## 4 Results
### Discussion  
The presented accuracy comparisons and error metrics are dependent on the used weather data, number and duration of flexibility requests and the magnitude of the curtailment. For a proof-of-concept of a newly developed transparent and simple method proposed in this paper, three summer weeks with different profiles have been selected. Each week gives a different result, which is to be expected when weeks with different profiles are compared. In particular weeks with a highly volatile irradiance profile have a large impact on the error metric (i.e., result in a worse performance). The error metrics of three additional weeks are presented in Appendix A. In future work, a broader statistical analysis is needed to ensure the results are valid in general. As the baseline is equal to the measurement when no flexibility is activated, the amount and duration of flexibility requests also influences the baselining error. Increasing the number and/or duration of activations will increase the error of the baseline, as for those periods the baseline is no longer equal to the measurement (thus the error is no longer zero). The error metrics should therefore not be interpreted in absolute sense. The MAE and RMSE provide a way to compare the four baselining methods, under the specific circumstances they have been tested for. This gives insight in their accuracy in similar situations. However, to get a better view, a simulation over a longer period of time would be required, including a platform to generate market-based flexibility activations. This is a topic for future research. When implementing a simulation over a longer period of time, including a platform to generate flexibility activations or requests, the availability of flexibility can also be taken into account. For this paper, it is assumed that when flexibility is requested by the DSO, the market will offer it and the flexibility is indeed available at the moment of delivery. However, in a real-life situation not every flexibility request may be fulfilled and not all promised flexibility will be delivered. The resulting uncertainty affects the baselines, as the historical measurements depend on the activation (no delivered flexibility means no user dilemma). This again impacts the performance. For method IV, the baseline is scaled using weather data. As the DSO does not necessarily have the same weather dataset as market parties have, it is important to evaluate the impact of using different datasets for the applied scaling, including using weather data from different locations, which is likely to happen given the limited amount of weather stations in relation to the spread of PV systems. To explore this we have used a weather dataset from the other side of the city. As the difference in output for baselining method IV was <2.5 W, or <1%, in our case the impact is negligible, but this topic also needs to be investigated further. Our new method IV introduces an improvement compared to baselining methods I, II and III, while keeping the method as simple and transparent as possible. However, the accuracy improvement depends on the weather, and during periods with highly volatile irradiance profiles, the error may be higher. In case this is not acceptable, and a DSO is willing to compromise on the simplicity and transparency criteria, alternative methods might be considered (e.g., methods in the calculated or machine learning domain, see Section 2.2). Alternatively, the DSO might implement a prognosis-based baseline. For this paper, we have not considered this method, as the nature of the problem of determining a baseline is not solved by shifting this responsibility from the DSO to a market party (e.g., aggregator). This paper focuses on a baselining method that can be used in combination with PV systems. Due to the strong dependency on a single parameter (i.e., irradiance), we expect this method might also apply to other flexibility sources that have a similar, single parameter, dependency (e.g., heat pumps, cooling installations).