Wind power’s contribution to power grid stability: Implications for market structure

Wind power’s contribution to power grid stability: Implications for market structure

Does it make sense to turn off wind turbines when power grids need to be stabilized?

This is a complicated question. On the one hand, the guaranteed access to the grid promised to renewable generation technologies such as wind and solar power has contributed significantly to the growth of these technologies. Especially in Germany, this priority has been a central tenant of the renewable energy law and the so called “Energiewende” in general. Germany has, however, also begun seeking to integrate renewable energy technologies into the broader energy market, by exposing them, through various adjustments in their feed-in tariffs, to the forces of supply and demand – at least in the short term. The “Market Premium” scheme was the first major example of this effort. This was an additional payment to renewables for adjusting their production dynamically when low spot market prices occur. Another renewables technology, biomass CHP, has benefited from providing negative reserve power to the grid, in addition to the market premium. As many biomass CHPs have a relatively constant generation profile, many can also be regularly depended upon to adjust their generation downwards on-demand. These plants provide exactly this service to the grid by participating in the market for secondary reserve power in Germany.

This study seeks to determine to what extent wind turbines can contribute to balancing electricity grids. We take the perspective of demand for balancing power from the network and apply public data from the German TSOs on the short-term demand for balancing energy at every instant. In an ex post analysis of bids in the market for secondary reserve power in Germany combined with the data on demanded reserve power, energy prices are determined for every 4 second interval of the time period analyzed. These prices are then set in relation to the opportunity costs faced by a wind turbine if it could choose to supply negative balancing energy by reducing its power generation on short notice. We show that the overall national demand for negative balancing power in the German grid is, unsurprisingly, highly correlated with wind power generation and, somewhat more surprisingly, that at times of high grid demand for negative balancing power, TSOs routinely pay for more expensive forms of negative balancing energy, although wind turbines could provide the same service more cheaply and with the same quality.

There are barriers to incorporating wind turbines into the reserve markets in Germany: A high degree of forecast accuracy is required in the short term in order to have a valid reference level of generation from which the turbines then are paid to deviate. Additionally, wind turbines cannot always dependably interrupt their generation of power. Most significantly, their power generation of course cannot be adjusted downward, if they are not generating any power due to a lack of wind. Yet if a wind turbine is generating power, modern systems can easily and quickly reduce the turbine’s power generation. Despite this short-term flexibility, the structure of the market prevents the participation of operators of wind turbines from bidding in auctions for providing secondary reserve power on a short term, reliable basis. A constant capacity for an entire week at a time is required to sell secondary reserve power to the grid (as of Dec. 2015, peak or offpeak for an entire week from Monday to Sunday). For tertiary reserve power, this requirement is less stringent (4 hour blocks are auctioned every day, on day in advance). However, overall demand for tertiary reserve is and has been, as of Dec. 2015, very low.

These points imply that taking advantage of wind turbines’ strengths in terms of flexibility means considering their integration into the market for secondary reserve power. We consider this question explicitly in the following analysis.

Price Elasticity of Demand for Reserve Power

We begin by determining the marginal costs for an additional MWh of secondary reserve power in the German market. The empirical distribution of these prices for 2014 is shown in the following figure:

Empirical distribution of maximum prices willing to be paid for secondary reserve power by TSOs in Germany. Based on reserve power demand in 4 second intervals in 2014 and reserve power supplier's bid data every auction period in 2014.
Figure 1: Empirical distribution of maximum prices willing to be paid for secondary reserve power by TSOs in Germany. Based on reserve power demand in 4 second intervals in 2014 and reserve power supplier’s bid data every auction period in 2014.

Figure 1 shows the entire range of data points in the distribution. The minimum price for 2014 was -6499.00 EUR/MWh. Note that negative prices here indicate a payment from the grid operator for using power or producing less power than originally planned. The maximum was 7200.00 EUR/MWh (ie, the price paid for additional energy delivered to the grid). The following table shows summary statistics for each of the four types of secondary reserve power in the Germany market: separate weekly auctions occur for each combination of positive and negative directions of reserve power as well as the two time periods HT (ie, “peak” hours Mon. – Fri. 8am-8pm) and NT (ie, off peak hours, Mon. – Sun. all time periods outside of peak hours).

