Solar-assisted district heating networks: Development and experimental validation of a novel simulation tool for the energy optimization

Abstract

Due to the growing interest in 4th and 5th generation district heating systems, characterized by lower working fluid temperatures compared to the past, poligenerative solar-assisted networks are experiencing increasing attention from the research community. In this framework, the adoption of large solar-based thermal systems will become more common for such applications. Therefore, optimising the design and operation of each solar field servicing a specific network will be crucial to improving its overall performance. In this context, the objective of the current research is to develop a method to maximise the performance of solar-assisted poligenerative district heating networks by optimising each associated solar system separately. To this end, a novel dynamic simulation tool for solar thermal field design and optimization has been developed and experimentally calibrated in the Simulink/Simscape environment. The tool is capable of investigating innovative control logics, and accurately assessing both the thermal and hydraulic behaviour of the systems. The latter is one of its novelty, as ignoring the hydronic behaviour of the system can lead to the adoption of flawed design or control logic. The resulting tool will enable accurate optimization of the design and management of each solar field servicing a given district heating network, thereby improving the efficiency of the system as a whole. In order to demonstrate the viability of the proposed method and the capabilities of the developed simulation tool, a proof-of-concept analysis is conducted on an existing solar thermal field serving the Geneva district heating network. Diverse control logics (including Rule-Based and Model Predictive Control) are evaluated for the selected case study. The performed analysis demonstrates that the proposed method has the potential to accomplish significant primary energy reductions (up to 10.3%) and CO2 emissions avoidance (up to 10.9 tCO2/year).