Bibliografías temáticas / Electricity modelling

Literatura académica sobre el tema "Electricity modelling"

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Publicado: 11 de marzo de 2023

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Artículos de revistas sobre el tema "Electricity modelling"

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Engle, Robert F., Chowdhury Mustafa y John Rice. "Modelling peak electricity demand". Journal of Forecasting 11, n.º 3 (abril de 1992): 241–51. http://dx.doi.org/10.1002/for.3980110306.

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BECKER, RALF, STAN HURN y VLAD PAVLOV. "Modelling Spikes in Electricity Prices*". Economic Record 83, n.º 263 (2 de enero de 2008): 371–82. http://dx.doi.org/10.1111/j.1475-4932.2007.00427.x.

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Hinz, Juri. "Modelling day‐ahead electricity prices". Applied Mathematical Finance 10, n.º 2 (junio de 2003): 149–61. http://dx.doi.org/10.1080/1350486032000130329.

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Escribano, Alvaro, J. Ignacio Peña y Pablo Villaplana. "Modelling Electricity Prices: International Evidence*". Oxford Bulletin of Economics and Statistics 73, n.º 5 (19 de abril de 2011): 622–50. http://dx.doi.org/10.1111/j.1468-0084.2011.00632.x.

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Sulistio, J., A. Wirabhuana y M. G. Wiratama. "Indonesia’s Electricity Demand Dynamic Modelling". IOP Conference Series: Materials Science and Engineering 215 (junio de 2017): 012026. http://dx.doi.org/10.1088/1757-899x/215/1/012026.

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Moore, Jared y Noah Meeks. "Hourly modelling of Thermal Hydrogen electricity markets". Clean Energy 4, n.º 3 (septiembre de 2020): 270–87. http://dx.doi.org/10.1093/ce/zkaa014.

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Abstract The hourly operation of Thermal Hydrogen electricity markets is modelled. The economic values for all applicable chemical commodities are quantified (syngas, ammonia, methanol and oxygen) and an hourly electricity model is constructed to mimic the dispatch of key technologies: bi-directional power plants, dual-fuel heating systems and plug-in fuel-cell hybrid electric vehicles. The operation of key technologies determines hourly electricity prices and an optimization model adjusts the capacity to minimize electricity prices yet allow all generators to recover costs. We examine 12 cost scenarios for renewables, nuclear and natural gas; the results demonstrate emissions-free, ‘energy-only’ electricity markets whose supply is largely dominated by renewables. The economic outcome is made possible in part by seizing the full supply-chain value from electrolysis (both hydrogen and oxygen), which allows an increased willingness to pay for (renewable) electricity. The wholesale electricity prices average $25–$45/MWh, or just slightly higher than the assumed levelized cost of renewable energy. This implies very competitive electricity prices, particularly given the lack of need for ‘scarcity’ pricing, capacity markets, dedicated electricity storage or underutilized electric transmission and distribution capacity.
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Poulin, Alain. "Characterization and modelling of electricity consumption". European Journal of Electrical Engineering 13, n.º 5-6 (30 de diciembre de 2010): 717–40. http://dx.doi.org/10.3166/ejee.13.717-740.

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Barndorff-Nielsen, Ole E., Fred Espen Benth y Almut E. D. Veraart. "Modelling Electricity Futures by Ambit Fields". Advances in Applied Probability 46, n.º 3 (septiembre de 2014): 719–45. http://dx.doi.org/10.1239/aap/1409319557.

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In this paper we propose a new modelling framework for electricity futures markets based on so-called ambit fields. The new model can capture many of the stylised facts observed in electricity futures and is highly analytically tractable. We discuss martingale conditions, option pricing, and change of measure within the new model class. Also, we study the corresponding model for the spot price, which is implied by the new futures model, and show that, under certain regularity conditions, the implied spot price can be represented in law as a volatility modulated Volterra process.
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STROMBACK, C. T. "MODELLING ELECTRICITY DEMAND IN WESTERN AUSTRALIA". Australian Economic Papers 25, n.º 46 (junio de 1986): 106–17. http://dx.doi.org/10.1111/j.1467-8454.1986.tb00837.x.

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Barndorff-Nielsen, Ole E., Fred Espen Benth y Almut E. D. Veraart. "Modelling Electricity Futures by Ambit Fields". Advances in Applied Probability 46, n.º 03 (septiembre de 2014): 719–45. http://dx.doi.org/10.1017/s0001867800007345.

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In this paper we propose a new modelling framework for electricity futures markets based on so-calledambit fields. The new model can capture many of the stylised facts observed in electricity futures and is highly analytically tractable. We discuss martingale conditions, option pricing, and change of measure within the new model class. Also, we study the corresponding model for the spot price, which is implied by the new futures model, and show that, under certain regularity conditions, the implied spot price can be represented in law as a volatility modulated Volterra process.
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Tesis sobre el tema "Electricity modelling"

1

Katsigiannakis, Konstantinos. "Electricity price risk : modelling the supply stack". Thesis, Imperial College London, 2006. http://hdl.handle.net/10044/1/7429.

