El convento de agustinas de Santa María de Gracia.

The realisation of the case study requires annual hourly profiles for the renewable energy production and the weather conditions. The profiles are calculated for a design reference year (DRY). The DRY is meant to provide the profiles for a representative year of a long-term period, e.g. a decade. The methodology followed for the calculation of the DRY is detailed in appendix D.1.

Weather data

The weather data (global radiation and air temperature measured 2m above ground) are obtained from the IDAWEB service provided by MeteoSwiss [10]. The measurements come from the 8 weather stations listed in table 3.3 for the years 2009-2015. The Swiss outdoors temperature is calculated as already described in section 3.5. The Swiss global radiation is calculated also through a weighted average of the data from the 8 weather stations. Since the radiation data is used for computing the Swiss PV electricity production profile, the weight of each station is only directly proportional to the sum of the buildings footprint. It is calculated in this way since it is assumed that PV panels will only be placed on roofs. Building footprint data is available in the RegBL database [204].

Electricity and space heating demand

The national electricity demand is obtained from [208]. Once the DRY is calculated, the demand related to electric SH (DEH and HPs) is removed. The SH related electricity consumption is computed with the methodology introduced in section 3.5, taking into consideration the exterior temperature for the DRY. The demand for SH (%shin [34] is assumed to follow the same profile like the demand

related to electric SH.

Hydro dams

There is no data source available giving the water inflows into the Swiss dams. On the other hand the electricity production, the electricity consumption of the pumps for pumping storage and the level of the dams can be obtained from [211] for every week aggregated at Swiss level. To obtain hourly values, the weekly data is interpolated. The water inflow of the Swiss dams (Einflow) is calculated with Eq. 3.47, where Eturbineand Epumpare the electricity produced by the turbines and the electricity consumed by the pumps, respectively.ΔLevel is the change in level of the electricity stored in the dams.

Einflow(t )= ΔLevel(t) + Eturbine(t )− Epump(t ) (3.47)

Wind electricity

In order to calculate the Swiss capacity factor for wind turbines, 18 new locations for wind parks are considered (see Appendix D.3). The wind park are part of the feasible locations considered in [15]. each of the locations is attributed to nearest MeteoSwiss weather stations for having access to wind speed data. This reduces the number of weather stations to 11. At the weather stations the speed is mesured at 10 m above ground level, then the wind speed is calculated at 50 m height using the conversion factors in table D.4. The power-speed curve of a Gamesa G128-4.5 MW wind turbine [212] is used to obtain the capacity factor profiles for each of the considered locations. The last step consists on solving the combinatorial problem that provides the weight of each of the weather station for the calculation of the Swiss wind turbine capacity factor, which must be equal to the reported capacity factor in [34]: 0.23.

PV electricity

The calculation of the Swiss PV electricity supply curved is based on the model for a photovoltaic system proposed by A. Ashouri et al. [213]. The model is used to generate the hourly Swiss specific photovoltaic electricity production (W/m2).The Swiss global radiation and outdoors temperature

for the reference year are an inputs to the model. The obtained profile is normalized to its annual average value and multiplied by the expected Swiss PV capacity factor in 2035: 0.113 [34].

Solar thermal

The heat supply profile from thermal solar panels is calculated using the panel efficiency equation in [63]. The parameters used for the efficiency calculation correspond to the flat place solar collector analysed in [214]. A mean temperature in the collector of 50°C is considered. As for the calculation of the capacity factor for PV, the Swiss global radiation and outdoors temperature for the DRY are considered for the calculation of the efficiency, and the subsequent specific thermal power (W/m2). The profile is then normalized to its maximum thermal power in order to obtain the capacity factor for Switzerland, which is further normalized to its average value and multiplied by the capacity factor reported in [34]: 0.113.

Electric vehicle usage

The characterization of the electric vehicles (EVs) usage profiles is necessary in order to determine the amount of cars available for smart charging, the level of their batteries and their expected electricity consumption at any time of the day. For this case study only private cars are considered. The behaviour of the Swiss cars fleet is modelled with 40 different typical cars. The 40 typical cars are divided by typical usage profile: work, education, shopping and leisure. Each category is represented with 10 cars. In [7], one can obtain the percentage of people on movement (mobile-people) by private car separated by reason for every day. The car usage profiles and their weight in the mobile- people curve are computed aiming to reproduce the mobile-people curves in [7]. Appendix D.4 contains the curves from [7] together with the profiles obtained when combining the 10 typical cars for each driving purpose. In order to obtain specific day mobile-people curve, the mix of mobility reasons for each type of day (Monday-Friday, Saturday and Sunday) are obtained from [7]. In Appendix D.4, the day specific private car mobility curves are compared to the percentage of cars on route for each type of day [8] for validating the generated car usage profiles.