[Objective] This study aims to reveal the influence law of temperature gradient on the insulation electric field distribution characteristics of the ±800 kV converter transformer valve-side bushing under high-current operation, thereby improving the scientificity and reliability of bushing insulation design in ultra-high-voltage direct current (UHVDC) transmission projects. [Method] Most existing studies on converter transformer bushings focus on their thermal or electric field characteristics, typically using 2D or 3D models to analyze steady-state temperature rise and electric field distribution under constant temperature. However, the nonlinear characteristics of dielectric parameters of insulating materials with temperature and electrothermal coupling effects are often overlooked. To address this gap, this study proposed a 2D coupled simulation method considering the temperature dependence of materials, and explored the evolution mechanism of insulation performance from the perspective of electrothermal interaction. A 2D finite element model was established to simulate the structure of a ±800 kV valve-side bushing, with measured temperature-dependent conductivity data of resin-impregnated paper (RIP) from existing literature incorporated. Based on COMSOL Multiphysics, the temperature field under current-carrying conditions was calculated, and electrothermal coupled electrostatic field analysis was further conducted to quantitatively reveal the influence law of temperature gradients on electric field distribution. [Result] Simulation results show that under a 6.736 kA DC current, the maximum temperature of the bushing reaches 130.7 ℃, mainly concentrated in the middle-lower part of the conductor rod and the flange area, with a radial temperature gradient of up to 37.7 ℃ at the capacitor core near the flange. The temperature gradient causes significant changes in the spatial distribution of material conductivity, resulting in the radial electric field strength in the capacitor core shifting from the original 6.2~7.6 kV/mm to 2.8~12.4 kV/mm. The maximum electric field strength increases by 63.2%, and the electric field distribution pattern reverses from "weak in the middle and strong at both ends" to "weak inside and strong outside". [Conclusion] The temperature gradient significantly affects the spatial distribution of conductivity in the capacitor core material, resulting in electric field distortion. The two-dimensional electrothermal coupled simulation quantitatively reveals the interaction mechanism between the temperature field and the electric field. The research findings provide a theoretical basis and technical support for the insulation design optimization and performance verification of ±800 kV converter transformer valve-side bushings.
Florian Redder, Philipp Althaus, Eziama Ubachukwu
et al.
Abstract Successful adaptation to climate change requires resilient, reliable, and efficient energy systems. To unlock energy efficiency potentials in buildings, an intelligent, user-centered approach is vital. However, this requires handling diverse data on the energy system. Therefore, technologies for harmonizing, storing, and visualizing data, as well as managing physical devices and users are needed. This work assesses existing and required Information and Communication Technologies (ICT) for intelligent building energy system operation. We propose an intermediate architecture based on Internet of Things (IoT) core principles and feature insights from its implementation within the Living Lab Energy Campus (LLEC) at Forschungszentrum Jülich. We present an approach for integrating existing ICT components, such as building energy metering and central Heating, Ventilation and Air Conditioning (HVAC) management, and propose a comprehensive data collection and distribution infrastructure. We establish IoT-enabled applications for energy system monitoring, user engagement, advanced building operation, and device identification and management. We evaluate our ICT setup through functional and performance assessments. We find that heterogeneous data can be reliably collected, distributed, and managed using standardized interfaces, state-of-the-art databases, and cutting-edge software components. For the buildings operated through the ICT infrastructure, data transmission availability is above 98.90 %, mean time to repair (MTTR) is less than 2.68 h, and mean time between failures (MTBF) is in the range of 242.67 h to 1092.00 h, evaluated over a period of three months. Our approach promotes the early real-world adoption of intelligent building control prototypes and their sustainable development. We demonstrate the proposed ICT setup through an experimental study that applies a cloud-based Model Predictive Controller (MPC) to a real building space. Our results provide a comprehensive discussion of the required ICT setup for intelligent building energy system control in real-world environments, and highlight important design strategies that reduce the conceptual overhead and facilitate implementation in similar projects.
Syed Ashraf Ali, Sohail Imran Saeed, Jehanzeb Khan
et al.
Abstract This paper proposes a Hybrid Stackelberg-Markov framework for adaptive load scheduling and dynamic pricing in smart grids. The framework integrates a Stackelberg game to model the interaction between the utility and consumers with a Markov process that captures consumer behavioral dynamics. By combining economic incentives with behavioral adaptation, the model achieves a balance between reducing the peak-to-average ratio (PAR), lowering consumer costs, and increasing utility profit. Simulation results demonstrate that the proposed approach reduces PAR by 43% compared with the baseline, decreases average consumer costs by 28%, and improves utility profit by 10%. The behavioral state analysis further shows that most consumers transition into the Content state, indicating long-term acceptance of dynamic pricing strategies. Moreover, the computational analysis confirms faster convergence and reduced run time compared with conventional demand response schemes. These results establish the proposed framework as a scalable and practical demand response solution for modern smart grids.
