Artículos de revistas: "Electricity modelling"

1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Dodds, G. I., G. W. Irwin y W. C. Beattie. "Electricity demand modelling from disaggregate data". International Journal of Electrical Power & Energy Systems 12, n.º 1 (enero de 1990): 50–60. http://dx.doi.org/10.1016/0142-0615(90)90021-3.

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12

Martín-Rodríguez, Gloria y José Juan Cáceres-Hernández. "Modelling the hourly Spanish electricity demand". Economic Modelling 22, n.º 3 (mayo de 2005): 551–69. http://dx.doi.org/10.1016/j.econmod.2004.09.003.

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13

Yin, Xunhu, Huahua Wu, Huangxing Su, Zhiquan Meng y Xingchao Yang. "Electricity-heat integrated agent modelling for participation in spot electricity market". IOP Conference Series: Earth and Environmental Science 983, n.º 1 (1 de febrero de 2022): 012020. http://dx.doi.org/10.1088/1755-1315/983/1/012020.

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Abstract Due to the limitation of market rules, many small capacity power plants cannot directly participate in the spot electricity market, reducing the ability of the market to optimize the allocation of power generation resources. Among these power plants, the electric power output of some combined heat and power (CHP) plants is constrained by their heat output, which should be considered in the dispatch. Therefore, the electricity-heat integrated agents are defined and introduced to aggregate small capacity power plants and participate in the spot electricity market. Firstly, the model of electricity-heat integrated agents is formulated based on the aggregation of the internal generators and CHP units. Then, the framework of the spot electricity market with the participation of electricity-heat integrated agents is constructed. Moreover, the market clearing model is further developed by combining the electricity-heat integrated agent model and the security constrained unit commitment model. Simulation results in the case verify the effectiveness of the proposed model and technique.

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14

Edomah, Norbert. "Modelling Future Electricity: Rethinking the Organizational Model of Nigeria’s Electricity Sector". IEEE Access 5 (2017): 27074–80. http://dx.doi.org/10.1109/access.2017.2769338.

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15

Szabó, László, Ágnes Kelemen, András Mezősi, Zsuzsanna Pató, Enikő Kácsor, Gustav Resch y Lukas Liebmann. "South East Europe electricity roadmap – modelling energy transition in the electricity sectors". Climate Policy 19, n.º 4 (10 de octubre de 2018): 495–510. http://dx.doi.org/10.1080/14693062.2018.1532390.

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16

Reade, Samantha, Temesgen Zewotir y Delia North. "Modelling household electricity consumption in eThekwini municipality". Journal of Energy in Southern Africa 27, n.º 2 (20 de julio de 2016): 38. http://dx.doi.org/10.17159/2413-3051/2016/v27i2a1340.

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South African municipalities are faced with the challenges of growing demand for services. This study models the energy consumption estimation practice within the Durban municipal area. It was found that an estimation technique that accounts for the seasonal and monthly effects, as well as residential type, predicts monthly individual household electricity consumption with minimum error. Models that were developed may be used to estimate electricity consumption for household billings within a municipality.

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17

Jones, Owen Dafydd. "Modelling electricity power cuts in the UK". ANZIAM Journal 47 (8 de marzo de 2007): 603. http://dx.doi.org/10.21914/anziamj.v47i0.1064.

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18

Londoño Hernández, Sandra Milena y Carlos Arturo Lozano Moncada. "A review of electricity market modelling tools". Ingeniería e Investigación 29, n.º 3 (1 de septiembre de 2009): 67–73. http://dx.doi.org/10.15446/ing.investig.v29n3.15185.

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Deregulating electricity markets around the world in the search for efficiency has introduced competition into the electricity marketing and generation business. Studying interactions amongst the participants has thus acquired great importance for regulators and market participants for analysing market evolution and suitably defining their bidding strategies. Different tools have therefore been used for modelling competitive electricity markets during the last few years. This paper presents an analytical review of the bibliography found regarding this subject; it also presents the most used tools along with their advantages and disadvantages. Such analysis was done by comparing the models used, identifying the main market characteristics such as market structure, bid structure and kind of bidding. This analysis concluded that the kind of tool to be used mainly depends on a particular study’s goal and scope.

