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Journal articles on the topic 'Electricity modelling'

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Author: Grafiati
Published: 11 March 2023

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1

Engle, Robert F., Chowdhury Mustafa, and John Rice. "Modelling peak electricity demand." Journal of Forecasting 11, no. 3 (April 1992): 241–51. http://dx.doi.org/10.1002/for.3980110306.

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2

BECKER, RALF, STAN HURN, and VLAD PAVLOV. "Modelling Spikes in Electricity Prices*." Economic Record 83, no. 263 (January 2, 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, no. 2 (June 2003): 149–61. http://dx.doi.org/10.1080/1350486032000130329.

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4

Escribano, Alvaro, J. Ignacio Peña, and Pablo Villaplana. "Modelling Electricity Prices: International Evidence*." Oxford Bulletin of Economics and Statistics 73, no. 5 (April 19, 2011): 622–50. http://dx.doi.org/10.1111/j.1468-0084.2011.00632.x.

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5

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

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6

Moore, Jared, and Noah Meeks. "Hourly modelling of Thermal Hydrogen electricity markets." Clean Energy 4, no. 3 (September 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, no. 5-6 (December 30, 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, and Almut E. D. Veraart. "Modelling Electricity Futures by Ambit Fields." Advances in Applied Probability 46, no. 3 (September 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, no. 46 (June 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, and Almut E. D. Veraart. "Modelling Electricity Futures by Ambit Fields." Advances in Applied Probability 46, no. 03 (September 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, and W. C. Beattie. "Electricity demand modelling from disaggregate data." International Journal of Electrical Power & Energy Systems 12, no. 1 (January 1990): 50–60. http://dx.doi.org/10.1016/0142-0615(90)90021-3.

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12

Martín-Rodríguez, Gloria, and José Juan Cáceres-Hernández. "Modelling the hourly Spanish electricity demand." Economic Modelling 22, no. 3 (May 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, and Xingchao Yang. "Electricity-heat integrated agent modelling for participation in spot electricity market." IOP Conference Series: Earth and Environmental Science 983, no. 1 (February 1, 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, and Lukas Liebmann. "South East Europe electricity roadmap – modelling energy transition in the electricity sectors." Climate Policy 19, no. 4 (October 10, 2018): 495–510. http://dx.doi.org/10.1080/14693062.2018.1532390.

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16

Reade, Samantha, Temesgen Zewotir, and Delia North. "Modelling household electricity consumption in eThekwini municipality." Journal of Energy in Southern Africa 27, no. 2 (July 20, 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 (March 8, 2007): 603. http://dx.doi.org/10.21914/anziamj.v47i0.1064.

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18

Londoño Hernández, Sandra Milena, and Carlos Arturo Lozano Moncada. "A review of electricity market modelling tools." Ingeniería e Investigación 29, no. 3 (September 1, 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, and Daniel Mitchell. "Modelling electricity prices: a time change approach." Quantitative Finance 16, no. 7 (February 2, 2016): 1089–109. http://dx.doi.org/10.1080/14697688.2015.1125521.

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20

Balachandra, P., and Vijay Chandru. "Modelling electricity demand with representative load curves." Energy 24, no. 3 (March 1999): 219–30. http://dx.doi.org/10.1016/s0360-5442(98)00096-6.

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21

Dilaver, Zafer, and Lester C. Hunt. "Modelling and forecasting Turkish residential electricity demand." Energy Policy 39, no. 6 (June 2011): 3117–27. http://dx.doi.org/10.1016/j.enpol.2011.02.059.

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22

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

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23

Clements, A. E., R. Herrera, and A. S. Hurn. "Modelling interregional links in electricity price spikes." Energy Economics 51 (September 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, no. 2 (April 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, and Peter Molnár. "Electricity consumption modelling: A case of Germany." Economic Modelling 55 (June 2016): 92–101. http://dx.doi.org/10.1016/j.econmod.2016.02.010.

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26

Ansu-Mensah, Peter, and Paul Adjei Kwakwa. "Modelling electricity consumption in Ghana: the role of financial development indicators." Green Finance 4, no. 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, and Iddi S. N. Mkilaha. "Modelling energy supply options for electricity generations in Tanzania." Journal of Energy in Southern Africa 26, no. 3 (September 23, 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, and Fang Chen. "Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images." Applied Sciences 12, no. 8 (April 14, 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, and Esra Adıyeke. "Comparison of Electricity Spot Price Modelling and Risk Management Applications." Energies 13, no. 18 (September 10, 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, and Jānis Zuters. "Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia." Economics and Business 29, no. 1 (August 1, 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., and 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, no. 8 (September 23, 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, and Michael C. Georgiadis. "A Review on the Complementarity Modelling in Competitive Electricity Markets." Energies 14, no. 21 (November 1, 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, and Arifin Rosyadi. "Indonesia’s Electricity Dynamic Modelling toward Its National Policies." International Journal of Modeling and Optimization 8, no. 6 (December 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, and Obioma G. Onukwube. "MODELLING THE GENERATION/TRANSMISSION AND DISTRIBUTION OF ELECTRICITY." Far East Journal of Applied Mathematics 114 (August 20, 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, no. 3 (June 30, 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, no. 4 (October 1, 2011): 275–81. http://dx.doi.org/10.3844/jmssp.2011.275.281.

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37

Irwin, G. W., W. Monteith, and W. C. Beattie. "Statistical electricity demand modelling from consumer billing data." IEE Proceedings C Generation, Transmission and Distribution 133, no. 6 (1986): 328. http://dx.doi.org/10.1049/ip-c.1986.0048.

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38

Cartea, Álvaro, Marcelo G. Figueroa, and Hélyette Geman. "Modelling Electricity Prices with Forward Looking Capacity Constraints." Applied Mathematical Finance 16, no. 2 (April 2009): 103–22. http://dx.doi.org/10.1080/13504860802351164.

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39

Frikha, Noufel, and Vincent Lemaire. "Joint Modelling of Gas and Electricity Spot Prices." Applied Mathematical Finance 20, no. 1 (March 2013): 69–93. http://dx.doi.org/10.1080/1350486x.2012.658220.

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40

Bucovetchi, Olga, Cristina Petronela Simion, and 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, and K. B. Debnath. "Modelling community electricity demand for UK and India." Sustainable Cities and Society 55 (April 2020): 102054. http://dx.doi.org/10.1016/j.scs.2020.102054.

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42

De Sanctis, Angela, and Carlo Mari. "Modelling spikes in electricity markets using excitable dynamics." Physica A: Statistical Mechanics and its Applications 384, no. 2 (October 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, no. 2 (January 1993): 149–59. http://dx.doi.org/10.1016/0306-2619(93)90064-v.

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44

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

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45

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

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46

Ge, Yan, Chengke Zhou, and Donald M. Hepburn. "Domestic electricity load modelling by multiple Gaussian functions." Energy and Buildings 126 (August 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, and Wolf Fichtner. "Integrated modelling of European electricity and hydrogen markets." Applied Energy 328 (December 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, no. 3 (December 12, 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, and Ricardo Covas. "Electricity Spot Price Forecast by Modelling Supply and Demand Curve." Mathematics 10, no. 12 (June 11, 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, no. 2010 (January 1, 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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