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Cost-benefit analysis of the meteorological information for the electric sector in Spain

This study examines the economic impact of 24-hour weather forecasts on the cost reduction of the electricity sector in Spain. It analyzes the potential savings and error prevention in the sector, as well as the cost reduction with the use of weather forecasts and renewable energy sources. The study also estimates the reduction in producer losses and the improvement in social efficiency.

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Cost-benefit analysis of the meteorological information for the electric sector in Spain

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  1. Cost-benefit analysis of the meteorological information for the electric sector in Spain Francisco Espejo Gil Área de Relaciones Internacionales e Institucionales Agencia Estatal de Meteorología fespejog@aemet.es

  2. Context • The Spanish Ministries of Industry, Energy and Tourism and of Finances and Public Administrations through the National Observatory on Telecommunications and Information Society (ONTSI) study the value of the public information and its re-use by the infomediary sector (driven by the EU INSPIRE directive) • ONTSI contacted AEMET to carry out a SEB study of the meteorological forecasts on the energy sector in Spain. • IClaves and ACAP were awarded by ONTSI/red.es with the contract to carry out the study in four months AEMET, Agencia Estatal de Meteorología

  3. Objective • Estimation of the economic impact of 24 h weather forecasts on the reduction of costs of the electricy sector in Spain in 2013 • References • Teisberg, Weither and Khotanzad (2005) (demand, USA) • Leviakängas (2007, Croatia) and Leviakängas and Hautala (2009, Finland), savings and error-prevention • NREL-GE Energy (2010, USA, cost reduction with the use of weather forecasts and renewable sources) AEMET, Agencia Estatal de Meteorología

  4. Spanish framework • No such study so far • High presence of renewable energy sources (40% in 2013) AEMET, Agencia Estatal de Meteorología

  5. Conceptual framework • Efficient (ideal) economic system AEMET, Agencia Estatal de Meteorología

  6. Conceptual framework • But in Spain the electric sector is not balanced: The mean price of energy is below its cost Loss of social efficiency (resources devoted to generate additional quantities of the good costlier than the benefit generated by them) AEMET, Agencia Estatal de Meteorología

  7. Conceptual framework • Weather forecasts help diminish the generating cost of energy by better forecasting demand in next 24h, resulting supply response (lowering the supply curve) and thus approaching the system to its balance AEMET, Agencia Estatal de Meteorología

  8. Scenarios to be evaluated • No weather forecasts • Current situation • Quasi-perfect weather forecasts AEMET, Agencia Estatal de Meteorología

  9. Empirical analysis Q=246,313 GWh (mean demand in 2013) P=154,8 €/MWh (mean price 2013) a=-0.24 (mean elasticity of the electric energy price vs. demand, estimated from different authors) AEMET, Agencia Estatal de Meteorología

  10. Empirical analysis • Main unit costs by technology AEMET, Agencia Estatal de Meteorología

  11. Cost of the weather information • AEMET has an analytical accountability system breaking down the cost of each service provided The revenue obtained by AEMET from the electric sector Is 14,4% of its commercial activity. Therefore (?), the cost of generating this information is assumed to be 14,4% of the cost of producing commercial products => 657,004 € Alternative is an analysis of joint cost AEMET, Agencia Estatal de Meteorología

  12. Demand forecast (how much the demand curve moves downwards) • After Teisberg et al. (2005) The economic value of temperature forecasts in electricity generation. Bull. AMS 86(12), 1765-1771 • For Spain it is assumed same %_cost reduction as in the South US (for climate and procedural reasons), that is 0,54% reduction using weather forecasts, plus an extra 0,23% using perfect forecasts. • With data from Spain, operational costs 33.19€/MWh • Mean reduction in production costs using weather forecasts: 0,0054*33.19€/MWh=0.179€/MWh • Using perfect forecasts: 0,0023*33.19€/MWh=0.076€/MWh AEMET, Agencia Estatal de Meteorología

  13. Use of renewable energy • After GE Energy/NREL, but estimates for USA 2017 whereas the case is real in Spain • Cost savings using renewable sources, from no weather forecasts to forecasts: 12.52€/MWh (in € of 2013) • Extra cost savings using renewable sources using perfect weather forecasts: 1.39€/MWh • As in Spain the penetration of renewables in the energy sector (wind+solar) in 2013 was 26%, the Spanish figures are (using the mean between 2 calculation methods): • 3.95€/MWh using weather forecasts • Extra 0.41€/MWh using perfect weather forecasts, but that would imply no extra worn out from inefficient use of fossil fuel powerplants, that is an additional saving of 0.157€/MWh AEMET, Agencia Estatal de Meteorología

  14. In summary AEMET, Agencia Estatal de Meteorología

  15. All this means a benefit for the consumers of 90,582 M€ AEMET, Agencia Estatal de Meteorología

  16. But, as in Spain the price is below the costs, what we get using weather forecasts is a reduction in the losses of the producers AEMET, Agencia Estatal de Meteorología

  17. That is, using weather forecasts, there is a reduction in the producer losses of 1,017 M€. Using perfect weather forecasts would be an additional reduction in these losses of 140 M€. AEMET, Agencia Estatal de Meteorología

  18. As for the reduction of the loss of social efficiency, there is a 25,5 M€ reduction, using weather forecasts. An additional 2,9 M€ reduction would be achieved using perfect forecasts AEMET, Agencia Estatal de Meteorología

  19. RESULTS AEMET, Agencia Estatal de Meteorología

  20. RESULTS: CBA AEMET, Agencia Estatal de Meteorología

  21. Remarks: • A Monte-Carlo sensitivity analysis of the results was made to check their robustness (positive) • Limitations of this study • The neo-classical model of aggregated supply-demand curves is a quite strong assumption, particularly in a heavily regulated market such as the electric one • The demand curve is non-linear in practice (many market segments) • A mean cost curve is used instead of a demand curve, considering the profit against the producer’s surplus • Some data are calculated from assumptions from other studies and markets (elasticity, benefits, demand forecast…) These should be calculated for Spain in further studies • The effect of the renewables is taken from a prognosis in the USA, whereas in Spain these are already implemented and data could be calculated • The costs of the own electric system to obtain weather info from other sources than AEMET has not been considered, neither the post-processing costs. • Given the confines of the study a few other simplifying assumptions were made • Essential is however whether the study serves the purpose despite simplifications AEMET, Agencia Estatal de Meteorología

  22. ¡Muchas gracias! Thank you! «Parque eólico La Muela» de Willtron. Disponible bajo la licencia CC BY-SA 3.0 vía Wikimedia Commons - http://commons.wikimedia.org/wiki/File:Parque_e%C3%B3lico_La_Muela.jpg#/media/File:Parque_e%C3%B3lico_La_Muela.jpg AEMET, Agencia Estatal de Meteorología

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