1 Electricity Spot Markets and Renewables A Feedback Analysis Carlo Obersteiner, Christian Redl Energy Economics Group (EEG), Vienna University of Technology Gusshausstrasse 25-29/373-2, A-1040 Vienna, Austria Tel , Fax Web: Within the EU, electricity from renewable energy sources (RES-E) is supported in order to achieve the targets defined in directive 2001/77/EC. In general, the supported electricity replaces conventional generation and therefore affects power market prices even if RES-E is not directly traded on power markets (e.g. as it is the case in Germany and Austria). This lowers, on the one hand, the burden of RES-E support for the consumer but also affects the market value of RES-E generation. Especially for wind power this might become dominant in markets with increasing wind power shares. In the following article the effects of RES-E generation on the price formation on the EEX spot market are identified. Furthermore, the consequences of this price (decreasing) effect on the market value of wind energy are evaluated. Keywords: Price modelling, Spot markets, Marketing renewables 1. Introduction In 2001, a new European directive on the promotion of electricity produced from renewable energy sources entered into force in order to facilitate an increasing share of renewables in the electricity sector. This increase is to be achieved by applying special support schemes in the Member States (Directive 2001/77/EC). In general, the supported electricity replaces conventional generation and therefore affects power market prices even if RES-E is not directly traded on wholesale power markets (e.g. as it is the case in Germany and Austria). This, on the one hand, indirectly lowers the burden of specific RES-E support for the consumer but also affects the market value of RES-E generation. Especially for wind power this effect might become dominant in markets with increasing wind power shares.
2 Hence, the major objective of this article is i) to identify the effects of RES-E generation on the price formation on the EEX spot market for electricity by using an econometric model as well as a fundamental marginal cost model ( Macro approach ) and ii) to evaluate the effect on the market value of wind energy ( Micro approach ). The article proceeds as follows: In section 2 the results of the market modelling approach are documented. Section 3 discusses and quantifies mechanisms that are affecting the market value of wind power based on case studies. Finally, section 4 concludes. 2. Price effects of RES-E on the EEX spot market With the liberalisation of the European electricity sector and the introduction of competition, a transition from a cost based price regulation towards a market orientated price formation took place. In a competitive power market, the wholesale price is determined by the generation costs of the marginal technology (i.e. the SRMC of the most expensive plant which is needed to satisfy demand merit order principle). In many Member States, electricity produced from renewable energy sources is not directly traded on these wholesale power markets. 1 Instead, for example in a feed-in tariff system, transmission system operators (TSOs) are obliged to treat RES-E as top priority and producers receive certain remuneration. Therefore, ceteris paribus, RES-E replaces conventional electricity in order to meet given electricity demand which corresponds to a shift of the residual demand curve to the left. As a result, assuming a convex supply curve, wholesale prices decrease when RES-E generation increases (see Fig. 1). 2 1 E.g. as it is the case in Austria, Germany and France. 2 See also Sensfuss et al. (2007).
3 Price [ /MWh] Demand Supply Price (System marginal costs) excl. RES-E Price (System marginal costs) incl. RES-E Quantity [MW] Fig. 1. Price effect of increasing RES-E generation in the conventional wholesale power market. In order to assess the price effect of RES-E generation on the EEX spot market a marginal cost modelling approach is used. As the countries Austria, France, Germany and Switzerland are not separated by cross-border transmission capacity bottlenecks, electricity can be traded virtually without limitations between these countries. This, in turn, causes prices to converge due to arbitrage reasons. Hence, when modelling EEX prices the whole Central European electricity sub market consisting of the four mentioned countries has to be considered. Fig. 2 shows the results of this modelling approach. RES-E generation 3 significantly affects the EEX spot market already although in the Central European sub market fossil fuelled plants are the price setting technologies. However, increased renewable electricity generation leads to a lower residual demand which has to be met by conventional plants. Due to the stepped supply curve plants with a higher electrical efficiency are more likely to be at the margin in case of high renewable production. 3 In this paper the term RES-E refers to all renewable energy sources except hydro power.
