# Introduction to SDOM

This page provides comprehensive introduction to the Storage Deployment Optimization Model.

## Overview

SDOM (Storage Deployment Optimization Model) is an open-source, high-resolution grid capacity-expansion framework developed by the National Laboratory of the Rockies (NLR). It’s purpose-built to optimize the storage portfolio considering diverse storage technologies, leveraging hourly temporal resolution and granular spatial representation of Variable Renewable Energy (VRE) sources such as solar and wind.

## How SDOM Works

At its core, SDOM models the gap between electricity demand and fixed generation by optimizing:

- **Variable Renewable Energy (VRE)**: Solar PV and wind capacity deployment
- **Energy Storage**: Multiple storage technologies (Li-Ion, CAES, PHS, H2, etc)
- **Thermal Generation**: Balancing thermal units capacity deployment
- **System Operation**: Hourly dispatch over 8760 hours (1 year)


SDOM is particularly well-suited for figure out the required capacity to meet a carbon-free generation mix target by:
- 📆 Evaluating required optimal short, long-duration and seasonal storage portfolios
- 🌦 Analyzing complementarity and synergies among diverse VRE resources and load profile
- 📉 Assessing curtailment and operational strategies under various grid scenarios

An illustrative figure below shows the flow from inputs to optimization results, enabling exploration of storage needs under varying renewable integration levels.

![SDOM illustrative flow](sdom_illustration.png)


### Input Data

- Load profiles (hourly demand)
- Fixed generation profiles (nuclear, hydro, other renewables)
- VRE capacity factors and cost data
- Storage technology characteristics
- Thermal generator parameters
- System scalars (discount rate, carbon targets, etc.)
- [Click here for detailed input files description](inputs.md)

### Outputs

- Optimal technology portfolio capacities
- Hourly dispatch profiles for each technology
- Operational metrics (curtailment, storage cycling, costs)
- System-level cost breakdowns (CAPEX, OPEX)

## Simplified Mathematical Formulation

SDOM is formulated as a **Mixed-Integer Linear Programming (MILP)** problem that minimizes total system cost:

$$
\min \text{Total Cost} = \text{CAPEX} + \text{Fixed O&M} + \text{Variable O&M} + \text{Fuel Costs}
$$

Subject to:
- Energy balance constraints ($supply = demand every hour$)
- Capacity constraints ($generation ≤ installed capacity$)
- Storage state-of-charge constraints
- Carbon-free or renewable energy targets
- Technology-specific operational limits

## Model Components
SDOM uses at its core [Pyomo](https://pyomo.readthedocs.io/en/stable/index.html).
The SDOM Pyomo model is organized into **Blocks** for each technology:

```python
model.pv                # Solar PV generation
model.wind              # Wind generation
model.storage           # Energy storage systems
model.thermal           # Thermal balancing units
model.hydro             # Hydropower
model.nuclear           # Nuclear (fixed)
model.other_renewables  # Other renewables such as Geothermal or Biomass
model.demand                # Load profile
model.imports           # Cross-border imports (optional)
model.exports           # Cross-border exports (optional)
```

## Key Features

### Temporal Resolution
- Full chronological 8760-hour simulation
- No time-step aggregation or representative periods
- Captures diurnal, weekly, and seasonal patterns

### Storage Representation
- Multiple storage technologies simultaneously
- Separate power (MW) and energy (MWh) capacity optimization
- Round-trip efficiency modeling
- Coupled vs. decoupled charge/discharge power

### Spatial Resolution
- Fine-grained VRE resource representation
- Multiple solar and wind plant locations
- Geographic diversity captured in capacity factors

### Flexibility
- Multiple hydropower formulations (run-of-river, monthly budget, daily budget)
- Optional import/export modeling
- Configurable carbon-free generation targets

## Computational Considerations

- **Copper Plate Assumption**: No transmission constraints for computational efficiency
- **Solver Compatibility**: Tested with CBC (open-source) and HiGHS solvers
- **Scalability**: 8760-hour problem with typical scenarios solves in minutes to hours
  - Close to 100% free carbon target scenarios tend to be the more complex problems to solve.
  - Also, scenarios where multiple storage technologies are being modelled and SDOM is optimizing both power and energy capacity tend to be harder to solve.

## Next Steps

- [Learn about input data requirements](inputs.md)
- [Run your first SDOM optimization](running_and_outputs.md)
- [Explore the Pyomo model structure](exploring_model.md)