Hasil untuk "Electricity"

G. Temple

Xuezhi Liu, Jianzhong Wu, Nicholas Jenkins et al.

Energy supply systems are usually considered as individual sub-systems with separate energy vectors. However, the use of Combined Heat and Power (CHP) units, heat pumps and electric boilers creates linkages between electricity and heat networks. Two combined analysis methods were developed to investigate the performance of electricity and heat networks as an integrated whole. These two methods were the decomposed and integrated electrical-hydraulic-thermal calculation techniques in the forms of power flow and simple optimal dispatch. Both methods were based on models of the electrical network, hydraulic and thermal circuits, and the coupling components, focusing on CHP units and circulation pumps. A case study of Barry Island electricity and district heating networks was conducted, showing how both electrical and heat demand in a self-sufficient system (no interconnection with external systems) were met using CHP units. The comparison showed that the integrated method requires less iteration than the decomposed method.

Mohammad A. Salahuddin, Khorshed Alam

Axel Kleidon, Harald Lesch

The energy transition is also about switching to electricity-based technologies such as heat pumps and electric mobility. They avoid heat as an intermediate step and are therefore much more efficient. This can significantly reduce the demand for primary energy in the future, which can then be fully covered by the expansion of renewable energies. Entropy and the maximum possible combustion temperature can be used to understand why combustion is so inefficient.

Bartosz Uniejewski, Florian Ziel

Electricity price forecasting is a critical tool for the efficient operation of power systems and for supporting informed decision-making by market participants. This paper explores a novel methodology aimed at improving the accuracy of electricity price forecasts by incorporating probabilistic inputs of fundamental variables. Traditional approaches often rely on point forecasts of exogenous variables such as load, solar, and wind generation. Our method proposes the integration of quantile forecasts of these fundamental variables, providing a new set of exogenous variables that account for a more comprehensive representation of uncertainty. We conducted empirical tests on the German electricity market using recent data to evaluate the effectiveness of this approach. The findings indicate that incorporating probabilistic forecasts of load and renewable energy source generation significantly improves the accuracy of point forecasts of electricity prices. Furthermore, the results clearly show that the highest improvement in forecast accuracy can be achieved with full probabilistic forecast information. This highlights the importance of probabilistic forecasting in research and practice, particularly that the current state-of-the-art in reporting load, wind and solar forecast is insufficient.

Gianfranco Huaccho Zavala, Thomas Gheeraert, Thomas Gheeraert et al.

This work presents the further development and application of the multi-physics coupled code Serpent/subchanflow for analyzing cores loaded with fuel assembly designs characterized by complex geometries, such as the VVR-KN fuel assembly. A high-detail steady-state analysis of one VVR-KN fuel assembly is presented and discussed. The VVR-KN is a plate-type fuel assembly, arranged coaxially with hexagonal fuel-plate tubes. Its particular geometry layout configuration challenges both their neutronic and thermal-hydraulic modeling. In this work, the versatility of Serpent’s multi-physics interface is exploited by using the unstructured mesh-based interface to update the properties of the fuel and coolant materials in a coupled neutronic/thermal-hydraulic simulation; these properties are solved and provided by the thermal-hydraulic code Subchanflow. Both neutronic and thermal-hydraulic models are developed for a single fuel assembly of 6.83 cm distance pitch and 60 cm active height, and state conditions for the simulations are defined. Typical material composition and main thermal properties for the fuel-meat (UO2-Al) and aluminum cladding (SAV-1) materials are extracted from references. This work paves the way for multi-physics analysis of research reactors with non-regular plates or subchannel geometries.

Jayasri Nemala, Devi V.S. Anusuya, Tewari Preeti et al.

