Hasil untuk "Electricity"

Ahmet Emir Şener, Burak Kocuk, Tuğçe Yüksel

The decarbonization of energy systems at energy-intensive sites is an essential component of global climate mitigation, yet such transitions involve substantial capital requirements, ongoing technological progress, and the operational complexities of renewable integration. This study presents a dynamic strategic planning framework that applies multi-stage stochastic programming to guide clean electricity transitions at the campus level. The model jointly addresses technology investment, storage operation, and grid interaction decisions while explicitly incorporating uncertainties in future technology cost trajectories and efficiency improvements. By enabling adaptive, stage-wise decision-making, the framework provides a structured approach for large electricity consumers seeking to achieve self-sufficient and sustainable energy systems. The approach is demonstrated through a case study of Middle East Technical University (Ankara, Turkey), which has committed to achieving carbon-neutral electricity by 2040. Through the integration of solar photovoltaics, wind power, and lithium-ion batteries, the model links long-term investment planning with operational-level dynamics by incorporating high-resolution demand and meteorological data. Our findings from the case study, sensitivity analyses, and comparisons with simplified models indicate that accounting for uncertainty and temporal detail is crucial for both the economic viability and operational feasibility of campus-scale clean electricity transitions.

Sven Otto, Luis Winter

We propose a function-on-function linear regression model for time-dependent curve data that is consistently estimated by imposing factor structures on the regressors. An integral operator based on cross-covariances identifies two components for each functional regressor: a predictive low-dimensional component, along with associated factors that are guaranteed to be correlated with the dependent variable, and an infinite-dimensional component that has no predictive power. In order to consistently estimate the correct number of factors for each regressor, we introduce a functional eigenvalue difference test. While conventional estimators for functional linear models fail to converge in distribution, we establish asymptotic normality, making it possible to construct confidence bands and conduct statistical inference. The model is applied to forecast electricity price curves in three different energy markets. Its prediction accuracy is found to be comparable to popular machine learning approaches, while providing statistically valid inference and interpretable insights into the conditional correlation structures of electricity prices.

Toma Masaki, Kanta Tachibana

This study proposes a novel framework for long-term electricity demand prediction based solely on historical consumption data, without relying on external variables such as temperature or economic indicators. The method combines Non-negative Tensor Factorization (NTF) to extract low-dimensional temporal features from multi-way electricity usage data, with a Genetic Algorithm that optimizes the hyperparameters of time series models applied to the latent annual factors. We model the dataset as a third-order tensor spanning electric utilities, industrial sectors, and years, and apply canonical polyadic decomposition under non-negativity constraints. The annual component is forecasted using autoregressive models, with hyperparameter tuning guided by the prediction error or reconstruction accuracy on a validation set. Comparative experiments using real-world electricity data from Japan demonstrate that the proposed method achieves lower mean squared error than baseline approaches without tensor decomposition or evolutionary optimization. Moreover, we find that reducing the model's degrees of freedom via tensor decomposition improves generalization performance, and that initialization sensitivity in NTF can be mitigated through multiple runs or ensemble strategies. These findings suggest that the proposed framework offers an interpretable, flexible, and scalable approach to long-term electricity demand prediction and can be extended to other structured time series forecasting tasks.

Timothy Weber, Cheng Cheng, Harry Thawley et al.

Fossil gas is sometimes presented as an enabler of variable solar and wind generation beyond 2050, despite being a primary source of greenhouse gas emissions from methane leakage and combustion. We find that balancing solar and wind generation with pumped hydro energy storage eliminates the need for fossil gas without incurring a cost penalty. However, many existing long-term electricity system plans are biased to rely on fossil gas due to using temporal aggregation methods that either heavily constrain storage cycling behaviour or lose track of the state-of-charge, failing to consider the potential of low-cost long-duration off-river pumped hydro, and ignoring the broad suite of near-optimal energy transition pathways. We show that a temporal aggregation method based on 'segmentation' (fitted chronology) closely resembles the full-series optimisation, captures long-duration storage behaviour (48- and 160-hour durations), and finds a near-optimal 100% renewable electricity solution. We develop a new electricity system model to rapidly evaluate millions of other near-optimal solutions, stressing the importance of modelling pumped hydro sites with a low energy volume cost (<US$50 per kilowatt-hour), long economic lifetime (~75 years), and low real discount rate akin to other natural monopolies (<=3%). Almost every region of the world has access to sufficient 50 - 5000 gigawatt-hour off-river pumped hydro options that enable them to entirely decarbonise their future electricity systems.

