Dynamic Energy Research Articles

Integrative Review-Based Conceptual Modeling: An Agent-Based Modeling Synthesis of Dynamic Energy Tariff Research and Models

Adoption of dynamic energy tariffs by households is crucial for the transition to carbon-neutral energy systems. Influencing the adoption patterns of these tariffs necessitates an examination of the drivers, decision components, and contextual factors influencing household decisions. Few computational models address this comprehensively, often omitting non-financial decision variables. Moreover, methodologically robust integrative reviews on this topic are scarce. To address this gap, this paper develops a concept-centered integrative review methodology aimed at deriving computer models for socio-techno-economic simulations of household adoption of sustainable technologies. The methodology encompasses five sequential phases: Setup, Literature Search, Analysis, Synthesis and Conceptual Model, and Discussion. To illustrate the methodology, it is applied to the case of household adoption of dynamic energy tariffs, resulting in an abstract conceptual model adaptable to local contexts. The review reveals a lack of consensus on modeled tariffs but highlights the significance of tariff complexity, relative advantage, household heterogeneity, and various agent properties. It also identifies potential improvements in model fundamentals, particularly spatial modeling. The developed process model focuses on the stages ‘knowledge’, ‘decision’, and ‘reevaluation’. The article contributes by presenting a comprehensive review scheme and delivering a concept-centered integrative review along with an explicit conceptual model derived from it.

How do Ritz Vectors React to the Imposed Load?

All real structures behave dynamically when loaded or displaced. Additional inertial forces are equal to the force in acceleration using Newton's second law. If the forces or the change of places are applied very slowly, the inertial forces can be ignored and a static analysis can be done. Therefore, it can be said that dynamic analysis is a simple extension of static analysis. In addition, all potential real structures have unlimited degrees of freedom. Therefore, the most critical part of structural analysis is creating a model with a limited number of degrees of freedom that has several almost massless members and several nodes, which can estimate the behavior of the structure appropriately. The mass of the structure can be concentrated in the nodes. Also, for a linear elastic system, the stiffness characteristics of the members can be estimated very accurately - according to the experimental data - although the estimation of dynamic loading, energy loss, and boundary conditions can be very difficult. Therefore, the nonlinear dynamic analysis method is usually used in theoretical studies and is not used in routine designs. So, it is a worthwhile task to propose a simple and computer method for seismic estimation of structures. For this purpose, POA has been introduced. In many cases, we can get more valuable information from POA than nonlinear dynamic analysis, this method is simple and economical and has attracted more attention nowadays.

A Comprehensive Multi-Objective Energy Management Approach for Wearable Devices with Dynamic Energy Demands

Recent advancements in low-power electronics and machine-learning techniques have paved the way for innovative wearable Internet of Things (IoT) devices. However, these devices suffer from limited battery capacity and computational power. Hence, energy harvesting from ambient sources has emerged as a promising solution for powering low-energy wearables. Optimal management of the harvested energy is crucial for achieving energy-neutral operation and eliminating the need for frequent recharging. This task is challenging due to the dynamic nature of harvested energy and battery energy constraints. To tackle this challenge, we propose tinyMAN, a reinforcement learning-based energy management framework for resource-constrained wearable IoT devices. tinyMAN maximizes the target device utilization under battery energy constraints without relying on the harvested energy forecast, making it a prediction-free approach. It achieves up to 17% higher utility while reducing battery constraint violations by 80% compared to prior work. We also introduce tinyMAN-MO, a multi-objective extension of tinyMan for applications with time-varying energy demands. It learns the tradeoff between meeting the application’s energy demand and maintaining the battery energy level. We deployed our framework on a wearable device prototype using TensorFlow Lite for Micro, leveraging its small (less than 120 KB) memory footprint. Evaluations show that tinyMAN-MO operates within 10% of the Pareto-optimal solutions with only 1.98 ms execution time and 23.17 μJ energy consumption overhead.

