Marco Nicolosi and Michaela Fürsch The Impact of an ... · 246 ZfE Zeitschrift für...

246 ZfE Zeitschrift für Energiewirtschaft 03 | 2009

THE IMPACT OF AN INCREASING SHARE OF RESE ON THE CONVENTIONAL POWER MARKET THE EXAMPLE OF GERMANY

Kontakt

Marco NicolosiMichaela Fürsch

Institute of Energy EconomicsUniversity of CologneAlbertus-Magnus-Platz50923 Köln, Germany

[email protected]@uni-koeln.de

Abstract

The intermittency of wind power has a decreasing eff ect on day-ahead spot pric-es. Data from Germany illustrate this eff ect and explain the underlying relation-ships. This short-term price eff ect leads to an adaptation process in the conven-tional generation capacity mix. In the long-run, a higher peak load plant share is required to cope with the increasing volatility of the residual demand. The re-sult is an adapted merit-order. This merit-order intersects with an increasingly volatile residual demand curve and leads to a higher price volatility in the pow-er market, which is going to trigger further adaptations. Therefore this article concludes with a list of open research questions, which can be derived from the illustrated relationship. These research questions should be investigated as soon as possible in order to induce the required adaptations in time.

Die unstete Windenergieeinspeisung hat einen preissenkenden Eff ekt am day-ahead Großhandel. Daten aus Deutschland zeigen diesen Eff ekt und erklären die zugrunde liegenden Zusammenhänge. Dieser Preiseff ekt gilt in der kurzen Frist und führt zu einem Anpassungsprozess im konventionellen Kraftwerkspark. Um die zunehmend volatile residuale Nachrage zu bedienen, ist in der langen Frist ein höherer Anteil an Spitzenlastkraftwerken nötig, was eine Veränderung der Merit-Order zur Folge hat. Wenn diese angepasste Merit-Order auf eine zu-nehmend volatile residuale Nachfrage triff t, führt dies zu einem Anstieg der Preisvolatilität. Der zukünftige Strommarkt zeichnet sich folglich durch eine hö-here Preisvolatilität aus, was weitere Anpassungsprozesse nach sich zieht. Der Artikel schließt daher mit einer Aufl istung off ener Forschungsfragen, die sich aus dem dargestellten Zusammenhang ableiten und schnellstmöglich unter-sucht werden sollten, um die benötigten Anpassungen einleiten zu können.

Marco Nicolosi and Michaela Fürsch

The Impact of an increasing share of RES-E on the Conventional Power Market – The Example of Germany

1. Introduction

The EU “climate package” has been adopt-ed by the EU Parliament on December 17th 2008.1 This package includes different di-rectives, which define political targets of 20% CO2 reduction, 20% energy efficien-cy increase and a 20% share of energy from renewable energy sources (RES) on the gross final consumption of energy until 2020. The renewables directive defines the RES targets for all individual Member States (MS). The allocation of renewable shares between the sectors electricity, heating and cooling as well as transport is under the responsibility of the individual MS. Until June 30th 2010, the MS need to

1 EU Parliament, 2008.

provide national action plans to the EU commission.2 While some countries have already defined RES-E targets for 2020 (e.g. Germany 30%), others still have no long term strategy. This article focuses on the effects on the electricity sector. The Renewable Roadmap of the EU Commis-sion calculated that approx. 34 % of the gross electricity consumption needs to be based on RES in order to reach the ambi-tious targets.

The generation of electricity from re-newable energy sources (RES-E) has an impact on the conventional power market besides the pure crowding out effect due to the prioritised RES-E infeed. Already in 2006, Neubarth et al. showed that the

2 Article 4, European Parliament, 2008.

wind power infeed has a wholesale power price reducing effect. Neubarth et al. used an econometric approach to find out that with every GW of wind power infeed in one particular hour, the day-ahead power price at the EEX is reduced by 1.9 € in this hour. This interdependence is true for the short-term perspective and an important information especially for traders. In the long run, however, the conventional gen-eration capacities adapt and the price re-ducing tendency will lead to a higher vol-atility of power prices. This perspective is an extrapolation of various analyses (Bode, 2006; Morthorst, 2007; Sensfuß et al., 2008; Sáenz de Miera et al., 2008), which ignore the dynamic adaptation and state that RES-E have a price reducing effect due to which the end consumer realises net savings.

