Grey-box models for prediction and control of solar district heat plants

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Abstract

It has become clear that the global energy system needs to shift away from fossil fuels towards clean, renewable energy sources. Experience in the past decades has shown that at-plate solar heat panels can play a role in the energy system of the future, in particular in solar collector fields for district heat generation. This thesis concerns the dynamical modelling of such solar district heat plants
(SDHPs). As the upswing of SDHPs occurred rather recently, research on their optimal control is ongoing. The main challenge for control is to adapt to fluctuations in solar radiation and other inputs in an optimal way, ensuring a high and stable outlet water temperature to the grid while minimizing flow fluctuations. Many modelling efforts of single collectors as well as full solar heat
fields have been reported, although mostly in the form of detailed physical models.

In this thesis, a new approach for describing the dynamics of a large at-plate solar field is proposed. We develop a continuous-discrete stochastic state space model suitable for prediction and control. This model form combines knowledge from physics and information from data, thereby allowing for relatively simple formulations while modelling complex dynamics. Retaining a physically meaningful model formulation has additional advantages for model development, as analysis of residuals and outputs can provide information on suitable model extensions. A basic model was formulated and systematically extended in a forward selection procedure, using a.o. likelihood ratio tests. The
model development was based on May 2017 measurements from an Aalborg CSP solar heat plant in the municipality of Solrød , Denmark. The models are implemented using the R-package CTSM-R, which includes parameter estimation methods based on maximum likelihood estimation and the
extended Kalman filter.

It is found that the developed model is suitable for very short term (minutes to hours ahead) to short term (day ahead) prediction, as needed for control and heat output forecasting of a SDHP. It includes several new aspects compared to existing models, the most notable being non-parametric modelling of shading effects and a split of total radiation into diffuse and direct components. Including these elements improves model predictions considerably, and allows for asymmetric panel effciency over the day. Detailed analysis of the model's predictive performance is provided, including a comparison to current ISO standard model and the current Solrød control scheme, as well as a cross-validation on data from different seasons. In future work, the model's performance when using input predictions from weather forecasts should be tested. The model should further be used in a model predictive control scheme in order to improve current SDHP control strategies. This would lead to a smoother pump operation, thereby reducing electricity consumption, costs, and greenhouse gas emissions.