Energy resources’ seasonal/daily dependencies under coupled operation of day-ahead markets

References

• ANRE. 2014. Ordin ANRE nr.82/2014 - privind aprobarea Regulamentului de organizare și funcționare a pieței pentru ziua următoare de energie electrică cu respectarea mecanismului de cuplare prin preț a piețelor și modificarea unor acte normative care reglementează piața pentru ziua următoare de energie electrică, Monitorul Oficial al României, Partea I [ANRE Order no. 82/2014 - regarding the approval of the Regulation for the organization and functioning of the Day-Ahead electricity market, respecting the mechanism of price coupling of the markets and the modification of some normative acts that regulate Day-Ahead electricity market, the Official Journal of Romania, Part I], no.668/11.IX.2014. https://portal.anre.ro/PublicLists/Ordin.
   Google Scholar
• Azzuni, A., and C. Breyer. 2018. Definitions and dimensions of energy security: A literature review. WIREs Energy Environment 7:e268. doi:10.1002/wene.268.
   Web of Science ®Google Scholar
• Bello, A., J. Reneses, A. Muñoz, and A. Delgadillo. 2016. Probabilistic forecasting of hourly electricity prices in the medium-term using spatial interpolation techniques. International Journal of Forecasting 32 (3):966–80. doi:10.1016/j.ijforecast.2015.06.002.
   Web of Science ®Google Scholar
• Bridge, G., B. Özkaynak, and E. Turhan. 2018. Energy infrastructure and the fate of the nation: Introduction to special issue. Energy Research & Social Science 41:1–11. doi:10.1016/j.erss.2018.04.029.
   Web of Science ®Google Scholar
• Brockwell, P. J., and R. A. Davis. 2016. Introduction to time series and forecasting. 3rd ed. Switzerland: Springer International Publishing. doi:10.1007/978-3-319-29854-2.
   Google Scholar
• Brown, M. B., and A. B. Forsythe. 1974. Robust tests for the equality of variances. Journal of the American Statistical Association 69 (346):364–67. doi:10.1080/01621459.1974.10482955.
   Web of Science ®Google Scholar
• Bunn, D., A. Andresen, D. Chen, and S. Westgaard. 2016. Analysis and forecasting of electricity price risks with quantile factor models. The Energy Journal 37 (1):101–22. doi:10.5547/01956574.37.1.dbun.
   Web of Science ®Google Scholar
• Bunn, D., and A. Gianfreda. 2010. Integration and shock transmissions across European electricity forward markets. Energy Economics 32 (2):278–91. doi:10.1016/j.eneco.2009.09.005.
   Web of Science ®Google Scholar
• Bushnell, J., and K. Novan. 2018. Setting with the sun: the impacts of renewable energy on wholesale power markets. Energy Institute at Haas working papers 292, August. http://ei.haas.berkeley.edu/support/.
   Google Scholar
• Castagneto-Gissey, G., M. Chavez, and F. De Vico Fallani. 2014. Dynamic Granger-causal networks of electricity spot prices: A novel approach to market integration. Energy Economics 44:422–32. doi:10.1016/j.eneco.2014.05.008.
   Web of Science ®Google Scholar
• Chauvenet, W. 1863. A manual of spherical and practical astronomy, Vol II: Theory and use of astronomical instruments. Philadelphia: J. B. Lippincott & Company.
   Google Scholar
• Colton, J., K. Corscadden, S. Fast, M. Gattinger, J. Gehman, M. Hall Findlay, D. Morgan, J. Sayers, J. Winter, and A. Yatchew. 2016. Energy projects, social licence, public acceptance and regulatory systems in Canada: A white paper. SPP Research Papers 9 (20), the School of Public Policy. University of Calgary, Canada.
   Google Scholar
• de Menezes, L. M., and M. Houllier. 2016. Reassessing the integration of European electricity markets: A fractional cointegration analysis. Energy Economics 53:132–50. doi:10.1016/j.eneco.2014.10.021.
