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Decentralised energy systems provide the potential for adding energy system flexibility by separating demand/supply dynamics with demand side management and storage technologies. They also offer an opportunity for implementing technologies which enable sector coupling benefits, for example, heat pumps with controls set to use excess wind power generation. Gaps in this field relating to planning-level modelling tools have previously been identified: thermal characteristic modelling for thermal storage and advanced options for control. This paper sets out a methodology for modelling decentralised energy systems including heat pumps and thermal storage with the aim of assisting planning-level design. The methodology steps consist of: 1) thermal and electrical demand and local resource assessment methods, 2) energy production models for wind turbines, PV panels, fuel generators, heat pumps, and fuel boilers, 3) bi-directional energy flow models for simple electrical storage, hot water tank thermal storage with thermal characteristics, and a grid-connection, 4) predictive control strategy minimising electricity cost using a 24-hour lookahead, and 5) modelling outputs. Contributions to the identified gaps are examined by analysing the sensible thermal storage model with thermal characteristics and the use of the predictive control. Future extensions and applications of the methodology are discussed.

Heat pump and thermal storage sizing studies require modelling to ensure capital and operational costs are minimised. Modelling should consider added flexibility, e.g. grid services, sector coupling benefits, e.g. utilising excess wind production, and access to electricity markets, e.g. time-of-use tariffs. This paper presents a two-step methodology for sizing heat pump and thermal storage systems with a time-of-use electricity tariff. The first step is a modelling method for decentralised energy systems, with the broader aim of assisting planning-level design, and consists of resource assessment, demand assessment, electrical components, thermal components, storage components, and control strategies. The second step is a parametric analysis of heat pump and thermal storage size combinations. This is then applied to a sizing study for an existing residential district heating network including a time-of-use electricity tariff. The performance metrics: % of heat pump thermal output at low-cost period, % of heat demand met by heat pump, electricity import cost, and capital cost, were plotted and tabulated to compare sizing combinations. Graphs explore the operation of the heat production units and the thermal storage. Future development involving use of model predictive control and grid services, and alternative applications including operational planning and feasibility studies, are then discussed.