Buildings in Hot Climate Zones—Quantification of Energy and CO2 Reduction Potential for Different Architecture and Building Services Measures

Reducing energy and associated greenhouse gas emissions in buildings is one of the key aspects of climate change on a global level. To put the building sector on a low carbon development path, policies and adequate financing play a crucial role in each region. In the global South, policies and regulations related to the decarbonization of the building stock are increasingly being implemented. For policy and decision makers, adequate data on the status quo of the building stock, as well as the quantification of energy reduction measures, are essential to make informed decisions on the building regulatory and funding framework. The objective of this study is to provide data-driven insights into the potential for energy and CO2 reduction in buildings across various hot climate zones in the Global South. A simulation-based approach was employed to model five different building types, ranging from residential homes to office buildings, under a variety of architectural and building services scenarios. The simulations were conducted using the dynamic building energy simulation tool EnergyPlus, which assessed the impact of various energy-saving measures under both current and projected future climate conditions. This study concludes that optimizing passive design features, such as improved windows, solar shading, and reflective surfaces, in conjunction with active systems like decentralized cooling units and renewable energy integration, can result in a notable reduction in energy demand and emissions. Our findings provide a robust basis for policymakers to develop targeted energy efficiency strategies for buildings in hot climate zones, which will play a crucial role in achieving climate goals in the Global South.

Comparative analysis of rural communities’ tradeoffs in large-scale and small-scale renewable energy projects in Kenya

AbstractIn Kenya's dynamic renewable energy landscape, characterized by complex policy frameworks, complex land tenure regimes, and diverse community dynamics, this qualitative research investigates the mechanisms and motivations guiding community decision-making when trading land for electricity access within the context of renewable energy projects. Through the lens of the Institutional Analysis and Development (IAD) framework, particularly the rules in use, this study unravels the complexities of rural communities’ trade-offs inherent in both large-scale and small-scale renewable energy projects. Data was collected through in-depth interviews, focus group discussions, and participant observations in rural communities engaged in these projects. The findings offer new insights into communities’ decision-making processes and institutional dynamics in shaping outcomes, with a focus on land rights and land use implications. The analysis highlights the relational nature of the trade-offs, influenced by factors such as land tenure systems, project scale, electricity access, traditional knowledge, and local context, supporting the importance of understanding communities’ diverse roles and positions, power dynamics, and governance structures. Overall, this study contributes to a deeper understanding of the complexities surrounding land-electricity trade-offs in renewable energy projects in rural areas, emphasizing the need for adaptable strategies to address evolving community needs and challenges.

Comparative Study of Time Series Analysis Algorithms Suitable for Short-Term Forecasting in Implementing Demand Response Based on AMI

This paper compares four time series forecasting algorithms—ARIMA, SARIMA, LSTM, and SVM—suitable for short-term load forecasting using Advanced Metering Infrastructure (AMI) data. The primary focus is on evaluating the applicability and performance of these forecasting models in predicting electricity consumption patterns, which is a critical component for implementing effective demand response (DR) strategies. The study provides a comprehensive analysis of the predictive accuracy, computational efficiency, and scalability of each algorithm using a dataset of real-time electricity consumption collected from AMI systems over a designated period. Through extensive experiments, we demonstrate that each algorithm has distinct strengths and weaknesses depending on the characteristics of the dataset. Specifically, SVM exhibited superior performance in handling nonlinear patterns and high volatility, while SARIMA effectively captured seasonal trends. LSTM showed potential in modeling complex temporal dependencies but was sensitive to hyperparameter settings and required a substantial amount of training data. This research offers practical guidelines for selecting the optimal forecasting model based on data characteristics and application needs, contributing to the development of more efficient and dynamic energy management strategies. The findings highlight the importance of integrating advanced forecasting techniques into smart grid systems to enhance the reliability and responsiveness of DR programs. This study lays a solid foundation for future research on integrating these forecasting models into real-world AMI applications to support effective demand response and grid stability.

Energy Market Price Forecasting and Financial Technology Risk Management Based on Generative AI

Abstract: The volatility of global energy markets, particularly electricity prices, plays a crucial role in influencing international economic activities. With the ongoing global energy transition and the push for low-carbon development, predicting electricity prices has become increasingly important for policymakers and market participants. This paper explores the forecasting capabilities of the ARIMA and LSTM models in analyzing electricity prices in the United States, drawing from data spanning 2001 to 2024.ARIMA, a traditional time series model, is valued for its simplicity and effectiveness in capturing linear trends, while LSTM, a deep learning-based model, excels at handling long-term dependencies in complex datasets. This study reveals that while both models offer valuable insights, each exhibits limitations. ARIMA struggles with non-linear patterns and volatility, whereas LSTM tends to underestimate extreme price values. The findings highlight the potential of hybrid models that combine traditional and machine learning approaches to enhance forecasting accuracy in the increasingly dynamic energy market. This research provides essential guidance for improving decision-making processes in the context of the global shift towards clean energy.

Unveiling hidden scaling relations in dissipative relaxation dynamics of strongly correlated quantum impurity systems.

