8 Limitations
The results obtained in this study must be contrasted by the limitations of this work. The limitations of this work can be separated between the limitations met because of the optimization and modeling tool used in this and the one that are a consequence of the used methodology.
8.1 Optimization and modeling tools limitations
The results presented in this study were based on data from the Buildings database from data generated by the REHO optimization tool. These tools, besides the fact that their represent a great help for characterizing the energy data of a territory and for designing energy systems, presents some limitations that must be pointed out.
The first limitation concerns the energetic needs of the analyzed neighborhood of Les Vergers : By comparing the synthetic load curves of the 34 buildings of the neighborhood, generated by QBuildings, with the actual electricity consumption of these buildings, it appeared that the annual electricity demand of the 34 buildings were underestimated by 30% (with respect to the actual load curve). This is a significant fraction of the total annual electricity demand and hence influence the outcomes of this study : The energetic performances of the energy system obtained with REHO are most probably overestimated in this work. Especially the drawn conclusions about self-sufficiency of the neighborhood should be revised downwards if the electricity needs of the buildings, hence of the neighborhood, is bigger than estimated.
The second identified limitation concerns the modeling of electricity sells price from the gird for the neighborhood in REHO. Here, as the electricity price is scalar parameter in REHO (1-dimensional), the SIG professional tariffs (proBT and MT), which vary between peak hours (HP) and off-peak hours (OPH), were only estimated through a mean price of energy over the 8760 hours of the year. This may have lead to erroneous conclusions on the balancing sheet of some of the stakeholders. Although, it is thought that this is only a minor limitation as the use of the mean price should be a good estimation for the total balancing sheet of each stakeholders.
8.2 Methodology limitations
The methodology of this study has obviously a big impact on its results.
First, the choices made for modeling flexibility in the neighborhood need to be analyzed. Indeed it is the insertion of a district battery in the energy system of the neighborhood that was used to model flexibility. This made sens since a battery acts as a energy buffer provider. However, the costs associated with its installation and operation does not necessarily represent the costs associated with the usage of other means of providing flexibility as mentioned in section 5.4.2.2. Here it was decided to ignore the CAPEX pf the battery in the development of the electricity sells price of the battery owner. Moreover, a mean price \(p_{battery}^*\) over the 6 scenarii that included a battery were used. These choices led to an electricity price that does not reflect the total costs associated with the use of a battery nor reflect the price of implementing flexibility by other means in the neighborhood.
Finally, the biggest issue of this study is that the financial study was done without optimizing any of the mentioned stakeholders portfolio. Indeed, in this work, the energy system of Les Vergers neighborhood is optimized considering that neighborhood is one unique stakeholder who deals with the grid. Yet, this is obviously not the case as the stakeholders within the neighborhood are multiple annd have different interests. In this study, the energy system found is obtained by optimizing the OPEX of this hypothetical neighborhood stakeholder, without taking into account the cost associated with energy flows inside the neighborhood. Once the energy system was found and that the enegy flows inside the neighborhood and between the neighborhood and the grid are fixed, the financial study was conducted. However, the price associated with each kWh exchanges between stakeholders within the neighborhood may have led to a differente energy system and hence to different economical outcomes.