Commit 09ef9d28 authored by Fabian Neumann's avatar Fabian Neumann
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update tutorial-6 bonus notebooks

parent 8a870662
%% Cell type:markdown id: tags:
## Tutorial VI.1
Consider a long-term multi-year investment problem where **CSP (Concentrated Solar Power)** has a learning curve such that
$$LCOE = c_0 \left(\frac{x_t}{x_0}\right)^{-\gamma} + c_1$$
where $c_0$ is cost at start, $c_1$ is material cost and $x_t$ is cumulative
capacity in the investment interval $t$. Thus, $x_0$ is the initial cumulative CSP capacity.
Additionally, there are **nuclear** and **coal** generators for which there is no potential for reducing their LCOE.
We build an optimisation to minimise the cost of supplying a flat demand $d=100$ with the given technologies between 2020 and 2050, where a CO$_2$ budget cap is applied.
> **Hint:** Problem formulation is to be found further along this notebook.
**Task:** Explore different discount rates, learning rates, CO$_2$ budgets. For instance
* No learning for CSP and no CO$_2$ budget would result in a coal-reliant system.
* A CO$_2$ budget and no learning prefers a system built on nuclear.
* A CO$_2$ budget and learning results in a system with CSP.
%% Cell type:markdown id: tags:
***
## Imports
%% Cell type:code id: tags:
``` python
from pyomo.environ import ConcreteModel, Var, Objective, NonNegativeReals, Constraint, Suffix, exp
from pyomo.opt import SolverFactory
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('bmh')
%matplotlib inline
```
%% Cell type:markdown id: tags:
## Parameters
%% Cell type:code id: tags:
``` python
techs = ["coal","nuclear","CSP"]
colors = ["#707070","#ff9000","#f9d002"]
parameters = pd.DataFrame(data=[[50.,100.,150.], # LCOE EUR/MWh_el
[1.,0.,0.], # emissions tCO2/MWh_el
[50.,100.,35.], # LCOE long-term potential EUR/MWh_el
[1e6,1e6,200]], # Volume in GW until (today - base) LCOE reduces by 1/e
index=["LCOE","emissions","base LCOE","volume"],
columns=techs)
parameters = pd.DataFrame(data=[[50.,100.,150.,"LCOE EUR/MWh_el"],
[1.,0.,0., "tCO2/MWh_el"],
[50.,100.,35., "EUR/MWh_el"],
[1e6,1e6,200,"GW"]],
index=["current LCOE","specific emissions","potential LCOE","current volume"],
columns=techs+["unit"])
parameters
```
%%%% Output: execute_result
coal nuclear CSP
LCOE 50.0 100.0 150.0
emissions 1.0 0.0 0.0
base LCOE 50.0 100.0 35.0
volume 1000000.0 1000000.0 200.0
coal nuclear CSP unit
current LCOE 50.0 100.0 150.0 LCOE EUR/MWh_el
specific emissions 1.0 0.0 0.0 tCO2/MWh_el
potential LCOE 50.0 100.0 35.0 EUR/MWh_el
current volume 1000000.0 1000000.0 200.0 GW
%% Cell type:code id: tags:
``` python
#discount rate
rate = 0.05
#demand in GW
demand = 100.
years = list(range(2021,2050))
#learning rate of CSP
gamma_csp = 0.4
# carbon budget in average tCO2/MWh_el
co2_budget = 0.2
# considered years
years = list(range(2020,2050))
```
%% Cell type:markdown id: tags:
## Build Model
> **Note:** We use [`pyomo`](https://pyomo.readthedocs.io/en/stable/) for building optimisation problems in python. This is also what `pypsa` uses under the hood.