   Web of Science ®Google Scholar
• Dickey, D. A., and W. A. Fuller. 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 4 (4):1057–72. doi:10.2307/1912517.
   Google Scholar
• Dillig, M., M. Jung, and J. Karl. 2016. The impact of renewables on electricity prices in Germany – An estimation based on historic spot prices in the years 2011–2013. Renewable and Sustainable Energy Reviews 57:7–15. doi:10.1016/j.rser.2015.12.003.
   Web of Science ®Google Scholar
• Do, L.-P.-C., Š. Lyócsa, and P. Molnár. 2019. Impact of wind and solar production on electricity prices: Quantile regression approach. Journal of the Operational Research Society 70 (10):1752–68. doi:10.1080/01605682.2019.1634783.
   Web of Science ®Google Scholar
• Doukas, H., A. Flamos, and J. Psarras. 2011. Risks on the security of oil and gas supply. Energy Sources, Part B: Economics, Planning, and Policy 6 (4):417–25. doi:10.1080/15567240903047442.
   Web of Science ®Google Scholar
• EC, COM. 2017a. 53 final Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee, the Committee of the Regions and the European Investment Bank. 2017. Second Report on the State of the Energy Union, EC, Brussels, 1. 2.2017. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=COM:2017:0053:FIN
   Google Scholar
• EC, COM. 2017b. 53 final Annex 2. 2017. The Energy Union’s five dimensions: Policy observations at Member State and EU level to the Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee, the Committee of the Regions and the European Investment Bank Second Report on the State of the Energy Union, Brussels, 1.2.2017. https://ec.europa.eu/commission/sites/beta-political/files/annex2-2nd-report-state-energy-union_en.pdf
   Google Scholar
• Fanelli, V., and M. D. Schmeck. 2019. On the seasonality in the implied volatility of electricity options. Quantitative Finance 19 (8):1321–37. doi:10.1080/14697688.2019.1582792.
   Web of Science ®Google Scholar
• González-Gómez, M. 2017. Estimating the long-run impact of guaranteed prices on wind and solar power in Germany. Energy Sources, Part B: Economics, Planning, and Policy 12 (8):692–98. doi:10.1080/15567249.2016.1269141.
   Web of Science ®Google Scholar
• Huisman, R., and M. Kiliç. 2013. A history of European electricity day-ahead prices. Applied Economics 45 (18):2683–93. doi:10.1080/00036846.2012.665601.
   Web of Science ®Google Scholar
• Krishnan, R. 2016. Energy security through a framework of country risks and vulnerabilities. Energy Sources, Part B: Economics, Planning, and Policy 11 (1):32–37. doi:10.1080/15567249.2011.563260.
   Web of Science ®Google Scholar
• Kruyt, B., D. P. van Vuuren, H. J. M. de Vries, and H. Groenenberg. 2009. Indicators for energy security. Energy Policy 37 (6):2166–81. doi:10.1016/j.enpol.2009.02.006.
   Web of Science ®Google Scholar
• Kumar, S., D. Schönheit, M. Schmidt, and D. Möst. 2019. Parsing the Effects of Wind and Solar Generation on the German Electricity Trade Surplus. Energies 12 (18):3434. doi:10.3390/en12183434.
   Web of Science ®Google Scholar
• Kyritsis, E., and J. Andersson. 2019. Causality in quantiles and dynamic relations in energy markets: (De)tails matter. Energy Policy 133. doi:10.1016/j.enpol.2019.110933.
   Web of Science ®Google Scholar
• Kyritsis, E., J. Andersson, and A. Serletis. 2017. Electricity prices, large-scale renewable integration, and policy implications. Energy Policy 101:550–60. doi:10.1016/j.enpol.2016.11.014.