In the longer run, the interaction be-tween total load and wind power infeed places an increasing challenge on the con-ventional power market due to which the market needs to adapt. The challenging situation comes from the combination of the total demand and the wind power in-feed by forming the residual load, which becomes increasingly volatile and places more extreme situations on the power market. Even without wind power infeed, fluctuating demand has always been one of the main characteristics of power mar-

ZfE Zeitschrift für Energiewirtschaft 03 | 2009 247


kets. At the current level of installed wind power capacity (case of Germany), these demand fluctuations are greater than the deviation of the wind power infeed. In combination, wind power infeed and de-mand lead to higher fluctuations than the demand alone. This is illustrated in this article by empirical data from Germany in 2008.

The first part of this article shows through which interactions the wind pow-er infeed affects the power prices in the short-term. In the second part, the long run perspective is introduced and adapta-tional tendencies on the conventional power market are shown. The third part shows how this adaptation influences the power market. Finally, some conclusions are drawn and further research topics are provided.

2. Short Term Price Effects

It has been shown in various studies and through different methodologies (Neu-barth et al., 2006; Bode 2006; Morthorst, 2007; Sensfuß, 2008), that the infeed of electricity from wind power plants has a price reducing effect. This is not surpris-ing, since the provision of “free power” to the conventional power market reduces the demand for conventional power and thereby lowers the intersection between the demand and supply curve, which means basically that a cheaper power plant sets the power price. The power is free to the conventional power market for two reasons. First, the variable costs of wind power are virtually zero. The wind power costs are mainly based on the investment costs, which mean fixed costs. These fixed costs are not relevant for the conventional power market, since the pricing is purely based on the variable costs. Second, in feed-in tariff support schemes, such as in Germany, the remuneration of wind pow-er is based on fixed tariffs for every pro-duced kWh independent of the time of generation. Therefore, the wind power be-comes fed into the market whenever the wind blows. Figure 1 shows, which effect the wind power infeed has on the pricing mechanism. The wind power infeed is il-lustrated as a shift of the supply curve (S1 to S2), which forms a lower intersection with the demand (D) to reduce the power price (P1 to P2).

The relation shown in Figure 1 explains, why the infeed of wind power reduces the

power prices. However, by simply looking on the empirical relationship between wind power infeed and the power price, one receives only a relatively weak corre-lation of -0.24 in 2008. These two param-eters are plotted in Figure 2 to illustrate the relationship.

Visually, one can hardly see a relation-ship between these two factors. It is more important that the wind power infeed fluctuated between 129 MW and 19,040 MW in 2008, while the installed capacity at the end of the year has been 23,900 MW. However, the average infeed has been 4,600 MW per hour, which is strongly in-fluenced by the high outliers, because the median has been 3,142 MW per hour.

As explained in the introduction, the challenging situation for the power mar-ket is not evoked by the wind power infeed alone, but by the interaction of the wind power with the total load. That is why in the next steps, a closer look on these inter-actions is required. In a first step, total load becomes plotted together with the re-sulting EEX spot power prices.

It can be observed in Figure 3 that the correlation between load and price is much higher than between wind power infeed and price, which is not surprising since this is the fundamental relationship in economics. The respective correlation co-efficient is 0.69. Total load has also a strong fluctuation. It fluctuates between 34,312

MW and 76,763 MW. The average load has been 56,419 and the median 56,061 which shows that there is not a strong effect of only few outliers.

The interesting question at this point is how the load and the wind power infeed relate to each other. Therefore these two parameters are plotted against each other in Figure 4.

There are four basic situations in Figure 4 that require a closer look. First, the situ-ation in which there is a low load and low wind power infeed. This is not a challeng-ing issue since the power market can eas-ily cope with this situation. Second, when high wind power infeed meets high load, the situation is favourable due to the fact that a cheaper conventional power plant can set the price. The third situation how-ever is more crucial. In case low load and high wind power infeed meet, the conven-tional power market needs to ramp down the base load plants, which could become economically and technically problemat-ic. The fourth situation is the match of high load and low wind power infeed. In this case there must be enough peak load capacity installed in order to have an ade-quate system, which can cope with the de-mand.

