Updates to result-summaries

[maartenbrinkerink]

Description

-Added global level gen-shares
-Added CCS to 'clean' technologies in constants
-Added filter to gen share calculations to only include 'ELC' fuels in the calculations. Contributions of technologies to other fuels (e.g. RES generation targets (e.g. T01)) led to double counting of generation values.
-Added TRN to costs calculations.

[trevorb1]

No required changes from me. Everything looks good and runs fine! :)

Only question is the need of the following filtering in some of the scripts. See comments for context

df = df.loc[df.index.get_level_values("FUEL").str.startswith('ELC')]

[trevorb1]

Missing type hint!

Suggested change

	def format_global_values(production: pd.DataFrame, emissions: pd.DataFrame):
	def format_global_values(production: pd.DataFrame, emissions: pd.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame]:

[trevorb1]

Probably better to change country to an optional argument, as there is no else condition applied to it.

def calculate_emission_intensity(
    production: pd.DataFrame, emissions: pd.DataFrame
    production: pd.DataFrame, 
    emissions: pd.DataFrame,
    Optional[country]: bool = False
) -> pd.DataFrame:
...

[trevorb1]

Probably want to create a copy of the filtered data, not the pre-filtered data!

Suggested change

	df1 = df.copy()[
	(df.index.get_level_values("TECHNOLOGY").str.startswith("TRN"))
	]
	df1 = df[
	(df.index.get_level_values("TECHNOLOGY").str.startswith("TRN"))
	].copy()

[trevorb1]

I find grouping the logic in the if else statement together easier to read, but Im not fussed either way. Here is a suggestion if you do want to apply it. :)

Suggested change

	def get_transmission_cost(discounted_cost_tech: pd.DataFrame, country: bool) -> pd.DataFrame:
	df = discounted_cost_tech.copy()
	df1 = df.copy()[
	(df.index.get_level_values("TECHNOLOGY").str.startswith("TRN"))
	]
	if country:
	r = "COUNTRY"
	df1[r] = df1.index.get_level_values("TECHNOLOGY").str[3:6]
	else:
	r = "NODE"
	df1[r] = df1.index.get_level_values("TECHNOLOGY").str[3:8]
	df2 = df.copy()[
	(df.index.get_level_values("TECHNOLOGY").str.startswith("TRN"))
	]
	if country:
	r = "COUNTRY"
	df2[r] = df2.index.get_level_values("TECHNOLOGY").str[8:11]
	else:
	r = "NODE"
	df2[r] = df2.index.get_level_values("TECHNOLOGY").str[8:13]
	for df in [df1, df2]:
	df['VALUE'] = df['VALUE'] / 2
	df = pd.concat([df1, df2])
	return (
	df.reset_index()[["REGION", r, "YEAR", "VALUE"]]
	.groupby(["REGION", r, "YEAR"])
	.sum()
	)
	def get_transmission_cost(discounted_cost_tech: pd.DataFrame, country: bool) -> pd.DataFrame:
	cost = discounted_cost_tech[
	(discounted_cost_tech.index.get_level_values("TECHNOLOGY").str.startswith("TRN"))
	].copy()
	df1 = cost.copy()
	df2 = cost.copy()
	if country:
	r = "COUNTRY"
	df1[r] = df1.index.get_level_values("TECHNOLOGY").str[3:6]
	df2[r] = df2.index.get_level_values("TECHNOLOGY").str[8:11]
	else:
	r = "NODE"
	df1[r] = df1.index.get_level_values("TECHNOLOGY").str[3:8]
	df2[r] = df2.index.get_level_values("TECHNOLOGY").str[8:13]
	for df in [df1, df2]:
	df['VALUE'] = df['VALUE'] / 2
	df = pd.concat([df1, df2])
	return (
	df.reset_index()[["REGION", r, "YEAR", "VALUE"]]
	.groupby(["REGION", r, "YEAR"])
	.sum()
	)

[trevorb1]

Whats the need of this filtering?