Summary statistics: SRL energy prices 2014
Min Median Mean Max
NEG_HT -6499.0 0.4 -41.5 28.8
NEG_NT -6499.0 -4.0 -45.3 15.1
POS_HT 39.8 72.7 99.4 6500.0
POS_NT 38.9 64.5 83.1 7200.0

The range of figure 1’s x-axis is too wide to show important details of the price distribution. In figure 2 we zoom in on the range of prices between -300 and +300:

Figure 2: Empirical distribution of maximum prices willing to be paid for secondary reserve power by TSOs in Germany. Based on reserve power demand in 4 second intervals in 2014 and reserve power supplier’s bid data every auction period in 2014. Truncated x-axis range (-300,300).
Figure 2: Empirical distribution of maximum prices willing to be paid for secondary reserve power by TSOs in Germany. Based on reserve power demand in 4 second intervals in 2014 and reserve power supplier’s bid data every auction period in 2014. Truncated x-axis range (-300,300).

Both figure 1 and figure 2 include the prices paid for by the TSO to providers of all four forms of secondary reserve power (NEG_HT, NEG_NT, POS_HT, and POS_NT). The separation in the distribution between bids by negative power suppliers and positive power suppliers is clearly visible in figure 2. Positive values on the x-axis in these figures represent payments for generating power. Negative values indicate payments to users of power for using power (or generating less). The right side of the graph in figure 2 shows the prices paid to POS_HT and POS_NT suppliers. The left side respectively shows the prices paid to NEG_HT and NEG_NT market participants (either users of power or generators that can generate less). Note that many suppliers of NEG power are willing to pay prices greater than zero to supply negative power. (Such bidders could include eg, power-to-heat plants which can sell the product of their electricity use as heat in a district heating network.) Figure 3 shows the median prices per calendar week in 2014 for each of the four products.

Figure 3: Median energy prices for secondary reserve power per product and calendar week in 2014.
Figure 3: Median energy prices for secondary reserve power per product and calendar week in 2014.

Figures 4 and 5 show the maximum and minimum energy prices in the same time period:

Figure 4: Maximum energy prices for secondary reserve power in each calendar week in 2014 (Germany).
Figure 4: Maximum energy prices for secondary reserve power in each calendar week in 2014 (Germany).
Figure 5: Minimum energy prices for secondary reserve power in each calendar week in 2014 (Germany).
Figure 5: Minimum energy prices for secondary reserve power in each calendar week in 2014 (Germany).

Note that due to our definition of negative energy prices implying a payment to the plant or power user for taking power out of the grid, the maximum prices for negative reserve power are relatively stable compared to the maximum prices paid for positive reserve power. This is because the TSO calls upon the least expense providers first, before calling upon plants with much higher bids for their energy price. For the same reason, this relationship is reversed in the minimum prices. The minimum prices paid for negative reserve power are volatile because the grid does not consistently need the most expensive providers of negative power to remain stable. According to the top part of figure 5, for example, the time period between weeks 4 and 22 consistently needed the most expensive forms of negative reserve power. In many weeks in the later half of the year, the grid did not require these expensive reserve sources, especially during the daytime hours between the 35th and the 42nd weeks. The minimum energy prices for positive reserve power were practically always called upon and far less volatile.

Appendix

Summary Statistics of Energy Prices for Secondary Reserve Power

The following table shows the same summary data on prices broken down by calendar week:

