Examples

This page provides practical examples for common OptiPlant.jl use cases, from basic single-scenario runs to advanced multi-scenario analyses and dashboard visualization.

Basic Examples

Single Scenario Optimization

The simplest way to run OptiPlant is with a single scenario using the example data:

# Basic setup
using OptiPlantPtX

# Configure paths (adjust to your installation)
Main_folder = "C:/path/to/OptiPlant"
Project = "Base" 
Inputs_file = "Input_data_example"
Solver = "HiGHS"  # or "Gurobi" if available

# Run single scenario
N_scen_0 = 1; N_scen_end = 1
include("examples/Run.jl")

This will:

Load the example techno-economic data
Run optimization for scenario 1
Save results in Base/Results/

Quick Test Run

For faster testing, limit the simulation to one week:

# In your main run script
TMstart = 1; TMend = 168  # First week (168 hours)
Tbegin = 1; Tfinish = 168

# Keep other settings the same
include("Run.jl")

Technology Comparison Examples

Electrolyzer Technology Assessment

Compare Alkaline (AEL) vs. PEM electrolyzers for hydrogen production:

Setup scenarios in Excel (ScenariosToRun sheet):

Scenario | Location | Fuel     | Electrolyser | Profile  | Year
1        | Denmark  | Hydrogen | AEL         | DK1_2019 | 2019
2        | Denmark  | Hydrogen | PEM         | DK1_2019 | 2019

Run comparison:

N_scen_0 = 1; N_scen_end = 2  # Run both scenarios
include("Run.jl")

# Results will be in separate CSV files:
# - Scenario_1.csv (AEL results)  
# - Scenario_2.csv (PEM results)

Key metrics to compare:

Production cost (EUR/kg H₂)
Investment requirements (M€)
Full load hours
Electrical consumption (kWh/kg)

Fuel Production Comparison

Compare hydrogen vs. ammonia production at the same location:

Scenario | Location    | Fuel    | Electrolyser | Profile   
1        | Antofagasta | Hydrogen| PEM         | ANF_2019  
2        | Antofagasta | Ammonia | PEM         | ANF_2019

This compares:

System complexity (H₂ only vs. H₂ + NH₃ synthesis)
Investment costs
Production costs
Resource utilization

Location Assessment Examples

Multi-Location Analysis

Evaluate the same system across different renewable resource locations:

# Define locations in scenarios
locations = ["Denmark", "Antofagasta", "Bornholm", "Faroes"]
scenarios = 1:length(locations)

N_scen_0 = 1; N_scen_end = 4
include("Run.jl")

Excel setup:

Scenario | Location    | Fuel     | Profile   | Year
1        | Denmark     | Hydrogen | DK1_2019  | 2019
2        | Antofagasta | Hydrogen | ANF_2019  | 2019  
3        | Bornholm    | Hydrogen | BOR_2019  | 2019
4        | Faroes      | Hydrogen | FAR_2019  | 2019

Compare:

Renewable resource quality (capacity factors)
Electricity prices
Resulting production costs
Optimal system sizing

Resource Quality Impact

Analyze how renewable resource profiles affect system design:

# Use profiles from different years for the same location
profiles = ["DK1_2018", "DK1_2019", "DK1_2020"]

# Or compare onshore vs offshore wind profiles
profiles = ["DK1_onshore_2019", "DK1_offshore_2019"]

Advanced Examples

Sensitivity Analysis

Test system response to parameter variations using scenario definitions:

In Excel Scenarios_definition sheet:

Reference | Scenario_name    | Parameter_changed | New_value
Base_case | High_CAPEX      | Investment        | 1200000
Base_case | Low_CAPEX       | Investment        | 800000  
Base_case | High_efficiency | Electrical cons.  | 45
Base_case | Low_efficiency  | Electrical cons.  | 60

Run sensitivity study:

Scenarios_set = "Scenarios_sensitivities"  # Different scenario sheet
N_scen_0 = 1; N_scen_end = 4
include("Run.jl")

Multi-Year Analysis

Analyze system performance across multiple years:

# Setup scenarios for different years
years = [2018, 2019, 2020]
profiles = ["DK1_2018", "DK1_2019", "DK1_2020"]

# Configure in Excel and run
N_scen_0 = 1; N_scen_end = 3
include("Run.jl")

This helps understand:

Inter-annual variability impact
System robustness to weather variations
Long-term performance expectations

Parallel Processing

For large scenario sets, use parallel processing:

# Use parallel version for multiple scenarios
include("Run_multi_scenarios_para.jl")

# Or manually configure parallel workers
using Distributed
addprocs(4)  # Use 4 CPU cores

@everywhere using OptiPlantPtX
# Run scenarios in parallel

Dashboard Examples

Interactive Analysis with Streamlit

Set up Python environment and run dashboards:

# Create Python environment
python -m venv .venv
.\.venv\Scripts\Activate.ps1

# Install requirements  
pip install -r requirements.txt

# Run dashboards
streamlit run src/PlotGraphs/Dashboard_CO2.py      # CO₂ analysis
streamlit run src/PlotGraphs/Dashboard_Daily.py    # Daily operation  
streamlit run src/PlotGraphs/Dashboard_Scenarios.py # Scenario comparison

Dashboard Configuration

Data Loading:

Dashboards read from Base/Results/ folder
Select scenario CSV files interactively
Debug panel shows data parsing issues

Visualization Options:

Capacity plots: Investment and installed capacity by unit
Daily profiles: Hourly operation patterns
Economic analysis: Cost breakdown and sensitivity
CO₂ analysis: Emission factors and carbon intensity

Custom Dashboard Setup

Create custom visualizations:

import streamlit as st
import pandas as pd
import plotly.express as px

# Load OptiPlant results
df = pd.read_csv("Base/Results/YourScenario/Scenario_1.csv")

# Create custom plots
fig = px.bar(df, x='Type of unit', y='Installed capacity', 
             title='System Configuration')
st.plotly_chart(fig)

Configuration Examples

Custom Input Data

Create new input file based on existing template:

Copy template:

# Copy existing input file
cp("Base/Data/Inputs/Input_data_example.xlsx", 
   "Base/Data/Inputs/My_custom_input.xlsx")

Modify parameters in Excel sheets:
- Data_base_case: Update techno-economic parameters
- Selected_units: Choose active units
- ScenariosToRun: Define your scenarios
Update run script:
```
Inputs_file = "My_custom_input"
```

New Location Setup

Add custom location with renewable profiles:

Create profile folder:
```
Base/Data/Profiles/My_Location/
```

Add profile CSV with columns:

Hour,Wind_offshore,Wind_onshore,Solar_PV,Electricity_price
1,0.42,0.38,0.0,45.2
2,0.48,0.41,0.0,43.8
...

Configure in scenarios:

Location | Profile_name     | Profile_folder_name
My_Location | Custom_2019   | My_Location

Technology Addition

Add new unit type to the system:

Define in techno-economics sheet:
- Add row with unit parameters
- Set investment costs, efficiency, etc.
Update unit selection:
- Include in Selected_units sheet
- Set to 1 for active scenarios
Configure connectivity:
- Define input/output flows
- Set mass/energy balances

Troubleshooting Examples

Common Issues and Solutions

Infeasible Solution:

# Check if renewable resources are sufficient
# Reduce load or increase renewable capacity

# Verify unit compatibility  
# Check Selected_units sheet for required units

File Path Errors:

# Use absolute paths
Main_folder = "C:/Users/YourName/Documents/OptiPlant"

# Verify folder structure exists
if !isdir(joinpath(Main_folder, Project, "Data"))
    error("Project folder not found")
end

Performance Issues:

# Start with shorter time periods
TMstart = 1; TMend = 24  # One day only

# Use HiGHS for initial testing  
Solver = "HiGHS"

# Disable complex constraints initially
Option_ramping = false
Write_flows = false

Debugging Workflow

Start simple: Single scenario, short time period
Verify data: Check input file formatting and completeness
Test solver: Ensure HiGHS/Gurobi installation works
Scale gradually: Add complexity incrementally
Check results: Verify outputs are reasonable

Next Steps

Review Installation for setup details
Check Usage for configuration options
Explore API Reference for detailed function documentation
Join GitHub discussions for community support