1. Introduction
Intermittent renewable sources such as wind energy are subject to rapid and unpredictable changes. In this context, if producers are part of a liberalized electricity market, they are exposed to penalties related to the costs of regulating the grid.
The centerpiece of modern electricity markets is the day-ahead, or spot market, where most electricity purchases and sales take place. The day-ahead market is a one-off auction where producers and consumers submit their bids and offers before 12:00 for each hour of following day, and the price for each hour is determined by balancing supply and demand. Thus, offers are made between 12 and 35 h before the delivery. Following the announcement of the day-ahead prices, the intraday market opens, allowing the participants to adjust their positions taken in the day-ahead market as new information becomes available. The intraday market is a continuous trading venue, similar to a stock trading platform, where bids and offers for delivery hours, half-hours or quarter-hours of the next day are placed in the order book, and matching bids/offers are executed immediately, resulting in a transaction. The electricity market opens at 15:00 on the previous day and the trading continues until 5 to 30 min before delivery for each unit, but significant liquidity usually only becomes available 5 to 6 h before delivery.
On delivery, producers are charged for any negative imbalance, defined as the difference between real production and the aggregate market position (day-ahead plus intraday). Positive imbalances give rise to payments. However, the corresponding imbalance prices may be lower than the market prices that the producer would have obtained in the day-ahead or intraday market. The actual balancing prices are computed post-factum, depending on the costs of the grid regulation [
1].
Several studies showed that accurate forecasts can be used to enhance the value of wind energy production. Roulston et al. [
2] compared the use of forecast from the European Centre for Medium-Range Weather Forecasts (ECMWF) for 1 to 10 days lead time, and the climatology to bid in the day-ahead market in the UK. They find that with forecasts based on ECMWF, the daily income is higher on 60% of the days compared with climatology. Moreover, the weekly income is higher on 80% of the weeks. Pinson et al. [
3] showed that one way to define sophisticated strategies to participate in the Danish day-ahead market is to provide, in addition to forecasts, information about their uncertainty. Barthelmie et al. [
4] studied the use of forecasts up to 36 h lead time for trading in the day-ahead market and show that a perfect forecast leads to a price advantage of about 4.5 £ per MWh. As in [
2], their study is based on the UK market. Fabbri et al. [
5] assessed the costs of forecasting errors for wind energy producers bidding in the Spanish day-ahead and intraday market. Considering three case studies (one wind farm, 15 wind farms and the total Spanish production), they show that these costs can reach as much as 10% of the total generator energy income. Usaola et al. [
6] showed that revenues can be increased with an hourly wind energy forecast from 1 h to 48 h lead-time, based on in situ measurements and numercial weather prediction (NWP) model outputs, and used to bid on the day-ahead and intraday markets. Using the rules of the Spanish electricity market, they show that with such a forecast, the income is reduced by 7.5% with respect to perfect forecast due to forecasting error. Its is reduced by 9.5% when persistence is used and 10% when no forecast is used. Matevosyan et al. [
7] described a method to simulate the Nordic power market (Norway, Finland, Sweden, and Denmark). It is based on imbalance price to minimize imbalance costs. They combine this model with wind energy forecasts from 18 h to 41 h lead time, to build a stochastic optimization model in order to generate optimal wind power production bids for the day-ahead market.
An economic quantification of the value of the forecast is a way of assessing its performance for users who are more interested in maximizing revenue from the use of the forecast than in the forecast itself.
Most of the studies mentioned above focus on wind energy forecasting in order to sell energy on the day-ahead market. However, the added value of nowcasting, to bid on the intraday market, is rarely investigated. The goal of this paper is therefore to quantify the economic value of nowcasting for a producer with a lead time ranging from 30 min to 3 h. We focus on the value of nowcasting for balancing wind production in the intraday market, to minimize the imbalance penalties. Our tests are conducted using the data from six specific wind farms situated at different locations in France; two of these farms are analyzed in more detail. We use the French electricity market rules and data from EPEX Spot.
After the introduction, the methodology is described in
Section 2, with the overview of the data sets (in situ measurements, NWP outputs and price data), the details of the nowcasting model used in the study and the description of the French electricity market. In
Section 3, the two sources of uncertainty faced by the wind producer are discussed. They include the forecasting model uncertainty, which is expected to decrease with shorter lead times, and on the other hand, the market price volatility, which is expected to increase with shorter lead times. To identify the impact of the uncertainties on the revenues of producers,
Section 4 compares various scenarios: the case where no nowcast is available, the case where a perfect nowcast is available, and the case where a realistic nowcast is available. The impact of these three scenarios is analyzed in depth with respect to the contribution of the two sources of variability. Finally, one-year income obtained using several nowcasts is compared at six existing wind farms.
