Optimising District Heating Substations with EnCoDaPy: A Modular Python Framework for Local Control and Energy Management

Increasing demands on district heating networks, coupled with the heat transition in Germany, require innovative operational management strategies. This paper introduces the EnCoDaPy (Energy Control and Data Preparation in Python) framework, which provides modular, real-time and scalable energy management solutions. By integrating with platforms such as FIWARE, EnCoDaPy enables the seamless communication and control of decentralised energy systems. The framework offers a modular configuration, including validation, to facilitate error detection and customisation. A use case involving a district heating substation controller illustrates how the framework can optimise energy efficiency and self-consumption by combining a centralised schedule creation with local implementation. Simulations highlight the benefits of the rule-based approach, while also acknowledging its limitations when it comes to handling complex systems. This suggests that there is a need for the future integration of model-based predictive control. The case study also highlights the potential for optimising the operational management of district heating substations. EnCoDaPy provides a robust foundation for research and industrial applications. Additionally, there are plans for further development and validation.