Md Shafayet-Ul-Islam, Abdul Kuddus, Md Kabiruzzaman
et al.
Copper-based chalcogenide quaternary semiconductors have emerged as promising candidates for next-generation photovoltaic (PV) devices, owing to their unique electronic and photonic properties coupled with environmentally friendly compositions. This study explores the potential of copper-based absorber materials, specifically Cu2FeSnS4 (CFTS), as an absorber in heterojunction solar cells with Cu-/Ni-metal oxides back surface field (BSF) and SnS2 buffer layers using the SCAPS-1D Simulator. Initially, we assess the performance of CFTS-absorber solar cells and compare the key photovoltaic metrics with those of other Cu-based semiconductors including CuInxGa(1-x)Se2 (CIGS), Cu2ZnSnS4 (CZTS), Cu2CoSnS4 (CCTS), Cu2NiSnS4 (CNTS), Cu2BaSnS4 (CBTS), Cu2MnSnS4 (CMTS), to identify the most promising absorber. Subsequently, we optimize the layer properties, including active layer thickness, free-carrier concentration, bulk and interface defect density, and carrier recombination in potential CFTS. Further, we examine the impact of defects, and carrier recombination, including radiative, Shockley-Read-Hall (SRH), and Auger recombination. These detailed studies yield improved and competitive photoconversion efficiency, (PCE) of 27.31% (compared to 24.68%, without BSF) with open circuit voltage, (VOC) of 1.36 V, short-circuit current density, (JSC) of 22.28 mA/cm² and fill factor, (FF) of 90.47% for Cu2O, whereas the PCE of 26.97% with VOC of 1.07 V, JSC of 28.82 mA/cm² and FF of 86.91% for NiOx BSF layer in Au/Mo/BSF(Cu2O and NiOx)/CFTS/SnS2/ZnMgO/ZnO:Al/Pt configurations under optimized conditions. The enhanced charge separation and carrier collection efficiencies reveal the strong potential of CFTS absorber heterostructures with Cu2O/NiOx, SnS2, and bi-layer ZnMgO/ZnO:Al as BSF, buffer, and window layers, repectively, providing insights and resources for developing high-efficiency CFTS-based photovoltaic devices.
Energy industries. Energy policy. Fuel trade, Renewable energy sources
Marcel van der Westhuizen, Amare Abebe, Eleonora Di Valentino
We present an overview of the main results from our two companion papers that are relevant for observational constraints on interacting dark energy (IDE) models. We provide analytical solutions for the dark matter and dark energy densities, $ρ_{\rm dm}$ and $ρ_{\rm de}$, as well as the normalized Hubble function $h(z)$, for eight IDE models. These include five linear IDE models, namely $Q=3H(δ_{\rm dm} ρ_{\rm dm} + δ_{\rm de} ρ_{\rm de})$ and four special cases: $Q=3Hδ(ρ_{\rm dm}+ρ_{\rm de})$, $Q=3Hδ(ρ_{\rm dm}-ρ_{\rm de})$, $Q=3Hδρ_{\rm dm}$, and $Q=3Hδρ_{\rm de}$, together with three non-linear IDE models: $Q=3Hδ\left( \tfrac{ρ_{\rm dm} ρ_{\rm de}}{ρ_{\rm dm}+ρ_{\rm de}} \right)$, $Q=3Hδ\left( \tfrac{ρ_{\rm dm}^2}{ρ_{\rm dm}+ρ_{\rm de}} \right)$, and $Q=3Hδ\left( \tfrac{ρ_{\rm de}^2}{ρ_{\rm dm}+ρ_{\rm de}} \right)$. For these eight models, we present conditions to avoid imaginary, undefined, and negative energy densities. In seven of the eight cases, negative densities arise if energy flows from DM to DE, implying a strong theoretical preference for energy transfer from DE to DM. We also provide conditions to avoid future big rip singularities and evaluate how each model addresses the coincidence problem in both the past and the future. Finally, we propose a set of approaches and simplifying assumptions that can be used when constraining IDE models, by defining regimes that restrict the parameter space according to the behavior researchers are willing to tolerate.
Isidora Abasolo Farfán, Carolina Bonacic Castro, René Garrido Lazo
et al.
