2.2.1. Representative Weather Selection

Since the scenarios are weather-driven, it is necessary to identify the years representing the various scenarios in the region. With this in mind, a metric system was created based on the notion that comfortable temperature levels are between 18 and 22 °C. Under 18 °C, people will start using their heaters, and above 22 °C, they will start using their air-conditioners.

Therefore, the metric system considers the mean and peak deviations per month from these values. Since the focus is to cover extreme cases, the years were ranked based on the summer-warmness and winter-coldness. Based on the rankings, six years were selected, as seen in Table 4. The frequency of occurrence was calculated based on the highest *R*<sup>2</sup> compared to the representative year from 1990 to 2019. The legends are used mainly in the figures in the results section.



\* Occurrence in the past 30 years from 1990 to 2019.

It can be seen in Figure 2 that around August, 2013 has the highest peak and mean positive temperature deviation, while around February, 2012 has the highest peak and mean negative temperature deviation as intended by the sampling. 2014 has the lowest deviation overall since it is the lowest in summer and in the winter. The other representative years fall between these extreme cases.

**Figure 2.** Temperature deviation of the representative year from the comfortable temperature of 18 °C to 22 °C.

#### 2.2.2. Weather-Based Solar Generation

The weather-based solar power generation calculation was mainly based on the TMY to power tutorial written by the developers of *pvlib* [28]. Since the approach requires both the direct-normal irradiance (DNI) and diffuse horizontal irradiance (DHI), and JMA only provides the global horizontal irradiance (GHI), the built-in function *pvlib*.*irradiance*.*erbs* was used to estimate the DNI and DHI. The Erbs model [29] estimates the diffused fraction of GHI to calculate DHI and uses the solar zenith to calculate DNI. By providing the timezone, longitude, latitude, and altitude data along with the hourly GHI data from JMA, the DNI and DHI were calculated using *pvlib*'s built-in functions. Besides the irradiance data, the power generation calculation also requires temperature data to account for the impact of temperature on solar cells' efficiency. The solar-capacity-weighted mean was used for both the GHI and temperature since the solar power generation distribution is proportional to the generation capacity of each prefecture. Subsequently, the power generation values of a 208 W Kyocera Solar Panel and an ABB Micro 250 W micro-inverter were calculated using *pvlib*.*pvsystem*.*sapm* and *pvlib*.*inverter*.*sandia*. The resulting hourly annual generation was scaled by 208 W to represent the maximum power output for PyPSA.
