## 4 Discussion  
Results show that availability is a stronger limiting factor to bid size and revenue than reliability. To make this effect visible, an 'always reliable' strategy was implemented, in which 100% availability was not a prerequisite. With this strategy, NA-fines were calculated, but did not affect the bid size and revenue. The NA-fines were displayed to give insights into the fines that would result if the aggregator would apply this strategy. Given the high risk of fines, the 'always reliable' strategy does not seem realistic to apply in practice. However, implementing it in the model shows that it is difficult for the TSO to apply NA-fines in practice. For the 'always reliable' strategy to be implemented successfully, a perfect knowledge about frequency deviations is required. In practice, prediction algorithms might make a rough estimation of the frequency deviations, but perfect knowledge about the frequency deviations one week in advance is not feasible. Therefore, selecting a bid size with the 'always reliable' strategy is merely a theoretical concept. An important factor when switching heating systems for DR is that the comfort of households should not be jeopardized. Ideally, the effect of heat pump switching on household temperature should be included in the model by simulating household temperature. However, the dataset lacked the information required to perform this kind of analysis. Therefore, instead of temperature boundaries, a limit was set in this model on the length of time that heat pumps could be continuously switched. This limit was set to 15 min, after which a period of non-availability was implemented. During this period, the power consumption of the heat pump follows the baseline, as it would without any interference of a third-party aggregator. An important factor for discussion in this study is the low data quality and availability resulting in many gaps and periods with irregular values. According to the method described, these gaps and constant values were either filled or filtered out, resulting in a small but reliable sample of data. Part of the data was excluded, decreasing the amount of viable data. Eight weeks of data were missing in December and January, usually the coldest months with the highest heating potential, which might lead to a slight underestimation of the potential for FCR. To correct for the small sample size, the portfolio of households has been scaled up to mimic a larger portfolio. By doing so, data has been duplicated to generate a 10 MW portfolio. This process might influence the results of this study, since these duplication methods lead to multiple heat pumps with the same fluctuation. In practice, 20,000 heat pumps, each with a unique baseline, will generate a more stable baseline when combined. With the frequency data, these problems did not occur. Certain research design choices were implemented to provide a reliable but rather conservative estimation of the economic and technical potential of residential heat pumps in the Dutch FCR market. This was due to the implications of the data availability and quality originating from this early pilot demonstration project. The main contribution of this paper is the proposed framework and the method and logic behind the model, which can be replicated for similar studies. Another factor that may influence the outcome of this study is the resolution of the dataset. The household data was provided on a 5-min basis, whereas the frequency data was provided on a 10-second basis. In order to reduce the complexity of the model, the 5-min resolution was used as the model resolution. The frequency data was therefore down sampled from 10 s. to 5 min by using the methods described in Section 2.4  
As a consequence, short term frequency deviations (within a 5-min time framework) could not be taken into account. For this reason, the FCR specification of 30 s response time could not be taken into account either. TSO-regulation on what is considered an IR-event or NA-event is ambiguous.