Simon Hirsch

Probabilistic electricity price forecasting (PEPF) is vital for short-term electricity markets, yet the multivariate nature of day-ahead prices - spanning 24 consecutive hours - remains underexplored. At the same time, real-time decision-making requires methods that are both accurate and fast. We introduce an online algorithm for multivariate distributional regression models, allowing an efficient modelling of the conditional means, variances, and dependence structures of electricity prices. The approach combines multivariate distributional regression with online coordinate descent and LASSO-type regularization, enabling scalable estimation in high-dimensional covariate spaces. Additionally, we propose a regularized estimation path over increasingly complex dependence structures, allowing for early stopping and avoiding overfitting. In a case study of the German day-ahead market, our method outperforms a wide range of benchmarks, showing that modeling dependence improves both calibration and predictive accuracy. Furthermore, we analyse the trade-off between predictive accuracy and computational costs for batch and online estimation and provide an high-performing open-source Python implementation in the ondil package.

Huanyu Chang, Yong Zhao, Yongqiang Cao et al.

Ensuring the security of agricultural systems is essential for achieving national food security and sustainable development. Given that agricultural systems are inherently complex and composed of coupled subsystems—such as water, land, and energy—a comprehensive and multidimensional assessment of system security is necessary. This study focuses on Northeast China, a major food-producing region, and introduces the concept of agricultural system coupling security, defined as the integrated performance of an agricultural system in terms of resource adequacy, internal coordination, and adaptive resilience under external stress. To operationalize this concept, a coupling security evaluation framework is constructed based on three key dimensions: reliability (Rel), coordination (Cor), and resilience (Res). An Agricultural System Coupling Security Index (AS-CSI) is developed using the entropy weight method, the Criteria Importance Through Intercriteria Correlation (CRITIC) method, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, while obstacle factor diagnosis is employed to identify key constraints. Furthermore, bivariate and trivariate Copula models are used to estimate joint risk probabilities. The results show that from 2001 to 2022, the AS-CSI in Northeast China increased from 0.38 to 0.62, indicating a transition from insecurity to relative security. Among the provinces, Jilin exhibited the highest CSI due to balanced performance across all Rel-Cor-Res dimensions, while Liaoning experienced lower Rel, hindering its overall security level. Five indicators, including area under soil erosion control, reservoir storage capacity per capita, pesticide application amount, rural electricity consumption per capita, and proportion of agricultural water use, were identified as critical threats to regional agricultural system security. Copula-based risk analysis revealed that the probability of Rel–Cor reaching the relatively secure threshold (0.8) was the highest at 0.7643, and the probabilities for Rel–Res and Cor–Res to reach the same threshold were lower, at 0.7164 and 0.7318, respectively. The probability of Rel–Cor-Res reaching the relatively secure threshold (0.8) exceeds 0.54, with Jilin exhibiting the highest probability at 0.5538. This study provides valuable insights for transitioning from static assessments to dynamic risk identification and offers a scientific basis for enhancing regional sustainability and economic resilience in agricultural systems.

Yanbo Tao, Yixun Xue, Yuan Du et al.

IntroductionThe accelerated development of renewable energy sources has confronted substantial challenges, primarily attributable to their intermittency and uncertainty. Consequently, the integration of green electricity has become a pressing concern. Hydrogen production from water electrolyzer has emerged as a key method for promoting local wind and solar energy consumption. However, extant studies tend to neglect the value of hydrogen as a chemical feedstock and rely on simplified linear models to describe the characteristics of electro-hydrogen coupling devices. This has resulted in discrepancies between optimization decisions and actual operational performance.MethodsTo address this gap, the present paper employs a nonlinear semi-empirical model with a focus on electrolyzer and fuel cell. It describes the energy conversion between electricity and hydrogen more accurately based on electrochemical mechanisms. On this basis, considering the dual value of hydrogen energy as both “energy carrier” and “chemical raw material”, the operation optimization model of electric-hydrogen coupling system for chemical parks is established. Furthermore, a convexification method for coupling device constraints is proposed to enhance solution efficiency.Results and DiscussionThe findings of the study demonstrate that the semi-empirical model provides a more accurate representation of actual equipment performance, thereby preventing deviations between real-world operation and outcomes derived from optimization. Furthermore, the collaborative optimization strategy that accounts for hydrogen’s dual value has been shown to significantly improve the system’s economic benefits.

Khoryfatul Munawaroh, Rizki Kurnia Tohir, Nurika Arum Sari et al.