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19

Li, Lingfei, Rafael Mendoza-Arriaga, Zhiyu Mo y Daniel Mitchell. "Modelling electricity prices: a time change approach". Quantitative Finance 16, n.º 7 (2 de febrero de 2016): 1089–109. http://dx.doi.org/10.1080/14697688.2015.1125521.

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20

Balachandra, P. y Vijay Chandru. "Modelling electricity demand with representative load curves". Energy 24, n.º 3 (marzo de 1999): 219–30. http://dx.doi.org/10.1016/s0360-5442(98)00096-6.

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21

Dilaver, Zafer y Lester C. Hunt. "Modelling and forecasting Turkish residential electricity demand". Energy Policy 39, n.º 6 (junio de 2011): 3117–27. http://dx.doi.org/10.1016/j.enpol.2011.02.059.

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22

Eriksson, Clas, Johan Lindén y Christos Papahristodoulou. "Modelling the value of variable renewable electricity". Energy Procedia 158 (febrero de 2019): 3358–62. http://dx.doi.org/10.1016/j.egypro.2019.01.960.

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23

Clements, A. E., R. Herrera y A. S. Hurn. "Modelling interregional links in electricity price spikes". Energy Economics 51 (septiembre de 2015): 383–93. http://dx.doi.org/10.1016/j.eneco.2015.07.014.

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24

Read, E. G. "OR Modelling for a Deregulated Electricity Sector". International Transactions in Operational Research 3, n.º 2 (abril de 1996): 129–37. http://dx.doi.org/10.1111/j.1475-3995.1996.tb00041.x.

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25

Do, Linh Phuong Catherine, Kuan-Heng Lin y Peter Molnár. "Electricity consumption modelling: A case of Germany". Economic Modelling 55 (junio de 2016): 92–101. http://dx.doi.org/10.1016/j.econmod.2016.02.010.

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26

Ansu-Mensah, Peter y Paul Adjei Kwakwa. "Modelling electricity consumption in Ghana: the role of financial development indicators". Green Finance 4, n.º 1 (2021): 54–70. http://dx.doi.org/10.3934/gf.2022003.

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<abstract> <p>Access to electricity is touted as one of the ways of reducing poverty and improving the livelihoods of people. However, an increased consumption may also contribute to higher carbon dioxide emissions. While many studies have therefore assessed the determinants of electricity consumption for developing countries that have a lower electricity consumption and inadequate supply to meet demand, the effect of financial development on electricity consumption has been mixed. Consequently, this study models electricity consumption in Ghana with special attention on the effect of financial development. The results show that price reduces electricity consumption while income and population density increase consumption of electricity. When financial development is represented by domestic credit to private sector, domestic credit to private sector by banks and broad money supply, the effect is negative on electricity consumption. However, the effect is positive when financial development is represented by foreign direct investment. A financial index constructed from the four indicators shows financial development reduces electricity consumption in Ghana. Among other things the policy implication includes the need to formulate appropriate policy based on a specific indicator for financial development.</p> </abstract>

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27

Kichonge, Baraka, Geoffrey R. John y Iddi S. N. Mkilaha. "Modelling energy supply options for electricity generations in Tanzania". Journal of Energy in Southern Africa 26, n.º 3 (23 de septiembre de 2015): 41–57. http://dx.doi.org/10.17159/2413-3051/2015/v26i3a2128.

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The current study applies an energy-system model to explore energy supply options in meeting Tanzania’s electricity demands projection from 2010 to 2040. Three economic scenarios namely; business as usual (BAU), low economic consumption scenario (LEC) and high economic growth scenario (HEC) were developed for modelling purposes. Moreover, the study develops a dry weather scenario to explore how the country’s electricity system would behave under dry weather conditions. The model results suggests: If projected final electricity demand increases as anticipated in BAU, LEC and HEC scenarios, the total installed capacity will expand at 9.05%, 8.46% and 9.8% respectively from the base value of 804.2MW. Correspondingly, the model results depict dominance of hydro, coal, natural gas and geothermal as least-cost energy supply options for electricity generation in all scenarios. The alternative dry weather scenario formulated to study electricity system behaviour under uncertain weather conditions suggested a shift of energy supply option to coal and natural gas (NG) dominance replacing hydro energy. The least cost optimization results further depict an insignificant contribution of renewable energy technologies in terms of solar thermal, wind and solar PV into the total generation shares. With that regard, the renewable energy penetration policy option (REPP), as an alternative scenario suggests the importance of policy options that favour renewable energy technologies inclusion in electricity generation. Sensitivity analysis on the discount rate to approximate the influence of discount rate on the future pattern of electricity generation capacity demonstrated that lower values favour wind and coal fired power plants, while higher values favour the NG technologies. Finally, the modelling results conclude the self-sufficiency of the country in generating future electricity using its own energy resources.

