1. IDENTIFYING THE DRIVERS FOR INCREASING WIND CAPACITY. THE CASE OF SPAIN.
Callejas-Albiñana, Fernando
Quintana-Rojo, Consolación
Tarancón-Morán, Miguel-Ángel

2. Index: Introduction. The importance of Wind Energy.
Factors influencing wind capacity behaviour.
Tests to assess the quality of the estimates.
The empirical study. An econometric model.
Concluding remarks.
Questions and Improvements.

3. Introduction. The importance of Wind Energy.
Lower energy dependence
Access to energy in difficulty area
Economic development and employment
Deployment of Renewable Energy
Measurements for descarbonization of Economy
Improvement Energy Efficiency
COUNTRY APPROACHES AGAINST CLIMATE CHANGE
TARGET 2020

4. ELECTRIC POWER GENERATION
Introduction. The importance of Wind Energy.
Deployment of Renewable Energy
ELECTRIC POWER GENERATION
HEATING AND COOLING
TRANSPORT
Source: European Commision

5. Introduction. The importance of Wind Energy.
Evolution of energy production by different technologies in Spain. TWh.
ELECTRIC POWER GENERATION
Electrical energy generation by renewable energy in Spain. TWh.
ONSHORE WIND ENERGY
45.6% TOTAL 2014 REE

6. Installed capacity of wind power generation in Spain. TWh.
Introduction. The importance of Wind Energy.
Installed capacity growing until 2007
Decreasing during
Installed capacity of wind power generation in Spain. TWh.
POLICY FACTORS
FEED IN TARIFFS - FEED IN PREMIUMS. Strong expansion until 2017
Moratorium 2012 for new projects
Royal Decrete Law 9/2013 and 24/2013  the end of Feed in Tariffs
Econometric model explaining the installed capacity of wind power generation and the impact in its growth
Factors influencing wind capacity behaviour in Spain

7. Factors influencing wind capacity behaviour.
Menz y Vachon (2006)
Carley (2009)
Marques et al. (2010)
Mitchel et al. (2011)
Del Río y Tarancón (2012)
Zhao et al. (2013)
Aguirre e Ibikunle (2014)
Technological
Electricity-Market
Socio-economic
Policy

8. Factors influencing wind capacity behaviour.
Type
Factor
Technological factors.
Cost of wind energy technology.
Wind energy potencial.
Electricity-Market Factors
Electricity prices.
Electricity demand.
Financial capability of wind operators.
Contribution of traditional energy sources to the electricity mix.
Contribution of “low-carbon” technologies (hydro, nuclear, solar) to the electricity mix.
Prices of fuels.
Administrative and grid connection barriers.
Socio-economic factors
Income.
Social acceptability (NIMBY syndrome).
Awareness of the global warming problem (CO2).
Contribution to economic development.
Policy Factors
International commitment (deployment targets).
Security of energy supply.
Public finance of renewable support.
Policy stability.
Factors should not be considered in isolation
Many factors as variables.
MULTICOLLINEARITY problems among variables.
AKAIKE’s INFORMATION CRITERION

9. Expected relation with dependent variable
The empirical study. An econometric model.
Variable
Definition
Equation
Expected relation with dependent variable
WINDCAP
Installed Wind Capacity
Dep Eq. 1
---
IRON
Iron price
Exp. Eq. 1
Iron Price is 80% of Steel Price which is the main raw material of the wind turbine infrastructure. Therefore, it can be considered a proxy variable for the cost of building new wind capacity.
(-)
ELECON
Domestic consumption of electricity
Dep. Eq. 2
Greater electricity consumption levels encourage the installation of new generating capacity.
(+)
GHG_PC
Greenhouse gases emissions per capita
Dep. Eq. 3
An increase in GHG emissions indicates a loss in the renewable energy impulse in contrast with other types of conventional energies that emit this GHG in their combustion processes.
FIT_99_13
Dummy variable: representative of Feed in tariffs system, between 1998 and 2013
A major support scheme involves greater new wind capacity

10. The empirical study. An econometric model.
GDP
Gross domestic product (constant 2010)
Exp. Eq. 2
An increase in economic activity can be expected to induce more investment in new infrastructures, because of the possibility of recovering the investment and obtaining returns.
(+)
PRICEL
Price of electricity for domestic final consumption
Dep. Eq. 4
The higher the Price of electricity, the lower consumption of electricity.
(-)
SHAREFUELS
Share of electricity production from traditional energy sources
Exp. Eq. 3
These traditional energy sources are those that emit GHG in the processes of combustion
ENERGINT
Energy intensity.
The greater energy intensity, the greater need to consume energy, part of which is generated by processes that emit GHG into atmosphere.
LTIR
Real long-term interest rate.
Higher intest rates constrain investment, economic and productive activity, leading to lower volume of pollutant emissions.
PRIGAS
Price of natural gas
Exp. Eq. 4
Natural gas is one of the main raw materials used in the generation of electricity by some conventional technologies. The Price of electricity will depend on the Price of this raw material
TAX_SHARE
Share of taxes on the price of domestic electricity
The higher taxes, the greater the final Price of electricity

11. IRON
Modelo 2: Adj. R2 =
GDP
ELECON
PRIGAS
PRICEL
WINDCAP
SHAREFUELS
TAX_SHARE
GHG_PC
ENERGINT
Modelo 4: Adj. R2 =
LTIR
Modelo 3: Adj. R2 =
FIT_99_13
Modelo 1: Adj. R2 =

12. Tests to assess the quality of the estimates.Ho
Equation 1
Equation 2
Equation 3
Equation 4
Ramsey-RESET test
The model does not have omitted variables (correct functional form)
No-Rejected
Rejected
Shapiro-Wilk normality test
The errors are normally distributed
Breusch-Pagan test
The errors have a constant variance (homoskedasticity)
Durbin-Watson test
The errors are not autocorrelated

13. The empirical study. An econometric model.
Equation 1
AR(1)
Equation 2
AR (1)
Equation 3
Equation 4
WINDCAP
Dependent
IRON
-0,342**
***
ELECON
0,9998***
1.0262***
GHG_PC
-0,6467***
***
FIT_99_13
0,102*
0.1165***
PRICEL
-0,1034*
0.0103
GDP
1,028***
0.9759***
0,151**
SHAREFUELS
0,4237***
ENERGINT
0,5***
LTIR
-0,1898*
PRIGAS
-0,1898**
TAX_SHARE
1,056***
Intercept
0.0024
AR(1) coef. -
Adj. R-Squared
0,9895
0,9689
0,9571
0,9956
AIC
Signif. codes: 0 ‘***’ ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

14. Concluding remarks.
STANDARDISED COEFFICIENTS
WORK IN PROGRESS
Allows to compare the economic relevance of different factors.
THE MOST RELEVANT VARIABLES:
ELECON
GHG_PC
GDP
ENERGINT
TAX_SHARE
THE LEAST RELEVANT VARIABLES:
FIT_99_13
PRICEL
LTIR
Several limitations of our model, but they will be corrected

15. Questions and Improvements.

16. IDENTIFYING THE DRIVERS FOR INCREASING WIND CAPACITY. THE CASE OF SPAIN.
Callejas-Albiñana, Fernando
Quintana-Rojo, Consolación
Tarancón-Morán, Miguel-Ángel