Section 5 discusses the results, summarizes the main outcome and provides perspectives.
It is essential to mention that in practice, small wind producers cannot access the market directly, but sell their energy through an aggregator. This being said, the methodology of this study remains valid, it would simply be necessary to transpose it to the aggregator scale, and only the numbers presented would vary.
3. Sources of Uncertainty in Wind Farm Income
This study highlights two main sources of uncertainty in wind farm income. The first one is related to nowcasting errors. Nowcasting errors are expected to decrease when the delivery time comes closer.
Figure 5 displays for the two wind farms the Normalized Root Mean Square Error (NRMSE) of the four available nowcasts from
,
and ECMWF models and from persistence. The NRMSE is defined as follows.
where
is the
i-th energy forecast and
is the corresponding observation,
N refers to the number of forecasts used to compute the NRMSE, and
is the rated power (12 MW at Parc de Bonneval and 9.2 MW at Parc de la Vènerie).
As a reminder, is a linear regression with assimilation of observations and is a linear regression without assimilation.
Figure 5 displays the NRMSE of the nowcasts as a function of lead time before the delivery date. For the two wind farms, the performance of ECMWF and
do not depend on the time horizon. The NRMSE is around 11% at Parc de Bonneval and between 11% and 14% at Parc de la Vènerie. When in situ measurements are added as explanatory variables in
, the error decreases as the forecast time horizon gets closer to the delivery time. The error from
increases from 7.9% at Parc de Bonneval for 30-min lead time, to 10.8% for 2 h lead time. For more details on the forecasting errors, interested readers are referred to [
10].
Using
nowcast, the optimal strategy should thus be to adjust the position in the intraday market as late as possible, i.e., 30 min before the delivery date. However, one needs to take into account the impact of price volatility which measures the risk associated with price fluctuations. If volatility is low, most of the time, an average price is obtained which means the associated risk is low. However, when the volatility is high it is whether possible to get a very good price (low purchase and high sale) or a very bad one (high purchase and low sale). As a result, transactions are much more risky. Intraday electricity market prices are non stationary. The number of transactions per unit time and the average price volatility strongly depend on the time remaining until delivery [
14] and increase when the delivery time draws near.
Figure 6 displays the annual price variations on intraday in 2015 as a function of the balancing time all delivery date combined. The closer the delivery date, the larger the price variations. Moreover, the variations are larger for the buying price than for the selling price. It starts between 12.1 € and 12.3 € 3 h before the delivery date, up to between 13.4 € and 14.5 € 30 min before the delivery date. A larger variation means more risk for the producer. Consequently, from
Figure 6, the optimal scenario seems to be a balancing time as early as possible.
Since the forecasting model is expected to be the most accurate close to the delivery date, while larger price variations introduce additional uncertainty at this time, one can assume the existence of an optimal time to balance in the intraday market. As expected from
Figure 5, the optimal time should depend on the nowcast model. In the following, the results are analyzed using
model which displays the best performance.
4. Results
To determine the optimal balancing time in the intraday market, we introduce three case studies:
The case without nowcast available. In this case, the difference between the production sold in the day-ahead market and the real production is compensated via imbalance settlement price.
The case with a perfect nowcast available. There is no imbalance penalty.
The case with a realistic nowcast available. The quantities sold in the intraday and day-ahead markets differ from the real production, so the producer may have to pay an imbalance penalty.
To evaluate the impact of the four available nowcasts (
,
, ECMWF or persistence), we define a score that represents the difference in income between the case where a realistic nowcast is used and the case without nowcast available:
The score is computed weekly and is expressed in euros per MW of installed capacity. It is positive (resp. negative) for income gain (resp. loss) consequent to the use of nowcast information.
Figure 7 displays
at Parc de Bonneval and Parc de la Vènerie, for the four nowcasts. At Parc de Bonneval, except for a strong peak from persistence in March,
nowcasts outperform the others. The quantity
is on average positive showing the added-value of the use of nowcast information. The annual averaged values are 58.0 €/MW for
, 17.3 €/MW for
, 15.2 €/MW for ECMWF and 35.1 €/MW for persistence. However, the variation range of
can reach 100% at times. At Parc de la Vènerie, such as at Parc de Bonneval, the use of a nowcasts increases the income. This income increase is larger than at Parc de Bonneval, except for ECMWF. The annual averaged values are 84.1 €/MW for
, 24.5 €/MW for
, 2.8 €/MW for ECMWF and 67.0 €/MW for persistence. For the two wind farms,
model shows the best performance.