Understanding the time evolution of strongly correlated open quantum systems (OQSs) in response to perturbations (quenches) is of fundamental importance to the precise control of quantum devices. It is, however, rather challenging in multi-impurity quantum systems because such evolution often involves multiple intricate dynamical processes. In this work, we apply the numerically exact hierarchical equations of motion approach to explore the influence of two different types of perturbations, i.e., sudden swapping of the energy levels of impurity systems and activating the inter-impurity spin-exchange interaction, on the dissipation dynamics of the Kondo-correlated two-impurity Anderson model over a wide range of energetic parameters. By evaluating the time-dependent impurity spectral function and other system properties, we analyze the time evolution of the Kondo state in detail and conclude a phenomenologically scaling relation for Kondo dynamics driven by these perturbations. The evolutionary scaling relationship is not only related to the Kondo characteristic energy TK but also significantly affected by the simultaneous non-Kondo dynamic characteristic energy. We expect these results will inspire subsequent theoretical studies on the dynamics of strongly correlated OQSs.

Energy-Aware Register Allocation for VLIW Processors

AbstractThe efficiency of VLIW processors can be improved by reducing the energy consumption associated with accessing the register-file. This paper presents an energy-aware register allocation approach to reduce the dynamic energy consumption of a scheduled program by using an evolutionary algorithm approach and a register access transition energy model. Additionally, the influence of instruction scheduling on energy-aware register allocation is analyzed. By using the proposed energy-aware compiler backend on synthetic applications with random data in the register-file, the dynamic energy consumption of the total processor core for an exemplary VLIW processor can be reduced by up to $$\varvec{21\%}$$ 21 % compared to a heuristic register allocation. In real-world applications from the domain of hearing aids, energy consumption can be reduced by up to $$\varvec{42.7\%}$$ 42.7 % for single applications, and $$\varvec{17.6\%}$$ 17.6 % average across a range of applications.

Influences of Strain Rate and Density on the Dynamic Material Properties and Energy Dissipation Characteristics of Iron Tailing Porous Concrete

Influences of Strain Rate and Density on the Dynamic Material Properties and Energy Dissipation Characteristics of Iron Tailing Porous Concrete

Dynamic splitting tensile properties of high-strength ultrahigh-toughness cementitious composites (HS-UHTCCs)

In this study, the splitting tests were conducted to examine the effect of strain rate on the splitting tensile performance of high-strength ultrahigh-toughness cementitious composites (HS-UHTCCs). Hydraulic machine and split Hopkinson pressure bar (SHPB) were used to investigate the tensile properties of HS-UHTCCs. The strain rates ranged from 10-6 to 10-3 s−1 for hydraulic machine experiments and from 5 to 15 s−1 for SHPB tests, respectively. The splitting tensile strength, tensile stress-strain curves, dynamic increase factor (DIF) and energy absorption ability of HS-UHTCCs were analyzed. The new DIF models proposed for HS-UHTCCs fit well with the experimental data. In addition, the failure modes of specimens and fibers were investigated. The splitting tensile strain was obtained from digital image correlation (DIC) tests. The results indicate that the strain rate has a significant effect on the splitting tensile strength and energy absorption ability of HS-UHTCCs. On the contrary, the average splitting tensile strain showed a decreasing trend with the increase of strain rate. Besides, the DIF model of HS-UHTCCs was proposed with experimental data. The failure modes of specimens changed at high strain rates, transitioning from remaining intact to completely splitting into half. Two failure patterns of polyethylene (PE) fibers included pull-out failure and rupture were observed, while steel fibers experienced pull-out failure.

Development of Promising CDK5 Inhibitors Using Structure‐Based Pharmacophore Modeling, Molecular Docking, and Molecular Dynamics Approach

AbstractCancer is highlighted as one of the deadliest diseases globally, with CDK5 identified as a key enzyme in cancer progression. Despite its potential as a therapeutic target, developing CDK5 inhibitors has been challenging. We used multicomplex‐based pharmacophore modeling on CDK5 complexes, identifying hydrophobic groups, hydrogen bond donors, and acceptors as crucial inhibition features. Validated models were used for the virtual screening of drug‐like natural product databases. Thereafter, the screened candidates were selected to study their binding pattern and binding efficiency in the enzyme. Four molecules were shortlisted and analyzed for electrostatic potential (ESP) energy maps. Molecular dynamic simulations and free energy calculations on the docked complexes revealed stable behavior for all, with three (CNP0299652, CNP0362830, and CNP0009633) showing higher Poisson Boltzmann surface area continuum solvation (MM‐PBSA) binding scores than the reference. These candidates demonstrated drug‐like characteristics, crucial amino acid interactions, favorable electron potentials in ESP plots, stable dynamicigher free energy, highlighting their potential as CDK5 inhibitors.