Geothermal Power Plants are one of the geothermal energies that can be used as a source of electricity. One of the geothermal powers in Lampung is the Ulubelu PLTP located in Tanggamus Regency. As an energy-producing agency, Ulubelu PLTP also contributes to preserving flora and fauna in their work area. This is shown through the planting of several types of wood plants to improve the flora in their work area. This study aims to record the types of plants that exist, the abundance of their types, climatic and edaphic factors, as well as recommendations for types that can be used for the enrichment of flora types in the Ulubelu PLTP work area. The vegetation analysis method used to collect flora data is a census method divided into 5 observation lines. The dominant and codominant types found at the tree level are Acacia mangium and Erythrina variegata, at the pole level Leucaena leucocephala and Toona sureni, at the pile level Gliricidia sepium and Syzygium myrtifolium, and at the lower plant level are Imperata cylindrica and Mikania micrantha. In addition to commercial types, some types have the potential to be invasive in the Ulubelu PLTP. The study also recommends that plant species be restored to increase species diversity and vegetation density. The types recommended for restoration based on vegetation analysis data are those that have aesthetic value, those that produce fruit or flowers that can present animals, and the protected types or types that can be used in addition to their wood.

Wataru Nakanishi, Noriko Kaneko

Electric road system (ERS) is expected to make electric vehicles (EVs) more popular as EVs with Dynamic Wireless Power Transfer (DWPT) system can be charged while driving on ERS. Although some studies dealt with ERS implementation, its toll system has not been explored yet. This paper aims at a fundamental analysis on impact of ERS toll system on a traffic assignment. We conduct assignments on a simple network where two vehicle types (EVs with DWPT and others) are co-existing. The results under two toll systems showed some undesirable situations, such as total travel time was not minimised, total charged volume was not optimised, and ERS was not utilised. The occurrence of them depended on the ratio of EVs, battery level, value of electricity, and toll price. The difficulty to control such situations by toll price was discussed as the battery level and value of electricity may vary over time.

Abdulwahab Rawesat, Pericles Pilidis

In the quest for achieving decarbonisation, it is essential for different sectors of the economy to collaborate and invest significantly. This study presents an innovative approach that merges technological insights with philosophical considerations at a national scale, with the intention of shaping the national policy and practice. The aim of this research is to assist in formulating decarbonisation strategies for intricate economies. Libya, a major oil exporter that can diversify its energy revenue sources, is used as the case study. However, the principles can be applied to develop decarbonisation strategies across the globe. The decarbonisation framework evaluated in this study encompasses wind-based renewable electricity, hydrogen, and gas turbine combined cycles. A comprehensive set of both official and unofficial national data was assembled, integrated, and analysed to conduct this study. The developed analytical model considers a variety of factors, including consumption in different sectors, geographical data, weather patterns, wind potential, and consumption trends, amongst others. When gaps and inconsistencies were encountered, reasonable assumptions and projections were used to bridge them. This model is seen as a valuable foundation for developing replacement scenarios that can realistically guide production and user engagement towards decarbonisation. The aim of this model is to maintain the advantages of the current energy consumption level, assuming a 2% growth rate, and to assess changes in energy consumption in a fully green economy. While some level of speculation is present in the results, important qualitative and quantitative insights emerge, with the key takeaway being the use of hydrogen and the anticipated considerable increase in electricity demand. Two scenarios were evaluated: achieving energy self-sufficiency and replacing current oil exports with hydrogen exports on an energy content basis. This study offers, for the first time, a quantitative perspective on the wind-based infrastructure needs resulting from the evaluation of the two scenarios. In the first scenario, energy requirements were based on replacing fossil fuels with renewable sources. In contrast, the second scenario included maintaining energy exports at levels like the past, substituting oil with hydrogen. The findings clearly demonstrate that this transition will demand great changes and substantial investments. The primary requirements identified are 20,529 or 34,199 km² of land for wind turbine installations (for self-sufficiency and exports), and 44 single-shaft 600 MW combined-cycle hydrogen-fired gas turbines. This foundational analysis represents the commencement of the research, investment, and political agenda regarding the journey to achieving decarbonisation for a country.

Faiz Ahmad, Peter B Monk, Akhlesh Lakhtakia

The building sector accounts for 36% of energy consumption and 39% of energy-related greenhouse-gas emissions. Integrating bifacial photovoltaic solar cells in buildings could significantly reduce energy consumption and related greenhouse gas emissions. Bifacial solar cells should be flexible, bifacially balanced for electricity production, and perform reasonably well under weak-light conditions. Using rigorous optoelectronic simulation software and the differential evolution algorithm, we optimized symmetric/asymmetric bifacial CIGS solar cells with either (i) homogeneous or (ii) graded-bandgap photon-absorbing layers and a flexible central contact layer of aluminum-doped zinc oxide to harvest light outdoors as well as indoors. Indoor light was modeled as a fraction of the standard sunlight. Also, we computed the weak-light responses of the CIGS solar cells using LED illumination of different light intensities. The optimal bifacial CIGS solar cell with graded-bandgap photon-absorbing layers is predicted to perform with 18%–29% efficiency under 0.01–1.0-Sun illumination; furthermore, efficiencies of 26.08% and 28.30% under weak LED light illumination of 0.0964 mW cm ^−2 and 0.22 mW cm ^−2 intensities, respectively, are predicted.

