https://github.com/ppadillaq/sammoo
Optimization of Solar Industrial Process Heat (SIPH) systems with Concentrated Solar Power (CSP) by the use of NREL-PySAM package to System Advisor Model (SAM), in the context of my Master's degree of Research in Industrial Technology at the Universidad Nacional de Educación a Distancia (UNED) in Spain.
https://github.com/ppadillaq/sammoo
modelling-and-simulation multiobjective-optimization renewable-energy
Last synced: 23 days ago
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Optimization of Solar Industrial Process Heat (SIPH) systems with Concentrated Solar Power (CSP) by the use of NREL-PySAM package to System Advisor Model (SAM), in the context of my Master's degree of Research in Industrial Technology at the Universidad Nacional de Educación a Distancia (UNED) in Spain.
- Host: GitHub
- URL: https://github.com/ppadillaq/sammoo
- Owner: ppadillaq
- License: bsd-3-clause
- Created: 2024-12-29T09:12:47.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2026-03-14T12:36:59.000Z (about 2 months ago)
- Last Synced: 2026-03-14T22:24:17.447Z (about 2 months ago)
- Topics: modelling-and-simulation, multiobjective-optimization, renewable-energy
- Language: Python
- Homepage:
- Size: 3.85 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# sammoo
**SAMMOO: System Advisor Model Multi-Objective Optimization**
**Python-based Framework for Multi-Objective Optimization of Renewable Energy Systems using NREL's System Advisor Model (SAM)**
Optimization of Solar Industrial Process Heat (SIPH) systems using Concentrated Solar Power (CSP) technologies through the NREL [PySAM](https://nrel-pysam.readthedocs.io/en/latest/) package (System Advisor Model). This project was developed in the context of my Master's Thesis in Research in Industrial Technologies at the Universidad Nacional de Educación a Distancia (UNED), Spain.
---
## 🌞 Overview
This Python package integrates:
- **System Advisor Model (SAM)** via NREL PySAM for the simulation of parabolic trough systems.
- **ParMOO** for multi-objective optimization using surrogate models.
It allows you to:
- Run parametric studies on industrial CSP systems.
- Optimize system configurations with multiple economic and technical objectives.
- Analyze Pareto fronts and export results.
## 📦 Features
- Modular simulation wrapper (`ConfigSelection`) for different SAM configurations.
- Integration with PySAM modules: `TroughPhysicalIph`, `LcoefcrDesign`, `CashloanHeat`, etc.
- Multi-objective optimization engine (`ParMOOSim`) using ParMOO.
- Automatic switching from sequential to batch acquisition.
- Exportable plots and CSV reports.
## Thermal Load Profiles
### 🔥 ThermalLoadProfileLPG
The `ThermalLoadProfileLPG` class generates a realistic hourly **thermal load profile** from monthly LPG consumption (in tonnes), compatible with SAM’s “User-defined thermal load profile” input.
- Working schedule: Monday to Friday, 6:00–19:00
- Outputs hourly thermal demand in kJ/h, kWh, and kW
- Includes:
- Yearly and weekly plotting
- CSV export
- Energy balance summary
#### Example usage:
```python
from sammoo.profiles.thermal_load_lpg import ThermalLoadProfileLPG
monthly_data = {1: 5000, 2: 4000, ..., 12: 3000}
profile = ThermalLoadProfileLPG(monthly_data)
profile.plot_year()
profile.export_csv("thermal_profile.csv")
```
## 🗂️ Project Structure
```
sammoo/
├── sammoo/ # Core package
│ ├── __init__.py
│ ├── version.py
│ ├── config_selection.py # ConfigSelection class
│ ├── parmoo_simulation.py # ParMOOSim class
│ ├── components/ # New components module (v0.2.0)
│ │ ├── __init__.py
│ │ ├── thermal_load_lpg.py # ThermalLoadProfileLPG class (enhanced)
│ │ ├── weather_design_point.py # Weather-based DNI calculation helper
│ │ └── solar_loop_configuration.py # Solar field / loop setup helpers
│ ├── resources/
│ │ ├── solar_resource/
│ │ │ └── seville_spain.csv # Default weather CSV
│ │ └── collector_data/
│ │ └── iph_collectors_parameters.csv # Industrial Process Heat collectors database
│ └── templates/ # JSON SAM templates
│ ├── __init__.py
│ └── iph_parabolic_commercial_owner/
│ ├── __init__.py
│ └── *.json
├── examples/ # Example usage scripts
│ ├── op1_single_objective_comparison.py
│ ├── opt2_design_point_multiobj.py
│ ├── opt3_multiobj_row_distance.py
│ └── opt4_startup_shutdown.py
├── README.md
├── CHANGELOG.md
├── pyproject.toml
├── MANIFEST.in
├── LICENSE
```
## 📂 Example Scripts
The `examples/` folder contains several usage scenarios of the `sammoo` package:
- `opt1_single_objective_comparison.py`: Compare optimal designs for different single-objective formulations (e.g., LCOE, Payback, LCS, NPV).