Summary statistics: SRL energy prices 2014 by calendar week
Min Median Mean Max
Calendar Week 1
  NEG_HT -5400.0 -5.9 -29.9 27.3
  NEG_NT -6000.0 -87.0 -141.1 11.1
  POS_HT 74.9 88.4 113.1 401.1
  POS_NT 68.7 84.0 121.7 1104.0
Calendar Week 2
  NEG_HT -6000.0 0.7 -6.3 25.2
  NEG_NT -6000.0 -12.0 -52.3 9.8
  POS_HT 75.9 96.0 114.1 237.7
  POS_NT 72.7 81.0 98.4 294.5
Calendar Week 3
  NEG_HT -1122.0 15.0 10.5 25.2
  NEG_NT -6000.0 1.1 -44.2 14.8
  POS_HT 75.9 94.0 105.7 444.0
  POS_NT 72.7 85.0 95.5 2100.0
Calendar Week 4
  NEG_HT -6000.0 17.4 10.9 23.2
  NEG_NT -6000.0 4.3 0.3 15.1
  POS_HT 79.0 94.0 97.9 440.0
  POS_NT 73.5 90.4 106.8 2100.0
Calendar Week 5
  NEG_HT -6000.0 16.5 -24.9 25.9
  NEG_NT -6000.0 5.0 -17.1 15.1
  POS_HT 80.9 88.3 89.9 374.0
  POS_NT 76.4 81.0 85.7 790.0
Calendar Week 6
  NEG_HT -6000.0 17.1 -26.6 25.0
  NEG_NT -6000.0 4.6 -33.0 12.4
  POS_HT 79.1 83.2 87.2 481.7
  POS_NT 74.9 78.5 80.4 303.0
Calendar Week 7
  NEG_HT -6000.0 0.4 -237.0 25.2
  NEG_NT -6000.0 -43.7 -98.0 13.3
  POS_HT 76.4 78.1 123.9 2500.0
  POS_NT 74.2 74.9 81.1 2100.0
Calendar Week 8
  NEG_HT -6000.0 -9.1 -131.5 25.1
  NEG_NT -6000.0 -29.0 -40.8 8.1
  POS_HT 70.3 73.3 79.8 2500.0
  POS_NT 70.0 73.3 78.2 644.5
Calendar Week 9
  NEG_HT -6000.0 -15.0 -61.1 23.1
  NEG_NT -6000.0 -23.9 -34.7 7.6
  POS_HT 66.7 68.0 68.5 175.0
  POS_NT 67.6 67.9 71.4 248.3
Calendar Week 10
  NEG_HT -6000.0 -20.0 -61.3 12.2
  NEG_NT -6000.0 -18.0 -41.5 9.9
  POS_HT 63.0 65.8 73.5 275.0
  POS_NT 63.8 65.0 74.7 221.3
Calendar Week 11
  NEG_HT -6000.0 -12.0 -86.7 12.5
  NEG_NT -6000.0 -22.0 -85.9 10.1
  POS_HT 62.0 63.5 90.0 2500.0
  POS_NT 63.0 65.4 73.7 603.9
Calendar Week 12
  NEG_HT -6000.0 -4.0 -93.1 6.0
  NEG_NT -6000.0 -27.0 -63.2 2.9
  POS_HT 46.7 64.0 98.5 2500.0
  POS_NT 59.7 64.5 73.3 1175.0
Calendar Week 13
  NEG_HT -6000.0 -6.7 -74.3 6.2
  NEG_NT -6000.0 -20.0 -51.3 1.5
  POS_HT 56.5 63.1 120.5 2500.0
  POS_NT 59.7 64.1 67.6 777.0
Calendar Week 14
  NEG_HT -6000.0 -3.5 -130.6 5.8
  NEG_NT -6000.0 -20.0 -43.9 1.6
  POS_HT 58.0 61.8 319.0 6000.0
  POS_NT 59.6 61.1 83.6 2100.0
Calendar Week 15
  NEG_HT -6000.0 -4.1 -67.5 4.0
  NEG_NT -6000.0 -37.0 -135.5 0.0
  POS_HT 59.2 61.0 66.2 617.0
  POS_NT 59.6 60.7 83.3 6000.0
Calendar Week 16
  NEG_HT -6000.0 -10.0 -61.3 3.5
  NEG_NT -6000.0 -27.4 -143.6 0.0
  POS_HT 57.4 61.3 75.6 603.7
  POS_NT 57.3 60.5 82.2 706.2
Calendar Week 17
  NEG_HT -5500.0 -10.0 -36.8 2.7
  NEG_NT -6000.0 -22.0 -85.3 0.0
  POS_HT 57.0 59.9 70.2 316.2
  POS_NT 56.8 60.5 103.7 5999.0
Calendar Week 18
  NEG_HT -5999.0 -9.9 -39.1 2.4