This study is concerned with the coordinated charging pattern of plugin electric vehicles (PEVs) by using a simulation and control framework. The first of these is to develop a novel control technique based on a grid structure to manage the charging power of PEVs in reaction to fluctuating renewable energy sources. The grid is assumed to control and communicate instantly and directly through a common control signal the electricity used for PEV charging. Based on the principle of market-based demand modeling, the subsequent theoretical formulation involves a system of partial differential equations for concurrent PEV charging. It is then applied to future real world driving data and compared to a PEV Monte Carlo model. Moreover, the principles of SM control are introduced to synthesize the robust output feedback controller for the system without state error. The fluctuating PEV count is addressed by focusing on the sole observable output: the instantaneous mismatch of supply and demand of renewable electricity by customers. The performance of the controller is evaluated in the present research based on a real wind power state trajectory through numerical simulations of the system.

S. Makonin, Bradley Ellert, I. Bajić et al.

With the cost of consuming resources increasing (both economically and ecologically), homeowners need to find ways to curb consumption. The Almanac of Minutely Power dataset Version 2 (AMPds2) has been released to help computational sustainability researchers, power and energy engineers, building scientists and technologists, utility companies, and eco-feedback researchers test their models, systems, algorithms, or prototypes on real house data. In the vast majority of cases, real-world datasets lead to more accurate models and algorithms. AMPds2 is the first dataset to capture all three main types of consumption (electricity, water, and natural gas) over a long period of time (2 years) and provide 11 measurement characteristics for electricity. No other such datasets from Canada exist. Each meter has 730 days of captured data. We also include environmental and utility billing data for cost analysis. AMPds2 data has been pre-cleaned to provide for consistent and comparable accuracy results amongst different researchers and machine learning algorithms. Design Type(s) observation design • time series design • data integration objective Measurement Type(s) electricity consumption • natural gas consumption • water consumption • weather record Technology Type(s) electricity meter • gas meter • water meter • weather station Factor Type(s) energy supply function Sample Characteristic(s) Province of British Columbia • building Design Type(s) observation design • time series design • data integration objective Measurement Type(s) electricity consumption • natural gas consumption • water consumption • weather record Technology Type(s) electricity meter • gas meter • water meter • weather station Factor Type(s) energy supply function Sample Characteristic(s) Province of British Columbia • building Machine-accessible metadata file describing the reported data (ISA-Tab format)

A. Dedinec, S. Filiposka, Aleksandar Dedinec et al.

Xiaping Zhang, M. Shahidehpour, Ahmed Alabdulwahab et al.

Subir Majumder, Ignacio Aravena, Le Xie

In recent years, power grids have seen a surge in large cryptocurrency mining firms, with individual consumption levels reaching 700MW. This study examines the behavior of these firms in Texas, focusing on how their consumption is influenced by cryptocurrency conversion rates, electricity prices, local weather, and other factors. We transform the skewed electricity consumption data of these firms, perform correlation analysis, and apply a seasonal autoregressive moving average model for analysis. Our findings reveal that, surprisingly, short-term mining electricity consumption is not directly correlated with cryptocurrency conversion rates. Instead, the primary influencers are the temperature and electricity prices. These firms also respond to avoid transmission and distribution network (T&D) charges - commonly referred to as four Coincident peak (4CP) charges - during the summer months. As the scale of these firms is likely to surge in future years, the developed electricity consumption model can be used to generate public, synthetic datasets to understand the overall impact on the power grid. The developed model could also lead to better pricing mechanisms to effectively use the flexibility of these resources towards improving power grid reliability.

Linian Wang, Jianghong Liu, Huibin Zhang et al.

Accurate day-ahead electricity price forecasting is essential for residential welfare, yet current methods often fall short in forecast accuracy. We observe that commonly used time series models struggle to utilize the prior correlation between price and demand-supply, which, we found, can contribute a lot to a reliable electricity price forecaster. Leveraging this prior, we propose a simple piecewise linear model that significantly enhances forecast accuracy by directly deriving prices from readily forecastable demand-supply values. Experiments in the day-ahead electricity markets of Shanxi province and ISO New England reveal that such forecasts could potentially save residents millions of dollars a year compared to existing methods. Our findings underscore the value of suitably integrating time series modeling with economic prior for enhanced electricity price forecasting accuracy.