%% Cell type:code id: tags:
``` python
model = ConcreteModel("discounted total costs")
```
%% Cell type:markdown id: tags:
$$G_{t,a}$$
%% Cell type:code id: tags:
``` python
model.generators = Var(techs, years, within=NonNegativeReals)
```
%% Cell type:markdown id: tags:
$$LCOE_{t,a}$$
%% Cell type:code id: tags:
``` python
model.costs = Var(techs, years, within=NonNegativeReals)
```
%% Cell type:markdown id: tags:
The objective is to minimise the system costs:
$$\min \quad \sum_{t\in T, a\in A} G_{t,a}\cdot LCOE_{t,a} \cdot \frac{8760}{10^6\cdot (1+r)^{t}}$$
%% Cell type:code id: tags:
``` python
# in billion EUR
model.objective = Objective(expr=sum(model.generators[tech,year]*model.costs[tech,year]*8760/1e6/(1+rate)**(year-years[0])
for year in years
for tech in techs))
```
%% Cell type:markdown id: tags:
Add a constraint such that demand is met by generator dispatch:
$$\forall a\in A: \quad d = \sum_{t \in T} G_{t,a}$$
%% Cell type:code id: tags:
``` python
def balance_constraint(model, year):
return demand == sum(model.generators[tech,year] for tech in techs)
model.balance_constraint = Constraint(years, rule=balance_constraint)
```
%% Cell type:markdown id: tags:
Add a constraint on carbon dioxide emissions:
$$\sum_{t\in T, a\in A} G_{t,a} \cdot e_{t} \leq \hat{e} \cdot |A| \cdot d$$
%% Cell type:code id: tags:
``` python
def co2_constraint(model):
return 0.2*30*demand >= sum(model.generators[tech,year]*parameters.at["emissions",tech] for tech in techs for year in years)
return co2_budget*len(years)*demand >= sum(model.generators[tech,year]*parameters.at["specific emissions",tech] for tech in techs for year in years)
model.co2_constraint = Constraint(rule=co2_constraint)
```
%% Cell type:code id: tags:
``` python
def cost_constraint(model,tech,year):
def lcoe_constraint(model,tech,year):
if tech != "CSP":
return model.costs[tech,year] == parameters.at["LCOE",tech]
return model.costs[tech,year] == parameters.at["current LCOE",tech]
else:
#return model.costs[tech,year] == parameters.at["base LCOE",tech] \
# + (parameters.at["LCOE",tech] - parameters.at["base LCOE",tech])*exp(-sum(model.generators[tech,yeart] for yeart in years if yeart < year)/parameters.at["volume",tech])
return model.costs[tech,year] == parameters.at["LCOE",tech]*(1+sum(model.generators[tech,yeart] for yeart in years if yeart < year))**(-0.4)
model.cost_constraint = Constraint(techs, years, rule=cost_constraint)
return model.costs[tech,year] == parameters.at["current LCOE",tech]*(1+sum(model.generators[tech,yeart] for yeart in years if yeart < year))**(-gamma_csp)
model.lcoe_constraint = Constraint(techs, years, rule=lcoe_constraint)
```
%% Cell type:code id: tags:
%% Cell type:markdown id: tags:
``` python
# in billion EUR
model.objective = Objective(expr=sum(model.generators[tech,year]*model.costs[tech,year]*8760/1e6/(1+rate)**(year-2021)
for year in years
for tech in techs))
```
> **Hint:** You can print the model formulation with `model.pprint()`
%% Cell type:markdown id: tags:
## Solve Model
%% Cell type:code id: tags:
``` python
opt = SolverFactory("ipopt")
```
%% Cell type:code id: tags:
``` python
results = opt.solve(model,suffixes=["dual"],keepfiles=False)
```
%% Cell type:markdown id: tags:
## Results
%% Cell type:markdown id: tags:
Optimised cost:
%% Cell type:code id: tags:
``` python
print(model.objective())
```
%%%% Output: stream
230.3039488909157
231.63436487305015
%% Cell type:markdown id: tags:
The unoptimized cost (where everything is supplied by coal) is:
%% Cell type:code id: tags:
``` python
print(8760*demand*parameters.at["LCOE","coal"]*len(years)/1e6)
print(8760*demand*parameters.at["current LCOE","coal"]*len(years)/1e6)
```
%%%% Output: stream
1270.2
1314.0
%% Cell type:markdown id: tags:
Plotting the development of the technology mix of the optimal solution over time:
%% Cell type:code id: tags:
``` python
capacities = pd.DataFrame(0.,index=years,columns=techs)
for year in years:
for tech in techs:
capacities.at[year,tech] = model.generators[tech,year].value
```
%% Cell type:code id: tags:
``` python
fig, ax = plt.subplots()
fig.set_size_inches((10,6))
capacities.plot(kind="area",stacked=True,color=colors,ax=ax)
ax.set_xlabel("year")
ax.set_ylabel("capacity [GW]")
```
%%%% Output: execute_result
Text(0, 0.5, 'capacity [GW]')
%%%% Output: display_data
![](data:image/png;base64,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)
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)
%% Cell type:markdown id: tags:
Plotting the development of the costs of the technology over time:
%% Cell type:code id: tags:
``` python
costs = pd.DataFrame(0.,index=years,columns=techs)
for year in years:
for tech in techs:
costs.at[year,tech] = model.costs[tech,year].value
```
%% Cell type:code id: tags:
``` python
fig, ax = plt.subplots()
fig.set_size_inches((10,6))
costs.plot(color=colors,ax=ax,linewidth=3)
ax.set_xlabel("year")
ax.set_ylabel("capacity [GW]")
ax.set_ylabel("costs [billion EUR]")
ax.set_ylim([0,160])
```
%%%% Output: execute_result
(0, 160)
%%%% Output: display_data
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)
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)
%% Cell type:code id: tags:
``` python
```
......
