## 3 Results
### Impact in Performance of Targeting One Service over the Non-Target Services  
This section explores how performing optimal scheduling targeting a particular service, affect the performance of the non-targeted services. If so, it would be of interest to know in which way and to what extend those other services are affected. In order to answer this question, the best combination of scheduling strategy+forecast, for each one of the services, is run for the test period. As usual, the output of the scheduling module passes through the balancing module before outputing the final power profiles. In this way, the performance of each scheduling strategy regarding its target and not-target services can be cross-compared, in order to see the impact among the different strategies. All of these results are summarized in Table 4, where the numbers in bold represent the best values that were obtained for each service, and the positive/negative bias represents a relative increase/decrease of the corresponding performance indicator with respect to the best performance obtained for that service. In this table, the percentage with respect to the best result of the GPP indicator represents the percentage-difference in terms of the annual fee that must be paid, depending on the contracted power required, as it was considered to be more meaningful for the purposes of the comparison of this indicator. There are two main results to take away from this table, with the first one being the fact that the best performance in each service, is obtained when using the scheduling-strategy-plus-forecast that targets that service. This validates the usefulness of the proposed scheduling strategies, as they allow for producing improvements in performance in every service studied. The second interesting fact, and related to the previous one, is that a scheduling strategy always under-performs for those services it does not target. For instance, in the case of EC, the best performance (0.165 E /kWh) is obtained with the EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.5}\) scheduling strategy. Its performance in the other services is always sub-optimal. Regarding CO\({}_{2}\), the best performance is obtained with the CO\({}_{\text{min}}\)-AnEn\({}_{\tau=0.3}\) strategy (63 gCO\({}_{2}\)/kWh), and the GPP\({}_{\text{min}}\)-AnEn\({}_{\tau=0.4}\) yields the best performance in GPP (15 kW). Regarding the grid-commitment, it is not favored by any scheduling module, but by the balancing module, that is present for all of the scheduling strategies. However, based on the results obtained for the test period, it was found that the EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.1}\) combination, produced the best annual performance for this service, among the different strategies. Another important fact that comes back again in this result is that, in order to obtain the best performance in each service, different (quantile) forecasts must be used (that are not always the ones presenting the smallest absolute errors rMAE, rRMSE). That highlights the importance of the versatility that is provided by use of the quantile forecasts obtained with the AnEn method, which outperform the results obtained with the reference NWP forecasts. Now, it is reasonable to wonder if it is possible to find a strategy that produces a good compromise, in terms of performance, among all services. It is true that, sometimes, the price to pay in order to achieve the best performance in one-target-service is big (regarding the remaining services), and the superiority in the target service can be marginal, with respect to the other strategies. For instance, if we take the EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.1}\) strategy, it yields the best performance for the grid-commitment service, with a value of 99.9%. However, the advantage in the performance of this indicator with respect to the other strategies is not huge, as the worst performer in this indicator is the EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.5}\) strategy, which under-performs by only 1.2%.