4 The marginal cost model indicates, ceteris paribus, a system marginal cost increase of 9% in the EEX market in the period from January 2005 to December 2006 in case of no RES-E generation [ /MWh] SRMC incl. RES-E SRMC excl. RES-E Jan 05 Mrz 05 Mai 05 Jul 05 Sep 05 Nov 05 Jan 06 Mrz 06 Mai 06 Jul 06 Sep 06 Nov 06 Fig. 2. System marginal costs in the Central European electricity sub market with and without RES-E generation. Source: UCTE, BAFA, own calculations When assessing different influence parameters on electricity wholesale prices empirically, statistical regression analyses are a useful tool. Hence, to estimate the influence of various parameters on the EEX spot price, a simple linear regression model is considered. Besides RES-E and hydro generation prices certainly are influenced by generation costs of conventional fossil fuelled power plants. A linear regression model is applied to test this hypothesis: LnSpotBase = b1 + b2lnres + b3lnsrmcccgt + b4lnsrmc HC (1) 4 In a perfectly competitive electricity market system marginal costs equal wholesale prices.
5 where SpotBase is the the monthly spot market average for baseload at the EEX, RES is the monthly generation of all renewable energy sources in the Central European sub market, and SRMC CCGT and SRMC HC are short run marginal generation costs of CCGT and hard coal plants respectively. 5 The theoretical explanations at the beginning of this chapter let us to anticipate a negative sign of parameter b 2 and positive signs of parameters b 3 and b 4 respectively. 6 Table 1 shows the results of the econometric model for monthly baseload power prices on the EEX for the period January 2005 to December RES-E generation leads to price reductions in all load segments, where the highest reductions occur during peak load, followed by baseload and off-peak power. This can be explained by the higher slope of the supply curve when approaching system capacity (see also Sensfuss et al. (2007)). 7 Table 1. Results of the regression analysis for Ln SpotBase (t-statistics in brackets). n=24 b 1 (constant term) 15,02 (4,51) b 2 (RES generation) -1,42 (-4,62) b 3 (SRMC CCGT ) 0,71 (3,61) b 4 (SRMC HC ) -0,02 (-0,08) R 2 (R 2 corr) 0,70 (0,65) DW 2,14 ADF (95% critical value) -3,54 (-4,63) As predicted, total RES-E generation has a (significant) dampening impact on power prices whereas generation costs of CCGT plants increase the price level. Interestingly, production costs of hard coal 5 Simplified, short run generation costs of fossil fuelled power plants are calculated as follows: p primary pco2 fco2 SRMC = + ; where SRMC are short run marginal costs [ /MWh], p primary are primary η η energy prices [ /MWh], η is the efficiency of the power plant, p CO2 is the price of emission allowances [ /t CO 2 ], and f CO2 is the CO 2 -emission factor of the fuel [t CO 2 /MWh primary ]. 6 See also Neubarth et al. (2006) on the negative effects of increasing wind power feeds into the German grid on EEX prices. 7 The regression results for off-peak and peak prices are available on request.
6 plants have a negligible effect on EEX electricity prices. This stresses the higher influence of the CCGT technology on the price formation compared to coal plants during the examination period Marketing wind power The market value of electricity is a key parameter for investments in power generation technologies. For RES-E technologies this is true if support is coupled to whole sale electricity prices (like e.g. in quota systems with TGC or bonus systems). Under support schemes that are uncoupled from power markets (like e.g. feed-in tariffs) the market value becomes of interest as soon as the generation unit drops out of the support system, i.e. after the guaranteed support period. Wind power is a very promising RES-E technology but due to its stochastic nature marketing is more challenging than for other technologies. Therefore the objective of the following analyses is to better understand mechanisms that are affecting the market value of wind power. The market value of power generation is basically determined by the level of whole sale electricity prices which show seasonal as well as diurnal fluctuations. Therefore the market value of a specific generation technology depends on seasonal and diurnal supply characteristic. For wind power due its average base characteristic usually the base price is used as a benchmark for estimating its market value. However due the variability and the limited predictability revenues on wholesale electricity markets are reduced depending on the share of wind power in the corresponding markets. In order to quantify these effects and to determine the key influencing parameters, the market value of wind power is evaluated within selected case studies for Germany and Austria. Effect of variability As already demonstrated on a monthly basis RES-E generation reduces the residual demand that has to be met by conventional generation and therefore affects electricity prices. For wind power the same effect can be observed on the spot market due to diurnal variations in generation. The correlation between spot market price and wind power affects in turn the resulting market value of wind power. 8 Redl and Haas (2007) show similar results for prices of electricity futures traded at EEX.