  NEG_NT -5998.0 -43.0 -79.7 0.0
  POS_HT 57.0 61.0 73.0 4888.0
  POS_NT 56.0 61.0 88.5 5999.0
Calendar Week 19
  NEG_HT -6499.0 -33.3 -116.5 1.1
  NEG_NT -6499.0 -35.8 -54.1 0.1
  POS_HT 56.4 67.5 108.7 5999.0
  POS_NT 56.3 73.2 83.5 3500.0
Calendar Week 20
  NEG_HT -6485.0 -25.8 -80.2 0.0
  NEG_NT -6488.0 -50.5 -137.4 -2.8
  POS_HT 56.5 64.8 102.7 2001.0
  POS_NT 56.0 73.8 78.8 4000.0
Calendar Week 21
  NEG_HT -6000.0 -7.0 -59.2 -0.3
  NEG_NT -6000.0 -41.0 -75.7 -6.8
  POS_HT 55.4 65.0 121.3 4444.0
  POS_NT 56.0 67.0 73.0 4444.0
Calendar Week 22
  NEG_HT -6000.0 -9.9 -24.8 -0.2
  NEG_NT -6000.0 -24.0 -36.8 -7.0
  POS_HT 55.0 77.0 96.6 4000.0
  POS_NT 56.0 60.0 65.6 3500.0
Calendar Week 23
  NEG_HT -5999.0 -9.9 -71.0 0.3
  NEG_NT -5999.0 -15.8 -20.1 -5.9
  POS_HT 55.5 58.0 66.6 308.1
  POS_NT 55.7 58.2 75.3 1164.0
Calendar Week 24
  NEG_HT -523.8 -7.4 -18.1 -0.9
  NEG_NT -6000.0 -13.4 -53.9 -6.8
  POS_HT 55.5 65.0 144.2 6000.0
  POS_NT 55.0 57.0 113.9 3500.0
Calendar Week 25
  NEG_HT -4444.0 -13.0 -63.9 -1.3
  NEG_NT -6000.0 -2.8 -12.2 -1.3
  POS_HT 55.1 56.3 72.4 3500.0
  POS_NT 55.0 56.0 74.2 480.0
Calendar Week 26
  NEG_HT -6000.0 -2.7 -37.5 -0.1
  NEG_NT -180.0 -1.0 -4.3 -0.7
  POS_HT 55.0 56.2 98.5 669.0
  POS_NT 50.0 64.5 66.8 3500.0
Calendar Week 27
  NEG_HT -5998.0 -14.9 -117.2 0.0
  NEG_NT -5998.0 -0.8 -70.5 -0.2
  POS_HT 55.0 55.9 146.2 3327.0
  POS_NT 50.0 64.0 109.0 480.0
Calendar Week 28
  NEG_HT -547.0 -0.3 -11.6 2.0
  NEG_NT -4949.0 0.7 -20.1 1.7
  POS_HT 52.8 55.9 78.5 6000.0
  POS_NT 48.0 53.5 97.6 6000.0
Calendar Week 29
  NEG_HT -207.2 -2.9 -13.2 2.1
  NEG_NT -4499.0 -0.5 -14.2 2.1
  POS_HT 51.8 55.0 72.5 384.0
  POS_NT 48.0 73.0 116.8 6001.0
Calendar Week 30
  NEG_HT -598.0 -1.0 -22.6 2.0
  NEG_NT -899.0 -0.5 -9.3 1.7
  POS_HT 49.0 52.1 77.4 6001.0
  POS_NT 47.5 50.0 71.7 4000.0
Calendar Week 31
  NEG_HT -5999.0 -5.0 -22.8 11.1
  NEG_NT -5997.0 -1.9 -36.8 3.7
  POS_HT 47.5 51.0 60.5 6001.0
  POS_NT 46.9 50.0 57.3 308.0
Calendar Week 32
  NEG_HT -200.0 0.9 -4.5 3.2
  NEG_NT -6000.0 0.0 -58.4 5.2
  POS_HT 46.1 47.0 50.3 151.0
  POS_NT 46.1 54.9 61.8 6001.0
Calendar Week 33
  NEG_HT -6000.0 -1.0 -61.0 4.8
  NEG_NT -6000.0 -2.0 -81.1 5.7
  POS_HT 44.9 47.1 50.1 853.8
  POS_NT 44.7 46.0 49.3 489.1
Calendar Week 34
  NEG_HT -6000.0 -2.0 -78.5 4.0
  NEG_NT -1159.0 -8.0 -8.8 5.1
  POS_HT 44.5 47.7 68.3 6002.0
  POS_NT 44.0 46.5 71.8 6001.0
Calendar Week 35
  NEG_HT -3333.0 0.6 -4.1 3.5
  NEG_NT -6000.0 -2.0 -4.5 4.4
  POS_HT 43.4 45.2 48.1 999.0
  POS_NT 42.8 43.8 54.6 6001.0
Calendar Week 36
  NEG_HT -44.0 2.0 1.9 4.0