This study introduces a method for identifying territories ideal for establishing photovoltaic (PV) plants for green hydrogen (GH2) production in the Antofagasta region of northern Chile, a location celebrated for its outstanding solar energy potential. Assessing the viability of PV plant installation necessitates a balanced consideration of technical aspects and socio-environmental constraints, such as the proximity to areas of ecological importance and indigenous communities, to identify potential zones for solar and non-conventional renewable energy (NCRE)-based hydrogen production.To tackle this challenge, we propose a methodology that utilizes geospatial analysis, integrating Geographic Information System (GIS) tools with sensitivity analysis, to determine the most suitable sites for PV plant installation in the Antofagasta region. Our geospatial analysis employs the QGIS software to identify these optimal locations, while sensitivity analysis uses the Sørensen–Dice coefficient method to assess the similarity among chosen socio-environmental variables.Applying this methodology to the Antofagasta region reveals that a significant area within a 15 km radius of existing road networks and electrical substations is favorable for photovoltaic projects. Our sensitivity analysis further highlights the limiting effects of socio-environmental factors and their interactions. Moreover, our research finds that enlarging areas of socio-environmental importance could increase the total hydrogen production by about 10% per commune, indicating the impact of these factors on the potential for renewable energy production.
Jannis Langer, Francesco Lombardi, Stefan Pfenninger
et al.
Indonesia has large renewable energy resources that are not always located in regions where they are needed. Sub-sea power transmission cables, or island links, could connect Indonesia’s high-demand islands, like Java, to large-resource islands. However, the role of island links in Indonesia’s energy transition has been explored in a limited fashion. Considering Indonesia’s current fossil fuel dependency, this is a critical knowledge gap. Here we assess the role of island links in Indonesia’s full power sector decarbonisation via energy system optimisation modelling and an extensive scenario and sensitivity analysis. We find that island links could be crucial by providing access to the most cost-effective resources across the country, like onshore photovoltaics (PV) and hydropower from Kalimantan and geothermal from Sumatera. In 2050, 43 GW of inter-island transmission lines enable 410 GW _p of PV providing half of total generation, coupled with 100 GW of storage, at levelised system costs of 60 US$(2021)/MWh. Without island links, Java could still be supplied locally, but at 15% higher costs due to larger offshore floating PV and storage capacity requirements. Regardless of the degree of interconnection, biomass, large hydro, and geothermal remain important dispatchable generators with at least 62 GW and 23% of total generation throughout all tested scenarios. Full decarbonisation by 2040 mitigates an additional 464 MtCO _2 e compared to decarbonisation by 2050, but poses more challenges for renewables upscaling and fossil capacity retirement.
Renewable energy sources, Energy industries. Energy policy. Fuel trade
A. Zhadyranova, M. Koussour, V. Zhumabekova
et al.
Motivated by anomalies in cosmic microwave background observations, we investigate the implications of $f(Q, T)$ gravity in Bianchi type-I spacetime, aiming to characterize the universe's spatially homogeneous and anisotropic properties. By using a linear combination of non-metricity $Q$ and the energy-momentum tensor trace $T$, we parametrize the deceleration parameter and derive the Hubble solution, which we then impose in the Friedmann equations of $f(Q, T)$ gravity. Bayesian analysis is employed to find the best-fit values of model parameters, with $1-σ$ and $2-σ$ contour plots illustrating the constraints from observational data, including $H(z)$ data and the Pantheon+ sample. Our analysis reveals a transition from a decelerated to an accelerated expansion phase, with the present deceleration parameter indicating an accelerating universe. The energy density gradually decreases over time, approaching zero for the present and future, indicating continuous expansion. The anisotropic pressure, initially notably negative, transitions to slightly negative values, suggesting the presence of dark energy. The evolving equation of state parameter $ω$ exhibits behavior akin to phantom energy, influenced by spacetime anisotropy. Violations of the null energy condition and the strong energy condition imply phantom-like behavior and accelerated expansion.
Jose Quintana, Alejandro Ramos, Moises Diaz
et al.
Supercapacitors are increasingly used as energy storage elements. Unlike batteries, their state of charge has a considerable influence on their voltage in normal operation, allowing them to work from zero to their maximum voltage. In this work, a theoretical and practical analysis is proposed of the energy efficiency of these devices according to their working voltages. To this end, several supercapacitors were subjected to charge and discharge cycles until the measurements of current and voltage stabilized. At this point their energy efficiency was calculated. These charge-discharge cycles were carried out: i) without rest between charging and discharging; and ii) with a rest of several minutes between the two stages. Using the information obtained from the tests, the energy efficiency is shown plotted against the minimum and maximum working voltages. By consulting the data and the graphs, the ideal working voltages to optimize the energy efficiency of these devices can be obtained.