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28

Gao, Xumiao, Mingquan Wu, Ju Gao, Li Han, Zheng Niu y Fang Chen. "Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images". Applied Sciences 12, n.º 8 (14 de abril de 2022): 3971. http://dx.doi.org/10.3390/app12083971.

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The accurate estimation of electricity consumption and its spatial distribution are important in electricity infrastructural planning and the achievement of the United Nations Sustainable Development Goal 7 (SDG7). Electricity consumption can be estimated based on its correlation with nighttime lights observed using remote sensing imagery. Since night-light images are easily affected by cloud cover, few previous studies have estimated electricity consumption in cloudy areas. Taking Cambodia as an example, the present study proposes a method for denoising night-light images in cloudy areas and estimating electricity consumption. The results show that an exponential model is superior to linear and power function models for modelling the relationship between total night-light data and electricity consumption in Cambodia. The month-specific substitution method is best for annual night-light image synthesis in cloudy areas. Cambodia’s greatest electricity consumption occurs in its four most economically developed cities. Electricity consumption spreads outwards from these cities along the main transport routes to a large number of unelectrified areas.

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29

Çanakoğlu, Ethem y Esra Adıyeke. "Comparison of Electricity Spot Price Modelling and Risk Management Applications". Energies 13, n.º 18 (10 de septiembre de 2020): 4698. http://dx.doi.org/10.3390/en13184698.

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In dealing with sharp changes in electricity prices, contract planning is considered as a vital risk management tool for stakeholders in deregulated power markets. In this paper, dynamics of spot prices in Turkish electricity market are analyzed, and predictive performance of several models are compared, i.e., time series models and regime-switching models. Different models for derivative pricing are proposed, and alternative portfolio optimization problems using mean-variance optimization and conditional value at risk (CVaR) are solved. Expected payoff and risk structure for different hedging strategies for a hypothetical electricity company with a given demand are analyzed. Experimental studies show that regime-switching models are able to capture electricity characteristics better than their standard counterparts. In addition, evaluations with various risk management models demonstrate that those models are highly competent in providing an effective risk control practice for electricity markets.

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30

Bobinaite, Viktorija y Jānis Zuters. "Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia". Economics and Business 29, n.º 1 (1 de agosto de 2016): 12–26. http://dx.doi.org/10.1515/eb-2016-0017.

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AbstractThe paper aims at modelling the electricity generator’s expectations about price development in the Latvian day-ahead electricity market. Correlation and sensitivity analysis methods are used to identify the key determinants of electricity price expectations. A neural network approach is employed to model electricity price expectations. The research results demonstrate that electricity price expectations depend on the historical electricity prices. The price a day ago is the key determinant of price expectations and the importance of the lagged prices reduces as the time backwards lengthens. Nine models of electricity price expectations are prepared for different natural seasons and types of the day. The forecast accuracy of models varies from high to low, since errors are 7.02 % to 59.23 %. The forecasting power of models for weekends is reduced; therefore, additional determinants of electricity price expectations should be considered in the models and advanced input selection algorithms should be applied in future research. Electricity price expectations affect the generator’s loss through the production decisions, which are made considering the expected (forecasted) prices. The models allow making the production decision at a sufficient level of accuracy.

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31

Meszaros, M. T. y S. O. Bade Shrestha. "Modelling of green electricity effects on oligopoly market". Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy 225, n.º 8 (23 de septiembre de 2011): 1007–15. http://dx.doi.org/10.1177/0957650911416560.

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This article builds a simulation model to analyse the effect of a change in the main policy variables of the British Renewable Obligation System, i.e. how the increase in the minimum requirement of green electricity can affect the prices and quantities in the electricity market under oligopoly market structure. The results show that the increasing quota obligation increases the price of electricity. The outcomes of this computational model emphasize the importance of the capacity limit which can constrain the market power and increase the competition in the market. In addition, the simulation shows that the integration of fossil-fuel and renewable producers has very small effect on production and prices because of the capacity limits.

