Hasil untuk "Electricity"

D. Kraemer, Bed Poudel, H. Feng et al.

Annette Evans, V. Strezov, T. Evans

A. Züttel, A. Remhof, A. Borgschulte et al.

Zhuhua Zhang, Xuemei Li, Jun Yin et al.

D. Connolly, H. Lund, B. Mathiesen

M. Guney, Yalçın Tepe

Usama Al-mulali, I. Ozturk, H. Lean

Hongjun Wang, Tao Li, Zhiliang Dong

INTRODUCTION: This paper examines the stability of small disturbances in wind farm grid-connected systems within the framework of power system resilience. With increasing renewable integration, minor disturbances can escalate into cascading failures, threatening grid reliability. OBJECTIVES: The goal is to build a short-term voltage prediction model by integrating Topological Data Analysis (TDA) with Deep Belief Networks (DBN) and to propose a coordinated reactive power control strategy that enhances system dynamic performance under small disturbances. METHODS: The study adopts a VSC-HVDC system based on Modular Multilevel Converters (MMC) to model wind farm connectivity. A cluster-based reactive power control approach is applied by grouping wind turbines with similar operational characteristics. Small disturbance signals are simulated, and both unified and decentralised Doubly Fed Induction Generator (DFIG) control schemes are compared using impedance modelling and time-domain analysis. RESULTS: Simulations indicate that small AC-side disturbances have a significant impact on reactive power and system voltage, whereas DC-side faults affect frequency stability. The decentralised DFIG coordination strategy achieved a lower network loss (0.467 MW) compared to the unified approach (0.473 MW) while also improving reactive power allocation and system responsiveness. CONCLUSION: By combining TDA and DBN with decentralised control, the proposed model enhances the stability of small disturbances in wind-integrated power systems. It enhances fault tolerance, mitigates power fluctuations, and facilitates the resilient operation of renewable-rich grids.

Lefeng Cheng, Pengrong Huang, Mengya Zhang et al.

This paper addresses the challenge of fostering cooperation among virtual power plant (VPP) operators in competitive electricity markets, focusing on the application of evolutionary game theory (EGT) and static reward–punishment mechanisms. This investigation resolves four critical questions: the minimum reward–punishment thresholds triggering stable cooperation, the influence of initial market composition on equilibrium selection, the sufficiency of static versus dynamic mechanisms, and the quantitative mapping between regulatory parameters and market outcomes. The study establishes the mathematical conditions under which static reward–punishment mechanisms transform competitive VPP markets into stable cooperative systems, quantifying efficiency improvements of 15–23% and renewable integration gains of 18–31%. Through rigorous evolutionary game-theoretic analysis, we identify critical parameter thresholds that guarantee cooperation emergence, resolving longstanding market coordination failures documented across multiple jurisdictions. Numerical simulations and sensitivity analysis demonstrate that static reward–punishment systems enhance cooperation, optimize resources, and increase renewable energy utilization. Key findings include: (1) Reward–punishment mechanisms effectively promote cooperation and system performance; (2) A critical region exists where cooperation dominates, enhancing market outcomes; and (3) Parameter adjustments significantly impact VPP performance and market behavior. The theoretical contributions of this research address documented market failures observed across operational VPP implementations. Our findings provide quantitative foundations for regulatory frameworks currently under development in seven national energy markets, including the European Union’s proposed Digital Single Market for Energy and Japan’s emerging VPP aggregation standards. The model’s predictions align with successful cooperation rates achieved by established VPP operators, suggesting practical applicability for scaled implementations. Overall, through evolutionary game-theoretic analysis of 156 VPP implementations, we establish precise conditions under which static mechanisms achieve 85%+ cooperation rates. Based on this, future work could explore dynamic adjustments, uncertainty modeling, and technologies like blockchain to further improve VPP resilience.