Wei Zhang, Yongzhe Chen, Qinghua Ji et al.

Issarachai Ngamroo, Wikorn Kotesakha, Suntiti Yoomak et al.

This study aims to evaluate the impact of the distributed generator (DG) connection to the grid. The simulated results present the parameters of the system required to install DG on the end of the main distribution feeder. Various parameters, such as voltage, current, and protective relay coordination are modelled after the actual provincial electricity authority (PEA) distribution system. Various case studies compared the coordination without and with DG connections to the grid by finding the difference of protective devices. The results indicate that the malfunction can be fixed in order of priority protective devices, which operate according to the parameter setting. Additionally, the coordinate functions between the recloser and fuse devices in both phase and ground configurations in the operating zone prevented the drop-out fuse melting or burning out. Based on the result, this problem is fixed by providing a directional recloser device and increasing the fuse-link rated with 40k installation for replacing the conventional sizing, which can improve the performance in case of fault occurrence to investigate the reliability and stability of the distribution system.

Yanchun XU, Zhongyao FAN, Sihan SUN et al.

Since the fault current output from the doubly-fed wind farm has frequency deviation characteristics and contains large harmonic components, when an internal fault occurs in the transmission transformer for the doubly-fed wind farm, the ratio of the second harmonic to the fundamental wave in the differential current of the transformer increases, which makes the differential protection of the transformer face the risk of delay action. Moreover, when the system fails, a large number of non-periodic components in the fault current output from the doubly-fed wind farm will make the current transformer in the transmission transformer more prone to saturation, resulting in reduced reliability of the differential protection of traditional transformers. This paper proposes a differential protection scheme for the transmission transformer for large-scale wind farms based on detrended analysis. Firstly, the sampling current is processed by detrended analysis through the sliding data window to obtain the detrended residual function, and then the slope characteristics of the current waveform are utilized to complete the effective distinction between the magnetizing inrush current and the fault differential current (including the current transformer saturation state) of the transformer. The proposed protection scheme is validated to be applicable under different operating conditions by building a transmission system for the doubly-fed wind farm in PSCAD.

Yongxin Zhang, Qikun Feng, Shaolong Zhong et al.

Abstract In the process of coping with energy and environmental protection issues, technologies such as energy materials, energy devices, and energy systems have made great progress. With excellent performance, film capacitors play an increasingly important role in energy‐related fields. With the increase of application scenarios and the continuous development of film material technology, it is urgent to establish a better theoretical connection from films to capacitors. First, the main components of the capacitor including the film and the positional relationships among them are given. Then, from the two perspectives of indirect calculation according to the volume and the direct calculation according to the winding process, the equation between the dielectric constant of films and the corresponding capacitance of capacitors is established. Further, the measurement data and error analysis results of the built test platform prove the accuracy and great potential of the proposed calculation methods. In addition, error sources, including film thickness uniformity, are listed. Finally, the challenges faced by the proposed calculation methods and the paths that can be referenced for future research are summarised and discussed.

Xiaofeng YANG, Yihang FANG, Pengzhen ZHAO et al.

In order to achieve the goal of “double carbon”, the development of wind power generation technology is essential. At the same time, with the increasing complexity of power grid, the real-time detection and accurate evaluation of the state of wind turbines and other power equipment are becoming increasingly important. In recent years, the development of big data technology and the improvement of power equipment data monitoring technology makes possible the application of big data technology in power equipment state recognition. Compared with the conventional methods, the above-mentioned methods are independent of accurate empirical thresholds or quantitative models, and have better adaptability to the rapid increase and variability of data. Thus, this paper applies the unsupervised (K-means) and supervised (BPNN) machine learning methods to state recognition of wind turbines, while exploring the variation of accuracy and computational efficiency after the application of dimensionality reduction methods. The results show that both machine learning methods are effective in state recognition of wind turbines, while the dimensionality reduction method can effectively improve the computational efficiency with limited accuracy loss.

Fan Wu, Fei Cao, Xurong Zhang et al.

