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Understanding the contribution a resource can make to the power system could indicate where its value lies. This paper estimates the capacity value of a load-shifting resource which is capable of providing multiple services. The capacity value represents the contribution of a resource to generation adequacy and an understanding of this contribution is important to compare how different power system resources can assist power system operators and planners. Additionally, policy-makers and market operators need an appreciation of the capacity value of different resources in order to design capacity remuneration mechanisms. A building energy model coupled with a power system model, co-optimizing the supply-side and the demand-side, is employed in this paper to estimate the capacity value of a specific load-shifting resource. The resource examined is electric thermal storage heating devices for space and water heating. Ireland is used as a test case. It was found that these load-shifting devices can provide an adequacy contribution to the power system and thus have a capacity value. The capacity value, for the Irish case, can be up to 26% for the DR resource in question for the given year but the values are typically much lower due to operational constraints (reserve provision) and due to occupancy profile impacts. The results highlight the need for holistic modeling of demand response resources, as well as the need for additional work for different load-shifting resources and more data. [ABSTRACT FROM AUTHOR]

Building stock modelling usually deploys representative building archetypes to obtain reliable results of annual energy heating demand and to minimise the associated computational cost. Available methodologies define archetypes considering only the physical characteristics of buildings. Uniform occupancy schedules, which correspond to national averages, are generally used in archetype energy simulations, despite evidence of occupancy schedules which can vary considerably for each building. This paper presents a new methodology to define occupancy-integrated archetypes. The novel feature of these archetype models is the integration of different occupancy schedules within the archetype itself. This allows building stock energy simulations of national population subgroups characterised by specific occupancy profiles to be undertaken. The importance of including occupant-related data in residential archetypes, which is different than the national average, is demonstrated by applying the methodology to the UK national building stock. The resultant occupancy-integrated archetypes are then modelled to obtain the annual final heating energy demand. It is shown that the relative difference between the heating demand of occupancy-integrated archetypes and uniform occupancy archetypes can be up to 30%. [ABSTRACT FROM AUTHOR]

Developing BEPS models which predict energy usage to a high degree of accuracy can be extremely time consuming. As a result, assumptions are often made regarding the input data required. Making these assumptions without introducing a significant amount of uncertainty to the model can be difficult, and requires experience. Even so, rules of thumb from one geographic region are not automatically transferrable to other regions. This paper develops a methodology which can be used to determine useful guidelines for defining the most influential input data for an accurate BEPS model. Differential sensitivity analysis is carried out on parametric data gathered from five archetype dwelling models. The sensitivity analysis results are used in order to form a guideline minimum set of accurately defined input data. Although the guidelines formed apply specifically to Irish residential dwellings, the methodology and processes used in defining the guidelines is highly repeatable. The guideline minimum data set was applied to practical examples in order to be validated. Existing buildings were modelled, and only the parameters within the minimum data set are accurately defined. All building models predict annual energy usage to within 10% of actual measured data, with seasonal energy profiles well-matching. [ABSTRACT FROM AUTHOR]