Renewable energies and electricity prices in Spain

Transcription

1 Renewable energies and electricity prices in Spain Liliana Gelabert*, Xavier Labandeira** and Pedro Linares*** Instituto de Empresa*, Universidade de Vigo**, Universidad Pontificia Comillas*** and Fundación de Estudios de Economía Aplicada (FEDEA) IAEE 2009 European Conference Vienna

2 Overview I. Motivation II. Related literature III. Econometric analysis IV. Results V. Conclusions

3 I. Motivation Advantages of renewable energies Climate Action Program Promotion policies Debate on final cost for small consumers, large consumers, competitiveness, etc.

4 II. Related literature Theoretical literature Amundsen and Mortensen (2001) Jensen and Skytte (2002) Fisher (2006) Empirical literature a) Simulations Sensfuss et al (2008) Linares et al (2008) b) Expost analysis Sanez de Miera et al (2008) Rathmann (2007)

5 III. Econometric analysis Daily electricity prices and use of technologies in Spain for (Source: REE and OMEL)

6 III. Econometric analysis Descriptive Statistics All the sample (N=1095) Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev. Min. Max. ELECPRICE TOTDEM SPREGIME (19.56%) (17.94%) (20.29%) (19.27%) HIDRO (6.97%) (9.69%) (9.70%) (8.79%) NUCLEAR (21.68%) (22.56%) (19.73%) (21.32%) COAL (28.80%) (23.95%) (25.29%) (26.01%) COMBCYCLE (18.71%) (23.94%) (24.21%) (22.29%) FUELGAS (4.28%) (1.91%) (0.76%) (2.32%) * All figures correspond to daily averages. Electricity prices are in Euros per MWh. Total demand and total generation by energy source are in GWh. Proportion of total generation corresponding to each energy source in parentheses. Total daily observations: (*)FUELGAS is zero for 61 daily observations.

7 III. Econometric analysis Descriptive Statistics All the sample (N=1095) Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev. Min. Max. ELECPRICE TOTDEM SPREGIME (19.56%) (17.94%) (20.29%) (19.27%) HIDRO (6.97%) (9.69%) (9.70%) (8.79%) NUCLEAR (21.68%) (22.56%) (19.73%) (21.32%) COAL (28.80%) (23.95%) (25.29%) (26.01%) COMBCYCLE (18.71%) (23.94%) (24.21%) (22.29%) FUELGAS (4.28%) (1.91%) (0.76%) (2.32%) * All figures correspond to daily averages. Electricity prices are in Euros per MWh. Total demand and total generation by energy source are in GWh. Proportion of total generation corresponding to each energy source in parentheses. Total daily observations: (*)FUELGAS is zero for 61 daily observations.

8 III. Econometric analysis Descriptive Statistics All the sample (N=1095) Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev. Min. Max. ELECPRICE TOTDEM SPREGIME (19.56%) (17.94%) (20.29%) (19.27%) HIDRO (6.97%) (9.69%) (9.70%) (8.79%) NUCLEAR (21.68%) (22.56%) (19.73%) (21.32%) COAL (28.80%) (23.95%) (25.29%) (26.01%) COMBCYCLE (18.71%) (23.94%) (24.21%) (22.29%) FUELGAS (4.28%) (1.91%) (0.76%) (2.32%) * All figures correspond to daily averages. Electricity prices are in Euros per MWh. Total demand and total generation by energy source are in GWh. Proportion of total generation corresponding to each energy source in parentheses. Total daily observations: (*)FUELGAS is zero for 61 daily observations.

9 III. Econometric analysis Descriptive Statistics All the sample (N=1095) Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev. Min. Max. ELECPRICE TOTDEM SPREGIME (19.56%) (17.94%) (20.29%) (19.27%) HIDRO (6.97%) (9.69%) (9.70%) (8.79%) NUCLEAR (21.68%) (22.56%) (19.73%) (21.32%) COAL (28.80%) (23.95%) (25.29%) (26.01%) COMBCYCLE (18.71%) (23.94%) (24.21%) (22.29%) FUELGAS (4.28%) (1.91%) (0.76%) (2.32%) * All figures correspond to daily averages. Electricity prices are in Euros per MWh. Total demand and total generation by energy source are in GWh. Proportion of total generation corresponding to each energy source in parentheses. Total daily observations: (*)FUELGAS is zero for 61 daily observations.