Energy arbitrage is one of the most profitable sources of income for battery operators, generating revenues by buying and selling electricity at different prices. Forecasting these revenues is challenging due to the inherent uncertainty of electricity prices. Deep reinforcement learning (DRL) emerged in recent years as a promising tool, able to cope with uncertainty by training on large quantities of historical data. However, without access to future electricity prices, DRL agents can only react to the currently observed price and not learn to plan battery dispatch. Therefore, in this study, we combine DRL with time-series forecasting methods from deep learning to enhance the performance on energy arbitrage. We conduct a case study using price data from Alberta, Canada that is characterized by irregular price spikes and highly non-stationary. This data is challenging to forecast even when state-of-the-art deep learning models consisting of convolutional layers, recurrent layers, and attention modules are deployed. Our results show that energy arbitrage with DRL-enabled battery control still significantly benefits from these imperfect predictions, but only if predictors for several horizons are combined. Grouping multiple predictions for the next 24-hour window, accumulated rewards increased by 60% for deep Q-networks (DQN) compared to the experiments without forecasts. We hypothesize that multiple predictors, despite their imperfections, convey useful information regarding the future development of electricity prices through a "majority vote" principle, enabling the DRL agent to learn more profitable control policies.
Load uncertainty must be accounted for during design to ensure building energy systems can meet energy demands during operation. Reducing building load uncertainty allows for improved designs with less compromise to be identified, reducing the cost of decarbonizing energy usage. However, the building monitoring required to reduce load uncertainty is costly. This study uses Value of Information analysis (VoI) to quantify the economic benefit of practical building monitoring for supporting energy system design decisions, and determine if its benefits outweigh its cost. An extension of the VoI framework, termed 'On-Policy' VoI, is proposed, which admits complex decision making tasks where decision policies are required. This is applied to a case study district energy system design problem, where a Linear Program model is used to size solar-battery systems and grid connection capacity under uncertain building loads, modelled using historic electricity metering data. Load uncertainty is found to significantly impact both system operating costs ($\pm$30%) and the optimal system design ($\pm$20%). However, using building monitoring data to improve the design of the district reduces overall costs by less than 1.5% on average. As this is less than the cost of measurement, using monitoring is not economically worthwhile in this case. This provides the first numerical evidence to support the sufficiency of using standard building load profiles for energy system design. Further, reducing only uncertainty in mean load is found to provide most of the available decision support benefit, meaning using hourly measurement data provides little benefit for energy retrofit design.
Abstract Protecting cybersecurity is a non‐negotiable task for smart grids (SG) and has garnered significant attention in recent years. The application of artificial intelligence, particularly deep learning (DL), holds great promise for enhancing the cybersecurity of SG. Nevertheless, previous surveys and review articles have failed to comprehensively investigate the intersection between DL and SG cybersecurity. To address this gap, this study presents a survey of the latest advancements in DL technology and their relevance to SG cybersecurity. First, the functional mechanisms and scope of application of common DL techniques are explored. Subsequently, SG cyberthreats are categorised into distinct types of cyberattacks that have not been systematically examined in previous surveys. Based on this, a thorough review of the application of DL techniques in addressing each cyberthreat along with recommendations and a generalised framework for enhancing cyberattack detection using DL is offered. Finally, insights are provided into the emerging challenges presented by DL applications in SG cybersecurity that are yet to be widely acknowledged, and potential research avenues are proposed to address or alleviate these challenges.
Energy industries. Energy policy. Fuel trade, Production of electric energy or power. Powerplants. Central stations
With the growing need for sustainable energy solutions in urban areas, a strategic integration of energy supply, public transport modernization, and energy demand reduction for residential and commercial buildings has become crucial. While numerous efforts have showcased technological advancements and examined the functionality of energy systems, urban applications have not received adequate attention. To address this gap, this research proposes a feasibility analysis of hybrid energy systems in urban settings. The study incorporates a well-defined research purpose, a robust methodology, and a comprehensive approach to data collection, enabling a thorough investigation of technological, scientific, and industrial developments. There is a requirement for a total of 20,349 k Wh of energy per year in the study area, and it can be met with a PV array having a production capacity of 5591 k Wh per year and a capacity of 14,758 k Wh per year. It also explains that 27 % of the required energy can be harnessed from photovoltaics, and 73 % from a domestic diesel generator. The system emits 13,428 kg of Carbon dioxide (CO2), 33.4 kg of carbon monoxide (CO), 3.71 kg of unburned hydrocarbon, 2.52 kg of particulate matter, 26.7 kg of Sulphur dioxide, 298 kg of Nitrogen oxides. The study encompasses a comprehensive review of past, present, and future trends in energy system design, development, and implementation, providing valuable insights for urban planners and policymakers.
