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[Display omitted] This paper introduces a non-linear grey-box (GB) model based on stochastic differential equations that describes the heat dynamics of a school building in Denmark, equipped with a water-based heating system. The building is connected to a local district heating network through a heat exchanger. The heat is delivered to the rooms mainly through radiators and partially through a ventilation system. A monitoring system based on IoT sensors provides data on indoor climate in the rooms and on the heat load of the building. Using this data, we estimate unknown states and parameters of a model of the building's heating system using the maximum likelihood method. Important novelties of this paper include models of the water flow in the circuit and the state of the valves in the radiator thermostats. The non-linear model accurately predicts the indoor air temperature, return water temperature and heat load. The ideas behind the model lay a foundation for GB models of buildings that use different kinds of water-based heating systems such as air-to-water/water-to-water heat pumps. Such GB models enable model predictive control to control e.g. the indoor air climate or provide flexibility services. [ABSTRACT FROM AUTHOR]

Numerous cost-optimal studies have been undertaken by EU member states to define minimum energy performance requirements for buildings within that member state in accordance with the Energy Performance of Buildings Directive. As few of these studies have examined the effect of heating and ventilation system parameters on life cycle cost (LCC) and energy use in detail, this paper analysed 84 HVAC solution-sets, including parameters for the heating supply system, heating distribution system and ventilation system, for a case study in Norway. The choice of heating supply system was the dominant parameter, with ground source heat pump solutions offering the lowest purchased energy use and LCC. Electric water heating and district heating systems had low capital costs which make these options more attractive to property developers looking for a good return on investment. Using an exhaust ventilation system with an exhaust air heat pump lowered LCC and capital costs for ground source heat pump and district heating systems but does not meet current Norwegian energy regulations. Despite using more energy, a constant setpoint schedule resulted in a lower LCC than a variable setpoint system due to the variation in hourly energy pricing and the dominant role of monthly pricing based on peak load. The choice of yearly heating strategy and heat emitter had a relatively small impact on energy use, capital cost and LCC. With all the parameters combined, there was variation within each heating system of up to 21 kWh/(m2∙year) in energy use, 78 Euro/m2 in capital cost and 236 Euro/m2 in LCC. Therefore, it is important that such system parameters are considered in cost-optimal assessments to give a fair comparison of HVAC systems. [ABSTRACT FROM AUTHOR]

• Paper presents a novel approach for optimising the mix of heat supply technologies. • Heat supply choices are affected by local and national interactions with power. • Heat sector flexibility can support decarbonisation of electricity supply. • Emission limits and grid constraints shape the optimal heat plant portfolio. • Increasing heat storage capacity can help with local network constraints. Decarbonisation of the heating and cooling sector is critical for achieving long-term energy and climate change objectives. Closer integration between heating/cooling and electricity systems can provide additional flexibility required to support the integration of variable renewables and other low-carbon energy sources. This paper proposes a framework for identifying cost-efficient solutions for supplying district heating systems within both operation and investment timescales, while considering local and national-level interactions between heat and electricity infrastructures. The proposed optimisation model minimises the levelised cost of a portfolio of heating technologies, and in particular Combined Heat and Power (CHP) and polygeneration systems, centralised heat pumps (HPs), centralised boilers and thermal energy storage (TES). A number of illustrative case studies are presented, quantifying the impact of renewable penetration, electricity price volatility, local grid constraints and local emission targets on optimal planning and operation of heat production assets. The sensitivity analysis demonstrates that the cost-optimal TES capacity could increase by 41–134% in order to manage a constraint in the local electricity grid, while in systems with higher RES penetration reflected in higher electricity price volatility it may be optimal to increase the TES capacity by 50–66% compared to constant prices, allowing centralised electric HP technologies to divert excess electricity produced by intermittent renewable generators to the heating sector. This confirms the importance of reflecting the whole-system value of heating technologies in the underlying cost-benefit analysis of heat networks. [ABSTRACT FROM AUTHOR]

• Data-driven and comprehensive thermodynamic technology models are developed. • Multi-fidelity approaches are used to compare heat-generation solutions. • Centralised CHP systems appear to be the most profitable option at present. • Heat pumps can achieve a significant degree of heat decarbonisation (55–62%). • Thermodynamic models reduce uncertainty by 75% compared to black-box approaches. This paper presents a multi-scale framework for the design and comparison of centralised and distributed heat generation solutions. An extensive analysis of commercially available products on the UK market is conducted to gather information on the performance and cost of a range of gas-fired combined heat and power (CHP) systems, air-source heat pumps (ASHPs) and ground-source heat pumps (GSHPs). Data-driven models with associated uncertainty bounds are derived from the collected data, which capture cost and performance variations with scale (i.e. , size and rating) and operating conditions. In addition, a comprehensive thermoeconomic (thermodynamic and component-costing) heat pump model, validated against manufacturer data, is developed to capture design-related performance and cost variations, thus reducing technology-related model uncertainties. The novelty of this paper lies in the use of multi-fidelity approaches for the comparison of the economic and environmental potential of important heat-generation solutions: (i) centralised gas-fired CHP systems associated with district heating network; (ii) gas-fired CHP systems or GSHPs providing heat to differentiated energy communities; and (iii) small-scale micro-CHP systems, ASHPs or GSHPs, installed at the household level. The pathways are evaluated for the case of the Isle of Dogs district in London, UK. A centralised CHP system appears as the most profitable option, achieving annual savings of £13 M compared to the use of decentralised boilers and a levelised cost of heat equal to 31 £/MWh th. However, if the carbon intensity of the electrical grid continues to reduce at current rates, CHP systems will only provide minimal carbon savings compared to boilers (<6%), with heat pumps achieving significant heat decarbonisation (55–62%). Differentiating between high- and low-performance and cost heat pump designs shows that the former, although 25% more expensive, have significantly lower annualised costs (£4.9 M vs. £7.7 M) and 12% higher CO 2 -equivalent savings. The uncertainty propagation reveals that data-driven and detailed thermoeconomic models can reduce the uncertainty in the total annualised energy cost and emissions by more than 75% compared to simplified black-box approaches, while the economic comparison of centralised CHP systems and domestic heat pumps for various electricity and gas price scenarios provides guidance to support energy policy. [ABSTRACT FROM AUTHOR]