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Advanced Exergy Analysis of a Pumped Thermal Energy Storage

forthebadge

DOI

This is a Python script that models a simple Pumped Thermal Energy Storage (high temperature HP and ORC) and performs an advanced exergetic analysis.

This is part of my research work at Department of Energy Engineering and Climate Protection of the Technische Universität Berlin.

Table of contents

Installation

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To set up the project environment, follow these steps:

  1. Install Python: Ensure you have Python installed on your system. Version 3.9 is recommended for compatibility. You can download it from the official Python website.

  2. Clone the Repository: Download the project code by cloning the repository. Open your terminal or command prompt and run:

    git clone https://github.com/sertomas/adex-ptes.git

    Then, navigate to the project directory:

    cd adex-ptes

  3. Create a Virtual Environment: It's a good practice to create a virtual environment for Python projects. This isolates your project's dependencies from the rest of your system. Use the following command:

    python -m venv adex-cb

    After creating the virtual environment, you need to activate it. The activation command differs depending on your operating system:

    • On Windows: 
      .\adex-cb\Scripts\activate
    • On Unix or MacOS: 
      source adex-cb/bin/activate

  4. Install Required Dependencies: The project dependencies are listed in a YAML file. However, pip does not directly install packages from a YAML file (commonly used with Conda environments). If you are using Conda, you can create an environment from the YAML file directly. Otherwise, for pip, ensure you have a requirements.txt file or convert the YAML content to a pip-compatible format. Assuming you have a requirements.txt file or have converted the YAML file content:

    pip install -r requirements.txt

Note: If you intended to include instructions for installing dependencies using a YAML file with Conda, you might need to adjust the command for installing dependencies accordingly. For example:

conda env create -f adex-ptes.yaml

Then, activate the Conda environment:

conda activate <env_name>

Ensure you replace <env_name> with the name of your environment as specified in the YAML file.

Usage

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  1. Run
    • To obtain all the results, run hp.py and orc.py. The equation system, the starting values and all the parameters are saved here.
    • The file functions.py contain general functions for the modeling of the equation system
    • The results from the advanced exergy analysis of the HP and the ORC are saved in /outputs/adex_hp and /outputs/adex_orc respectively.
    • All the diagrams created during the simulations are saved in /outputs/diagrams.
  2. Changes
    • If you want to change the fluid of the HP or of the ORC, or change the ambient conditions, you can do it in hp.py and orc.py.
    • If you want to change the design of your subsystem, you should change the equation system in hp.py and orc.py.

Methodology

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  1. Model and simulate the base case of the HP and the ORC:
    • The HP and the ORC of the CB are simulated using a self-made simulatenous solver.
    • The starting values, the design variables and the ambient conditions are provided.
    • The selection of the equations to correctly simulate the system is a crucial part.
    • If necessary, the decision variables are optimized to obtain the highest efficiency.
  2. Model and simulate the ideal case of the HP and the ORC:
    • Maintain constant output: Ensure the system's product remains is the same of the base case.
    • Eliminate exergy losses: Operate components adiabatically, except for those like condensers designed for heat dissipation.
    • Idealize components: Apply specific concepts to idealize all system components.
      • Compressors and pumps: Treated as isentropic compression processes.
      • Turbines: Idealized through isentropic expansion.
      • Throttling valves: Replaced with isentropic expanders for idealization.
      • Heat exchangers: Conceptualized with intermediate reversible cycles (e.g., Lorenz cycle), avoiding detailed cycle simulation.
    • Account for additional power flows: Adjust for power flows resulting from idealization, affecting total power consumption or fuel usage.
  3. Model and simulate the hybrid cases of the HP and the ORC:
    • Analyze each component individually: Consider each component in a real operation mode with others idealized, focusing on exergetic efficiency.
    • Use enthalpy and entropy values: Simplify analysis by avoiding direct exergy calculations.
    • Formulate efficiency equations: Integrate exergetic efficiency equations for standard components (compressors, expanders, heat exchangers) into the system's equation set.
  4. Correct the methods in case of errors:
    • In case of errors (e.g. negative temperature difference), the approach should be relaxed, in order to obtain reasonable results.

License

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MIT License (MIT). Please have a look at the LICENSE.md for more details.