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Maiorano, Annalisa. "Modelling and analysis of oligopolistic electricity markets". Thesis, Brunel University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367886.

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Jędrzejewski, Piotr. "Modelling the European High-voltage electricity transmission". Thesis, KTH, Energiteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284152.

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This Master’s thesis describes modelling of the cross-border electricity transmission network of Europe. Under this work an extension of The Open Source Energy Model Base for the European Union (OSeMBE) was developed, implementing interconnections to the already existing model. The model is built using the Open Source Energy Modelling System (OSeMOSYS). The purpose of the model is to find cost optimal shape of the electricity system of Europe in the modelling period from 2015 to 2050. The model was used to analyse plans for the development of the electricity interconnection network, defined by the European Union on the list of Projects of Common Interests. For the thesis four scenarios of the European electricity system’s future development were modelled. The aim was to analyse on which borders new interconnection capacity would be beneficial and to test the influence of the interconnection development on the whole electricity system, particularly generation capacities and CO2 emissions. The electricity flows were analysed on each border. For a better overview in the analysis four regions were defined. The regions are adequate to the four priority corridors for electricity defined in Trans-European Networks for Energy (TEN-E). The major finding of the scenario that optimized the capacity of the interconnections in Europe, was that only 16% of capacities planned as the PCI are needed to be built. Most of those capacities should be developed in the northern Europe, particularly on the subsea borders Germany-Norway, United Kingdom-Norway, Poland-Lithuania, but also land ones Finland-Sweden, Denmark-Germany. The analysis also included utilization factors of the interconnection lines. However, due to the simplifications and limitation of modelling tool OSeMOSYS, the results needs to be taken with certain dose of caution and may serve only for indicating the direction of further analysis. The work conducted under this Master’s thesis, might also be a base for the future work, such as deeper look on the already obtained data with purpose to find relationship between electricity generation sources being utilized and interconnections utilization. The model might be also improved by implementation interconnection representation to the borders which were omitted here due to the lack of cost data.
Detta examensarbetebeskriver modellering av Europas gränsöverskridande elektriska transmissionsnät. Under detta arbete utvecklades en utvidgning av Open Source Energy Model Base för Europeiska unionen (OSeMBE) för implementering av sammankopplingar med den redan existerande modellen. Modellen är byggd med hjälp av Open Source Energy Modeling System (OSeMOSYS). Syftet med modellen är att hitta en kostnadseffektiv form av Europas elsystem under modelleringsperioden 2015 till 2050. Modellen användes för att validera planer för utveckling av sammankoppling för elnätet, definierade av Europeiska unionen i listan över projekt av gemensamt intresse. Under denna avhandling modellerades fyra scenarier för det europeiska elsystemets framtida utveckling. Målet för scenarierna var att analysera för vilka gränser en ny sammankopplingskapacitet skulle vara till nytta, samt att testa påverkan av samtrafikutvecklingen på hela elsystemet, särskilt produktionskapacitet och koldioxidutsläpp. Därefter analyserades flödena av elektricitet vid varje gräns, och för att förenkla analysen delades området upp i fyra regioner. Regionerna är uppdelade i enlighet med de fyra prioriterade korridorerna för elektricitet, definierade i Transeuropeiska Nät för Energi (TEN-E). Det huvudsakligaresultatet i scenariot som optimerade kapaciteten för sammankopplingarna i Europa var att endast 16% av den kapacitet som planerades som PCI behöver byggas. De flesta av dessa kapaciteter bör utvecklas i norra Europa, särskilt vid havsgränserna Tyskland-Norge, Storbritannien-Norge, Polen-Litauen, men också Finland-Sverige och Danmark-Tyskland. Även användningsfaktorer för samtrafikledningarna analyserades i arbetet.
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Fanone, Enzo. "Three Essays on Modelling of Electricity Markets". Doctoral thesis, Università degli studi di Trieste, 2012. http://hdl.handle.net/10077/7424.

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Richardson, Ian. "Integrated high-resolution modelling of domestic electricity demand and low voltage electricity distribution networks". Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/7968.