To determine more precisely the added value of nowcast information, we separate the total income into three sources: income from day-ahead market, income from intraday market and income from imbalance settlement. Particular attention is given to
and
as they have a stronger dependence on the nowcast. We consider four balancing times: 30 min, 1 h, 2 h and 3 h before the delivery time. The quantity
is the same for all four scenarios, since it does not depend on the balancing time. For each balancing time, we compare two cases: the case with perfect nowcasts and the case with
nowcast. In each case, the quantity sold in the day-ahead market is based on the ECMWF forecasts (see
Section 2.2).
Figure 8 displays the daily income in euros per MW from the intraday market, averaged over each month, at Parc de Bonneval (
Figure 8a,b) and at Parc de la Vènerie (
Figure 8c,d). The two farms exhibit similar behavior whatever the forecast (realistic or perfect), with higher revenues during spring and summer (with an exception in May), than during fall and winter. The income from the intraday market is negative in winter and positive in summer. This means that in winter,
, and the balancing consists of buying energy in the intraday market, while in summer
, and the balancing consists of selling a surplus of energy in the intraday market. The difference between the four balancing strategies is more pronounced in the case of realistic forecast, as for perfect foreast, the difference is only due to the difference in market prices.
More quantitatively,
Table 3 shows how frequently the energy balanced in the intraday market
exceeds different thresholds. Four thresholds are considered:
−20% of the installed capacity (ICAP) (large negative forecast errors),
0 MWh (moderately negative forecast errors),
0 MWh (moderately positive forecast errors) and
20% ICAP (large positive forecast errors). The first four rows display the results for the four balancing strategies for
nowcast, while the last row displays the results for perfect nowcast. At Parc de Bonneval, the occurrence of positive errors (around 53%) is larger than that of negative errors (around 41%) for both
and perfect nowcasts. Therefore, in most cases, the balancing is done by selling excess energy in the intraday market. At Parc de la Vènerie, the occurrences of negative and positive errors are similar. However, the occurrence of large negative errors is significantly higher than the occurrence of large positive errors. Therefore, in most cases, the balancing consists of buying a lack of energy on intraday. This explains the results of
Figure 8. As the balancing at Parc de Bonneval consists mainly in selling an energy surplus, the income is therefore positive (
Figure 8a,b). However, as the balancing at Parc de la Vènerie consists mainly in buying an energy deficit, the income from this balancing tends to be negative (
Figure 8c,d).
The last source of income that depends on nowcasting and on balancing strategy is the imbalance settlement.
Figure 9 shows the monthly averages of the daily income from imbalance settlement
for Parc de Bonneval for 2015. The income is mostly negative at Parc de Bonneval, except in February. At Parc de la Vènerie, a similar curve is found but shifted to more positive values.
The income from imbalance is the difference between the real production and the production nowcast, multiplied by the imbalance settlement price.
Table 4 shows the same information as
Table 3 for the production imbalance
. This time, the occurrences of positive and negative errors are very similar for all balancing times. In these conditions, the positive errors cannot compensate for the large difference between settlement prices for positive and negative imbalance. Indeed, the average settlement price for positive imbalances in 2015 is 32 € while the average penalty for negative imbalances in 2015 is 45 €, explaing the negative income. Also, the occurrence of large errors increases with increasing lead time. When the nowcast is provided 30 min before the delivery date, the occurrenvce of large negative errors exceeds that of large positive errors, but the occurrence remains low (<
). When balancing is done 1 h before the delivery date, the occurrences of large negative and positive errors are very similar. Combined with the large difference between penalties for positive and negative imbalance, this suggests that such situations should lead to the lowest income from imbalance settlement. Finally, when balancing is done 2 h or 3 h before the delivery date, the occurrence of large positive errors iexceeds that of large negative errors suggesting a positive income from imbalance. At Parc de la Vènerie, the occurrence of positive errors is significantly larger than that of negative errors, compensating the difference in penalty rates. It therefore leads to a positive income from imbalance settlement. The different result for the two farms is linked to the nowcast error which put in evidence the impact of nowcast performance assessment. It also shows that for other farms, an a priori study of the nowcast bias is key information on the wind farm income.
To understand the combined effect of the three sources of income, we compare in
Figure 10 the total annual income for 2015 for the three case studies and for the four available nowcasts. For the two farms, and without surprise, the largest income is obtained when perfect forecast is available (which is never the case). The lowest income is found when no nowcast is used. At Parc de la Vènerie, this lowest income is also reached when using ECMWF forecast as a nowcasting tool as ECMWF forecast of wind energy production performs poorly at this location. Indeed, the forecasts are provided at 100 m, and despite height correction, there is still a chance of overestimating the production. Such a bias would produce negative imbalance, resulting in a loss of income. Without surprise, the
model, assimilating wind farm data and calibrated specifically on each wind farm outperforms the other models.
