PyPSA-DESOpt-Heat

PyPSA for District Energy System Optimization with focus on the Heat Sector

Intro

PyPSA-DESOpt-Heat is a linear programming district energy system optimization model for designing cost-optimal district energy systems. It focuses on distributed and building-specific heat supply and integrates sector coupling within energy system modeling.

For more details refer to the original research paper:

Trentmann, Lennart and Ceruti, Amedeo and Schweiger, Benedikt and Spliethoff, Hartmut, What district heating share is optimal for a municipality? A holistic approach including supply temperatures. Available at SSRN: https://ssrn.com/abstract=5986796 or http://dx.doi.org/10.2139/ssrn.5986796

Feature overview

• Sector-coupled district energy system optimization
  • Variable efficiency, price and demand timeseries
  • Greenfield and brownfield optimization
• Cost-optimal decision between district heating network (DHN) build-out and building-specific heat supply
• Preprocessing functions included for timeseries calcuation of COPs, PV, ST and standing losses of TES

Contents

• PyPSA-DESOpt-Heat
  • Intro
  • Feature overview
  • Contents
  • Description
    • Schematic overview of the urban energy system modeling approach
    • Components of the District Energy System Model
    • Timeseries Preprocessing
  • Install
  • Solver
  • Usage
  • Contribute
  • License
  • Example

Description

To run the model, ensure the following:

• All required PyPSA components are defined in the CSV network files located in the model folder.
• A cost function for the district heating network (DHN) build-out is specified in the cost_func_heat_grid.csv file.

Schematic overview of the urban energy system modeling approach

Components of the District Energy System Model

The components must be defined as follows:

1. buses:

name, carrier

2. carriers:

name,co2_emissions

3. loads:

name,bus,ts

where ts is the column name in timeseries.csv containing the series of demands in hourly resolution

4. generators:

name,bus,carrier,efficiency,build_year,lifetime,p_nom_extendable,p_nom_max,p_max_pu,p_min_pu,min_up_time,min_down_time,up_time_before,down_time_before,investment,fuel_costs,FOM,VOM,fuel_costs_ts,efficiency_ts,emission,p_max_pu_ts,p_min_pu_ts,p_nom_max_area,factor

To define the time series XX_ts, indicate the name of the relevant column in the timeseries.csv file, leaving the XX empty. An area constraint can be defined via the p_nom_max_area and factor columns, where the total usable area and the conversion factor in m²/MW are defined.

5. links:

name,bus0,bus1,carrier,efficiency,build_year,lifetime,p_nom_extendable,p_nom_max,p_max_pu,p_min_pu,min_up_time,min_down_time,up_time_before,down_time_before,ramp_limit_up,ramp_limit_down,bus2,efficiency2,bus3,efficiency3,investment,fuel_costs,FOM,VOM,efficiency_ts,fuel_costs_ts,p_max_pu_ts,p_min_pu_ts,picewise_costs_func,p_nom_max_area,factor

An additional convex cost function can also be defined by specifying the name of the CSV file containing the piecewise data. This file must contain the columns: costs, capacity and optional the efficiency.

6. storage_units:

name,bus,p_nom_extendable,p_nom_max,carrier,build_year,lifetime,cyclic_state_of_charge,max_hours,efficiency_store,efficiency_dispatch,standing_loss_ts,standing_loss,investment,FOM,VOM,p_nom_max_area,factor

The standing loss can either be defined as timeseries in standing_loss_ts or as constant value in standing_loss.

7. stores:

name,bus,carrier,e_nom_extendable,e_nom_min,e_nom_max,standing_loss,standing_loss_ts,build_year,e_cyclic,lifetime,investment,FOM,VOM,p_nom_max_area,factor,e_nom_min_cap,description,capital_cost

The cost function for the district heating network (DHN) can be calculated using the sensitivity mode of the topotherm model — a Pyomo-based mixed-integer linear programming (MILP) tool for district heating network design.

For more details, visit the topotherm GitHub repository or refer to the original research paper:

Lambert, Jerry and Ceruti, Amedeo and Spliethoff, Hartmut, Benchmark of Mixed-Integer Linear Programming Formulations for District Heating Network Design. Energy, Volume 308, 2024, 132885, ISSN 0360-5442, https://doi.org/10.1016/j.energy.2024.132885

For a more detailed description of the PyPSA components, refer to the PyPSA Documentation or consult the following publication:

T. Brown, J. Hörsch, D. Schlachtberger, PyPSA: Python for Power System Analysis, Journal of Open Research Software, 6(1), 2018. DOI: 10.5334/jors.188. Available at arXiv:1707.09913.

Timeseries Preprocessing

If generation timeseries of renewables like, PV, ST or COPs and standing loss timeseries of TES are needed run preprocessing_ts.py

The required technology data parameters must be specified in the relevant CSV files in the data folder.

Installation

1. Clone this repository and navigate into it:

   git clone https://github.com/<your-username>/PyPSA-DESOpt-Heat.git
   cd PyPSA-DESOpt-Heat
   

2. Create and activate a virtual environment for PyPSA-DESOpt-Heat. For example, using Anaconda:

   conda env create -f environment.yml
   conda activate PyPSA-DESOpt-Heat-v1.0
   

Solver

We recommend using gurobi, which offers free academic licenses. For installation instructions, see the installation guide.

Usage

Run the main optimization script: python main.py

Contribute

Contributions are welcome! Please open issues to discuss proposed changes or features before submitting pull requests. This helps ensure alignment with project goals.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Example

Below is an example visualization of district heating network heat supply: