1. 1 Machiel Mulder and Bert Scholtens Faculty of Economics and Business University of Groningen Influence of climate factors on the electricity price an econometric analysis on the Dutch market over 2006-2011

2. 2 1.Background –more wind, solar cells, CHP plants –higher temperatures as a result of climate change (?) 2.Method –reduced-form equation of electricity price –economic factors: demand, fuel price, market power –climate factors: wind, day light, river temperature 3.Data on the Dutch wholesale market 4.Results and conclusions Outline

3. 3 Background: more wind and solar cells Installed WIND capacity (GW) Installed SOLAR capacity (GW) Dutch market is closely linked to the German market (as part of the NWE market)

4. 4 Explaining prices = controlling for demand and supply factors - time patterns in demand - economic factors affecting demand - market power - fuel costs - environmental restrictions on generation - wind - day light * * * * * * * * * * * * * * * * * Price Quantity

5. 5 Log(APX) = β 0 + β 1 log(Demand(-1)) + β 2 log(RSI) + β 3 log(Gas price(-1)) + β 4 River Temperature + β 5 log(Wind speed – Netherlands) + β 6 log(Wind speed - Germany) + β 7 log(Day light) + ε Model

6. 6 Spot prices of electricity, gas and coal Volatility in day-ahead electricity price decreased strongly

7. 7 Demand Demand = production by centralised units + import – export It is included as a lagged variable (- 1) to control for endogeneity

8. 8 Competition: measured by the RSI RSI (per firm) = (capacity of other firms + import capacity ) / demand For each hour we include the RSI of the marginal firm

9. 9 Gas price Gas price = TTF day-ahead price It is included as a lagged variable (- 1) to control for endogeneity

10. 10 Temperature of river water The threshold is 23 degrees: above this temperature plants have to shut down This is included in the model as “number of degrees above 23”

11. 11 Wind speed and day light Wind speed is transformed in an indicator for the supply of wind energy on the basis of technical characteristics of wind generators Day light is expressed in number of minutes per day

12. 12 Data: correlation matrix APXDemand (-1) RSIGas price(-1) River temp Wind-NLWind- GER Day light APX1.00 Demand (-1).211.00 RSI-.40-.441.00 Gas price (-1).69-.05.041.00 River temp.10-.04.03 1.00 Wind-NL-.13-.002.06-.004-.101.00 Wind-GER-.18-.006.07-.03-.10.781.00 Day light-.18-.45.27-.14.17-.13-.171.00 Note: all variables are measures in logs (except River temp)

13. 13 Results Explanatory variables 200620072008200920102011 Log(demand(-1)).54*** (.10).20* (.11).16** (.07).26*** (.06).26*** (.05).14*** (.03) Log(RSI)-.60*** (.09) -.64*** (.11) -.17*** (.06) -.24*** (.04) -.26*** (.03) -.14*** (.02) Log(gas price(-1)).31*** (.07).33*** (.11).38*** (.12).36*** (.07).38*** (.09).60*** (.07) River temperature restriction.04 (.04)..05 (.19).05 (.05).03 (.04). Log(Wind-NL).01 (.008) -.003 (.006) -.02*** (.008) -.005 (.007) -.008* (.004) -.006 (.004) Log(Wind-GER)-.05*** (.01) -.05*** (.008) -.03*** (.008) -.04*** (.009) -.02*** (.005) -.03*** (.005) Log(Day light)-.61*** (.19) -.13 (.21).17 (.15) -.06 (.12) -.06 (.09).06 (.07) Month dummies (1-11) Day dummies (1-6) AR(1).40*** (.07).62*** (.06).58*** (.07).44*** (.07).50*** (.08).26*** (.07) AR(2)-.08 (.05) -.21*** (.05) -.06 (.05) -.08 (.06).01 (.06).04 (.06) Variance equation Constant.01*** (.002).01*** (.001).01*** (.001).01*** (.001).004*** (.0005).002*** (.0003) Residual(-1) 2.96*** (.17).86*** (.16).60*** (.16).25*** (.10).40*** (.11).61*** (.14) Adjusted R 2.75.82.76.83.76 F stat. ARCH LM.88.14.57.96.98.73 Nobservations356357366364365

14. 14 The electricity price has become more related to the price of gas The merit order has become flatter as changes in demand have a lower impact on the price The electricity markets seems to have become more competitive as the influence of pivotal players has reduced In spite of the increase in wind and solar capacity, the climate factors have less effect on the electricity price Conclusions