Also it is surprising at first sight that the scatter plot in Figure 4 shows hardly any observations with low load and high wind power infeed. Although there is no direct

Abb. 1 | The Eff ect of Wind Power Infeed on the Merit-Order

Power Price[€/MWh]

Demand[MW]

S1 S2

D

P1

P2

Wind Power Infeed

Power Price[€/MWh]

Demand[MW]

S1 S2

D

P1

P2

Wind Power Infeed

Source: Own illustration



Abb. 3 | Total Load and Power Prices in Germany (2008)

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Abb. 2 | Wind Power Infeed and EEX Power Prices in Germany (2008)

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Min: 129 MW Max: 19,040 MW

Source: Own illustration, data provided by BDEW and EEX.

Source: Own illustration, data provided by UCTE and EEX.



Abb. 4 | Total Load and Wind Power Infeed in Germany (2008)

link between both parameters, they are both linked to the weather. Since Germa-ny is a winter peaking country, the de-mand rises in autumn and reduces in spring. The wind power infeed has basi-cally the same structure and tends to peak in the winter months. However, this inter-pretation of Figure 4 explains only the ten-dencies, which are not compulsory. There are also hours with very low wind power infeed in the winter months and hours with high wind power infeed in summer months. Figure 5 shows this slight corre-lation between load and wind power in-feed as well as the high fluctuation of the parameters. In the embedded table it can be seen, that both, average load and aver-age wind power is lowest in summer and highest in winter.

The load/wind correlations in Figures 4 and 5 show that the fourth situation (high load/low wind) is the most urgent one be-cause it occurs statistically more often. However, the third situation (low load/high wind) has also serious effects as will be explained with Figure 6. Additionally, the low load and high wind power infeed situation is going to become more rele-vant, especially as soon as Germany de-

ploys offshore wind power, because off-shore wind power has supposed to have a higher utilization.

By now, we have seen that there is a neg-ative correlation between the wind power infeed and the power prices. Then we looked at the load and have seen that there is by definition a much stronger correla-tion. The next logical step is to combine these two parameters in order to receive the residual load. Of course there are oth-er factors, which also need to be consid-ered. The remaining renewables have been taken into account as aggregated to monthly bands. Unfortunately, one im-portant factor, the electricity infeed from heat driven combined heat and power plants (chp) could not become included due to the lack of available data. However, the included parameters already show a clear picture of the effects.

Figure 6 provides a very good intuition of the effects on the conventional power market, since it can be seen that the resid-ual load explains the power prices to a great degree. In order to really explain the power prices, other parameters would need to be considered, such as the interna-tional exchange as well as the fuel and CO2

prices. This of course is not the intention of this article. However, the residual de-mand is the parameter which is important for the further discussion, since this is the demand which needs to be met by the con-ventional power market. This is also indi-cated by the greater correlation coefficient of 0.79 between the residual load and the power price.

The impact of the two important situa-tions, which have been discussed in Figure 4 (high load/low wind and low load/high wind) can be clearly seen in Figure 6. Here, the power prices serve as interpretation of the challenging situation in the market, by either showing atypically high or low pric-es. In case of high residual load, the situa-tion in the power market becomes tight, which can be seen by atypically high pric-es as signs of scarcity in the market. In case of low residual demand, very low prices can be observed. Base load plants bid pric-es underneath their variable costs, which are mainly fuel and CO2 costs, if they want to avoid to ramp-down. This is rea-sonable if they are required in the hours immediately after the low demand. Then, they either need to ramp-up again, which requires additional fuel and CO2 costs or

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Source: Own illustration, data provided by UCTE and BDEW.