Yong Wei, Yang Yang, Yanyang Yang

Numerous wave-energy-enhancing systems (WESs) have been developed for advancing global marine energy development. However, WESs face several obstacles, including low efficiency, substantial efficiency fluctuations, and poor impact resistance. A cavity-type breakwater system that protects the coast and generates wave power generation functions was developed for deployment in Beibu Gulf, China, to address these obstacles. A full-chain energy conversion model encompassing wave dynamics, hydropneumatics, and electromechanical conversion was designed and used in physical experiments. A wave particle motion model was established using the three-dimensional nonlinear Stokes wave theory to study the wave characteristics. The improved Goda formula was used to calculate the impact force of the waves: a 55°-inclined breakwater panel generated a peak pressure of 215 kPa. A transient flow model was developed and applied for pressurized water channels showing that the single-impact flow rate was 12.7 m3/s with a channel diameter and length of 0.8 and 3.2 m, respectively. ANSYS Fluent fluid–structure interaction simulations were conducted, which verified that the air chamber pressure fluctuated between 18.6 and 248 kPa, the turbine speed stabilized at 1,120 ± 210 rpm, and the average annual output power per unit was 38.7 kW. A four-stage efficiency chain model was developed, achieving an overall conversion efficiency of 12.6%. Deploying 500 units in Beibu Gulf along with policy guidance could generate 274 million kWh of electricity annually, reducing the levelized cost of electricity from 0.453 to 0.422 CNY/kWh, with a payback period of 6.8 years. This study provides theoretical support and indicates the technical pathways for the coordinated development of coastal engineering and renewable energy.

Adela Bâra, Irina Alexandra Georgescu, Simona-Vasilica Oprea

The paper examines the price volatility, key determinants, and autoregressive distributed lag (ARDL) framework of Romania’s Intraday Continuous Market (IDC) during the summer months. The stability of the ARDL-ECM coefficients is assessed using the cumulative sum (CUSUM) test. We explore the interaction between IDC and Day-Ahead Market (DAM) prices, alongside the influence of economic and environmental variables, including traded volumes, consumption, export/import and the generation mix. Using hourly data and econometric techniques, we identify significant short- and long-run relationships between IDC prices and their drivers. DAM prices exhibit a strong positive impact on IDC prices, reflecting tight market integration. Higher shares of Renewable Energy Sources (RES) such as wind and solar are associated with increased IDC prices, highlighting challenges in integrating intermittent resources. Conventional sources, particularly coal and oil/gas, also elevate prices due to higher marginal costs. Conversely, electricity consumption is negatively related to IDC prices, suggesting that anticipated demand patterns may contribute to system stability. The findings carry implications for policymakers, indicating a need for enhanced forecasting, flexible resources and improved inter-market coordination to ensure price stability and efficient integration of RES.

Alireza Sarsangi Aliabad, Ara Toomanian, Majid Kiavarz et al.

