## 3 Results  
### Performance of the Proposed Scheduling Strategies  
Scheduling can be defined as the planning of the use of resources in a MG, for a given window of time in the future. Therefore, derived from this statement, the first advantage of performing scheduling is the ability to offer some services (such as the grid-commitment that is introduced in this work), which, due to its intrinsic nature, can only be offered if scheduling is performed.  
Table 2 summarizes the results in performance of three reference strategies and the three proposed EMS scheduling strategies, regarding the four services under study, denoted by their performance indicators: EC, CO\({}_{2}\), GPP, and GC. The values in bold represent the best performance that was  
Figure 6: Flow diagram of the reference \(PVB_{max}\) strategy. It works as a balancing strategy if real PV-production (\(P_{pv}^{mpt}\)) is given, while it performs scheduling if the PV forecasts are provided instead (\(P_{pv}^{forecast}\)).  
obtained for each indicator. In this table, a quantile forecast expressed, like: \(\text{AnEn}_{\tau=\text{x}_{1}:\text{x}_{2}}\) means that any probability level (i.e., quantile) between \(x_{1}\) and \(x_{2}\) produces the same-optimal-performance with the strategy being used.  
This cross-comparison against reference strategies is important, as some studies in the field only focus on developing new functionalities or proposing new EMS strategies, without benchmarking against basic reference cases. This is valuable when justifying the necessity or showing the added value of having a MG and/or implementing a novel EMS strategy. The first reference case is when we assume that there is no MG deployed (**NO MG** in Table 2); therefore, all of the consumption is supplied by the utility grid. The second and third strategies used as reference are the \(\text{PVB}_{\text{max}}\) balancing strategy and the \(\text{PVB}_{\text{max}}\) scheduling strategy, that are presented in detail in Section 2.4.2. A summary of the proposed strategies (scheduling and balancing), including the type of algorithm used, the target objective, as well as the reference to the objective function equations (when applicable), is presented in Table 1.  
In Table 2, we observe that, with the exception of the \(\text{CO}_{2\text{min}}\) strategy, all of the proposed scheduling strategies outperform all the reference strategies in the services they target. The exception with the \(\text{CO}_{2}\) content is comprehensible, as the embedded \(\text{CO}_{2}\) emissions (due to the manufacturing process) of the battery and PV panels are high, when compared with the carbon footprint of the energy coming from the French national grid.  
This result shows the importance of developing an EMS strategy that targets the specific service that we want to optimize, as it will most likely obtain a better performance than a strategmey that is not ant to optimize the same service.  
In this particular case, the scheduling method proposed proves to bring an added value to the MG in terms of performance, with respect to the strategy that is envisaged to be deployed in the Drahi-X building, as long as the proper forecasting method is chosen. This conclusion brings out a question: what does it mean \"a proper forecasting method\"? What characteristics define it? The answer to this question is addressed in the following section.