## 2 Materials and Methods
The system partition for this case study is depicted in Table 5.  
Toronto was chosen as the reference location, mainly because a lot of data are available and updated for Canadian territories (usually case studies or templates involving other countries are no more reliable especially for costs analysis). The city has both ground and satellite data to accurately  
Figure 3: Benchmark setting for energy production (screenshot by authors using RESCreen). \begin{table}
\begin{tabular}{c c} \hline POWER SYSTEM – TOTAL & \\ Capacity & 4167 kW \\ Electricity & 35,033 MWh \\ \hline GEOTHERMAL POWER & \\ Capacity & 3167 kW \\ Electricity & 26,273 MWh \\ \hline HYDRO TURBINE & \\ Capacity & 1000 kW \\ Electricity & 8760 MWh \\ \hline \end{tabular}
\end{table} 
Table 5: Definition of the system partition. map its territory characteristics. Moreover, Toronto is located in the extreme south-east of Canada, capital of the province of Ontario and most populous center of Canada with its 3,120,668 inhabitants. The city has shown its strong engagement in sustainable development, and in 2013, the City Council introduced a mandate to produce at least 5% of the electricity from renewable energy sources for all new installations. The use of solar photovoltaic, solar thermal, geothermal and biomass supports the environmental, energetic and economic objectives of the city. Many initiatives have been set up for education and training of the community and even for helping in designing new smart buildings for commercial and personal use. In Table 6, geographic and climate data for Toronto are provided. Wind turbine and photovoltaic plants, both of 1,000 kW, are considered. For the wind turbine system, the electricity produced will be around 3,000 MWh. As a benchmark for energy production costs, RESTcreen values are quite accurate for many installations in Canada, so it was automatically set at 100 CAS/MWh. A benchmark analysis for the wind turbine case located in Toronto is shown in Figure 4. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Unit** & **Location Area** & **Facility Location** & **Source of Data** \\ \hline Latitude & N.A. & 43.7 & 43.7 & N.A. \\ \hline Longitude & N.A. & \(-\)79.4 & \(-\)79.4 & N.A. \\ \hline Climate zone & N.A. & \(6\)A-Cold-Humid & Ground+NASA \\ \hline Elevation & M & 107 & 91 & Ground-Map \\ \hline Heating design temperature & \({}^{\circ}\)C & \(-\)17.1 & N.A. & Ground \\ \hline Cooling design temperature & \({}^{\circ}\)C & 28.8 & N.A. & Ground \\ \hline Earth temperature amplitude & \({}^{\circ}\)C & 21.4 & N.A. & NASA \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Location details for Toronto. Figure 4: Benchmark setting for energy production (screenshot by author using RESTcreen). Below, in Table 7, the initial target values of the proposed case are presented. The model of the wind turbine used for this project was manufactured by a Danish company, VESTAS. Many VESTAS turbines have been exported to Canada, as a symbol of European engagement in renewable energies. The plant, using this wind turbine model, is able to produce 3197 MWh with initial costs of 2,309,703 CA$(CAS$/$kW value is scaled with respect to the plant capacity and data are available in the software for defined range). The electricity export rate value has been changing across the years. To give an example, many countries started with incentives for renewable energies investors, by rewarding them with a feed-in tariff or feed-in premium. In this way, the electricity was paid more for than the real cost per unit of the electricity exported to the central grid. Nowadays, this scenario is no longer relevant because bonuses for renewable energy plants have been largely reduced, so the actual price paid per MWh to sustainable power plants is similar to all other energy source plants. Finally, the choice to put the electricity export rate at the same value as the benchmark energy production cost is good enough for an approximate evaluation.