Extended Abstract:1. IntroductionElectricity is an essential input for all production systems and a necessity for all modern families. Hence, relevant energy policies are needed to induce efficient electricity consumption in the residential sector in many countries due to the effects of global warming and security of energy supply. Forecasting electricity demand at a regional or national level is crucial for planning to ensure optimal energy management. Various factors influence household consumption patterns. Factors such as employment rate, residential area, distance from green space, etc. affect electricity consumption. The purpose of this study is to investigate the impact of various factors on electricity consumption in residential homes in Yazd city. The results of this study will be useful for making management decisions for planning to reduce electricity consumption.2. Research MethodologyThe present study was conducted in the city of Yazd, which has a hot and dry climate and is extremely hot in the summer. Data on electricity consumption of Yazd city subscribers was obtained from the provincial electricity distribution company for the years 2016 to 2019. Data related to the city's buildings, such as (current use, building height, area, building shape, and building age), as well as streets, existing street widths, and the location of parks and green spaces, were obtained from the municipality. Spatial configuration indices including: connectivity, depth, coherence and control were estimated. The urban physical parameters of the components of parcel area, building area, yard area, building height, building volume were calculated. Then, association rules were used to examine the existing relationships. Spatial Association Rules are a set of rules that describe the relationships between different features in spatial data. These rules are a capability to find unknown relationships in spatial data. Spatial association rules are rules that indicate the implication of a set of features on another set of features in a spatial database. These rules are introduced to discover the rules between products in large-scale transactional data. 3. Results and discussionResidential electricity consumption data was analyzed using Moran's spatial autocorrelation index and based on Euclidean distance. The results of the study of hot and cold spots of residential electricity consumption data in the study area showed that the distribution of electricity consumption in residential homes is asymmetrical. That is, the number of homes with very high electricity consumption is greater than the number of homes with very low electricity consumption.In total, 3.2 percent of the number of parcels in the region is made up of Low_High outliers and 4.7 percent is High_Low. In the present study, the Apriori algorithm was used. The Apriori algorithm is known as one of the main methods in data mining for discovering association rules. The results of the rule review using Apriori showed that in rule one: buildings with a height of 5 to 8 meters that are located in a new urban context are most likely (93%) to have an annual electricity consumption of more than 3,500 units. Rule two: buildings that are located in a new urban context and their control is less than 1 are most likely (87%) to have an annual electricity consumption of more than 3,500 units. Rule three: buildings that are located in parcels with an area of 150 to 250 square meters and a local connectivity of 2-3 are most likely (74%) to have an annual electricity consumption of more than 3,500 units. Rule four: buildings that are located in parcels with an area of 150 to 250 square meters and in a new urban context and with a yard area of less than 75 square meters are most likely (61%) to have an annual electricity consumption of more than 3,500 units.4. ConclusionAssociation rules are able to extract patterns that cannot be easily identified by traditional methods and provide useful information for optimizing energy consumption.One of the major challenges in using association rules in big data is the need for time-consuming and resource-intensive processing, especially when the data is complex and contains a large number of features. Association rules are usually designed for discrete data, and for numerical data, complex preprocessing such as converting the data to categorical values may be required. Also, the appropriate selection of parameters such as minimum support and confidence can be difficult and have a significant impact on the quality and applicability of the extracted results. It is suggested that in future studies, hourly electricity consumption data should be used if possible so that the effects of more factors can be examined. -

E. Bollen, E. Bollen, R. Hendrix et al.

<p>In this paper, we are concerned with data pertinent to <i>transportation networks</i>, which model situations in which objects move along a graph-like structure. We assume that these networks are equipped with <i>sensors</i> that monitor the network and the objects moving along it. These sensors produce <i>time series data</i>, resulting in sensor networks. Examples are river, road, and electricity networks.</p> <p>Geographical information systems are used to gather, store, and analyse data, and we focus on these tasks in the context of data emerging from transportation networks equipped with sensors. While tailored solutions exist for many contexts, they are limited for sensor-equipped networks at this moment. We view time series data as temporal properties of the network and approach the problem from the viewpoint of property graphs. In this paper, we adapt and extend the theory of the existing property graph databases to model spatial networks, where nodes and edges can contain temporal properties that are time series data originating from the sensors. We propose a language for querying these property graphs with time series, in which time series and measurement patterns may be combined with graph patterns to describe, retrieve, and analyse real-life situations. We demonstrate the model and language in practice by implementing both in Neo4j and explore questions hydrology researchers pose in the context of the Internet of Water, including salinity analysis in the Yser river basin.</p>

M. Ya. Kletsel, E. V. Petrova, S. S. Girshin et al.

The efficient and sustainable functioning of energy systems is a critical element for supplying of electricity necessary to maintain the vital functions of modern society. Therefore, the integration of meteorological data into the management of electrical grids is becoming increasingly important. Meteorological data, such as information on weather conditions, temperature, wind and precipitation, play an essential role in the operational and strategic management of power systems. Their use allows optimizing the operation of generating and distribution stations, using the maximum capacity of lines, as well as improve the planning of repair work and infrastructure upgrades. On the basis of the weather conditions, operators of electric power grids can make more informed decisions regarding the distribution and management of energy resources.The research is aimed at determining the role of meteorological data in the management strategies of modern energy systems.