10 III. Econometric analysis Descriptive Statistics All the sample (N=1095) Mean St.Dev. Mean St.Dev. Mean St.Dev. Mean St.Dev. Min. Max. ELECPRICE TOTDEM SPREGIME (19.56%) (17.94%) (20.29%) (19.27%) HIDRO (6.97%) (9.69%) (9.70%) (8.79%) NUCLEAR (21.68%) (22.56%) (19.73%) (21.32%) COAL (28.80%) (23.95%) (25.29%) (26.01%) COMBCYCLE (18.71%) (23.94%) (24.21%) (22.29%) FUELGAS (4.28%) (1.91%) (0.76%) (2.32%) * All figures correspond to daily averages. Electricity prices are in Euros per MWh. Total demand and total generation by energy source are in GWh. Proportion of total generation corresponding to each energy source in parentheses. Total daily observations: (*)FUELGAS is zero for 61 daily observations.

11 III. Econometric analysis Average generation levels by technology in each 25% demand percentile 1 st 2 nd 3 rd 1 st -3 rd Domestic Demand Special Regime Hydroelectric Nuclear Coal Combined Cycle Fuel/Gas All figures are expressed in GWh.

12 III. Econometric analysis Use of technologies and hourly electricity prices in Spain for (Source: REE and OMEL) Unit roots (Dickey and Fuller, 1979)

13 III. Econometric analysis Augmented Dickey Fuller test statistics (variables in levels) ELECPRICE TOTDEM SPREGIME HIDRO NUCLEAR COAL COMBCYCLE FUELGAS MacKinnon (1991) critical values for rejection of hypothesis of a unit root are (for 10% confidence level), (for 5% confidence level), and (for 1% confidence level).

14 III. Econometric analysis Use of technologies and hourly electricity prices in Spain for (Source: REE and OMEL) Test for unit roots (Augmented Dickey Fuller) Seasonality

15 III. Econometric analysis Use of technologies and hourly electricity prices in Spain for (Source: REE and OMEL) Test for unit roots (Augmented Dickey Fuller) Seasonality ΔELECPRICEt = β0 + β1 ΔTOTDEMt + β2 ΔSPREGIMEt + β3 ΔHIDROt + β4 ΔNUCLEARt + β5 ΔCOALt + β6 ΔCOMBCYCLEt + β7 ΔFUELGASt + β8 dummy_d1t+ + β13 dummy_d6t + β14 dummy_m1t + + β24 dummy_m11t + β25 dummy_y1t + β26 dummy_y2t + u t

16 IV. Results OLS estimation of daily changes in electricity prices Dependent variable: ΔELECPRICEt (1) (2) (3) (4) ΔTOTDEMt 1.607*** (0.142) 1.655*** (0.123) 3.854*** (0.456) ΔSPREGIMEt *** (0.179) *** (0.431) ΔHIDROt *** (0.672) ΔNUCLEARt *** (0.723) ΔCOALt *** (0.523) ΔCOMBCYCLEt *** (0.458) ΔFUELGASt 3.008*** (0.882) WEEKLY DUMMIES Yes Yes Yes Yes MONTHLY DUMMIES Yes Yes Yes Yes ANNUAL DUMMIES Yes Yes Yes Yes OBSERVATIONS Adjusted R-SQUARED 39.75% 49.67% 68.03% 75.52% All the models include an intercept; Standard errors in parenthesis are robust to heteroscedasticity and serial correlation (Newey and West, 1987); *** indicates p<0.001

17 IV. Results OLS estimation of daily changes in electricity prices Dependent variable: ΔELECPRICEt (1) (2) (3) (4) ΔTOTDEMt 1.607*** (0.142) 1.655*** (0.123) 3.854*** (0.456) ΔSPREGIMEt *** (0.179) *** (0.431) ΔHIDROt *** (0.672) ΔNUCLEARt *** (0.723) ΔCOALt *** (0.523) ΔCOMBCYCLEt *** (0.458) ΔFUELGASt 3.008*** (0.882) WEEKLY DUMMIES Yes Yes Yes Yes MONTHLY DUMMIES Yes Yes Yes Yes ANNUAL DUMMIES Yes Yes Yes Yes OBSERVATIONS Adjusted R-SQUARED 39.75% 49.67% 68.03% 75.52% All the models include an intercept; Standard errors in parenthesis are robust to heteroscedasticity and serial correlation (Newey and West, 1987); *** indicates p<0.001