%% Cell type:markdown id: tags:
# First optimise investments
# Tutorial VI.2
Let us consider a myopic dispatch problem that is solved with three different approaches:
1. [Investments are optimised for a single node.](#one)
2. [Each hour is optimised sequentially without foresight.](#two)
3. [No optimisation is used at all, just a merit order curve is used.](#three)
To simulate the available renewable energy we use data from [renewables.ninja](https://renewables.ninja)
**Task:** Take optimal investments and do dispatch without foresight and
without optimisation, winner is solution with least load-shedding.
%% Cell type:markdown id: tags:
***
<a id='one'></a>
# Investment optimisation for a single node
%% Cell type:code id: tags:
``` python
import urllib.request as dl
import os
import logging
logging.basicConfig(level=logging.ERROR)
import pypsa
import pandas as pd
from pyomo.environ import Constraint
import json
import xarray as xr
import matplotlib.pyplot as plt
plt.style.use('bmh')
%matplotlib inline
```
%% Cell type:code id: tags:
``` python
weather_dir = "../whobs-server/data/"
files = ['ninja_pv_europe_v1.1_sarah.nc',
'ninja_wind_europe_v1.1_current_on-offshore.nc']
url_base = "https://model.energy/data/"
for f in files:
if not os.path.isfile(f):
print("Download file: {}".format(f))
dl.urlretrieve(url_base+f, f)
else:
print("No need to download file: {}".format(f))
```
%%%% Output: stream
No need to download file: ninja_pv_europe_v1.1_sarah.nc
No need to download file: ninja_wind_europe_v1.1_current_on-offshore.nc
%% Cell type:code id: tags:
``` python
#read in renewables.ninja solar time series
solar_pu = xr.open_dataset(weather_dir + 'ninja_pv_europe_v1.1_sarah.nc')
solar_pu = xr.open_dataset('ninja_pv_europe_v1.1_sarah.nc')
#read in renewables.ninja wind time series
wind_pu = xr.open_dataset(weather_dir + 'ninja_wind_europe_v1.1_current_on-offshore.nc')
wind_pu = xr.open_dataset('ninja_wind_europe_v1.1_current_on-offshore.nc')
def annuity(lifetime,rate):
if rate == 0.:
return 1/lifetime
else:
return rate/(1. - 1. / (1. + rate)**lifetime)
```
%%%% Output: error
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/software/anaconda3/envs/esm-tutorials/lib/python3.7/site-packages/xarray/backends/file_manager.py in acquire(self, needs_lock)
166 try:
--> 167 file = self._cache[self._key]
168 except KeyError:
~/software/anaconda3/envs/esm-tutorials/lib/python3.7/site-packages/xarray/backends/lru_cache.py in __getitem__(self, key)
41 with self._lock:
---> 42 value = self._cache[key]
43 self._cache.move_to_end(key)
KeyError: [<function _open_netcdf4_group at 0x7fa84cce60d0>, ('/home/ws/sp2668/bwSyncAndShare/teaching/whobs-server/data/ninja_pv_europe_v1.1_sarah.nc', CombinedLock([<unlocked _thread.lock object at 0x7fa84cd35f80>, <unlocked _thread.lock object at 0x7fa84cd35f58>])), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('group', None), ('persist', False))]
During handling of the above exception, another exception occurred:
FileNotFoundError Traceback (most recent call last)
<ipython-input-2-77803472364e> in <module>
2
3 #read in renewables.ninja solar time series
----> 4 solar_pu = xr.open_dataset(weather_dir + 'ninja_pv_europe_v1.1_sarah.nc')
5
6 #read in renewables.ninja wind time series
~/software/anaconda3/envs/esm-tutorials/lib/python3.7/site-packages/xarray/backends/api.py in open_dataset(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables, backend_kwargs, use_cftime)
361 if engine == 'netcdf4':
362 store = backends.NetCDF4DataStore.open(
--> 363 filename_or_obj, group=group, lock=lock, **backend_kwargs)
