Correlation Between the Prices in Elbas and Prices in the Balancing

The prices in Elbas should reflect the expectations of the market, and hence give some price signals for the balancing market. As previously mentioned, the last accepted trade in an hour sets the price in Elbas.

Figure 4.6: Correlation between prices in Elbas and the balancing market

As seen in Fig. 4.6, a linear trend is observed between the prices in Elbas and the prices in the balancing market. In this case the correlation is 0,5400 in total. The correlation of upward regulation is 0,5967 and for downward regulation it is 0,7089. Furthermore, a strong seasonal autocorrelation is observed for the prices in Elbas, as shown in Fig. 4.7.

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Figure 4.7: Autocorrelation in Elbas

The initial thought was therefore to use the strong autocorrelation in Elbas to predict an Elbas price in the upcoming hours. Thereafter, a linear model model would be constructed using the correlation between the price in Elbas and price of balancing shown in Fig. 4.6. With the proper regression coefficient in place the model would be run a large number of times and the result compared with the actual price of balancing in the hour at hand. With a large number of samples, the expected value and standard deviation of the modelling of the balancing price could be found. The next step would then be to find the error function of the wind power forecast. When this is found a simulation of the best way to handle the balances would have been implemented and the results presented as a probability density function. This is done by creating scenarios combining the the wind forecast error and the price in the balancing market. For each scenario it would be investigated if it is better to handle the balancing intraday or settle it in the balancing market. When this is done for a large number of scenarios a distribution for the cost of balance handling would emerge. One probability density function for handling the balance intraday and one for letting it settle in the balancing market. Whichever function would have the lowest expected value and standard deviation would then be the most promising way to handle the balances. It was, however, pointed out by Gro Klæboe that the results in Fig. 4.6 were a false positive as both the balancing price and the intraday price is correlated to the spot price. The initial thought was therefore to use the strong autocorrelation in Elbas to predict a Elbas price in the upcoming hours. Thereafter, a linear model model would be constructed using the correlation between the price in Elbas and price of balancing shown in Fig. 4.6. With the proper regression coefficient in place the model would be run a large number of times and the result compared with the actual price of balancing in the hour at hand. With a large sample of results the mean and standard deviation of the balancing price could be found. The next step would then be to find the error function of the wind power forecast. When this is found, a simulation of the best way to handle the balances would have been implemented and the results presented as a probobility density function. This is done by creating scenarios for

the wind forecast error and the price in the balancing market. For each scenario it would be investigated if it is better to handle the balancing intraday or settle it in the balancing market. When this is done for a large number of scenarios a distribution for the cost of balance handling would emerge. One probobility density function for handling the balance intraday and one for letting it settle in the balancing market. Whichever function would have the lowest expected value and standard deviation would then be the most promising way to handle the balances.

As mentioned it was pointed out that the results were a false positive, as both the balancing price and the intraday price is correlated to the spot price. Therefore the same correlation was checked by subtracting the spot price as follows.

prBM = BalancingP rice ElspotP rice (4.3)

prElb = ElbasP rice ElspotP rice (4.4)

The scatter plot of prBM and prElb is shown in Fig. 4.8.

Figure 4.8: Correlation between prices in Elbas and the balancing market, when the spot price is subtracted

As seen in Fig. 4.8, no strong correlation is found between the prices in Elbas and the prices in the balancing market. These results indicate that the trend line found in Fig. 4.6 is a result of the volatility in the spot price. High spot prices are correlated with both the prices

in the intraday market and in the balancing market. Therefore a correlation between the two was found when the spot price of electricity was not subtracted. No correlation between the price in the intraday market and the balancing market tells us that these prices are random when compared to each other. This is a quite peculiar result as it is reasonable to assume that they would have some correlation.