  NEG_NT -6000.0 -1.4 -3.8 1.1
  POS_HT 41.8 50.8 86.8 6001.0
  POS_NT 40.3 41.9 47.0 6001.0
Calendar Week 37
  NEG_HT -569.0 3.6 3.3 5.1
  NEG_NT -6000.0 1.0 -9.7 1.6
  POS_HT 39.8 58.1 83.4 480.0
  POS_NT 39.2 40.9 63.3 894.4
Calendar Week 38
  NEG_HT -307.6 5.2 3.1 7.1
  NEG_NT -4800.0 2.0 -1.2 4.2
  POS_HT 43.4 101.5 120.0 6000.0
  POS_NT 38.9 90.0 107.0 6000.0
Calendar Week 39
  NEG_HT -140.0 6.8 2.2 10.2
  NEG_NT -6000.0 4.0 -20.4 8.3
  POS_HT 45.4 87.3 87.1 198.5
  POS_NT 42.9 85.4 80.9 326.1
Calendar Week 40
  NEG_HT -279.0 11.1 10.0 16.1
  NEG_NT -6000.0 7.3 -4.6 13.1
  POS_HT 49.2 81.4 89.5 6000.0
  POS_NT 44.8 82.4 96.2 6000.0
Calendar Week 41
  NEG_HT -2444.0 12.9 9.7 16.2
  NEG_NT -6000.0 10.7 3.6 13.2
  POS_HT 52.2 77.0 125.8 6000.0
  POS_NT 51.2 74.8 78.2 440.0
Calendar Week 42
  NEG_HT -6000.0 15.6 -59.2 17.2
  NEG_NT -6000.0 12.8 -10.1 14.4
  POS_HT 58.2 72.7 119.3 6000.0
  POS_NT 58.7 67.9 80.8 750.0
Calendar Week 43
  NEG_HT -6000.0 16.1 2.8 18.8
  NEG_NT -6000.0 -71.2 -54.7 13.8
  POS_HT 58.9 70.5 118.1 600.0
  POS_NT 59.4 68.5 112.1 6000.0
Calendar Week 44
  NEG_HT -6000.0 16.9 -23.4 19.1
  NEG_NT -6000.0 -14.0 -25.6 14.7
  POS_HT 59.3 80.0 110.0 756.8
  POS_NT 60.8 75.0 110.7 999.0
Calendar Week 45
  NEG_HT -650.0 17.0 8.3 20.1
  NEG_NT -657.0 0.0 -4.1 8.4
  POS_HT 61.8 86.0 98.3 678.7
  POS_NT 61.6 80.0 115.4 5990.0
Calendar Week 46
  NEG_HT -5727.0 13.2 12.7 20.1
  NEG_NT -6000.0 1.5 -42.2 7.7
  POS_HT 62.0 82.4 85.3 223.0
  POS_NT 60.0 63.2 76.0 879.0
Calendar Week 47
  NEG_HT -5444.0 15.0 6.4 22.1
  NEG_NT -6000.0 3.0 -8.9 8.1
  POS_HT 61.1 76.9 78.5 448.0
  POS_NT 60.0 69.0 71.8 480.0
Calendar Week 48
  NEG_HT -750.0 19.7 18.5 24.1
  NEG_NT -5387.0 4.4 -5.7 8.2
  POS_HT 61.0 73.7 82.4 6500.0
  POS_NT 59.3 67.8 68.3 146.2
Calendar Week 49
  NEG_HT -6000.0 23.1 21.8 26.6
  NEG_NT -1166.0 9.2 -0.9 10.4
  POS_HT 61.0 78.4 146.4 6000.0
  POS_NT 58.9 67.0 99.1 1174.0
Calendar Week 50
  NEG_HT -6000.0 24.8 -9.0 27.2
  NEG_NT -6000.0 7.8 -40.9 10.7
  POS_HT 63.1 96.7 112.5 294.0
  POS_NT 58.0 78.2 102.7 7200.0
Calendar Week 51
  NEG_HT -1128.0 -3.3 -7.4 28.8
  NEG_NT -6000.0 -13.8 -47.6 11.8
  POS_HT 62.9 93.3 99.4 235.0
  POS_NT 56.4 69.3 80.2 1140.0
Calendar Week 52
  NEG_HT -4931.0 -8.6 -35.1 18.0
  NEG_NT -4385.0 -50.0 -67.4 3.0
  POS_HT 58.8 85.9 88.3 193.4
  POS_NT 53.7 77.8 84.5 360.1
Calendar Week 53
  NEG_HT -822.0 -30.6 -70.1 11.0
  NEG_NT -683.7 -71.0 -81.9 2.7
  POS_HT 61.0 77.8 80.9 175.0
  POS_NT 57.0 78.4 90.8 555.0

Leave a Reply

Your email address will not be published. Required fields are marked *