Abb. 6 | Residual Load and Power Prices in Germany (2008)

Abb. 5 | Load and Wind Infeed Structure in Germany (2008)


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8760 hours in 2008

MW

LoadWindinfeedPolynomisch (Load)Polynomisch (Windinfeed)

average averageSeason Load Wind power infeedSpring (March-May) 55.984 4.171Summer (June-August) 53.315 3.247Autumn (September-November) 57.364 4.603Winter (December-February) 59.051 6.406

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Min: 22,425 MW

Max: 69,112 MW

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Min: 22,425 MW

Max: 69,112 MW

Source: Own Illustration, data provided by UCTE and BDEW.

Source: Own illustration, data provided by UCTE, BDEW and EEX.


Abb. 7 | Annual load duration curve with and without RES-E infeed in Germany (2008)


they are not able to ramp-up in time due to idle time restrictions and miss potential earnings. Basically, in these situations, variable costs become a matter of dynam-ic pricing due to opportunity costs.

Since these ramp-down situations be-came increasingly frequent, the EEX re-acted to this challenge by introducing the possibility of negative prices in October 2008. Before that, the floor has been at ze-ro (therefore Figure 6 tends to underesti-mate the amount of negative prices). If too much supply has been nominated due to the price restriction, they became reduced on a pro rata basis, which is not economi-cally efficient due to part load inefficien-cies or technical restrictions. With the possibility of negative prices, the power plant with the highest opportunity costs bids the lowest into the market and is al-lowed to run through the low load situa-tion.

3. Long-Term Effects on the Conventional Capacity

It has been shown that the wind infeed is not the parameter, which places challeng-

ing situations on the conventional power market but the residual load, which is a combination of the load and the wind power infeed. The question which natu-rally arises at this point is how the residu-al load further develops with an increas-ing RES-E share. To understand the load levels throughout a year, the annual load duration curve shows all individual load hours in a subsequent order. Figure 7 shows the annual load duration curve as well as the residual load duration curve. On the left side the highest demand is de-picted and on the right side the lowest.

As Figure 7 illustrates, the RES-E infeed pushes the load curve downwards. And it can be easily stated that an increasing RES-E share is going to push it further downwards. The more interesting detail is that the slope of the curve is going to be-come steeper. It can already be seen that the space between the two lines is smaller in the extreme situation on the left side than on the right side. It is important to understand, that hours in the two lines are not in the same order, but in subsequent order. Therefore, the hours on the left side of the residual load duration curve are the hours with a relatively high load and low

wind power infeed, while the hours on the right side are the ones with already rela-tively low load and relative high wind pow-er infeed. Again, it is the combination of these two parameters, which form the steeper slope of the residual load duration curve. The consequence for the conven-tional power market is illustrated in Fig-ure 8.

The upper right corner shows marginal cost curves with annuity capacity costs as starting point at the ordinate. It can be seen, that base load plants have relatively high investment costs and low variable costs (i.e. fuel and CO2 costs). Peak load plants on the other hand have low invest-ment costs and relatively high variable costs. The abscissa shows the annual utili-zation time at which the plant types are ef-ficient. Base load plants are economically viable when a high utilization time can be reached and peak load plants are the effi-cient choice when the utilization remains at a low level.3 In the lower right graph, the two annual load duration curves which have already been discussed are depicted. The shift of the shares of the different

3 see e.g. Stoft, 2002.

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Source: Own illustration, data provided by UCTE and BDEW.



Abb. 8 | Reaction of the power plant mix

power plant types can be seen in the low-er left graph. As the graph shows, through increasing RES-E infeed, the demand for base load capacity is reduced while the de-mand for peak load capacity is increased. It is important to note however that the base load capacity demand is not reduced to zero, empirically there still remains a need for new base load capacity in Germa-ny, especially as a considerable amount of base load capacities are decommissioned in the coming years. This shift stems from the relation between the relatively high RES-E infeed compared to its relatively low share of secured capacity, since the RES-E generation is not guaranteed in the hours of peak demand. However, through regional distribution, it is also unlikely that there is no wind simultaneously in all regions. That means a certain amount of wind capacity can be accounted as guar-anteed. This guaranteed capacity, which is called capacity credit, is able to substitute a certain amount of conventional capaci-ty in the power plant mix. Compared to the RES-E infeed however, the share of

substitutable capacity is relatively low. De-na (2005) has shown that a wind capacity of 14.5 MW in 2003 in Germany had a ca-pacity credit of between 7 and 9 %, mean-ing that between 1.0 and 1.3 GW conven-tional capacity can become substituted. One important implication is that an in-creasing penetration reduces the relative capacity credit. The above mentioned study also calculated that the considered 35.9 GW wind capacity in 2015 would be associated with a capacity credit of only 5 to 6%.The result of high RES-E infeed with a relatively low capacity credit is an in-crease in peak load capacity and a decrease in base load capacity.