Tancredi Testasecca, Manfredi Picciotto Maniscalco, Giovanni Brunaccini et al.

Solid oxide fuel cells (SOFC) could facilitate the green energy transition as they can produce high-temperature heat and electricity while emitting only water when supplied with hydrogen. Additionally, when operated with natural gas, these systems demonstrate higher thermoelectric efficiency compared to traditional microturbines or alternative engines. Within this context, although digitalisation has facilitated the acquisition of extensive data for precise modelling and optimal management of fuel cells, there remains a significant gap in developing digital twins that effectively achieve these objectives in real-world applications. Existing research predominantly focuses on the use of machine learning algorithms to predict the degradation of fuel cell components and to optimally design and theoretically operate these systems. In light of this, the presented study focuses on developing digital twin-oriented models that predict the efficiency of a commercial gas-fed solid oxide fuel cell under various operational conditions. This study uses data gathered from an experimental setup, which was employed to train various machine learning models, including artificial neural networks, random forests, and gradient boosting regressors. Preliminary findings demonstrate that the random forest model excels, achieving an R² score exceeding 0.98 and a mean squared error of 0.14 in estimating electric efficiency. These outcomes could validate the potential of machine learning algorithms to support fuel cell integration into energy management systems capable of improving efficiency, pushing the transition towards sustainable energy solutions.

Luiz Moreira Coelho Junior, Edvaldo Pereira Santos Júnior, Cleani Figueredo Fideles da Silva et al.

Abstract Bioelectricity generation from sugarcane is significant across Brazil and is related to regional market structure characteristics where the mills are located. To understand the distribution and conjuncture of this sector, this study analyzes the pattern of location, concentration and clustering of the bioelectricity supply from sugarcane bagasse in Brazil, for 2017 and 2022. The data were obtained from the Brazilian National Electric Energy Agency, and the methodology was based on concentration indices and scan statistics. The results showed that the Southeast region presented the most thermoelectric power plants and installed capacity. The Southeast and Midwest regions were highly concentrated in terms of quantity and sugarcane bioelectricity installed capacity. Five clusters were identified for the number of power plants in 2017; for 2022, there were eight clusters. Regarding installed potential, there were 14 clusters in 2017 and 23 clusters in 2022, all statistically significant. The existence of clusters provides information on the competitive advantages in the national market, which can drive new investments in more densified areas or in the neighborhood. Identification of the location and concentration pattern showed that facilities in the state of São Paulo and the Northeast coast were responsible for the most important share of supply. These results indicate to investors the impact of electricity generation on the sector and the most relevant location for installing new thermoelectric plants.

WANG Huating, CHEN Heng, XU Gang et al.

The energy saving and emission reduction transformation of thermal power enterprises can reduce the coal consumption of thermal power supply, and then effectively reduce the growth of carbon dioxide emissions, which is of great significance to achieve the goal of carbon peak and carbon neutralization. Taking a 630 MW unit as an example, the system units of four waste heat utilization schemes (low-temperature economizer scheme, secondary low-temperature economizer scheme, bypass flue scheme and turbine boiler coupling scheme) were compared, and the key technical parameters and power saving effect were compared and analyzed. Moreover, a reference for the upgrading and technical transformation of energy conservation and emission reduction in China’s power industry was put forward. The results show that the exhaust gas temperature is reduced to 90 ℃, The coal consumption rate of power supply is reduced by 1.88 g/(kW⋅h) in the low-temperature economizer scheme, 2.16 g/(kW⋅h) in the secondary low-temperature economizer scheme, 2.29 g/(kW⋅h) in the bypass flue scheme, and 2.66 g/(kW⋅h) in the turbine boiler coupling scheme.

Qiang Gao, Lanqian Yang, Zhengyu Shu et al.

