## 4 Discussion
Figure 8 gives an example of the corresponding investment cost (left) alongside the operation and maintenance cost (right) as a function of system capacity. Data for each of the four compared systems are obtained from the Danish technology data catalogues [40,41]. The comparison shows that for some systems such as district heating and gas, there would not be great differences in the resulting investment or operation and maintenance cost due to reduction of the required capacity. This differs for heat pumps, as an evidently considerable investment cost reduction can be achieved by reducing the size of a heat pump. The strongest cost-dependence on the size is observed for W/W heat pumps. Operation and maintenance costs for the two heat pumps are equal and remain the same for 160 and 400 kW systems, but they decrease for systems smaller than 160 kW. The investment, operation, and maintenance costs are only a couple of the parameters involved in the methodology. To identify which model parameters have the highest impact on the CEP and LCC results, a sensitivity analysis of energy and cost calculations is necessary. Furthermore, such will determine the robustness of the solutions and the methodology overall. The comparison of CEP and NPV of individual building elements showed the same disposition for nearly all investigated elements. This suggests that a selection of package contents based on NPV would not be very different to a selection based on CEP. However, as presented in the paper, the two parameters are obtained by different methods, where NPV requires a greater number of inputs and arguably grater complexity and time. The CEP provides the possibility for choosing actions by considering the cost of saved kWh and gives the possibility of applying different approaches for compiling a package. Sorting on an element level, one can apply the CEP to select specific types from a range of options. The applied methodology for the compilation of packages showed that selected actions vary with different project goals. Sorting across all possible investigated options provided the package with the most savings for the least cost. The CEP of renewable energy-producing systems was shown to be much lower than those of energy-efficiency actions by means of building renovation. This was further confirmed through LLC analysis, as nearly all investigated renewable solutions resulted in both lowered global cost and energy demand. The results indicate that with high self-consumption, large system sizes bring the biggest global cost reduction. Nevertheless, in a few scenarios with PV, a local optimal is present where the second largest system is with the lowest global cost. Figure 8: Investment (**left**) and operation and maintenance (**right**) costs as a function of system capacity for selected energy-producing systems. Comparing the methodology proposed in this paper with the work presented in [24], it can be argued that the CEP parameter helps to reduce the economic gap between cost-optimal and NZEB solutions, as it seeks to implement a package that delivers the most energy savings for the least investment. While the aim of the study [14] was to find building system configurations that comply with a minimum share of renewables in an Italian setting, the achieved results for applicable renewable systems are in good agreement with those found in this paper: namely, heat pumps combined with PV systems and small solar heating plants. In a similar manner, results related to cost-optimality presented in [31; 32] have a comparable composition of optimal package, as found in this paper. In fact, both papers found that interventions of multiple building parts and systems are necessary to reach NZEB. In [31], the cost-optimal package consists of basic insulation of all building elements, a heat pump or gas boiler, and renewable production. A number of similarities can also be observed between results for the cost-optimal renovation of a Finish apartment building provided in [29] and this study.