Nicholas R. Jaramillo, Cole A. Ritchie, Michelle L. Pantoya
et al.
A calibration-free multi-color pyrometry data analysis approach for determining the temporal change in the reciprocal temperature by only comparing the photomultiplier tube (PMT) responses to the system light emission is introduced. For Arrhenius reactions, analyzing the reciprocal temperature is particularly relevant for evaluating reactivity. The high accuracy of the proposed method is provided by eliminating the calibration step, which is made possible by considering the ratio of PMT signals as a function of time. The developed methodology is applicable to systems with continuous light emission spectra of the thermal nature that originate from condensed particulates. A demonstration of the data analysis approach was performed using aluminum powder burning in air. Four PMTs detected light emission during combustion that enabled analysis of six detector combinations to obtain a time-dependent signal ratio. Based on the temperature-dependent nature of light emission, the PMT response ratio provided the value of the reciprocal temperature change. All six detector combinations generated precisely coinciding results within time periods where the light emission trace behavior was relatively smooth that validated the data processing approach. It was also found that a non-smooth behavior of light emission led to significant deviations between outputs of different PMT combinations. This inconsistency between outputs was an indication of multi-temperature light emission whereas consistency between outputs corresponds to the single-temperature emission behavior. Using the calibration-free data processing approach, we isolated time periods where multi-temperature radiation is essential. Then, we further decoupled contributions from non-monotonic light emission signals and resolved two distinct temperatures responsible for observed radiation peculiarities.
Fuel, Energy industries. Energy policy. Fuel trade
Nanomaterials from gas-phase synthesis are tapping into new fields of application in research and industry due to their unique size-dependent properties. To produce tailored nanoparticles, the synthesis starting from the precursor decomposition to the particle formation must be fully understood. Tetramethylsilane is used as a precursor for the synthesis of silica nanomaterials in flames. The precursor starts to decompose by H-abstraction. Reaction rates for H-abstraction of tetramethylsilane (TMS) by +O/+H/+OH radicals are determined by means of quantum chemical calculations. The rate expressions are obtained in the temperature range from 300 to 1400 K: kH = 6,025,735 (T/K)2.1731exp(-(+2613.840 K)/T) cm3mol−1s−1 for the total bimolecular reaction coefficient of TMS with hydrogen atoms, kOH = 10,179,818 (T/K)1.7790 exp(-(+152.739 K)/T) cm3mol−1s−1 for TMS with OH radicals, and kO = 93,100 (T/K)2.4797 exp(-(+889.166 K)/T) cm3mol−1s−1 for TMS with O(3P) radicals, respectively. The reaction rates are implemented into the TMS reaction mechanism of Janbazi et al. [1], and a better agreement of the TMS reactivity in the flame is achieved. Experiments are conducted to obtain the mass deposition rates with a Quartz-Crystal-Microbalance (QCM) in a wide range of different equivalence ratios. The equivalence ratio is varied between ϕ = 0.6–1.2, and precursor amounts of 400, 600 and 800 ppm are used. These QCM-experiments are complementary to the MBMS-studies from Karakaya et al. [1–3] but use the same flame conditions to extend the data set. The results reveal that metastable particles exist in the reaction zone of the flame. Depending on flame conditions, their concentration decreases towards the end of the reaction zone, but particles subsequently grow again in the recombination zone of the flame. The mechanisms, which describe the reactivity of the metastable nanoparticles, are tentatively proposed. The understanding of the mechanisms can open up the way for tailored nanoparticles with different structures and stoichiometries.
Fuel, Energy industries. Energy policy. Fuel trade
Distribution system end users are transforming from passive to active participants, marked by the push towards widespread adoption of edge-level Distributed Energy Resources (DERs). This paper addresses the challenges in distribution system planning arising from these dynamic changes. We introduce a bottom-up probabilistic approach that integrates these edge-level DERs into the reliability evaluation process. Our methodology leverages joint probability distributions to characterize and model the penetration of rooftop photovoltaic (PV) systems and energy storage across a distribution network at the individual residential level. Employing a scenario-based approach, we showcase the application of our probabilistic method using a Monte Carlo Simulation process to assess average system reliability indices and their variations at the user level. To validate our approach, we applied this methodology to the RBTS test system across various adoption scenarios, effectively showcasing the capability of our proposed method in quantifying the variation in end-user reliability indices for each scenario within the distribution system.