Model Specifications

Model Specifications#

Overview#

This section provides detailed specifications for the Storage-in-OSeMOSYS models.

OSeMOSYS Integration#

Storage-in-OSeMOSYS builds upon the Open Source Energy Modelling System (OSeMOSYS) framework to provide advanced storage modeling capabilities.

Model Variants#

The framework supports four different temporal clustering approaches:

  1. Kotzur Method - Advanced temporal clustering using machine learning techniques

  2. Niet Method - Statistical clustering approach for representative periods

  3. Welsch Method - Time series aggregation with optimization

  4. 8760 Hours - Full year hourly resolution (no clustering)

Storage Technologies#

Storage Parameters#

  • StorageLevelStart - Initial storage level at the beginning of each time slice

  • StorageMaxChargeRate - Maximum charging rate for storage technologies

  • StorageMaxDischargeRate - Maximum discharging rate for storage technologies

  • StorageLevelSeasonEnd - Storage level at the end of each season

Storage Constraints#

The model implements various constraints to ensure realistic storage operation:

  • Energy balance constraints

  • Charging/discharging rate limits

  • Storage capacity constraints

  • Seasonal storage carryover

Technical Implementation#

Model Files#

  • osemosys_fast_Kotzur.txt - Kotzur method implementation

  • osemosys_fast_Niet.txt - Niet method implementation

  • osemosys_fast_Welsch.txt - Welsch method implementation

  • OSeMOSYS base model files for 8760-hour resolution

Data Processing#

The framework uses OtoOle for data conversion between different formats:

  • Excel to CSV conversion

  • CSV to datafile generation

  • Result processing and analysis

Future Development#

This section will be expanded with:

  • Detailed mathematical formulations

  • Parameter sensitivity analysis

  • Validation case studies

  • Performance benchmarking results

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