18 IV. Results OLS estimation of daily changes in electricity prices Dependent variable: ΔELECPRICEt (1) (2) (3) (4) ΔTOTDEMt 1.607*** (0.142) 1.655*** (0.123) ΔSPREGIMEt *** (0.179) ΔHIDROt ΔNUCLEARt ΔCOALt ΔCOMBCYCLEt 3.854*** (0.456) *** (0.431) *** (0.672) *** (0.723) *** (0.523) *** (0.458) ΔFUELGASt 3.008*** (0.882) WEEKLY DUMMIES Yes Yes Yes Yes MONTHLY DUMMIES Yes Yes Yes Yes ANNUAL DUMMIES Yes Yes Yes Yes OBSERVATIONS Adjusted R-SQUARED 39.75% 46.67% 58.03% 69.52% All the models include an intercept; Standard errors in parenthesis are robust to heteroscedasticity and serial correlation (Newey and West, 1987); *** indicates p<0.001

19 IV. Results OLS estimation of weekly changes in electricity prices Dependent variable: ΔELECPRICEt (1) (2) (3) (4) ΔTOTDEMt 2.221*** (0.380) 2.034*** (0.335) ΔSPREGIMEt *** (0.369) ΔHIDROt ΔNUCLEARt ΔCOALt ΔCOMBCYCLEt 1.692* (0.942) ** (0.937) (1.372) (1.058) (1.241) (0.086) ΔFUELGASt 6.001*** (1.567) MONTHLY DUMMIES Yes Yes Yes Yes ANNUAL DUMMIES Yes Yes Yes Yes OBSERVATIONS Adjusted R-SQUARED 7.88% 33.69% 51.89% 62.01% All the models include an intercept; Standard errors in parenthesis are robust to heteroscedasticity and serial correlation (Newey and West, 1987); *** indicates p<0.001

20 IV. Results OLS estimation of weekly changes in electricity prices Dependent variable: ΔELECPRICEt (1) (2) (3) (4) ΔTOTDEMt 2.221*** (0.380) 2.034*** (0.335) ΔSPREGIMEt *** (0.369) ΔHIDROt ΔNUCLEARt ΔCOALt ΔCOMBCYCLEt 1.692* (0.942) ** (0.937) (1.372) (1.058) (1.241) (0.086) ΔFUELGASt 6.001*** (1.567) MONTHLY DUMMIES Yes Yes Yes Yes ANNUAL DUMMIES Yes Yes Yes Yes OBSERVATIONS Adjusted R-SQUARED 7.88% 33.69% 51.89% 62.01% All the models include an intercept; Standard errors in parenthesis are robust to heteroscedasticity and serial correlation (Newey and West, 1987); *** indicates p<0.001

21 V. Conclusions A marginal increase of 1 GWh of electricity production using renewable energy sources is associated with a reduction of almost 2.5 Euros in electricity prices (around 5% of the average price for the analyzed period).

22 V. Conclusions A marginal increase of 1 GWh of electricity production using renewable energy sources is associated with a reduction of almost 2.5 Euros in electricity prices (around 5% of the average price for the analyzed period). The model also allows us to compute the average effect on electricity prices resulting from a switch between any two energy sources given a fixed level of electricity demand.

23 V. Conclusions A marginal increase of 1 GWh of electricity production using renewable energy sources is associated with a reduction of almost 2.5 Euros in electricity prices (around 5% of the average price for the analyzed period). The model also allows us to compute the average effect on electricity prices resulting from a switch between any two energy sources given a fixed level of electricity demand. Extensions: theoretical model, other control variables, simulations.

24 References Amundsen, E.S. and J.B. Mortensen (2001) The Danish Green Certificate System: some simple analytical results, Energy Economics 23: Jensen, S.G., K. Skytte (2002) Interactions between the power and green certificate markets. Energy Policy 30: Linares, P., Santos F.J. and M. Ventosa (2008) Coordination of carbon reduction and renewable energy support policies, Climate Policy 8: Fischer, C. (2006) How can Renewable Portfolio Standards lower electricity prices?, RFF Discussion Paper Sensfuss, F., M. Ragwitz, M. Genoese (2008) The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany. Energy Policy 36: Rathmann, M. (2007) Do support systems for RES-E reduce EU-ETS-driven electricity prices? Energy Policy 35: Sáenz de Miera, G., P. del Río, I. Vizcaíno (2008) Analysing the impact of renewable electricity support schemes on power prices: The case of wind electricity in Spain. Energy Policy 36: Wooldridge J., (2003) Introductory Econometrics: A Modern Approach, Cincinnati, OH: South-Western College Publishing. Newey,W. K., and K. D. West (1987) A Simple, Positive Semi-Definite Heteroskedasticity and Autocorrelation Consistent Covariance Matrix, Econometrica 55:

25 Thank you