7 This effect is exemplarily evaluated for the German balancing zone of Vattenfall on a quarterly basis for 2005 and Therefore in a first step the average wind generation is determined for every hour of the day based on historical data on wind power forecasts. The resulting average wind power profile is then assessed with average hourly spot market prices observed on the EEX. The volume weighted average price represents the market value of an anticipated generation technology with a constant average daily profile equal to that of wind power. This number is compared to the specific revenue for selling wind power on the EEX spot market, i.e. the volume weighted power price. Both numbers are finally compared with the average base price observed for the analysed period (see Fig. 3). 70 Average EEX base price Market value of average wind power profile Market value of forcasted wind power 60 Market value in /MWh Q Q Q Q Q Q Q Fig. 3. Comparison of the volume weighted market value of wind power in the Vattenfall area with EEX spot market prices. Source: Vattenfall, EEX, own calculations The results show a significant reduction in revenue between 1.5 and 9.4 /MWh for wind power compared to an average profile. On average the reduction amounts to 6.4 % of the reference market value. The result indicates that Vattenfall is trading wind power on the EEX spot market 9. The deviation between EEX base price and the market value of an average profile depends on the shape of 9 In Germany TSOs collect RES-E generation supported under the Renewable Energy Law and pass it on to suppliers according to their consumer demand in form of ex ante defined monthly generation bands. TSOs are thereby free to trade the difference quantities between the fixed band and the daily RES-E generation forecast bilaterally or on whole sale electricity markets.
8 the average quarterly profile. For the whole period of consideration the value of the average profile is slightly higher than the average EEX base price due to the peak characteristic of wind power in the balancing zone of Vattenfall (see Fig. 4). Average hourly wind power in MW 3,500 3,000 2,500 2,000 1,500 1, Q Q Q Q Q Q Q Average profile Fig. 4. Average daily profile of wind power generation in the Vattenfall area. Source: Vattenfall, own graphic. Similar analysis carried out for a sample of wind sites in Austria show, that there is no reduction in market value due to variability of wind power. This result indicates that for small volumes (compared to the overall market volume) the effect of variability is negligible and that there is no significant correlation between wind power in the two regions analysed. Effect of limited predictability Finally the impact of the limited predictability of wind power on its market value is discussed and assessed for a sample of wind sites in Austria. The Austrian power market is designed based on the concept of Programme Responsible Parties (PRPs) who submit their resulting power schedules for the following working day to the TSO that is responsible for the corresponding balancing zone. Deviations from schedules are settled with imbalance prices which are assessed ex post for every quarter of an hour.
9 Due to its stochastic nature, wind power faces a higher risk of imbalances and corresponding cost than other power generation technologies. The amount of imbalance is determined by the accuracy of wind power forecasts. Imbalance prices are principally based on spot prices and further determined by the overall imbalance of the balancing zone in a certain period. Imbalance prices are higher than spot prices when the system is short and vice versa. The resulting cost of imbalance therefore is even determined by the correlation between the imbalance of the power system and the PRP 10. Imbalances are modelled for various samples of 22 wind sites in Austria using a commercial wind forecasting tool developed by Siemens PSE. Wind power forecasts are based on historical hourly wind speed forecasts provided by the Austrian Central Institute for Meteorology and Geodynamics. Wind speeds are transformed into wind power data using a piecewise linear transformation matrix which is calculated offline based on a pivot analyses of historical wind power measurements and wind speed forecasts. Due to the lack of wind speed data, forecasts exceeding the one-day time horizon are reproduced by forecasts of the previous day. Currently wind power forecasts have to be performed for horizons >1 day as the EEX spot market closes on weekends and public holidays. In order to determine the impact of potential future changes of the regulatory framework on imbalance cost of wind power even the case of continuous day-ahead forecasts is evaluated. Analyses are carried out for different arrangements of wind sites in order to assess the dependency of forecast accuracy and corresponding imbalance cost on the cumulated capacity. Cumulated capacities of grouped wind sites range from 7.5 to 85 MW. Arrangements show a comparable spatial distribution in order to filter its influence on the forecast accuracy. Key data of the analysed arrangements are summarised in Table Analysis of the Austrian power system show that even for relatively small shares of wind power (<4 % of gross consumption), the imbalance of the power system is highly correlated with the forecast error of wind power.