Since there is already a conventional power mix installed, in the short run, this means basically that the utilization time of the installed capacity is going to be re-duced. However, the market signals for the long-term adaptation are already observ-able. Negative prices can be interpreted as demand for more f lexibility. Since base load plants bid negative due to their in-flexibility to ramp down, they basically

become penalised. In the longer run, this will influence investment decisions to-wards more peak load capacity.

4. Long-Term Market Effects

The reduction of base load capacity and increase in peak load capacity can be in-terpreted as an increase of the slope of the merit-order as Figure 9 illustrates. The old merit-order is illustrated as the dark curve in the background and the new merit-or-der – which has adapted to a more volatile residual load - is shown as transparent curve in the front. In the illustrated adap-tation process, the most inefficient baseload and medium load plants become decommissioned and peak load capacity becomes added. Total capacity decreases only slightly due to the relatively low ca-pacity credit of the installed wind capaci-ty. The two depicted residual load curves represent both the same level of total de-mand but differ with regard to the wind infeed, which is near to its maximum in

Available conventional capacity

Capacity/Load(GW)

Utilization time (h/a)

Annual costs

(€/kWa)Peakload-

plant

Baseload-plant

Medium load-plant

8760

Peakload-plant

Baseload-plant

Medium load-plant

8760without RES-E with RES-E


Shift of the annual load duration curve caused

by RES-E

Capacity-Credit

Available conventional capacity

Capacity/Load(GW)


Annual costs

(€/kWa)Peakload-

plant

Baseload-plant

Medium load-plant

8760

Peakload-plant

Baseload-plant

Medium load-plant

8760without RES-E with RES-E


Shift of the annual load duration curve caused

by RES-E

Capacity-Credit

Source: Adapted from Wissen/Nicolosi (2008), see also Nabe (2006).


Abb. 9 | Illustration of steeper Merit-Order


the first curve and near to its minimum in the second one.

Since the second residual load curve (with low wind infeed) intersects in the adapted merit-order a plant with higher marginal costs in this example, the price volatility increases compared to the case without adaptation. Of course, this illus-tration is a simplification of reality in which hourly dynamics and interdepend-ing markets play a crucial role.

Another effect, which increases price volatility, is an increasingly fluctuating re-sidual demand. Due to an increase in in-stalled wind power capacity, the average and maximum distance between residual load curve 1 and 2 increases.

Since fluctuating demand is one of the fundamental attributes of power markets, the power market always had to meet this flexibility requirement. In 2008, the total load deviated by 42,451 MW. This devia-tion is much greater than the difference between the minimal and maximal wind power infeed of 18,911 MW. Intuitively one could assume that the power market

is equipped for this f lexibility demand. However, the deviation of the residual de-mand of 46,687 MW is greater than the deviation of the total demand, which has already been shown in Figure 7. As the German environmental ministry plans to install 38 GW wind power capacity in 2020, this effect will increase. The maxi-mum residual demand (high load/low wind power infeed) is not going to change very much in the future, while the mini-mum residual demand (low load/high wind power infeed) will naturally decrease with increasing installed wind power ca-pacity.

The planned wind power capacity over-shoots the minimum load in Germany of 34,312 MW in 2008. This will further in-crease the flexibility requirement of the power market. An already existing flexi-bility that is crucial is the interconnector capacity (Net transfer capacity – NTC). According to Etso (2009), the overall ex-port NTC of Germany is 13,580 MW. This capacity depends strongly on the wind sit-uation since unplanned power flows often

reduce the available NTC. The corre-sponding import NTC is 16,900 MW, which is crucial in cases of high load and low wind power infeed. Nevertheless, the neighbouring countries are increasing their wind power capacity as well. Since the weather is strongly correlated between the countries, the load as well as the wind pattern is relatively alike at a certain point of time.