Grain Size Prediction in SCR420HB Hot Forging: Combining Phenomenological and JMAK Models with Experimental and Numerical Analysis

This study presents the enhancement of a phenomenological model for predicting grain size evolution during the hot deformation of SCR420HB bearing steel. The model now incorporates strain rate and temperature as controllable variables, thereby improving prediction accuracy for various combinations of these factors. Material’s microstructural constants, including initial grain size exponent, strain exponent, strain rate exponent, and dynamic recrystallization activation energy, were determined through FEM-coupled optimization techniques. Accurate flow stress data, crucial for determining the onset of dynamic recrystallization, was integrated to enhance the model's precision. The accuracy of the optimized model was validated by comparing predicted and experimental grain sizes across different stages of the hot forging process, demonstrating improved model performance. Additionally, the study provides insights into the sensitivity of the grain size to different deformation conditions, offering valuable guidance for industrial forging applications. This comprehensive approach ensures the model's robustness and practical applicability in real-world scenarios.

DEBEcoMod: A dynamic energy budget R tool to predict life-history traits of marine organisms across time and space

DEBEcoMod: A dynamic energy budget R tool to predict life-history traits of marine organisms across time and space

Dynamic Energy Management and Control of Networked Microgrids based on Load to Grid Services and Incentive-Based Demand Response Programs: A Multi-Agent Deep Reinforcement Learning Approach

This study has presented the energy management paradigm in a networked microgrid structure based on L2G services and considering incentive-based load response (IBDR) programs and energy market requirements to reduce operating costs, control and restore voltage and frequency index, providing the benefits of subscribers and distribution system operators. In this study, multi-objective functions such as optimal operation based on IBDR structure and energy market requirements, risk assessment, and L2G service approach are configured in the framework of central and local controllers. Optimal operation and risk assessment are analyzed by a multi-task learning algorithm based on multi-objective function and L2G service policies are evaluated based on multi-agent deep reinforcement learning. Control policies are sent by the communication system to the components affecting the optimal power distribution as well as the voltage and frequency controllers. L2G services have been evaluated in different scenarios such as plug-and-play operating conditions, load fluctuations, and operating in island mode. The results of optimal operation based on L2G services show that the IBDR program implementation reduces the total operation cost by 21%. Also, the total operating cost of the proposed framework is 13.97% less than the RL method and 27.8% less than the ANN method.

Anti-plane Yoffe-type crack in flexoelectric material

This work investigates the deformation and polarization fields around a finite anti-plane shear (mode III) crack growing dynamically under steady-state conditions. The leading tip of this finite crack breaks the material while the trailing tip heals it. This fast moving finite crack (referred to as a rupture “pulse” in the geophysics literature) propagates with a constant velocity and with the mechanical and the electrical fields that remain invariant with respect to an observer moving with the crack-tips. This problem belongs to the first type of steady state crack growth problems according to the classification of Freund. The “prototype” problem which refers to an isotropic, body subjected to fracture under tensile loading was first proposed and solved by Yoffe, while finite cracks (or shear pulses) were also analyzed by Freund and by Rice. In the above cases the material was assumed to be linear elastic. Our analysis extends these studies to flexoelectric materials, and it is both theoretical and numerical. It discusses the asymptotic structure of the crack-tip displacement and the polarization fields; it calculates the dynamic energy release rate and presents their dependence on crack-tip velocity. Comparisons are made to the available, classical, elasto-dynamic solutions and to the static case. The influence of the electrical properties of the material on strengthening is also analyzed. Dynamic fracture of flexoelectric materials is of relevance to both the study of earthquake source mechanics and to the analysis of the reliability of micro-electronic devices. This is because both rocks and ceramics are flexoelectric. Indeed, during earthquake rupture processes, dynamic, in-plane shear (Mode-II) and out of plane shear (Mode-III), cracks propagate along faults and exhibit both mechanical and electrical polarization signatures. At an entirely different length scale, flexoelectric ceramics are currently used as sensors and transducers and can experience dynamic shear failure along interfaces when subjected to dynamic loading (e.g. impact.). Failure by dynamic fracture can be detrimental to both their mechanical reliability and electrical functionality.