10 Table 2. Key data of analysed wind site arrangements. Cluster 7.5 Cluster 12.5 Cluster 30 Cluster 85 Number of aggregated wind sites Installed capacity MW 7,5 12,4 29,0 85,9 Installed capacity per site MW 1,1 1,0 1,8 3,9 Generation in analysed period Full load hours MWh h/a In literature different measures are used to describe the accuracy of wind power forecasts. In this paper the Mean Absolute Error (MAE) related to the mean wind power is used because it directly indicates the share of imbalances on total wind generation: MAPE where MAPE t N 1 P ( t) Pmeasure ( t) 100, 1 Pmeasure ( t) N forecast N t= 1 = N t= 1 Mean Absolute Error related to the mean power generation in % (Mean Absolute Percentage Error) Time interval (15 min) N Number of time intervals in consideration period (35040) P measure (t) mean measured power in time interval t P forcast (t) mean forecasted power in time interval t The analysis show that the forecast error decreases significantly for a sample of few spatially distributed sites compared to a single site forecast. A further extension of the forecasted sample allows for a moderate increase of forecast accuracy. A considerable decrease of the forecast error in the range of 20 % can be realised by applying continuous day-ahead forecasts instead of the current forecast practise which is determined by the lack of continuous day-ahead markets (see fig 5). With 50 % the MAPE is in the range of forecast errors published for the total wind power capacity supported under the Austrian Renewable Energy Law (see E-Control (2006). The forecast error decreased from 52.5 % in 2003 to 45.8 % in 2005 (for total capacities of 186 and 698 MW). In leading wind energy countries forecast error range from 25 to 35 %.
11 MAPE (related to mean wind power) 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% Current forecast practise Continuous day-ahead forecast 0% Single site average Cluster 7.5 Cluster 12.5 Cluster 30 Cluster 85 Fig. 5. Mean absolute percentage error of wind power forecasts for different arrangements of 22 Austrian wind sites. Source: IGWindkraft, ZAMG, own calculations. Imbalance costs are determined by assessing imbalances with historical clearing prices for the balancing zone of Verbund APG. As a reference these cost are calculated for the case of a perfect foresight of the average wind power on a quarterly basis. Deviations of the measured wind power from the mean power are assessed with imbalance prices. This reference case is compared with forecasts under the current regulatory framework and with continuous day-ahead forecasts (see Table 3). Table 3. Imbalance cost for different arrangements of wind sites and different forecast scenarios, average values for period 07/ /2006. Source: APCS, own calculations. Imbalance cost in /MWh Forecast of average generation Current forecast practice Continuous dayahead forecast Cluster Cluster Cluster Cluster The results show, that imbalance cost are in the range of /MWh for the reference case or 20 to 25 % of the average EEX base price in the analysed period. Imbalance cost can be reduced by 10 % by forecasting wind power and by another 30 % by applying continuous day-ahead forecasts. Although the forecast accuracy is higher for the 85 MW cluster imbalance cost are higher than for the 7.5 MW
12 cluster. This result indicates than besides the volume of imbalances also its correlation with imbalances of the balancing zone determines the resulting imbalance cost. Imbalance costs of wind power have been assessed for the Danish system in Holttinen (2004) and Morthorst (2003) for the years 2001 and 2002 respectively. These studies come up with numbers in the range from 2.3 to 3 /MWh. Kleinschmidt et al. (2006) calculates imbalance cost for a 100 MW onshore wind farm for the years 2004 and 2005 in the range from 5.6 to 8.8 /MWh. Even if these numbers have to be interpreted against the background of underlying assumptions which are not fully consistent, the comparison indicates that imbalance costs in Austria are in the upper range. 4. Conclusions Increasing RES-E generation leads to a reduction of the residual demand on conventional electricity wholesale markets. As a consequence, ceteris paribus, prices on these wholesale markets are expected to decrease when large amounts of RES-E are fed into the grid. Various studies support this theory (see e.g. Neubarth et al. (2006) and Sensfuss et al. (2007)). In our analysis, both a marginal cost model and an econometric model were used to assess this price (decreasing) impact of renewables on the EEX spot market. The model results indicate significant price reductions during the examination period. In turn, this decrease in the power price has to be taken into account when considering the consumers burden of RES-E support. Although wind power on average has a base characteristic as a first approximation (i.e. a constant profile), the market value of wind power due to variability and limited predictability is lower than the average base price. For both effects described the reduction of the market value results from a correlation between volumes (of wind generation/imbalances) and market prices (on the spot /balancing market). The market value decreases ceteris paribus for increasing market shares which has to be taken into account when assessing economics on wind power in the medium to long term. While the revenue loss on the spot market is widely independent of the regulatory framework charges for
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