5. Conclusion and further research topics

This article has shown that the wind pow-er infeed affects the wholesale power mar-ket through a shift of the residual demand. This leads in the long-run to a higher peak load capacity share in the conventional power market and a lower average utiliza-tion of the generating capacities. The rea-soning behind this is that intermitting RES-E, mainly wind power, provide a rel-atively high energy amount, but contrib-utes little to an adequate power system due

Hydro Nuclear Lignite Hardcoal CCGT OCGT

Residual Load 1 Residual Load 2

Capacity/Load[GW]

Price[€/MWh]

Price VolatilityOld Power Plant Mix

Additional Price VolatilityDue to adapted Mix

Capacity Credit

Hydro Nuclear Lignite Hardcoal CCGT OCGT

Residual Load 1 Residual Load 2

Capacity/Load[GW]

Price[€/MWh]

Price VolatilityOld Power Plant Mix

Additional Price VolatilityDue to adapted Mix

Capacity Credit

Source: Own illustration



to the low capacity credit. If the volatile wind power infeed leads to an increasing-ly fluctuating inelastic residual demand curve, the power price becomes increas-ingly volatile. Further research is neces-sary to find efficient ways to integrate higher RES-E shares into the market.

In order to achieve a higher contribu-tion to system adequacy but also reach the ambitious RES-E targets, a broader RES-E mix could increase the overall RES-E ca-pacity credit. Due to the lower likelihood of the absence of different RES (i.e. wind, sun irradiation, hydro, biomass) through the portfolio effect, the capacity credit could become greater than by focussing purely on the cheapest RE source. Through a greater RES-E capacity credit, the peak load and transmission grid capacity re-quirements would become lower, which in turn could lead to lower overall costs in the entire power system and lower price f luctuations. An assessment of the effi-cient solution requires a detailed analysis of the value of the capacity credit which could be reached under a more diversified geographical and technological RES-E de-ployment.

Additional measures to improve system adequacy could be the introduction of var-ious flexibility options to the power mar-ket which need to be analysed in more de-tail:

Grid enhancements, especially in inter- ■connector capacities increase the neces-sary f lexibility of the power system. How could an optimal grid infrastruc-ture look like?

Providing flexibilities to the RES-E sup- ■port could also reduce price effects. In case of high wind power infeed, negati-ve reserve power prices increase drama-tically due to the small amount of gene-rating plants in the market. If the sup-port scheme would allow for wind pow-er to reduce its infeed and provide

positive and negative reserve power, an overall cheaper solution could be reached.

Power storage (e.g. compressed air pow- ■er storage) could utilize the price diffe-rentials between low and high residual load times and reduce the need for grid enhancements. Currently, the Dena 2 grid-study analysis these effects.

A more flexible demand could also re- ■duce the price effects. There are various options in the current discussion. The f lexibility options of large industrial consumers are already realized to a high degree. More potential is available in the end consumer section, e.g. through the installation of smart grids. To realize this potential, great investments are ne-cessary in the corresponding infrastruc-ture. In the long-run it could provide si-gnificant savings in the whole system. If e.g. dish-washers, laundry machines, cooling and heating appliances as well as in the long run electro-vehicle char-ging could be managed according to the RES situation, savings in the overall sys-tem could become realized e.g. through savings in peak load capacity and grid investments.

The integration of the ambitious RES-E targets is going to change the economics of the power market. In the long-run the volatility of RE sources becomes more im-portant than the volatility of the overall load. Therefore, more flexibility in all sec-tors of the power market becomes increas-ingly valuable. Various open research questions need to be answered in the near future. Then political decision makers need to streamline the regulatory frame-work to support the required changes to-wards increasing flexibility in the power system in the short timeframe the climate package sets.

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Etso (2009): European Transmission System Op-3. erators, Online: www.etso-net.org.

European Commission (2007): Renewable En-4. ergy Road Map Renewable energies in the 21st century: building a more sustainable future, SEC(2006)1719, Brussels.

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