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Assessing the impact of domestic low-carbon technologies on the electricity distribution network requires a detailed insight into the operation of networks and the power demands of consumers. When used on a wide-scale, low-carbon technologies, including domestic scale micro-generation, heat pumps, electric vehicles and flexible demand, will change the nature of domestic electricity use. In providing a basis for the quantification of the impact upon distribution networks, this thesis details the construction and use of a high-resolution integrated model that simulates both existing domestic electricity use and low voltage distribution networks. Electricity demand is modelled at the level of individual household appliances and is based upon surveyed occupant time-use data. This approach results in a simulation that exhibits realistic time-variant demand characteristics, in both individual dwellings, as well as, groups of dwellings together. Validation is performed against real domestic electricity use data, measured for this purpose, from dwellings in Loughborough in the East Midlands, UK. The low voltage distribution network is modelled using real network data, and the output of its simulation is validated against measured network voltages and power demands. The integrated model provides a highly detailed insight into the operation of networks at a one-minute resolution. This integrated model is the main output of this research, alongside published articles and a freely downloadable software implementation of the demand model.
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Urquhart, Andrew J. "Accuracy of low voltage electricity distribution network modelling". Thesis, Loughborough University, 2016. https://dspace.lboro.ac.uk/2134/21799.

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The connection of high penetrations of new low carbon technologies such as PV and electric vehicles onto the distribution network is expected to cause power quality problems and the thermal capacity of feeder cables may be exceeded. Replacement of existing infrastructure is costly and so feeder cables are likely to be operated close to their hosting capacity. Network operators therefore require accurate simulation models so that new connection requests are not unnecessarily constrained. This work has reviewed recent studies and found a wide range of assumptions and approximations that are used in network models. A number of these have been investigated further, focussing on methods to specify the impedances of the cable, the impacts of harmonics, the time resolution used to model demand and generation, and assumptions regarding the connectivity of the neutral and ground conductors. The calculation of cable impedances is key to the accuracy of network models but only limited data is available from design standards or manufacturers. Several techniques have been compared in this work to provide guidance on the level of detail that should be included in the impedance model. Network modelling results with accurate impedances are shown to differ from those using published data. The demand data time resolution has been shown to affect estimates of copper losses in network cables. Using analytical methods and simulations, the relationship between errors in the loss estimates and the time resolution has been demonstrated and a method proposed such that the accuracy of loss estimates can be improved. For networks with grounded neutral conductors, accurate modelling requires the resistance of grounding electrodes to be taken into account. Existing methods either make approximations to the equivalent circuit or suffer from convergence problems. A new method has been proposed which resolves these difficulties and allows realistic scenarios with both grounded and ungrounded nodes to be modelled. In addition to the development of models, the voltages and currents in a section of LV feeder cable have been measured. The results provide a validation of the impedance calculations and also highlight practical difficulties associated with comparing simulation models with real measurement results.
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Sayin, Ipek. "Modelling Electricity Demand In Turkey For 1998-2011". Master's thesis, METU, 2013. http://etd.lib.metu.edu.tr/upload/12615515/index.pdf.

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This thesis estimates the quarterly electricity demand of Turkey. First of all proper seasonal time series model are found for the variables: electricity demand, temperature, gross domestic product and electricity price. After the right seasonal time series model are found Hylleberg, Engle, Granger and Yoo (1990) test is applied to each variable. The results of the test show that seasonal unit roots exist for the electricity price even it cannot be seen at the graph. The other variables have no seasonal unit roots when the proper seasonal time series model is chosen. Later, the cointegration is tested by looking at the vector autoregressive model. As the cointegration is seen vector error correction model is found. There is long-run equilibrium when the price is the dependent variable and independent variable is gross domestic product. Temperature is taken as exogenous variable and demand is not statistically significant.
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Figueroa, Marcelo Gustavo. "Modelling electricity markets : swing options and hybrid models". Thesis, Birkbeck (University of London), 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439778.

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Ma, Yuning. "Statistical modelling of rural distribution networks". Thesis, Queen's University Belfast, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269157.

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Chan, Kam Fong. "Modelling short-term interest rates and electricity spot prices /". [St. Lucia, Qld.], 2006. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19289.pdf.

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Libros sobre el tema "Electricity modelling"

1

Saltyte, Benth Jurate y Koekebakker Steen, eds. Stochastic modelling of electricity and related markets. New Jersey: World Scientific, 2008.

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Poppe, L. L. G. Analysis of the restructuring of the British electricity industry and modelling of the pool. Manchester: UMIST, 1995.

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S, Szmyd J. y Suzuki K. 1940-2007, eds. Modelling of transport phenomena in crystal growth. Southampton, [Eng.]: WIT Press, 2000.

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Golling, Christiane. A cost-efficient expansion of renewable energy sources in the European electricity system: An integrated modelling approach with a particular emphasis on diurnal and seasonal patterns. München: Oldenbourg Industrieverlag, 2012.

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Schetzen, Martin. Discrete systems laboratory using MATLAB. Australia: Brooks/Cole, 2000.

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Optimal design of control systems: Stochastic and deterministic problems. New York: M. Dekker, 1999.

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Mathematical Modelling of Contemporary Electricity Markets. Elsevier, 2021. http://dx.doi.org/10.1016/c2019-0-04254-9.

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Modelling prices in competitive electricity markets. Chicester, West Sussex, England: J. Wiley, 2004.

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Dagoumas, Athanasios. Mathematical Modelling of Contemporary Electricity Markets. Elsevier Science & Technology, 2021.

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Bunn, Derek W. Modelling Prices in Competitive Electricity Markets. Wiley, 2004.

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Capítulos de libros sobre el tema "Electricity modelling"

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Yıldırım, Miray Hanım, Ayşe Özmen, Özlem Türker Bayrak y Gerhard Wilhelm Weber. "Electricity Price Modelling for Turkey". En Operations Research Proceedings, 39–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29210-1_7.

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Ferreira, Paula y Elizabete Pereira. "Modelling Interconnected Renewable Electricity Systems". En Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 140–49. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45694-8_11.

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Motlagh, Omid, George Grozev y Elpiniki I. Papageorgiou. "A Neural Approach to Electricity Demand Forecasting". En Artificial Neural Network Modelling, 281–306. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28495-8_12.

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Kraft, Emil. "Overview French Electricity System". En Analysis and Modelling of the French Capacity Mechanism, 3–16. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-20093-0_2.

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Mariorano, A., Y. H. Song y M. Trovato. "Modelling and Analysis of Electricity Markets". En Power Systems, 13–49. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-3735-1_2.

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Castelli, Mauro, Matteo De Felice, Luca Manzoni y Leonardo Vanneschi. "Electricity Demand Modelling with Genetic Programming". En Progress in Artificial Intelligence, 213–25. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23485-4_22.

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Fritsche, Uwe R. "Modelling Externalities: Cost-Effectiveness of Reducing Environmental Impacts". En Integrated Electricity Resource Planning, 67–82. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1054-9_4.

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Garbacz, Christopher. "Residential Electricity Demand Modelling with Secret Data". En Regulating Utilities in an Era of Deregulation, 137–54. London: Palgrave Macmillan UK, 1987. http://dx.doi.org/10.1007/978-1-349-08714-3_9.

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Currie, Glen. "Modelling Consumer Roles in the Electricity System". En Australia’s Energy Transition, 55–83. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6145-0_4.

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Álvarez, C. y A. Gabaldón. "Distribution Load Modelling for Demand Side Management and End-Use Efficiency". En Integrated Electricity Resource Planning, 167–88. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1054-9_11.

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Actas de conferencias sobre el tema "Electricity modelling"

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Algora, Carlos. "Modelling And Manufacturing GaSb TPV Converters". En THERMOPHOTOVOLTAIC GENERATION OF ELECTRICITY: Fifth Conference on Thermophotovoltaic Generation of Electricity. AIP, 2003. http://dx.doi.org/10.1063/1.1539400.

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Hao, Shangyou y Fulin Zhuang. "A Novel Method for Decomposing Transmission Losses for Electricity Markets". En Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.735-082.

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Bojic, M., A. Patou-Parvedy y D. Nikolic. "On Photovoltaic Electricity Production by a Residential House in Reunion Island". En Modelling and Simulation. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.685-059.

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Martín, Diego. "Key Issues for an Accurate Modelling of GaSb TPV Converters". En THERMOPHOTOVOLTAIC GENERATION OF ELECTRICITY: Fifth Conference on Thermophotovoltaic Generation of Electricity. AIP, 2003. http://dx.doi.org/10.1063/1.1539399.

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Bauer, Thomas. "Heat Transfer Modelling of Glass Media within TPV Systems". En THERMOPHOTOVOLTAIC GENERATION OF ELECTRICITY: Sixth Conference on Thermophotovoltaic Generation of Electricity: TPV6. AIP, 2004. http://dx.doi.org/10.1063/1.1841890.

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Tiedemann, Kenneth H. "Modelling Electricity Demand Response: A Meta-Analysis". En Artificial Intelligence and Applications / Modelling, Identification, and Control. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.718-046.

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Tiedemann, Kenneth H. "Modelling Commercial Electricity Consumption by End-Use". En Artificial Intelligence and Applications / Modelling, Identification, and Control. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.718-028.

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"Using Genersys to model electricity generation expansion". En 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.c4.james.

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Nycander, Elis y Lennart Soder. "Modelling Prices in Hydro Dominated Electricity Markets". En 2022 18th International Conference on the European Energy Market (EEM). IEEE, 2022. http://dx.doi.org/10.1109/eem54602.2022.9921134.

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Selot, Florian y Bruno Robisson. "Formal modelling of the electricity balancing responsibility". En 2022 International Conference on Renewable Energies and Smart Technologies (REST). IEEE, 2022. http://dx.doi.org/10.1109/rest54687.2022.10022459.

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