# Mdp1 _sciences_
## 4 Discussion and Practical Implications  
As shown in the previous section, the forecasting methods used in this work assimilate exogenous data from two meteorological towers in addition to the target tower to accurately forecast the wind speed one and two time steps ahead; one of the extra towers is located at an upstream position with respect to the target tower, considering the predominant lobe of the bi-modal wind direction distribution at the site. An additional method, using only on-location records from the target tower, was used for reference purposes. The assimilation of exogenous data dramatically boosts the capability of predicting wind speed changes, particularly in the critically important case of two-time-steps-ahead forecasts, compared to the reference method. While the reference method detects only about 10% of large (\(\delta\)wvs \(<-1\) m/s) negative wind speed (ws) changes, the highest-ranking methods with exogenous data detect up to 40% of such events, while producing only about 20% false positives. The detection efficiency for incompliance events can be further boosted by building _ensemble_ methods, which combine the predicted incompliance events of several exogenous methods, leading to a detection efficiency of about 50%. The application of these forecasts to the FBC method translates into similar results for the _FBC efficiency_\(\eta_{\text{FBC}}\), defined as the additional compliance achieved by the FBC method, divided by the natural incompliance fraction, at any given critical slope value. \(\eta_{\text{FBC}}\) measures the capability of mitigating naturally occurring incompliance events. For the case of the target tower studied in this work, the ideal FBC efficiency is around 80%, where it is assumed that the wind speed changes one and two time steps ahead can be predicted perfectly. For the highest-ranking method implemented in this work, based on an ensemble approach, the realistic FBC efficiency is about 35%, i.e., a little below half the ideal value. The key advantage of the FBC method, as opposed to other curtailment strategies, such as _flat_ curtailment (e.g., methods reducing power output by a constant factor or limiting power to a constant maximum) or fixed positive ramp rate limitations, is its very low energy penalty. Particularly at high critical slopes, where compliance matters the most, the energy penalty is of the order of 0.1% for all methods; even for very small tolerable slopes, the curtailment cost is well below 1%. These numbers have to be compared with the tens of percents of energy which are lost by conventional curtailment approaches under similar conditions. Battery energy storage systems (BESS), finally, are well suited to  
Figure 11: Trajectories of all methods in the true-positive rate—false-positive plane. Note the semi-logarithmic ordinate. mitigate negative ramps but represent a significant upfront investment. A brief summary of the methods for ramp control is given in Table 3.  
The FBC efficiency can be further boosted by activating the FBC algorithm at _operational_ critical slope values that are less negative than the true system limit. In the indicative case of triggering curtailment at 75% of the true critical value, the FBC efficiency is increased to over 40% at high slopes, but the energy penalty now increases more drastically (compared to the variations among forecast methods), while still remaining well below 1% for relevant slope values. As pointed out before, the FBC method critically relies on the capability of forecasting future wind speed changes one, and more importantly, two time steps ahead. In this work, it was argued that this capability is typically limited by the negative correlation between the wind speed changes and the corresponding forecast errors. A simple probabilistic model was developed to demonstrate and explore this effect for idealized joint probability density functions (bi-pdfs) for the changes and forecast errors, and a geometrical explanation was offered.