- `opt2_design_point_multiobj.py`: Multi-objective optimization at a fixed design point (e.g., SM and storage hours), optionally applying a thermal load profile.
- `opt3_multiobj_row_distance.py`: Multi-objective optimization of solar field row spacing (`Row_Distance`) using ParMOO.
- `opt4_startup_shutdown.py`: Optimization focusing on startup/shutdown temperatures and their impact on techno-economic performance.
💡 **Note**: Example scripts (`examples/`) are not included in the PyPI installation.
If you want to explore the examples, please [clone the GitHub repository](https://github.com/ppadillaq/sammoo).
---
## 🚀 Quick Start
### ✅ Option 1: Install from PyPI *(recommended for users)*
```bash
pip install sammoo
```
You can now use the package in Python:
```bash
from sammoo import ConfigSelection, ParMOOSim
```
> 💡 **Note:** Example scripts (`examples/`) are not included in the PyPI installation.
> If you want to explore examples, clone the GitHub repository instead:
```bash
git clone https://github.com/ppadillaq/sammoo.git
cd sammoo
python examples/single_objective_comparison.py
```
### 🛠 Option 2: Install from source (for development)
If you want to work with the source code:
```bash
git clone https://github.com/ppadillaq/sammoo.git
cd sammoo
pip install -e .
```
### ▶️ Run an example
```bash
python examples/opt1_single_objective_comparison.py
```
## 🛠 Dependencies
- Python ≥ 3.10
- NREL PySAM
- ParMOO
- NumPy
- Matplotlib (for plotting)
Install them via:
```
pip install pysam parmoo numpy matplotlib
```
## 📈 Example Use Case
```python
# Minimal example: multi-objective optimization with a thermal load profile
from sammoo import ConfigSelection, ParMOOSim
from sammoo.components import ThermalLoadProfileLPG
# 1) (Optional) Generate an hourly thermal load profile from monthly LPG consumption (kg)
monthly_kg = {1: 11000, 2: 9000, 3: 8000, 4: 9500, 5: 9200, 6: 9800,
7: 8600, 8: 8700, 9: 9000, 10: 9400, 11: 8800, 12: 9600}
profile = ThermalLoadProfileLPG(monthly_kg=monthly_kg) # uses default efficiency and PCI
# 2) Define the design space (use "continuous" / "integer")
design_vars = {
"specified_solar_multiple": ([1.0, 3.0], "continuous"),
"tshours": ([2, 8], "integer"),
}
# 3) Define objectives (minimize LCOE, maximize LCS)
selected_outputs = ["LCOE", "-LCS"]
# 4) Create the SAM configuration (optionally set collector/HTF)
cfg = ConfigSelection(
config="Commercial owner",
selected_outputs=selected_outputs,
design_variables=design_vars,
collector_name="Power Trough 250", # optional
htf_name="Therminol VP-1", # optional
verbose=0
)
# Apply the demand profile → sets timestep_load_abs and sizes q_pb_design
profile.apply_to_config(cfg)
# 5) Run the optimization (increase search_budget and sim_max for better results)
opt = ParMOOSim(cfg, search_budget=20, auto_switch=True)
opt.solve_all(sim_max=50)
# 6) Display results
print(opt.get_results())
```
## ☀️ Weather Data
The simulation requires a weather file (`file_name`) in CSV format. You can:
- ✅ Provide your own via `user_weather_file="path/to/weather.csv"`
- 🔁 Or let the framework use a built-in default (Tucson, AZ)
Your custom weather files must follow the TMY3 format used by SAM.
## 📚 Publications
Version **0.3.0** of this project was used in the development of the following master's thesis in the Master's programme in *Research in Industrial Technologies* (Energy Engineering track), UNED (Spain):
- Padilla-Quesada, P. (2025). *Sammoo: Un marco de optimización multiobjetivo basada en simulaciones para el diseño de sistemas solares térmicos de calor de proceso industrial* [Trabajo Fin de Máster, Universidad Nacional de Educación a Distancia (UNED)]. https://hdl.handle.net/20.500.14468/31636
[Download full thesis (PDF)](https://e-spacio.uned.es/bitstreams/87c9541c-8e90-4a36-a15d-b0c7b5e2132f/download)
Future versions may include broader support for other CSP technologies, reinforcement learning-based controllers, and integration with digital twin platforms.
## 📄 License
This project is licensed under the **BSD 3-Clause License**.
See the [`LICENSE`](./LICENSE) file for full text.
## 👤 Author
Pedro Padilla Quesada
MSc in Research in Industrial Technologies
UNED, Spain