def extend(serie,
endpoint,
newname,
past=capacity_factor_past,
future=capacity_factor_PDP7A):
"""Extend a series by interpolating between 2020, 2025, 2030 and a 2050 endpoint."""
r = past[serie]
a = (r.loc[2013] + r.loc[2014] + r.loc[2015]) / 15
b = future.loc[2020, serie] / 5
c = future.loc[2025, serie] / 5
d = future.loc[2030, serie] / 5
s = pd.Series(name=newname,
data=[
4 * a + b, 3 * a + 2 * b, 2 * a + 3 * b, a + 4 * b,
5 * b, 4 * b + c, 3 * b + 2 * c, 2 * b + 3 * c,
b + 4 * c, 5 * c, 4 * c + d, 3 * c + 2 * d,
2 * c + 3 * d, c + 4 * d, 5 * d
],
index=range(2016, 2031))
r = r.append(s)
d *= 5
e = endpoint
s = pd.Series(name=newname,
data=[d + (e - d) * (i + 1) / 20 for i in range(20)],
index=range(2031, 2051))
return r.append(s)
def residual_value(additions, plant_accounting_life, technology):
"""Return the residual value of the generation capacity at model end year.
A Series equal 0 for all years except at the end.
"""
lifetime = plant_accounting_life[technology]
idx = additions.index
n = len(idx)
remaining_fraction = pd.Series(0, index=idx)
for i in range(min(lifetime, n)):
# On average, plant opens middle of the year
remaining_fraction.iloc[n - i - 1] = 1 - (i + 0.5) / lifetime
result = pd.Series(0, index=years, name=technology)
result[2050] = (remaining_fraction * additions[technology]).sum()
return result
def by_median(fuel, col):
"""Median of observed costs, defined as 'refering to a year prior year of publication'."""
data = view[fuel]
past_data = data[data.Year < data.PublicationYear]
s = pd.Series(past_data[col].median(), index=years, name=fuel)
if VERBOSE:
myplot(data, fuel, col, s, " (median of " + str(len(past_data)) + ") ")
return s
def fill_in(serie):
"""Return the investments needed to reach the capacity objectives.
Approximately because cast as integer
"""
capacity_2015 = capacity_past.cumsum().loc[2015, serie.name]
a = (serie[2020] - capacity_2015) / 15
b = (serie[2025] - serie[2020]) / 15
c = (serie[2030] - serie[2025]) / 15
return pd.Series(name=serie.name,
data=[
a, 2 * a, 3 * a, 4 * a, 5 * a, b, 2 * b, 3 * b, 4 * b,
5 * b, c, 2 * c, 3 * c, 4 * c, 5 * c
],
index=range(2016, 2031),
dtype="int64")
def by_regression(fuel, col):
"""Regression, when the literature forecasts cost saving technical progress."""
data = view[fuel]
explaining = data.Year - start_year
explaining = sm.add_constant(explaining)
model = sm.OLS(data[col], explaining, missing='drop')
results = model.fit()
show(fuel, results.summary())
level = results.params.const
trend = results.params.Year if results.pvalues.Year < 0.05 else 0
s = pd.Series(np.linspace(level, level + trend * n_year, n_year),
index=years,
name=fuel)
if VERBOSE:
myplot(data, fuel, col, s, " (regression on " + str(len(data)) + ") ")
return s
def smooth(s):
"""Redistribute over time equally a mass concentrated in the last period.
Argument s is a numerical series with a numerical value in last position.
Values are and remain integers;
Metaphor: the benjamin shares apples with all his previous siblings.
Assume for example there are 4 brothers and the last one has 10 apples.
Then he should give each brother 2 apples, keeping 4 for himself.
>>> smooth(pd.Series([0, 0, 0, 10]))
0 2
1 2
2 2
3 4
dtype: int64
"""
n = len(s)
total = s.iloc[-1]
annual = int(total // n)
final = int(total - annual * (n - 1))
data = [annual] * (n - 1) + [final]
assert sum(data) == total
return pd.Series(data, s.index)
of the electricity production (3.7%).
The figure adds that: Imports represented 1.5%, we infer that the production number is the total
domestic supply.
The Gas cheese slice label is "Gas Turbine" which includes Oil fueled gas turbines.