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32

Dimitriadis, Christos N., Evangelos G. Tsimopoulos y Michael C. Georgiadis. "A Review on the Complementarity Modelling in Competitive Electricity Markets". Energies 14, n.º 21 (1 de noviembre de 2021): 7133. http://dx.doi.org/10.3390/en14217133.

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In recent years, the ever-increasing research interest in various aspects of the electricity pool-based markets has generated a plethora of complementarity-based approaches to determine participating agents’ optimal offering/bidding strategies and model players’ interactions. In particular, the integration of multiple and diversified market agents, such as conventional generation companies, renewable energy sources, electricity storage facilities and agents with a mixed generation portfolio has instigated significant competition, as each player attempts to establish their market dominance and realize substantial financial benefits. The employment of complementarity modelling approaches can also prove beneficial for the optimal coordination of the electricity and natural gas market coupling. Linear and nonlinear programming as well as complementarity modelling, mainly in the form of mathematical programs with equilibrium constraints (MPECs), equilibrium programs with equilibrium constraints (EPECs) and conjectural variations models (CV) have been widely employed to provide effective market clearing mechanisms, enhance agents’ decision-making process and allow them to exert market power, under perfect and imperfect competition and various market settlements. This work first introduces the theoretical concepts that regulate the majority of contemporary competitive electricity markets. It then presents a comprehensive review of recent advances related to complementarity-based modelling methodologies and their implementation in current competitive electricity pool-based markets applications.

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33

Sulistio, Joko y Arifin Rosyadi. "Indonesia’s Electricity Dynamic Modelling toward Its National Policies". International Journal of Modeling and Optimization 8, n.º 6 (diciembre de 2018): 311–14. http://dx.doi.org/10.7763/ijmo.2018.v8.670.

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34

Amuji, Harrison O., Uchenna U. Moneke, Chinemerem Igboanusi y Obioma G. Onukwube. "MODELLING THE GENERATION/TRANSMISSION AND DISTRIBUTION OF ELECTRICITY". Far East Journal of Applied Mathematics 114 (20 de agosto de 2022): 49–64. http://dx.doi.org/10.17654/0972096022014.

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35

Son, Young Sook. "Prediction of Electricity Sales by Time Series Modelling". Korean Journal of Applied Statistics 27, n.º 3 (30 de junio de 2014): 419–30. http://dx.doi.org/10.5351/kjas.2014.27.3.419.

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36

S. "Forecasting and Modelling Electricity Demand Using Anfis Predictor". Journal of Mathematics and Statistics 7, n.º 4 (1 de octubre de 2011): 275–81. http://dx.doi.org/10.3844/jmssp.2011.275.281.

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37

Irwin, G. W., W. Monteith y W. C. Beattie. "Statistical electricity demand modelling from consumer billing data". IEE Proceedings C Generation, Transmission and Distribution 133, n.º 6 (1986): 328. http://dx.doi.org/10.1049/ip-c.1986.0048.

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38

Cartea, Álvaro, Marcelo G. Figueroa y Hélyette Geman. "Modelling Electricity Prices with Forward Looking Capacity Constraints". Applied Mathematical Finance 16, n.º 2 (abril de 2009): 103–22. http://dx.doi.org/10.1080/13504860802351164.

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39

Frikha, Noufel y Vincent Lemaire. "Joint Modelling of Gas and Electricity Spot Prices". Applied Mathematical Finance 20, n.º 1 (marzo de 2013): 69–93. http://dx.doi.org/10.1080/1350486x.2012.658220.

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40

Bucovetchi, Olga, Cristina Petronela Simion y Radu D. Stanciu. "Object-oriented Modelling Applied to Electricity Critical Infrastructures". Procedia Technology 19 (2015): 651–56. http://dx.doi.org/10.1016/j.protcy.2015.02.092.

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41

Jenkins, D. P., S. Patidar, P. McCallum y K. B. Debnath. "Modelling community electricity demand for UK and India". Sustainable Cities and Society 55 (abril de 2020): 102054. http://dx.doi.org/10.1016/j.scs.2020.102054.

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42

De Sanctis, Angela y Carlo Mari. "Modelling spikes in electricity markets using excitable dynamics". Physica A: Statistical Mechanics and its Applications 384, n.º 2 (octubre de 2007): 457–67. http://dx.doi.org/10.1016/j.physa.2007.05.015.