   Web of Science ®Google Scholar
• Lago, J., F. De Ridder, P. Vrancx, and B. De Schutter. 2018. Forecasting day-ahead electricity prices in Europe: The importance of considering market integration. Applied Energy 211:890–903. doi:10.1016/j.apenergy.2017.11.098.
   Web of Science ®Google Scholar
• Lisi, F., and F. Nan. 2014. Component estimation for electricity prices: Procedures and comparisons. Energy Economics 44 (C):143–59. doi:10.1016/j.eneco.2014.03.018.
   Google Scholar
• Lynch, M. Á., and J. Curtis. 2016. The effects of wind generation capacity on electricity prices and generation costs: A Monte Carlo analysis. Applied Economics 48 (2):133–51. doi:10.1080/00036846.2015.1076145.
   Web of Science ®Google Scholar
• Manbachi, M., A. Parsaeifard, and M.-R. Haghifam. 2015. Generation expansion planning of distributed generation sources in an energy market based on monte-carlo simulation and game theory. Energy Sources, Part B: Economics, Planning, and Policy 10 (2):139–47. doi:10.1080/15567249.2010.518216.
   Web of Science ®Google Scholar
• Martinez-Anido, C. B., G. Brinkman, and B. M. Hodge. 2016. The impact of wind power on electricity prices. Renewable Energy 94:474–87. doi:10.1016/j.renene.2016.03.053.
   Web of Science ®Google Scholar
• Mulder, M., and B. Scholtens. 2013. The impact of renewable energy on electricity prices in the Netherlands. Renewable Energy 57:94–100. doi:10.1016/j.renene.2013.01.025.
   Web of Science ®Google Scholar
• Newbery, D., G. Strbac, and I. Viehoff. 2016. The benefits of integrating European electricity markets. Energy Policy 94:253–63. doi:10.1016/j.enpol.2016.03.047.
   Web of Science ®Google Scholar
• Niederreiter, H. 1992. Random number generation and quasi-monte carlo methods. Philadelphia, PA./USA: Society for Industrial and Applied Mathematics.
   Google Scholar
• Nowotarski, J., J. Tomczy, and R. Weron. 2013. Robust estimation and forecasting of the long-term seasonal component of the electricity spot prices. Energy Economics 13:13–27. doi:10.1016/j.eneco.2013.04.004.
   Google Scholar
• Nowotarski, J., and R. Weron. 2016. On the importance of the long-term seasonal component in the day-ahead electricity price forecasting. Energy Economics 57:228–35. doi:10.1016/j.eneco.2016.05.009.
   Web of Science ®Google Scholar
• Nowotarski, J., and R. Weron. 2018. Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renewable and Sustainable Energy Reviews 81:1548–68. doi:10.1016/j.rser.2017.05.234.
   Web of Science ®Google Scholar
• Ochoa, C., and A. Ackere. 2015. Winners and losers of market coupling. Energy 80:522–34. doi:10.1016/j.energy.2014.11.088.
   Web of Science ®Google Scholar
• OPCOM. 2000. The government decision no. 627 of July 13, 2000. The Official Journal of Romania (357). of July 31, 2000.
   Google Scholar
• OPCOM. 2015. Raport anual de sinteză a rezultatelor functionării piețelor centralizate operate de OPCOM. 2015 [Annual summary report of the results of the operation of the centralized markets operated by OPCOM]. Accessed August 15, 2016. www.opcom.ro/opcom/uploads/doc/rapoarte/Companie/RA_2015_RO.pdf,
   Google Scholar
• OPCOM. [dataset]. Day-Ahead Market/RO trading results. Accessed January 15, 2016 and January 9, 2020. https://www.opcom.ro/raportPIPsiVolumTranzactionat.php?lang=ro.
   Google Scholar
• Panapakidis, A., and A. Dagoumas. 2016. Day-ahead electricity price forecasting via the application of artificial neural network based models. Applied Energy 172:132–51. doi:10.1016/j.apenergy.2016.03.089.
