Usage

This guide covers how to use OptiPlant.jl for Power-to-X system modeling and optimization, from basic single-scenario runs to advanced multi-scenario analyses.

Basic Concepts

System Architecture

OptiPlant.jl models Power-to-X fuel production systems using:

Units: Individual components (electrolyzers, storage, conversion units, etc.)
Time series: Hourly profiles for renewable energy and electricity prices
Scenarios: Different system configurations and operational parameters
Optimization: Linear programming to minimize total system costs

Folder Structure

OptiPlant follows a specific project structure:

Project_Name/
├── Data/
│   ├── Inputs/           # Configuration and techno-economic data
│   └── Profiles/         # Renewable energy and price time series
├── Results/              # Optimization results and outputs
└── Code/                 # Julia optimization scripts

Quick Start

Running Your First Optimization

Select your input data file in the main run script:

# In Run.jl or your main script
Inputs_file = "Input_data_example"  # Start with the example

Configure basic settings:

Solver = "HiGHS"          # Use open-source solver
Project = "Base"          # Project folder name
N_scen_0 = 1             # First scenario to run
N_scen_end = 1           # Last scenario to run

Run the optimization:

using OptiPlantPtX
include("Run.jl")  # Or execute in VS Code

Understanding Results

Results are automatically saved in Project/Results/ with:

Main results: Overall system economics and capacity
Hourly results: Time series of operation and flows
Data used: Input parameters for reproducibility

Configuration

Input Data Files

OptiPlant uses Excel files in Data/Inputs/ with structured sheets:

Core Sheets:

Databasecase: Techno-economic parameters

Type of units | Investment (EUR/Capacity) | Fixed O&M | Variable O&M | ...
Electrolyzer  | 1000000                  | 25000     | 0.01         | ...
H2_storage    | 500000                   | 10000     | 0.005        | ...

Selected_units: Enable/disable units for scenarios

Unit Type     | Scenario_1 | Scenario_2 | ...
Electrolyzer  | 1          | 1          | ...
Ammonia_plant | 1          | 0          | ...

ScenariosToRun: Define scenarios to execute

Scenario | Location    | Fuel     | Year | Profile  | Electrolyser | ...
1        | Denmark     | Hydrogen | 2019 | DK1_2019 | AEL          | ...
2        | Antofagasta | Ammonia  | 2019 | ANF_2019 | PEM          | ...

Profile Data

Time series data in Data/Profiles/Location/:

Renewable energy: Wind and solar capacity factors (0-1)
Electricity prices: EUR/MWh for each hour
Format: CSV with hourly data (8760 hours for full year)

Example profile structure:

Hour,Wind_offshore,Solar_PV,Electricity_price
1,0.45,0.0,45.2
2,0.52,0.0,43.8
...

Advanced Usage

Multi-Scenario Analysis

Run multiple scenarios in sequence:

N_scen_0 = 1      # Start scenario
N_scen_end = 10   # End scenario (runs scenarios 1-10)

Or use parallel processing:

include("Run_multi_scenarios_para.jl")  # Parallel execution

Scenario Definitions

Create advanced scenarios using the Scenarios_definition sheet:

Reference scenario | Scenario name | Parameter changed | New value
Base_case         | High_CAPEX    | Investment        | 1200000
Base_case         | Low_efficiency | Electrical cons.  | 55

This allows systematic sensitivity analysis by modifying specific parameters.

Time Period Configuration

Control simulation time periods:

# Full year simulation
TMstart = 1; TMend = 8760; Tbegin = 1; Tfinish = 8760

# Maintenance periods (exclude summer maintenance)
TMstart = 4000; TMend = 4876; Tbegin = 72; Tfinish = 8760

# Short test run (first week)
TMstart = 1; TMend = 168; Tbegin = 1; Tfinish = 168

Solver Configuration

HiGHS (Open Source)

Solver = "HiGHS"
# No additional configuration needed

Gurobi (Commercial)

Solver = "Gurobi"
# Requires license activation: grbgetkey YOUR_LICENSE_KEY

Output Options

Control result granularity:

# In scenario configuration
Write_flows = true   # Save detailed hourly flows
Option_ramping = true # Include ramping constraints

System Configuration Options

Available Technologies

OptiPlant includes models for:

Electrolyzers: AEL (Alkaline), PEM (Proton Exchange Membrane)
Storage: Hydrogen tanks, batteries
Conversion: Ammonia synthesis, methanol production
Renewable: Wind (onshore/offshore), Solar PV, CSP
Grid: Electricity import/export

Operational Constraints

Configure realistic operational limits:

# In techno-economic data
Max_Capacity = 100      # MW maximum size
Load_min = 0.1          # 10% minimum load
Ramp_up = 0.5          # 50% capacity/hour ramp rate  
Ramp_down = 0.7        # 70% capacity/hour ramp down

Economic Parameters

All economic data in EUR 2019:

Investment costs: EUR/capacity installed
Fixed O&M: EUR/capacity/year
Variable O&M: EUR/output
Fuel prices: EUR/output
Discount rate: Built into annuity factors

Results Analysis

Main Results Structure

Key output metrics for each unit:

Installed_capacity: Optimal capacity (MW or t/h)
Investment: Total and annualized investment (M€)
Production: Annual output (kton or GWh)
Full_load_hours: Capacity utilization
Production_cost: EUR/kg fuel or EUR/MWh

Interpreting Results

System Levelized Cost:

LCOF = (Annualized Investment + O&M + Fuel Costs) / Annual Production

Capacity Factor:

CF = Full Load Hours / 8760 hours

Economics:

Compare production costs across scenarios
Identify cost drivers (investment vs. operational)
Analyze sensitivity to key parameters

Common Use Cases

1. Technology Comparison

Compare different electrolyzer technologies:

# Scenario 1: AEL electrolyzer
# Scenario 2: PEM electrolyzer  
# Compare: investment costs, efficiency, flexibility

2. Location Assessment

Evaluate different sites:

# Multiple scenarios with different locations
# Compare: resource quality, electricity prices, LCOF

3. Sensitivity Analysis

Test parameter impacts:

# Vary: CAPEX (-20%, +20%), efficiency (±5%), fuel prices  
# Analyze: cost sensitivity, optimal design changes

4. Optimal Sizing

Find cost-optimal capacity:

# Enable: Option_max_capacity = true
# Result: Economically optimal plant size

Troubleshooting

Common Issues

Infeasible Solutions:

Check unit compatibility in Selected_units
Verify profile data completeness (8760 hours)
Ensure renewable resource adequacy

Slow Performance:

Reduce time resolution for initial studies
Disable ramping constraints for faster solving
Use Gurobi for large problems

File Path Errors:

Use absolute paths in configuration
Verify folder structure matches expected layout
Check Excel file names and sheet names

Performance Tips

Start simple: Use example data, single scenario, HiGHS solver
Scale up gradually: Add complexity after verifying basic functionality
Profile first: Test with short time periods before full year
Parallel processing: Use for multiple scenarios with sufficient CPU cores

Next Steps

Explore Examples for detailed use cases
Check API Reference for function documentation
Set up [Streamlit dashboards] for interactive visualization
Review the User Guide for additional configuration details