However, at Parc de Bonneval (resp. Parc de la Vènerie), the additional income of nowcast with respect to the absence of nowcast information is larger than 20 k€ (resp. 30 k€). The income loss of nowcast with respect to a perfect nowcast is around 30 k€ (resp. 13 k€). The difference between the balancing strategies is even much lower, with additional gain of the order of 1 to 2 k€. Given these numbers, it is clear that the balancing strategy is less important than having access to an accurate nowcast.
To extend the scope of this study, we apply the methodology to the six wind farms shown in
Figure 1. Results are shown in
Figure 11. As the data from wind farms are not available for the same year and for the sake of consistency, we make the following adjustments to the methodology:
for the three considered years, only 24 h lead time forecasts were available from ECMWF. To determine the day-ahead bids, we therefore use the forecasts starting at 1200 UTC on the day before delivery, with horizon at 12:00 on the delivery day. For the last twelve hours of the delivery day, we use the last available lead time from ECMWF.
we only have access to the intraday order book for 2015. For more consistency, we therefore use the intraday price data available on the EpexSPOT website for the 6 wind farms. The website provides the last intraday price, without distinction between purchase price and selling price. Then, to make the simulations more realistic, we add a spread to this price. As mention above, the average intraday selling price in 2015 is around 31 € while the average intraday buying price for the year 2015 is around 33 €, so the average spread in the intraday market is 2 €. Then, we add 1 € to the EPEX price to obtain the buying price and subtract 1 € to obtain the selling price.
Despite those simplifications, the conclusions are the same as those inferred from
Figure 10.
5. Conclusions
This paper aims at quantifying the economic value for a wind energy producer of nowcast from 30 min to 3 h lead time to bid on the intraday market, by using wind speed and energy production data of six wind farms as well as electricity prices. Based on these data, several key messages can be inferred. The optimal balancing time on the intraday market results from a trade-off between the degradation of the nowcast performance with increasing lead-time to delivery, and the increasing price volatility when approaching the delivery time. However, balancing at the optimal moment only brings an additional income of less than 1% of the annual income for the wind farms considered in the study, whereas the use of an accurate nowcast can bring an additional gain of about 3–4%.
A second key message is that the combination of wind energy nowcast errors and price volatility is non trivial as not only the distribution of the wind energy nowcast errors strongly impacts the income (symmetrical or asymmetrical error distribution) but also the difference of selling and buying price which can differ significantly on the imbalance market. Therefore, even a non-biased symmetrical nowcast error distribution can induce an income loss as the sold energy surplus cannot compensate financially the bought energy deficit. An asymmetrical nowcast error distribution can mitigate or amplify the price effect. Therefore an accurate assessment of the nowcast error distribution is key to anticipate the risk the wind energy producer takes when bidding on the intraday market, or worse the penalties on the imbalance market.
This study presents a scenario where the wind energy producers have access to the electricity market. However in practice this access is restricted to so-called aggregators. They link the electricity producers and the electricity market. After buying the production of partners, they sell it, either to customers or on the market. In this study, the production of each farm is individually sold on the market. The methodology is valid; however, the numbers mentioned are not realistic since they must be transposed to the aggregator level.
A second source of uncertainty, which is not addressed in this study, is related to the ECMWF forecasts 48 h ahead. These forecasts are used to sell the production on the day-ahead market. They are computed over a grid of 0.125° in latitude and longitude and a linear interpolation is performed in order to retrieve the forecast at the wind farm location. Such resolution is too coarse to account for local terrain inhomogeneities. Therefore, forecasts on such time and spatial resolution are often biased, as is shown in this study, with a significant impact on the income assessment. A statistical bias correction should be applied to reduce uncertainty and remove the bias. This could modify the quantity that needs to be balanced on intraday, and change the impact of the nowcasting models.
Another potential source of uncertainty affecting the results of our study is related to the use of market prices of electricity. Our study used mostly data from 2015 with some examples based on 2016 and 2017. However, the electricity markets are non stationary and the prices may fluctuate year to year due to external factors, such as the growing penetration of renewables, variations in fuel prices, changes in market structure, etc. Thus, specific income values given in our paper for specific wind farms may not be representative of the general situation. However, the relative performance of different forecasting methods is stable over different wind farms and different years.