Abstract To solve the problem of the mismatch of the wideband underdetermined direction of arrival (DOA) estimation under the condition of the On‐grid model, this paper extends the narrowband Off‐Grid model to wideband and proposes a new DOA estimation algorithm for Off‐Grid sources based on group sparsity. The proposed algorithm first obtains the preliminary estimation result under the current predefined discrete grid through the group sparsity wideband DOA estimation algorithm. Then, the Off‐Grid optimisation problem is adopted to calculate the Off‐Grid deviation vector. It is also assumed that the off‐grid deviation vectors of each frequency subband are exactly the same, thereby reducing the number of parameters to be estimated. Therefore, the proposed algorithm can not only maintain similar or even better estimation accuracy but also greatly reduce the computational complexity. Finally, simulation is conducted and the results verify the effectiveness and performance of the proposed method.

Amalija Božiček, Bojan Franc, Božidar Filipović-Grčić

Power systems and their primary components, mostly the transmission and distribution of overhead lines, substations, and other power facilities, are distributed in space and are exposed to various atmospheric and meteorological conditions. These conditions carry a certain level of risk for reliable electrical power delivery. Various atmospheric hazards endanger the operation of power systems, where the most significant are thunderstorms, wildfire events, and floods which can cause various ranges of disturbances, faults, and damages to the power grid, or even negatively affect the quality of life. By utilizing a weather monitoring and early warning system, it is possible to ensure a faster reaction against different weather-caused fault detections and elimination, to ensure a faster and more adequate preparation for fighting extreme weather events, while maintaining overhead line protection and fault elimination. Moreso, it is possible to bypass overhead lines that have the highest risk of unfavorable meteorological events and hazards, and reroute the energy, thus providing electricity to endangered areas in times of need while minimizing blackouts, and consequently, improving the quality of human life. This paper will present an analysis of the various risks of atmospheric phenomena, in the meteorological and climate context, and discuss various power system components, the power system control, operations, planning, and power quality. A concept with the main functionalities and data sources needed for the establishment of an early warning weather hazard system will be proposed. The proposed solution can be used as a utility function in power system control to mitigate risks to the power system due to atmospheric influences and ongoing climate change.

Mohamed Alwaeli, Viktoria Mannheim

Nuclear power can replace fossil fuels and will have a decisive impact on the change in the approach to conventional energy. However, nuclear (or radioactive) wastes are produced by the operation of the nuclear reactors should be safely and properly disposed of. This paper assesses the uranium resources and the global state of nuclear power plants and determines the energy mixes in different countries using the most nuclear energy. Furthermore, this paper analysed the nuclear waste management and disposal and the depletion of abiotic resources, and the primary energy sources of a basic production process using electricity mix and nuclear electricity for a basic production (PET bottle manufacturing) process. The life cycle assessment was completed by applying the GaBi 8.0 (version 10.6) software and the CML method. In this study, we limit our discussion to high-level nuclear waste (HLW) and spent nuclear fuel (SNF) waste. We do not consider waste generated from uranium mining and milling, which is usually disposed of in near-surface impoundments close to the mine or the mill. The investigation of waste management methods is limited to European countries. This research work is relevant because determining abiotic resources is important in a life cycle assessment and current literature available on LCA analysis for nuclear powers remains under-developed. These results can guide and compare manufacturing processes involving a nuclear electricity and electricity grid mix input. The results of this research can be used to develop production processes using nuclear energy with lower abiotic depletion impacts. This research work facilitates the industry in making predictions for a production-scale plant using an LCA of production processes with nuclear energy consumption.

Negin Zarbakhsh, M. Saeed Misaghian, Gavin Mcardle

COVID-19 non-pharmaceutical interventions (NPIs) are changing human mobility patterns; however, the effects on power systems remain unclear. Previous loads and timings along with weather features are often used in literature as input features in load forecasting, but these may be insufficient during COVID-19. As a result, this paper proposes an analytical framework to assess the impact of COVID-19 on power system operation as well as day-ahead electricity prices in Ireland. To improve peak demand forecasting during pandemics, we incorporate mobility, NPIs, and COVID-19 cases as complementary input features and representative of human behaviour changes. By defining different combinations of these explanatory features, several Machine Learning (ML) algorithms are applied and their performance is compared with the baseline scenario currently used in the literature. Using SHapley Additive Explanations (SHAP), we interpret the best performing model, Light Gradient Boosted Machine, to determine the influence of each feature on the predicted outcomes. We discover that typical load forecasting features still influence ML outcomes the most, but mobility-related changes are also significant. Our finding shows that NPIs impact human behaviour and electricity consumption during times of crisis and can be used in the context of load forecasting to assist policymakers and energy distributors.