Dirk Saelens

Coupling of dynamic building simulation with stochastic modelling of occupant behaviour in offices – a review-based integrated methodology

A comprehensive modular behavioural model for office buildings and its coupling to building simulation software is introduced, developed to be used in energy uncertainty analysis in a straightforward manner. The model includes the inherent variability in behaviour amongst individuals by defining representative active and passive users. The ratio of the latter serves as an input for the uncertainty analysis. The behavioural model consists of submodels for occupancy, use of shading system, window operation, control of artificial lighting, heat gains by appliances and the control of heating and cooling set points. All these submodels are selected from a literature review. The review revealed a lack of validation and intercomparison of the models as the principal weakness of the research field. The methodology is applied in a Monte Carlo analysis of the uncertainty on the simulated energy demands of an office building at the building level, yielding moderate uncertainties.

Rule-based demand-side management of domestic hot water production with heat pumps in zero energy neighbourhoods

Grid saturation has been reported in electricity distribution systems with a high penetration of photovoltaic (PV) systems. This saturation is often caused by overvoltage and results in curtailing or shutting down of the PV inverters, leading to a loss of renewable electricity generation. The presented work assesses the potential of rule-based demand-side management (DSM) applied to domestic hot water (DHW) production with heat pumps in dwellings for reducing the non-renewable energy use of the neighbourhood. The studied case consists of 33 single-family dwellings connected to a single phase distribution grid in a moderate European climate. Each dwelling is designed as a net-zero energy building by adequate design of a heat pump and PV system. A detailed dynamic simulation model is implemented by use of a cross-domain Modelica library for integrated district energy assessment. The user behaviour is obtained from a stochastic model based on Markov chains and survival analysis. Different rule-based DSM control strategies are applied to the individual dwellings DHW systems. The results show that for balancing the PV production, active thermal energy storage in the DHW storage tanks is very promising. Even with very basic control algorithms and small storage tanks of 0.3 m3, curtailing losses can be reduced by 74%. This represents a net energy saving on a neighbourhood level of 3.4%.

Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour

Occupant behaviour has long been of main interest in the domain of building energy-savings and indoor air quality and its importance is recognized by its wide coverage in the literature. In the recent developments of detailed transient building energy simulations, including the occupant behaviour as a boundary condition for the thermal comfort, system efficiency calculations have been a major research topic, given its significant impact. A simultaneous growing interest in district energy simulations raises similar questions at the aggregate level, where upscaling from the building to an aggregate neighbourhood level at the spatial scale of a low-voltage feeder results in a natural regression to the mean lowering uncertainty, compared to the level of the household. The presented work starts with the description of StROBe, a stochastic residential occupant behaviour for district energy simulations integrating the modelling of receptacle loads, internal heat gains, thermostat settings and hot water tappings based on occupancy and activity prerequisites. Given this model, the uncertainty for district energy simulations is addressed. The epistemic uncertainties are elaborated first by comparing the model results with the reference values and then denoting local disaggregation of demographic statistics as a possible main hiatus of general modelling methods for building energy occupant behaviour used at the neighbourhood level. Finally, the aleatory uncertainty caused by the StROBe in integrated district energy simulations is quantified. Here, the expected value of the objective functions has, to a large extent, the same minimizers as the measures of the proposed robustness. As such, optimizing an objective value for its expected value generally seems to result in a optimum near the optimum of robustness. However, 95% of the observed objectives lay between 0.81 and 1.6 times the expected value for a feeder larger than 10 houses, and between 0.88 and 1.3 times the expected value for more than 20 houses, denoting an overall ‘rather small’ uncertainty on the possible objective functions caused by the user behaviour. Furthermore, we show that the design of the building energy system has an impact on the robustness of the objective criteria and it could thus be minimized as part of an optimisation exercise.

Grid impact indicators for active building simulation

This paper gives an overview of grid impact indicators that could be used to assess the impact of the energy exchange with a single intelligent building on the electricity grid. Seven indicators are given, the capacity factor, loss of load probability, cover factor, one percent peak power, peaks above limit, dimensioning rate and kVA credit. An example of a building simulation is used to show what these indicators can teach us. For this example the indicators are calculated and an explanation of what they show is given. Finally a summary of the grid impact indicators is given in the conclusion of this paper.

Implementation and verification of the IDEAS building energy simulation library

Building and district energy systems become increasingly complex, requiring accurate simulation and optimization of systems that combine building envelope, heating ventilation and air conditioning,...

Optimal operation of building microgrids - comparison with mixed-integer linear and continuous non-linear programming approaches

Purpose This paper aims to present two mathematical models to solve the Energy Management problem of a building microgrid (MG). In particular, it proposes a deterministic mixed integer linear programming (MILP) and non-linear programming (NLP) formulations. This paper focuses on the modelling process and the optimization performances for both approaches regarding optimal operation of near-zero energy buildings connected to an electric MG with a 24-h time horizon. Design/methodology/approach A general architecture of a MG is detailed, involving energy storage systems, distributed generation and a thermal reduced model of the grid-connected building. A continuous non-linear model is detailed along with linearizations for the mixed-integer liner formulation. Multi-physic, non-linear and non-convex phenomena are detailed, such as ventilation and air quality models. Findings Results show that both approaches are relevant for solving the energy management problem of the building MG. Originality/value Introduction and modelling of the thermal loads within the MG. The resulting linear program handles the mutli-objective trade-off between discomfort and the cost of use taking into account air quality criterion. Linearization and modelling of the ventilation system behaviour, which is generally non-linear and non-convex equality constraints, involving air quality model, heat transfer and ventilation power. Comparison of both MILP and NLP methods on a general use case provides a solution that can be interpreted for implementation.