Basic Run with Pyomo¶

This tutorial demonstrates how to run the EnergyScope model using Pyomo instead of AMPL. Pyomo is an open-source optimization modeling language that doesn't require commercial solvers.

Imports¶

First, we import the essential libraries and modules:

• run_pyomo_model from energyscope.energyscope: Function to run the Pyomo-based model.
• pyomo_ch from energyscope.models: The Pyomo model configuration for Switzerland.
• postprocessing from energyscope.result: Functions for processing results after optimization.
• plot_sankey from energyscope.plots: Function to generate Sankey diagrams for visualizing energy flows.

In [1]:

from energyscope.energyscope import run_pyomo_model
from energyscope.models import pyomo_ch
from energyscope.result import postprocessing
from energyscope.plots import plot_sankey

from energyscope.energyscope import run_pyomo_model from energyscope.models import pyomo_ch from energyscope.result import postprocessing from energyscope.plots import plot_sankey

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Run the Model¶

Run the Pyomo model with a single function call. The model uses HiGHS as the solver (open-source, no license required).

Parameters:

• model: The PyomoModel instance (e.g., pyomo_ch)
• objective: Objective function to minimize ("gwp" for emissions, "cost" for total cost)

In [2]:

result = run_pyomo_model(pyomo_ch, objective="gwp")

result = run_pyomo_model(pyomo_ch, objective="gwp")

Inspect Results¶

The result object contains variables, parameters, sets, and objectives from the solved model.

In [3]:

# View objectives
print("Total GWP:", result.objectives['TotalGWP'].values[0][0])
print("Total Cost:", result.objectives['TotalCost'].values[0][0])

# View objectives print("Total GWP:", result.objectives['TotalGWP'].values[0][0]) print("Total Cost:", result.objectives['TotalCost'].values[0][0])

Total GWP: 14118.02386754794
Total Cost: 44634.461179539176

In [4]:

# View installed capacities (F_Mult)
f_mult = result.variables['F_Mult']
f_mult[f_mult['F_Mult'] > 0].head(10)

# View installed capacities (F_Mult) f_mult = result.variables['F_Mult'] f_mult[f_mult['F_Mult'] > 0].head(10)

Out[4]:

	F_Mult	Run
index
BUS_COACH_CNG_STOICH	16.7	0
BUS_COACH_DIESEL	16.7	0
BUS_COACH_FC_HYBRIDH2	16.7	0
BUS_COACH_HYDIESEL	16.7	0
CAR_BEV	16.7	0
CAR_DIESEL	16.7	0
CAR_FUEL_CELL	16.7	0
CAR_GASOLINE	16.7	0
CAR_HEV	16.7	0
CAR_NG	16.7	0

---

Post-Processing¶

Apply post-processing to compute Key Performance Indicators (KPIs) and prepare the data for visualization.

In [5]:

result = postprocessing(result)

result = postprocessing(result)

In [6]:

# View annual data for a specific technology
result.postprocessing['df_annual'].head(10)

# View annual data for a specific technology result.postprocessing['df_annual'].head(10)

Out[6]:

		C_inv	C_maint	Annual_Prod	F_Mult	tau	C_op	C_inv_an	Annual_Use	Category	Category_2	Sector
index	Run
BUS_COACH_CNG_STOICH	0	0.0	0.0	0.000	16.7	1.03215	0.0	0.0	0.000	MOB_PUBLIC	Mobility	Mobility
BUS_COACH_DIESEL	0	0.0	0.0	0.000	16.7	1.03215	0.0	0.0	0.000	MOB_PUBLIC	Mobility	Mobility
BUS_COACH_FC_HYBRIDH2	0	0.0	0.0	0.000	16.7	1.03215	0.0	0.0	0.000	MOB_PUBLIC	Mobility	Mobility
BUS_COACH_HYDIESEL	0	0.0	0.0	0.000	16.7	1.03215	0.0	0.0	0.000	MOB_PUBLIC	Mobility	Mobility
CAR_BEV	0	0.0	0.0	21907.395	16.7	1.03215	0.0	0.0	21907.395	MOB_PRIVATE	Mobility	Mobility
CAR_DIESEL	0	0.0	0.0	14604.930	16.7	1.03215	0.0	0.0	14604.930	MOB_PRIVATE	Mobility	Mobility
CAR_FUEL_CELL	0	0.0	0.0	14604.930	16.7	1.03215	0.0	0.0	14604.930	MOB_PRIVATE	Mobility	Mobility
CAR_GASOLINE	0	0.0	0.0	14604.930	16.7	1.03215	0.0	0.0	14604.930	MOB_PRIVATE	Mobility	Mobility
CAR_HEV	0	0.0	0.0	0.000	16.7	1.03215	0.0	0.0	0.000	MOB_PRIVATE	Mobility	Mobility
CAR_NG	0	0.0	0.0	0.000	16.7	1.03215	0.0	0.0	0.000	MOB_PRIVATE	Mobility	Mobility

---

Visualize Results with a Sankey Diagram¶

A Sankey diagram visualizes energy flows within the system.

In [7]:

fig = plot_sankey(result, aggregate_grid=False) #  aggregate_grid=False as the model don't have grid layers
fig.show(renderer="notebook")

fig = plot_sankey(result, aggregate_grid=False) #  aggregate_grid=False as the model don't have grid layers fig.show(renderer="notebook")

Save the Diagram¶

Optionally save the diagram as an HTML file:

In [8]:

# fig.write_html("tutorial_output/sankey_pyomo_ch.html")

# fig.write_html("tutorial_output/sankey_pyomo_ch.html")

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Compare with Cost Optimization¶

Run the model again with cost minimization to compare results.

In [9]:

result_cost = run_pyomo_model(pyomo_ch, objective="cost", id_run=1)

print("Cost optimization:")
print("  Total GWP:", result_cost.objectives['TotalGWP'].values[0][0])
print("  Total Cost:", result_cost.objectives['TotalCost'].values[0][0])

result_cost = run_pyomo_model(pyomo_ch, objective="cost", id_run=1) print("Cost optimization:") print(" Total GWP:", result_cost.objectives['TotalGWP'].values[0][0]) print(" Total Cost:", result_cost.objectives['TotalCost'].values[0][0])

Cost optimization:
  Total GWP: 26249.879832788352
  Total Cost: 13668.185984877611