M. G. Dubinina

The purpose of the study is to identify the impact of information and communication technologies and measures taken by telecommunications companies in China, Japan and South Korea on the environment of these countries.Materials and methods. Indexes of the ecological footprint (based on the Global Footprint Network data) and greenhouse gas emissions (based on the International Energy Agency data) for these countries are used as a measure of environmental assessment. Based on the Sustainability Reports of telecommunication companies in these countries (China Mobile, SK Telekom, KDDI and others), their strategies for environmental protection and achieving a zero carbon footprint are examined. The impact of information and communication technologies is assessed using indexes of the number of Internet users, fixed Internet access, mobile communications users per 100 people of the country’s population, the share of ICT goods and services in the total exports and imports of countries, as well as the growth index of IT investments in the private sector for Japan. For each country, a correlation matrix was constructed depending on the level of the logarithm of the ecological footprint (Y) on the logarithms of the listed indexes; the factors that most influence Y and are not multicollinear were selected. Based on the selected indexes, multiple regression models were developed for each country and their parameters were assessed.Results. For China and South Korea, a positive elasticity of the ecological footprint was obtained for the number of mobile phone users (for China) and fixed broadband Internet access (for South Korea). In addition, the import of ICT goods into a country reduces its environmental footprint, and the export of ICT services from the country leads to an increase in the index. For Japan, negative elasticities of the ICT sector indexes for the country’s ecological footprint were obtained, which is associated with measures taken by telecommunication companies to reduce their own consumption of electricity and other resources, as well as the widespread use of digital technologies for energy saving in other sectors of the Japanese economy.Conclusion. For China and South Korea, significant dependences of the country’s ecological footprint on the spread of digital technologies were obtained, and their diffusion entails an increase in the index. While this impact is not very large, the widespread adoption of 5G mobile communications in these countries should be taken into account, which could significantly increase the share of the ICT sector in the countries’ environmental footprint. At the same time, Japanese telecommunication companies are promoting environmental protection

Tingyi Chai, Chang Liu, Yichuan Xu et al.

The electricity consumption of the textile industry accounts for 2.12% of the total electricity consumption in society, making it one of the high-energy-consuming industries in China. The textile industry requires the use of a large amount of industrial steam at various temperatures during production processes, making its dispatch and operation more complex compared to conventional electricity–heat integrated energy systems. As an important demand-side management platform connecting the grid with distributed resources, a virtual power plant can aggregate textile industry users through an operator, regulating their energy consumption behavior and enhancing demand-side management efficiency. To effectively address the challenges in load regulation for textile industry users, this paper proposes a coordinated optimization dispatching method for electricity–steam virtual-based power plants focused on textile industrial parks. On one hand, targeting the impact of different energy prices on the energy usage behavior of textile industry users, an optimization dispatching model is established where the upper level consists of virtual power plant operators setting energy prices, and the lower level involves multiple textile industry users adjusting their purchase and sale strategies and changing their own energy usage behaviors accordingly. On the other hand, taking into account the energy consumption characteristics of steam, it is possible to optimize the production and storage behaviors of textile industry users during off-peak electricity periods in the power market. Through this electricity–steam optimization dispatching model, the virtual power plant operator’s revenue is maximized while the operating costs for textile industry users are minimized. Case study analyses demonstrate that this strategy can effectively enhance the overall economic benefits of the virtual power plant.

Jinping Zhao, Ali Arefi, Alberto Borghetti et al.

There is a general concern that the increasing penetration of electric vehicles (EVs) will result in higher aging failure probability of equipment and reduced network reliability. The electricity costs may also increase, due to the exacerbation of peak load led by uncontrolled EV charging. This paper proposes a linear optimization model for the assessment of the benefits of EV smart charging on both network reliability improvement and electricity cost reduction. The objective of the proposed model is the cost minimization, including the loss of load, repair costs due to aging failures, and EV charging expenses. The proposed model incorporates a piecewise linear model representation for the failure probability distributions and utilizes a machine learning approach to represent the EV charging load. Considering two different test systems (a 5-bus network and the IEEE 33-bus network), this paper compares aging failure probabilities, service unavailability, expected energy not supplied, and total costs in various scenarios with and without the implementation of EV smart charging.