Design of a hybrid solar and biomass-based energy system integrated with near-zero energy building: Techno-environment investigation and multicriteria optimization

The current research aims to meet the energy needs of a group of residential buildings in Norway with the least amount of carbon dioxide emission and the greatest amount of renewable energy. The supply side based on the renewable renewable-based energy system is planned and scaled using the real-time data use of domestic hot water (DHW), heating and electricity necessary for the buildings. PVT panels are used in the hybrid solar and biomass-based energy systems to produce both the DHW requirements and the electricity production. The digester is used with the heat pump, which generates heat. A double-effect absorption refrigeration system is also utilized to provide the necessary cooling requirements. On the supply side, the management of the heat streams and the redirection of flows are done using a rule-based regulating system. The whole plant's size is supplied, and the dynamic energy simulation is run. The primary deciding criteria for heating the buildings are the costs of electricity and biomass. The whole system is then optimized depending on operating circumstances, and the outcomes are compared to the design point. PVT may be used to create more than 80% of the yearly DHW. Furthermore, summertime radiation accounts for 64.8% of cooling output since it is more intense and may be converted into cooling energy. Digester/CC also contributes 66.55% of the building's heating, showing that the designers should rely on biomass as wintertime energy costs are higher. The parametric study demonstrates that increasing PVT time and tank capacity has varied effects on efficiency and emissions. Also, On winter days, there was a significant drop in renewable energy generation from summer to autumn, from 82.7 MWh to 28.52 MWh. The optimization results show that at the TOPSIS point, the total cost, efficiency, and emission index are 9.73 $/hr, 36.8%, and 7.75kg/MWh, respectively.

Dynamic energy efficient resource allocation in multi-user multi-IRS mmWave 6G networks

Dynamic energy efficient resource allocation in multi-user multi-IRS mmWave 6G networks

High-temperature heat pumps in industrial heating networks: A study on energy use, emissions, and economics

Industrial heat significantly contributes to global primary energy use and primarily relies on fossil-fuel combustion. Recovering residual heat in the industry offers a means to reduce overall energy use. However, the temperature and amount of residual heat available varies widely across industrial sites. Some may have a large amount of residual heat available at relatively high temperatures, while others may not have any residual heat available. Furthermore, for most industries, the residual heat available is at temperatures below 100 °C, while the heat demands are at higher temperatures. Hence, clustering these industries by industrial heating networks, using high-temperature heat pump integration, could offer a promising solution. Research on this concept regarding energy use, emissions and economics is however lacking in the literature. This study however distinguishes and subsequently compares three different heat network configurations in terms of their energy use, carbon emissions and financial appraisal. These configurations include a ‘consumer based heat upgrading network’, a ‘supplier based heat upgrading network’ and a ‘supplier based heat upgrading network with an additional hot water network’. For this purpose a generic methodology is developed, using first and second law principles completed with empirical data for performance and costs. The methodology is applied to data collected from ten companies clustered within the North Sea Port, Ghent (Belgium). The results indicate that the first configuration exhibits the most efficient use of energy and consequently also has the lowest carbon emissions. In addition it also has the lowest levelized cost of heat. This configuration shows, depending on maximum supply temperature of the heat pump, a potential reduction in carbon emissions ranging from 70 % to 80 % in comparison to natural gas boilers. Considering low gas prices, a positive financial appraisal is difficult without carbon taxation. On the other hand, an evaluation during the energy crisis of 2021–2022 indicates that the even without carbon taxation, the levelized cost of heat decreases by 19 % compared to a gas boiler at a maximum heat pump temperature of 160 °C. It was also found that in scenarios of dynamic energy prices a hybrid configuration of a consumer based heat upgrading network and a natural gas boiler could lower the LCOH compared to the individual solutions, by up to 7.6 % compared to the best individual solution. This is done by activating the technology with the lowest operational cost in each time frame.

The rebound law of micro-particle on amorphous alloys under high impact velocities

Compared to their crystalline counterparts, amorphous alloys due to disordered structures are expected to have higher elasticity of deformation. However, in this work, we use the micro-ballistic impact technique to show a smaller dynamic redound of micro-particles on a Zr-based amorphous alloy than that on its corresponding polycrystalline target. We find that the two alloys follow the same rebound law of micro-particle under low impact velocities, but with increasing impact velocity the amorphous alloy exhibits a faster decrease in rebound velocity of micro-particle. This lower rebound results from the easier activations of shear banding in glassy structures, thus contributing to more significant energy dissipation during the micro-particle impact. Further analyses imply that the amorphous alloys and their crystalline counterparts are more favorable in shock wave and projectile protection, respectively. This work is useful in the understanding of the dynamic elasticity and shock energy dissipation of amorphous alloys under micro-particle impacts.