364 elif engine == 'scipy':
365 store = backends.ScipyDataStore(filename_or_obj, **backend_kwargs)
~/software/anaconda3/envs/esm-tutorials/lib/python3.7/site-packages/xarray/backends/netCDF4_.py in open(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)
350 kwargs=dict(group=group, clobber=clobber, diskless=diskless,
351 persist=persist, format=format))
--> 352 return cls(manager, lock=lock, autoclose=autoclose)
353
354 @property
~/software/anaconda3/envs/esm-tutorials/lib/python3.7/site-packages/xarray/backends/netCDF4_.py in __init__(self, manager, lock, autoclose)
309
310 self._manager = manager
--> 311 self.format = self.ds.data_model
312 self._filename = self.ds.filepath()
313 self.is_remote = is_remote_uri(self._filename)
~/software/anaconda3/envs/esm-tutorials/lib/python3.7/site-packages/xarray/backends/netCDF4_.py in ds(self)
354 @property
355 def ds(self):
--> 356 return self._manager.acquire().value
357
358 def open_store_variable(self, name, var):
~/software/anaconda3/envs/esm-tutorials/lib/python3.7/site-packages/xarray/backends/file_manager.py in acquire(self, needs_lock)
171 kwargs = kwargs.copy()
172 kwargs['mode'] = self._mode
--> 173 file = self._opener(*self._args, **kwargs)
174 if self._mode == 'w':
175 # ensure file doesn't get overriden when opened again
~/software/anaconda3/envs/esm-tutorials/lib/python3.7/site-packages/xarray/backends/netCDF4_.py in _open_netcdf4_group(filename, lock, mode, group, **kwargs)
242 import netCDF4 as nc4
243
--> 244 ds = nc4.Dataset(filename, mode=mode, **kwargs)
245
246 with close_on_error(ds):
netCDF4/_netCDF4.pyx in netCDF4._netCDF4.Dataset.__init__()
netCDF4/_netCDF4.pyx in netCDF4._netCDF4._ensure_nc_success()
FileNotFoundError: [Errno 2] No such file or directory: b'/home/ws/sp2668/bwSyncAndShare/teaching/whobs-server/data/ninja_pv_europe_v1.1_sarah.nc'
%% Cell type:code id: tags:
``` python
assumptions_df = pd.DataFrame(columns=["FOM","fixed","discount rate","lifetime","investment","efficiency"],
index=["wind","solar","hydrogen_electrolyser","hydrogen_turbine","hydrogen_energy",
"battery_power","battery_energy"],
dtype=float)
assumptions_df["lifetime"] = 25.
assumptions_df.at["hydrogen_electrolyser","lifetime"] = 20.
assumptions_df.at["battery_power","lifetime"] = 15.
assumptions_df.at["battery_energy","lifetime"] = 15.
assumptions_df["discount rate"] = 0.05
assumptions_df["FOM"] = 3.
assumptions_df["efficiency"] = 1.
assumptions_df.at["battery_power","efficiency"] = 0.9
```
%% Cell type:code id: tags:
``` python
assumptions = {"wind" : True,
"solar" : True,
"battery" : True,
"hydrogen" : True,
"load" : 100,
"wind_cost": 1182,
"solar_cost" : 600,
"battery_energy_cost" : 100,
"battery_power_cost" : 100,
"hydrogen_electrolyser_cost" : 750,
"hydrogen_energy_cost" : 0.5,
"hydrogen_electrolyser_efficiency" : 80,
"hydrogen_turbine_cost" : 800,
"hydrogen_turbine_efficiency" : 60,
"discount_rate" : 0.05,
"hydrogen_energy_cost" : 0.5,
"hydrogen_electrolyser_efficiency" : 80,
"hydrogen_turbine_cost" : 800,
"hydrogen_turbine_efficiency" : 60,
"discount_rate" : 0.05,
"frequency" : 3,
"year" : 2011,
"country" : "GB"}
```
%% Cell type:code id: tags:
``` python
booleans = ["wind","solar","battery","hydrogen"]
floats = ["load","wind_cost","solar_cost","battery_energy_cost",
"battery_power_cost","hydrogen_electrolyser_cost",
"hydrogen_energy_cost",
"hydrogen_electrolyser_efficiency",
"hydrogen_turbine_cost",
"hydrogen_turbine_efficiency",
"discount_rate"]
threshold = 0.1
def error(message, jobid):
with open('results-solve/results-{}.json'.format(jobid), 'w') as fp:
json.dump({"jobid" : jobid,