D. Keles, Jonathan Scelle, Florentina Paraschiv et al.

Mansur Arief, Yan Akhra, Iwan Vanany

The rising demand for electric vehicles (EVs) worldwide necessitates the development of robust and accessible charging infrastructure, particularly in developing countries where electricity disruptions pose a significant challenge. Earlier charging infrastructure optimization studies do not rigorously address such service disruption characteristics, resulting in suboptimal infrastructure designs. To address this issue, we propose an efficient simulation-based optimization model that estimates candidate stations' service reliability and incorporates it into the objective function and constraints. We employ the control variates (CV) variance reduction technique to enhance simulation efficiency. Our model provides a highly robust solution that buffers against uncertain electricity disruptions, even when candidate station service reliability is subject to underestimation or overestimation. Using a dataset from Surabaya, Indonesia, our numerical experiment demonstrates that the proposed model achieves a 13% higher average objective value compared to the non-robust solution. Furthermore, the CV technique successfully reduces the simulation sample size up to 10 times compared to Monte Carlo, allowing the model to solve efficiently using a standard MIP solver. Our study provides a robust and efficient solution for designing EV charging infrastructure that can thrive even in developing countries with uncertain electricity disruptions.

Nkosingizwile Mazwi Mchunu, George Okechukwu Onatu, Trynos Gumbo

This paper discusses the impact of electricity blackouts and poor infrastructure on the livelihood of residents and the local economy of Johannesburg, South Africa. The importance of a stable electricity grid plays a vital role in the effective functioning of urban infrastructure and the economy. The importance of electricity in the present-day South Africa has not been emphasized enough to be prioritized at all levels of government, especially at the local level, as it is where all socio-economic activities take place. The new South Africa needs to redefine the importance of electricity by ensuring that it is accessible, affordable, and produced sustainably, and most of all, by ensuring that the energy transition initiatives to green energy take place in a planned manner without causing harm to the economy, which might deepen the plight of South Africans. Currently, the City of Johannesburg is a growing spatial entity in both demographic and urbanization terms, and growing urban spaces require a stable supply of electricity for the proper functioning of urban systems and the growth of the local economy. The growth of the city brings about a massive demand for electricity that outstrips the current supply of electricity available on the local grid. The imbalance in the current supply and growing demand for electricity result in energy blackouts in the city, which have ripple effects on the economy and livelihoods of the people of Johannesburg. This paper examines the impact of electricity blackouts and poor infrastructure on the livelihood of residents and the local economy of Johannesburg, South Africa.

Joseph de Vilmarest, Jethro Browell, Matteo Fasiolo et al.

Electricity load forecasting is a necessary capability for power system operators and electricity market participants. The proliferation of local generation, demand response, and electrification of heat and transport are changing the fundamental drivers of electricity load and increasing the complexity of load modelling and forecasting. We address this challenge in two ways. First, our setting is adaptive; our models take into account the most recent observations available, yielding a forecasting strategy able to automatically respond to changes in the underlying process. Second, we consider probabilistic rather than point forecasting; indeed, uncertainty quantification is required to operate electricity systems efficiently and reliably. Our methodology relies on the Kalman filter, previously used successfully for adaptive point load forecasting. The probabilistic forecasts are obtained by quantile regressions on the residuals of the point forecasting model. We achieve adaptive quantile regressions using the online gradient descent; we avoid the choice of the gradient step size considering multiple learning rates and aggregation of experts. We apply the method to two data sets: the regional net-load in Great Britain and the demand of seven large cities in the United States. Adaptive procedures improve forecast performance substantially in both use cases for both point and probabilistic forecasting.