kpi-calculator 0.1.1

Extend ESDL's with relevant key performance indicators.

KPI Calculator

A Python package for calculating Key Performance Indicators (KPIs) for heat network designs from ESDL files.

Part of the 'Nieuwe Warmte Nu Design Toolkit' project.

Quick Start

from kpicalculator import calculate_kpis

# Automatic: Loads time series from InfluxDB references in ESDL
results = calculate_kpis(esdl_file="path/to/model.esdl")
print(f"Total CAPEX: {results['costs']['capex']['All']} EUR")

See test

Installation

pip install git+https://github.com/Project-OMOTES/kpi-calculator.git@feature/database-connectivity # From development branch

pip install kpi-calculator  # Coming soon

Features

KPI Categories:

• Cost: CAPEX, OPEX, NPV, LCOE
• Energy: Consumption, production, efficiency
• Emissions: Total CO2, emissions per MWh

Data Sources:

• ESDL files with embedded cost data
• InfluxDB time series (automatic)
• XML time series files (testing)
• Pandas DataFrames (simulator integration)

Architecture:

• Modular adapters, calculators, and managers
• Full type safety with Pydantic v2
• Secure InfluxDB integration (setup guide)
• Comprehensive test coverage (83%+)

Usage

Basic Usage

from kpicalculator import calculate_kpis

# Production: Automatically loads time series from InfluxDB profiles in ESDL
results = calculate_kpis(esdl_file="model.esdl")

# With optional parameters
results = calculate_kpis(
    esdl_file="model.esdl",
    system_lifetime=30  # Default: 30 years
)

# Testing: Override with XML file when database is unavailable
results = calculate_kpis(
    esdl_file="model.esdl",
    time_series="timeseries.xml"  # For testing only
)

See tests

Data Source Options

Cost Data (priority order):

1. ESDL costInformation elements (production default)
2. CSV files (testing override only)

Time Series Data (priority order):

1. Pandas DataFrames (in-memory, simulator integration)
2. InfluxDB profiles (automatic from ESDL InfluxDBProfile references)
3. XML files (testing override when database unavailable)
4. None (asset-level calculations only)

Note: In production, time series are automatically loaded from InfluxDB when your ESDL file contains InfluxDBProfile references. You only need to specify time_series parameter for testing with XML files.

Database Setup: See Secure Database Setup for configuring InfluxDB credentials using environment variables or secure configuration files.

Simulator Integration

Pass time series data directly as pandas DataFrames:

import pandas as pd

# Create time series data indexed by datetime
datetime_index = pd.date_range("2024-01-01", periods=24, freq="H")

timeseries_data = {
    "asset_id_1": pd.DataFrame({
        "mass_flow": [2.5, 2.6, 2.4, ...],
        "temperature": [353.15, 353.20, ...],
        "heat_supplied": [100000, 102000, ...],
    }, index=datetime_index),

    "asset_id_2": pd.DataFrame({
        "heat_demand": [80000, 81000, ...],
    }, index=datetime_index),
}

results = calculate_kpis(
    esdl_file="model.esdl",
    timeseries_dataframes=timeseries_data,
    system_lifetime=30
)

Requirements for timeseries_dataframes:

• Keys: Asset IDs matching ESDL file
• Values: DataFrames with datetime index and property columns
• Properties: Any of mass_flow, pressure, temperature, volume_flow, heat_supplied, heat_demand, velocity, pressure_loss, heat_loss

Integration example: The simulator-worker converts its port-level tuple-column format to asset-level DataFrames before calling calculate_kpis(). See integration guide.

See test: test_timeseries_dataframes_integration

Testing with CSV Override

# Override ESDL costs for testing
results = calculate_kpis(
    esdl_file="model.esdl",
    pipes_cost="test_pipes.csv",
    assets_cost="test_assets.csv"
)

See test

Advanced: Batch Processing

from kpicalculator import KpiManager

# Batch process multiple scenarios
manager = KpiManager("unit_conversion.csv")
scenarios = [
    {"file": "scenario_1.esdl", "lifetime": 25},
    {"file": "scenario_2.esdl", "lifetime": 30},
    {"file": "scenario_3.esdl", "lifetime": 35}
]

for scenario in scenarios:
    manager.load_from_esdl(scenario["file"])
    results = manager.calculate_all_kpis(system_lifetime=scenario["lifetime"])
    # Compare results across scenarios

See test

Results Structure

{
    "costs": {
        "capex": {"All": 1000000, "HeatProducer": 500000, ...},
        "opex": {"All": 50000, ...},
        "npv": 850000,
        "lcoe": 45.5
    },
    "energy": {
        "consumption": 1e9,
        "production": 950000,
        "efficiency": 0.95
    },
    "emissions": {
        "total": 1200,
        "per_mwh": 1.26
    }
}

Supported ESDL Features

Cost Units:

• EUR, EUR/m, EUR/kW, EUR/MW, EUR/kWh, EUR/MWh, EUR/yr, %
• Automatic unit conversion (e.g., EUR/m × pipe length = total investment)

Time Series:

• InfluxDBProfile references (production)
• XML files (testing)
• Pandas DataFrames (simulator integration)

Asset Types:

• Producers, consumers, pipes, storage, conversion, pumps

Dependencies

Runtime:

• pandas ≥2.0.0
• numpy ~2.1.0
• pyesdl ~25.5.1
• pydantic ≥2.0.0
• influxdb ≥5.3.2
• xmltodict 0.14.2
• coloredlogs ~15.0.1

Development Status

Implemented:

• ESDL adapter with cost extraction
• Cost, energy, and emission calculators
• InfluxDB integration
• Security layer with input validation
• CI/CD pipeline with UV

Planned:

• MESIDO adapter
• OMOTES Simulator adapter
• Advanced caching

See Roadmap for details.

Contributing

This project is part of the OMOTES (Optimization and Modeling for Thermal Energy Systems) initiative.

Releases

Releases are automatically published to PyPI when a GitHub Release is created:

1. Ensure all changes are merged to main
2. Create and push a version tag: git tag -a v1.2.3 -m "Release 1.2.3" && git push origin v1.2.3
3. Create a GitHub Release from the tag
4. The CI workflow will automatically build, verify, and publish to PyPI

The workflow includes security scanning, build verification, and generates cryptographic attestations for supply chain security.

License

GNU General Public License v3.0