Building Your Own Optimization-Based Controller

Introduction

This section provides guidance on how to create your own custom optimization-based controller in HAMLET. By extending the existing framework, you can implement specialized control strategies tailored to your specific energy system requirements.

Prerequisites

Before building your own optimization-based controller, you should have:

  • A good understanding of mathematical optimization concepts

  • Familiarity with the HAMLET framework and its component models

  • Knowledge of Python programming

  • Understanding of the specific requirements for your energy system

General Steps

  1. Define Your Mathematical Formulation

    • Identify the objective function(s) for your controller

    • Determine the necessary constraints

    • Select appropriate decision variables

    • Consider time coupling and horizon requirements

  2. Choose an Implementation Approach

    • Linopy-based implementation

    • PyOptInterface-based implementation

    • Custom solver integration

  3. Implement the Controller

    • Create a new controller class

    • Implement the required methods

    • Define the optimization problem

    • Connect to the HAMLET framework

  4. Test and Validate

    • Verify mathematical correctness

    • Test with simple scenarios

    • Compare against existing controllers

    • Validate with realistic use cases

Extension Points

The HAMLET framework provides several extension points for custom controllers:

  • Custom objective functions

  • Component-specific constraints

  • Alternative solver configurations

  • Post-processing of optimization results

Best Practices

  • Start with a simplified version of your controller

  • Incrementally add complexity

  • Document your mathematical formulation

  • Use consistent naming conventions

  • Include appropriate tests

  • Consider computational efficiency