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43

Gustafsson, Stig-Inge. "Mathematical modelling of district-heating and electricity loads". Applied Energy 46, n.º 2 (enero de 1993): 149–59. http://dx.doi.org/10.1016/0306-2619(93)90064-v.

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44

Atalla, Tarek N. y Lester C. Hunt. "Modelling residential electricity demand in the GCC countries". Energy Economics 59 (septiembre de 2016): 149–58. http://dx.doi.org/10.1016/j.eneco.2016.07.027.

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45

Narajewski, Michał y Florian Ziel. "Econometric modelling and forecasting of intraday electricity prices". Journal of Commodity Markets 19 (septiembre de 2020): 100107. http://dx.doi.org/10.1016/j.jcomm.2019.100107.

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46

Ge, Yan, Chengke Zhou y Donald M. Hepburn. "Domestic electricity load modelling by multiple Gaussian functions". Energy and Buildings 126 (agosto de 2016): 455–62. http://dx.doi.org/10.1016/j.enbuild.2016.05.060.

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47

Hesel, Philipp, Sebastian Braun, Florian Zimmermann y Wolf Fichtner. "Integrated modelling of European electricity and hydrogen markets". Applied Energy 328 (diciembre de 2022): 120162. http://dx.doi.org/10.1016/j.apenergy.2022.120162.

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48

Samuel Asuamah Yeboah. "ECONOMETRIC MODELLING OF THE CONSUMPTION OF ELECTRICITY IN GHANA WHEN STRUCTURAL BREAKS EXIST". Journal of Economic Info 7, n.º 3 (12 de diciembre de 2020): 205–20. http://dx.doi.org/10.31580/jei.v7i3.1612.

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The research modelled electricity consumption for Ghana using annual data for the period 1971-2011, obtained from world development indicator. The research adopts the Gregory and Hansen model of cointegration for the estimation in the presence of structural breaks. The results reveal stable short run and long-run relationships among the explanatory variables and electricity consumption. The findings suggest that financial development explain electricity consumption in Ghana both in the short run and in the long run. The other variables (trade openness, price, and income) in the estimated model do not significantly explain electricity consumption. Therefore, they are not reliable policy variables in managing electricity consumption.

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49

Pinhão, Miguel, Miguel Fonseca y Ricardo Covas. "Electricity Spot Price Forecast by Modelling Supply and Demand Curve". Mathematics 10, n.º 12 (11 de junio de 2022): 2012. http://dx.doi.org/10.3390/math10122012.

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Electricity price forecasting has been a booming field over the years, with many methods and techniques being applied with different degrees of success. It is of great interest to the industry sector, becoming a must-have tool for risk management. Most methods forecast the electricity price itself; this paper gives a new perspective to the field by trying to forecast the dynamics behind the electricity price: the supply and demand curves originating from the auction. Given the complexity of the data involved which include many block bids/offers per hour, we propose a technique for market curve modeling and forecasting that incorporates multiple seasonal effects and known market variables, such as wind generation or load. It is shown that this model outperforms the benchmarked ones and increases the performance of ensemble models, highlighting the importance of the use of market bids in electricity price forecasting.

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50

Cahill, Brendan. "Wave farm modelling: harnessing Ireland’s greatest energy resource". Boolean: Snapshots of Doctoral Research at University College Cork, n.º 2010 (1 de enero de 2010): 22–25. http://dx.doi.org/10.33178/boolean.2010.6.

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Wave Energy Converters are being developed worldwide, including by a number of Irish companies, as a sustainable and environmentally-friendly means of generating electricity using the power of the ocean. By 2020 it is envisaged that hundreds of these devices will be deployed off the West Coast of Ireland in arrays known as Wave Farms and connected to the national electricity grid. The focus of my Ph.D. project, being carried out at the Hydraulics and Maritime Research Centre (HMRC) in UCC, is to create realistic models of these Wave Farms so that we can better understand their behaviour and ultimately optimise their performance. Nearly 95% of the electricity consumed in Ireland is generated from fossil fuels such as gas, coal and oil. As reserves of these resources begin to dry up, leading to scarcity of supply and increases in cost, and the environmental concerns about carbon dioxide emissions become more pronounced, ...

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Artículos de revistas sobre el tema "Electricity modelling"

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