recent years due to its significant impact on public health and energy and water consumptions. Extreme heat is particularly exacerbated by the urban heat island (UHI) effect in cities. Many studies have examined the relationship between urban form factors and surface UHI empirically. But few of them have studied how UHI changes in response to an extreme heat event, termed heat resilience in recent studies. Additionally, most of current studies used traditional regression models assuming linear relationships, which may not be the case for UHI effects. To address this gap, this study aims to identify nonlinear relationships between urban form factors and land surface temperature (LST) and heat resilience, using machine learning methods. The study adopted the gradient boosting decision tree (GBDT) models to predict LST and heat resilience and compared the findings with those using spatial regression models. The results suggest that the GBDT model has a higher prediction power than traditional regression, and the GBDT models show that the urban form factors have nonlinear relationships with LST and heat resilience under extreme heat. The findings of the study provide valuable guidance for urban planning practice aimed at prioritizing planning elements in urban form toward heat-resilient cities.

Abstract

Thermal striping is a phenomenon that the temperature constantly changes of the fluid and the contact solid structure, due to the mixing of fluids with different temperatures. When thermal striping occurs above the core of a nuclear reactor, it usually leads to fatigue failure of the solid structure near the center column. Existing research mainly focuses on the mixing phenomenon of different temperature fluids and temperature fluctuations in fluid by using CFD simulations. However, the experimental study on the temperature fluctuation characteristics of the surface and interior of the structure is limited. In this study, a thermal striping experiment setup based on coaxial jet is design and built. The temperature of the plate structure surface and interior was measured when cold and hot fluids were mixed in coaxial jet. The temperature distribution and fluctuation characteristics associated with thermal striping phenomena was analyzed.

Abstract

The LiNixCoyMnzO2 (NCM, x+y+z=1) cathode materials for lithium-ion batteries (LIBs), are at the forefront of advancements in various emerging technologies within the power and energy storage sectors. However, the diffusion-induced stresses resulting from lithium-ion intercalation and deintercalation can lead to mechanical damage in NCM polycrystalline particles and consequently impact the overall battery cycle life. This study employs finite element simulations and experiments to investigate the initiation and evolution of intragranular and intergranular cracks in NCM polycrystalline particles under different charging-discharging conditions. This work contributes to a deeper understanding of the mechanical responses of NCM materials within LIBs, it highlights the potential for optimizing battery cycling conditions to mitigate crack-related degradation, thereby extending the lifespan of lithium-ion batteries in practical applications.

Abstract

Amid the escalating concerns of climate change and the mounting research papers on carbon neutrality and waste-to-energy solutions, comprehending crucial knowledge and technological trends in the chemical and energy sectors has become challenging. This study presents a novel approach combining large language models (LLM) and knowledge graphs (KG) to facilitate AI-supported knowledge retrieval. This work establishes a knowledge graph in the biochemical industry with 6,461 nodes and 8,969 relationships, emphasizing material and energy flow integration with the autonomous AI workflow. The graph’s node attributes and relationships are analyzed using cosine similarities, with the capability to trace back to original literature through DOIs. This method not only underscores the relevance of node pairs in the graph but also links their similarities to the physical and chemical properties of materials. To sum up, this work provides an AI-enhanced tool that enables researchers and decision-makers to quickly build a knowledge base, learn about trends, and gain insights into specific fields.

Abstract

The power density and thermal efficiency of the electrochemical Brayton cycle (EBC) have been addressed through the conservation of energy. However, it remains a gap to determine the energy density of charging and discharging processes, a crucial aspect tied to energy storage/release density in EBC systems with integrated energy storage. This study derives the energy storage/release density and net energy density of the ideal EBC system, confirming key parameters including the change in state of charge ΔSOC and the dimensionless figure of merit αqv/cv (α: temperature coefficient, qv: specific charge, cv: specific heat) are confirmed. The theoretical energy storage/ release density approximates 10 W h L-1, and the net energy density is expected to reach 1.0 W h L-1. This study provides a valuable framework for assessing the energy storage performance of EBC systems.

Abstract

The United Kingdom has ambitious net zero targets of 50 GW of offshore wind and 10 GW of low carbon hydrogen by 2030. The increased uptake in renewable energy technologies requires significant infrastructure reform to enable connection to the existing energy system. This paper aims to outline a novel methodology capable of optimising large-scale projects with multiple energy vectors to maximise the highest return on investment and plan for the future. A novel framework is established that takes three key design parameters into account: cable landfall, offshore substations, and green hydrogen production.