Results Analysis

Results Analysis#

Overview#

This section covers the analysis and interpretation of Storage-in-OSeMOSYS model results.

Output Files#

CSV Results#

The model generates comprehensive CSV output files for analysis:

  • AccumulatedAnnualDemand.csv - Total demand accumulated annually

  • AnnualEmissions.csv - Annual emission levels by technology and fuel

  • CapacityFactor.csv - Technology capacity factors

  • StorageLevelStart.csv - Storage levels at each time slice

  • TotalCapacityAnnual.csv - Installed capacity by technology and year

Storage-Specific Outputs#

Storage modeling produces specialized output variables:

  • StorageLevelStart - Initial storage level for each time period

  • StorageCharge - Charging activity of storage technologies

  • StorageDischarge - Discharging activity of storage technologies

  • NewStorageCapacity - New storage capacity investments

Analysis Techniques#

Temporal Analysis#

  • Hourly Profiles - Detailed hourly operation patterns

  • Seasonal Patterns - Storage operation across seasons

  • Daily Cycles - Charging/discharging daily patterns

  • Peak Analysis - Peak demand and storage contribution

Comparative Analysis#

Compare results across different temporal clustering methods:

  1. Kotzur vs. Niet vs. Welsch - Clustering method comparison

  2. Clustered vs. 8760 - Computational efficiency vs. accuracy trade-offs

  3. Storage Impact - Effect of storage on system operation

Economic Analysis#

  • Investment Costs - Storage capacity investment patterns

  • Operational Costs - Variable costs and system efficiency

  • System Benefits - Grid stability and renewable integration benefits

Visualization#

Tools and Libraries#

  • Matplotlib - Basic plotting functionality

  • Plotly - Interactive visualizations

  • Seaborn - Statistical plotting

  • Pandas - Data manipulation and analysis

Performance Metrics#

Computational Performance#

  • Model Size - Number of variables and constraints

  • Solution Time - Computational time for different methods

  • Memory Usage - RAM requirements for large models

  • Convergence - Solver performance and stability

Model Accuracy#

  • Clustering Error - Accuracy of temporal clustering

  • Storage Representation - Fidelity of storage modeling

  • System Operation - Realistic operational patterns

Quality Assurance#

Validation Checks#

  • Energy Balance - Conservation of energy across all time periods

  • Storage Constraints - Physical limits and operational constraints

  • Economic Rationality - Cost-effective technology deployment

  • Technical Feasibility - Realistic operational parameters

Troubleshooting#

Common issues and solutions:

  • Empty result files → Check model constraints and data inputs

  • Infeasible solutions → Review storage and system constraints

  • Long solve times → Consider temporal clustering methods

  • Memory issues → Optimize model size and data handling

Future Enhancements#

Planned improvements to results analysis:

  • Automated report generation

  • Advanced visualization dashboards

  • Statistical analysis tools

  • Machine learning-based pattern recognition

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