Study on modelling and optimising controllers for heating systems in buildings

Abstract

The UK, like the rest of the world, is working toward net-zero carbon emissions by 2050. A significant contributor to national emissions (17%) is domestic space heating, making efficient building design and retrofitting crucial. This thesis presents methodologies for simulating, controlling, and analysing domestic heating systems to reduce energy use while maintaining thermal comfort. The core framework, Inverse Dynamics-based Energy Assessment and Simulation (IDEAS), was enhanced into IDEAS+ to better align with the UK’s Standard Assessment Procedure (SAP) for building regulations. Key improvements include: • A new thermal comfort algorithm for more accurate modelling of human heat perception and support for niche heating systems. • Dynamic free heat gain calculations, improving precision and SAP compliance. • An updated optimum start method to optimize heating schedules based on system capacity. IDEAS+ was first calibrated using direct electric heating, then applied to Gas Condensing Boilers (GCBs) and Air Source Heat Pumps (ASHPs). To further reduce emissions, optimizing control systems for dynamic energy markets was explored. Traditional optimization methods are slow, requiring iterative simulations. Instead, this thesis introduces a new method - OPTimal Inverse Control (OPTIC), which embeds cost-function optimization directly into IDEAS+’s inverse dynamics control. This allows real-time optimization alongside system operation, improving performance. OPTIC was tested with battery storage, dynamically adjusting to fluctuating energy prices and carbon intensity while ensuring thermal comfort. Two case studies demonstrated simulation based and practical based applications. One was a block of flats in Eastbourne, modelled in IDEAS+ for retrofit analysis, then simulated with a heat pump network and OPTIC-controlled storage as a simulation only based study. Whilst, the other was a commercial property with PV arrays, heat pumps, and battery storage, controlled via an Industrial PC running OPTIC-based C++/C# code at the property in a practical application. These methods provide scalable solutions for reducing emissions in residential and commercial heating systems, supporting the UK’s net-zero targets.