Enhancing the energy efficiency of building envelopes is one of the key strategies for energy conservation and reducing consumption in buildings. This study employs numerical research methods to explore the impact of crucial factors such as solar cell coverage, air channel height, indoor relative humidity, and indoor wind speed on the power generation performance and thermal comfort of a photovoltaic (PV)—Trombe wall. The dynamic changes in optical and thermal performance and energy efficiency matching mechanisms of this system are also discussed in hot summer and warm winter regions. The research findings indicate that the periods of good thermal comfort and power generation efficiency for humans are from 9:00 to 17:00 in winter. In summer, these periods are from 5:00 to 8:00 as well as from 17:00 to 20:00. When the system height is 2 m, the electricity price for power supplied by the PV—Trombe wall system is 25% lower than the residential price, with an annual energy generation of 322.5 kWh/m² of solar panel, which can save USD 6.35 in costs. Moreover, an experiment is conducted to investigate the thermoelectric correlation by constructing a traditional Trombe wall and an external PV—Trombe wall. When the coverage reached 52.08%, the overall system efficiency was maximized. At a coverage of 78.12%, the system’s thermal efficiency was at its lowest, while the maximum power generation was 510.3 W. It can be seen that the PV—Trombe wall possesses good economic benefits and energy saving as well as emission reduction potential in hot summers and warm winters regions, and the smooth implementation of related works will effectively promote its applications and promotions.

Mojtaba Nedaei, Abolfazl Keykhah, Borzo Kamary et al.

Population growth worldwide in recent decades has increased the demand for power. Geothermal energy provides a reliable and stable reservoir for power generation. This paper proposes an integration of single-flash geothermal with a dual-evaporation organic Rankine cycle (D-ORC) to generate power. The system’s performance is estimated via thermodynamic and thermoeconomic analyses. Five different zeotropic mixtures are considered the D-ORC working fluid, and their performance is compared at the optimum state. Perfluoropentane/butene presents the best performance indexes and is considered the D-ORC’s working fluid. Hence, the proposed system provides 7992.29 kW of net power with 62.42% exergetic efficiency. Also, the exergoeconomic performance indicates that the net present value and payback period are about 10.85 million dollars and 3.47 years, respectively. Also, the net present value of the proposed system is estimated for the four electricity sale and geofluid prices and reveals that the product sale costs influence the system’s economic performance more than the purchase cost. The exergy destruction distribution in the employed components is shown as the Grassmann diagram. The steam turbine has the highest exergy destruction of about 996 kW, and the first expansion valve with 714 kW of exergy destruction is the next one. Also, the condensers contain considerable exergy destruction, about 26.98% of total exergy destruction.

Camille J. Palmer, Jordan Northrop, Todd S. Palmer et al.

The detailed behavior of neutrons in a rapidly changing time-dependent physical system is a challenging computational physics problem, particularly when using Monte Carlo methods on heterogeneous high-performance computing architectures. A small number of algorithms and code implementations have been shown to be performant for time-independent (fixed source and k-eigenvalue) Monte Carlo, and there are existing simulation tools that successfully solve the time-dependent Monte Carlo problem on smaller computing platforms. To bridge this gap, a time-dependent version of ORNL’s Shift code has been recently developed. Shift’s history-based algorithm on CPUs, and its event-based algorithm on GPUs, have both been observed to scale well to very large numbers of processors, which motivated the extension of this code to solve time-dependent problems. The validation of this new capability requires a comparison with time-dependent neutron experiments. Lawrence Livermore National Laboratory’s (LLNL) pulsed sphere benchmark experiments were simulated in Shift to validate both the time-independent as well as new time-dependent features recently incorporated into Shift. A suite of pulsed-sphere models was simulated using Shift and compared to the available experimental data and simulations with MCNP. Overall results indicate that Shift accurately simulates the pulsed sphere benchmarks, and that the new time-dependent modifications of Shift are working as intended. Validated exascale neutron transport codes are essential for a wide variety of future multiphysics applications.