"""
# This implies a total production of 164310 * 0.985 = 161.8 TWh
# which compares to 159.7 TWh given in EVN 2016 report page 16
domestic_supply_2015 = 164310 # GWh
production_2015 = pd.Series(name=2015,
data={
"Coal": round(0.344 * domestic_supply_2015),
"BigHydro":
round(0.304 * domestic_supply_2015),
"GasTurbine":
round(0.300 * domestic_supply_2015),
"Renewable4":
round(0.037 * domestic_supply_2015),
"Import": round(0.015 * domestic_supply_2015)
})
production_2015[
"Oil"] = 450 # Source: Own estimate, same as 2015, we have no idea
production_2015["Gas"] = production_2015["GasTurbine"] - production_2015["Oil"]
production_2015["Wind"] = 240 # Source: Own estimate, 2014 + Bac Lieu 1 online
production_2015["Biomass"] = 60 # Continuity with 2014 level and trend
production_2015["Solar"] = 0 # Commercial solar power not allowed by law yet
addcol_Renewable(production_2015)
production_2015["SmallHydro"] = production_2015[
"Renewable4"] - production_2015["Renewable"]
CO2_captured = CO2_produced - CO2_emitted
CO2_emitted = EMISSION_FACTOR_withCCS * production
CO2_produced = CO2_factor_of_heat * heat_used
CO2_produced = CO2_factor_of_heat * production * heat_rate_CCS
CO2_produced = CO2_factor_of_heat * production * heat_rate_noCCS * (1 + energy_penalty)
CO2_produced = production * EMISSION_FACTOR_noCCS * (1 + energy_penalty)
CO2_captured = production * capture_factor
capture_factor = EMISSION_FACTOR_noCCS * (1 + energy_penalty) - EMISSION_FACTOR_withCCS
"""
capture_factor = pd.DataFrame(index=YEARS)
for fuel in SOURCES:
capture_factor[fuel] = pd.Series(0, index=YEARS) # gCO2/ kWh
capture_factor["CoalCCS"] = (EMISSION_FACTOR["Coal"] * (1 + energy_penalty) -
EMISSION_FACTOR["CoalCCS"])
capture_factor["GasCCS"] = (EMISSION_FACTOR["Gas"] * (1 + energy_penalty) -
EMISSION_FACTOR["GasCCS"])
capture_factor["BioCCS"] = (EMISSION_FACTOR["Biomass"] * (1 + energy_penalty) -
EMISSION_FACTOR["BioCCS"])
# %%
DISCOUNT_RATE = 0.06
START_CARBON_PRICE = 0
"BioCCS": 0
}
for y in range(2031, 2051):
additions.loc[y] = increment
#%% Old plant retirement program
plant_life = pd.Series({
"Coal": 40,
"Gas": 25,
"Oil": 30,
"BigHydro": 100,
"SmallHydro": 60,
"Biomass": 25,
"Wind": 20,
"Solar": 25,
"CoalCCS": 40,
"GasCCS": 25,
"BioCCS": 25,
"PumpedStorage": 100,
"Import": 100
})
retirement = pd.DataFrame()
for tech in plant_life.index:
retirement[tech] = additions[tech].shift(plant_life[tech])
retirement.fillna(0, inplace=True)
def as_zero(fuel, _):
"""Return a time series of zero."""
show(fuel + " set as zero for all years")
return pd.Series(0, index=years, name=fuel)
# and was built at a cost of 413 million yuan (53 million U.S. dollars).
# ==>
# 10^12 Wh / 8760 = 114 MW
# 53 / 114 = 0.465 $/W = 465 $ per kW
#
#
# http://news.xinhuanet.com/english/2017-04/19/c_136220293.htm
# PREY VENG, Cambodia, April 19 (Xinhua) : The 160-km project... line
# is capable of transmitting 150 megawatts (MW) electric power
# at the cost of 75 million U.S. dollars
# ==>
# 500 $ per kW
construction_cost_start_year_import = 500
construction_cost["Import"] = pd.Series(construction_cost_start_year_import,
index=years,
name="Import")
show("Overnight construction costs, $/kW ", start_year)
show(construction_cost.loc[start_year].round())
show()
show("Trends in overnight construction costs, $/kW/yr", start_year, "-",
end_year)
show(construction_cost.diff().loc[start_year].round(2))
show()
show("Overnight construction costs, $/kW")
show(construction_cost[sources].round())
# %% Fixed operating costs
# Convert all other Gas capacity to Gas CCS on 2035 - 2050
retrofit_rate_gas = (
(baseline.capacities.at[END_YEAR, "Gas"] - PILOT1_SIZE - PILOT2_SIZE -
additions.loc[RETROFIT_PERIOD, "Gas"].sum()) / len(RETROFIT_PERIOD))
retirement.loc[RETROFIT_PERIOD, "Gas"] += retrofit_rate_gas
additions.loc[RETROFIT_PERIOD, "GasCCS"] = retrofit_rate_gas
# Install new Gas CCS plants instead of simple Gas
additions.loc[RETROFIT_PERIOD, "GasCCS"] += additions.loc[RETROFIT_PERIOD,
"Gas"]
additions.loc[RETROFIT_PERIOD, "Gas"] = 0