   Web of Science ®Google Scholar
• Paraschiv, F., D. Emil, and R. Pietsch. 2014. The impact of renewable energies on EEX day-ahead electricity prices. Energy Policy 73:196–210. doi:10.1016/j.enpol.2014.05.004.
   Web of Science ®Google Scholar
• Pereira da Silva, P., and P. Horta. 2019. The effect of variable renewable energy sources on electricity price volatility: The case of the Iberian market. International Journal of Sustainable Energy 38 (8):794–813. doi:10.1080/14786451.2019.1602126.
   Web of Science ®Google Scholar
• Pineda, I., and P. Wilczek. 2012. Creating the internal energy market in Europe, ISBN: 978-2-930670-01-0. Brussels, Belgium: European Wind Energy Association – EWEA.
   Google Scholar
• Pollitt, M. G. 2019. The European single market in electricity: An economic assessment. Review of Industrial Organization 55:63–87. doi:10.1007/s11151-019-09682-w.
   Web of Science ®Google Scholar
• Raviv, E., K. Bouwman, and D. van Dijk. 2015. Forecasting day-ahead electricity prices: Utilizing hourly prices. Energy Economics 50:227–39. doi:10.1016/j.10.1016/j.eneco.2015.05.014.
   Web of Science ®Google Scholar
• Rintamäki, T., A. Siddiqui, and A. Salo. 2017. Does renewable energy generation decrease the volatility of electricity prices? An analysis of Denmark and Germany. Energy Economics 62:270–82. doi:10.1016/j.eneco.2016.12.019.
   Web of Science ®Google Scholar
• Rubinstein, R. Y., and D. Kroese. 2017. Simulation and the Monte Carlo method. Hoboken, NJ/USA: John Wiley & Sons.
   Google Scholar
• Transelectrica. [data set]. Transparency/Generation/Ex post information on actual generation/Hourly average. Accessed March 16, 2016. www.transelectrica.ro/web/tel/productie
   Google Scholar
• Tukey, J. W. 1949. Comparing Individual Means in the Analysis of Variance. Biometrics 5 (2):99–114. doi:10.2307/3001913.
   PubMed Web of Science ®Google Scholar
• Worthington, A. C., and H. Higgs. 2017. The impact of generation mix on Australian wholesale electricity prices. Energy Sources, Part B: Economics, Planning, and Policy 12 (3):223–30. doi:10.1080/15567249.2015.1060548.
   Web of Science ®Google Scholar
• Yang, Z., L. Ce, and L. Lian. 2017. Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Applied Energy 190:291–305. doi:10.1016/j.apenergy.2016.12.130.
   Web of Science ®Google Scholar
• Ziel, F., and R. Steinert. 2016. Electricity price forecasting using sale and purchase curves: The X-Model. Energy Economics 59 (435–54):435–54. doi:10.1016/j.eneco.2016.08.008.
   Google Scholar
• Ziel, F., R. Steinert, and S. Husmann. 2015a. Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets. Energy Economics 51:430–44. doi:10.1016/j.eneco.2015.08.005.
   Web of Science ®Google Scholar
• Ziel, F., R. Steinert, and S. Husmann. 2015b. Efficient modeling and forecasting of electricity spot prices. Energy Economics 47:98–111. doi:10.1016/j.eneco.2014.10.012.
   Web of Science ®Google Scholar
• Zipp, A. 2017. The marketability of variable renewable energy in liberalized markets – An empirical analysis. Renewable Energy 113:1111–21. doi:10.1016/j.renene.2017.06.072.
   Web of Science ®Google Scholar
• Zlatar, I., B. Kozan, R. Golob, and A. F. Gubina. 2014. The security of electricity supply: The economic downturn’s influence in slovenia. Energy Sources, Part B: Economics, Planning, and Policy 9 (4):351–59. doi:10.1080/15567249.2010.499410.
   Web of Science ®Google Scholar