# Ramp up some BioCCS, quadratically
BIOCCS_TREND = 10 # The increase of annual capacity installed (MW / yr)
bioCCS_2050 = BIOCCS_TREND * len(RETROFIT_PERIOD)
bioCCS_ramp = pd.Series(range(0, bioCCS_2050, BIOCCS_TREND), RETROFIT_PERIOD)
additions.loc[RETROFIT_PERIOD, "BioCCS"] = bioCCS_ramp
# Save a bit on Gas CCS, keeping the END_YEAR generation unchanged
savedGasCCS = (bioCCS_ramp * baseline.capacity_factor.at[END_YEAR, "BioCCS"] /
baseline.capacity_factor.at[END_YEAR, "GasCCS"])
additions.loc[RETROFIT_PERIOD, "GasCCS"] -= savedGasCCS
withCCS = Plan(additions, retirement[technologies], baseline.capacity_factor,
baseline.net_import)
withCCS.__doc__ = "With CCS"
if __name__ == '__main__':
if (len(sys.argv) == 2) and (sys.argv[1] == "summarize"):
withCCS.summarize()
if (len(sys.argv) == 3) and (sys.argv[1] == "plot"):
# Convert all other Gas capacity to Gas CCS on 2035 - 2050
retrofit_rate_gas = (
(baseline.capacities.at[end_year, "Gas"] - pilot1_size - pilot2_size -
additions.loc[retrofit_period, "Gas"].sum()) / len(retrofit_period))
retirement.loc[retrofit_period, "Gas"] += retrofit_rate_gas
additions.loc[retrofit_period, "GasCCS"] = retrofit_rate_gas
# Install new Gas CCS plants instead of simple Gas
additions.loc[retrofit_period, "GasCCS"] += additions.loc[retrofit_period,
"Gas"]
additions.loc[retrofit_period, "Gas"] = 0
# Ramp up some BioCCS, quadratically
bioCCS_trend = 10 # The increase of annual capacity installed (MW / yr)
bioCCS_2050 = bioCCS_trend * len(retrofit_period)
bioCCS_ramp = pd.Series(range(0, bioCCS_2050, bioCCS_trend), retrofit_period)
additions.loc[retrofit_period, "BioCCS"] = bioCCS_ramp
# Save a bit on Gas CCS, keeping the end_year generation unchanged
savedGasCCS = (bioCCS_ramp * baseline.capacity_factor.at[end_year, "BioCCS"] /
baseline.capacity_factor.at[end_year, "GasCCS"])
additions.loc[retrofit_period, "GasCCS"] -= savedGasCCS
withCCS = PowerPlan(additions, retirement[technologies],
baseline.capacity_factor, baseline.net_import)
withCCS.__doc__ = "With CCS"
if __name__ == '__main__':
if (len(sys.argv) == 2) and (sys.argv[1] == "summarize"):
withCCS.summarize()
if (len(sys.argv) == 3) and (sys.argv[1] == "plot"):
CO2_captured = CO2_produced - CO2_emitted
CO2_emitted = emission_factor_withCCS * production
CO2_produced = CO2_factor_of_heat * heat_used
CO2_produced = CO2_factor_of_heat * production * heat_rate_CCS
CO2_produced = CO2_factor_of_heat * production * heat_rate_noCCS * (1 + energy_penalty)
CO2_produced = production * emission_factor_noCCS * (1 + energy_penalty)
CO2_captured = production * capture_factor
capture_factor = emission_factor_noCCS * (1 + energy_penalty) - emission_factor_withCCS
"""
capture_factor = pd.DataFrame(index=years)
for fuel in sources:
capture_factor[fuel] = pd.Series(0, index=years) # gCO2/ kWh
capture_factor["CoalCCS"] = (emission_factor["Coal"] * (1 + energy_penalty)
- emission_factor["CoalCCS"])
capture_factor["GasCCS"] = (emission_factor["Gas"] * (1 + energy_penalty)
- emission_factor["GasCCS"])
capture_factor["BioCCS"] = (emission_factor["Biomass"] * (1 + energy_penalty)
- emission_factor["BioCCS"])
#%%
discount_rate = 0.06
start_carbon_price = 0
Source : IPCC SRREN Methodology Annex II, Methodology
Lead Authors: William Moomaw (USA), Peter Burgherr (Switzerland), Garvin Heath (USA),
Manfred Lenzen (Australia, Germany), John Nyboer (Canada), Aviel Verbruggen (Belgium)
Table A.II.4 page 982 "Aggregated results of literature review of LCAs of GHG emissions
from electricity generation technologies (g CO2eq/kWh)"
Median of the literature reviewed for Coal to Solar.
(Min + Max)/2 for CCS technologies
"""
from init import pd
EMISSION_FACTOR = pd.Series({
"Coal": 1001,
"Gas": 469,
"Oil": 840,
"BigHydro": 4,
"SmallHydro": 4,
"Biomass": 18,
"Wind": 12,
"Solar": 46,
"CoalCCS": (98 + 396) / 2,
"GasCCS": (65 + 245) / 2,
"BioCCS": (-1368 + (-594)) / 2
}) # gCO2eq / kWh
#Assumption: VN imports from China and Lao
EMISSION_FACTOR["Import"] = 0.5 * EMISSION_FACTOR[
"Coal"] + 0.5 * EMISSION_FACTOR["BigHydro"]
