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As renewable energies are incorporated in larger shares in the electricity grid and district heating and cooling networks, the integration of storage solutions becomes more important. Thermal Energy Storage is an effective way to store heat and utilize the synergies between different energy carriers. Stratified storage tanks are a promising technology because of their low cost, simplicity and reliability. However, the modeling of the thermocline region in a stratified tank remains a challenge. There is a need to develop a fast and accurate 1D model for simulations and optimizations of TES. In this paper, a new discretization scheme is applied to the vertical axis of the storage tank. Orthogonal Collocation accurately represents the temperature profiles inside the storage tank with less points than the traditional multinode model, therefore running faster. Oscillations appear in the temperature profiles computed with orthogonal collocation if the thermocline represented is too steep and a low number of discretization points is used. But if a realistic thermocline is used as initial condition, the model performs well. Thus, it is appropriate to represent the real behavior of a storage tank, where uniform temperature conditions are avoided. Orthogonal Collocation on Finite Elements runs even faster and represents a good perspective for optimization studies. The model developed in this paper is validated with real data from a solar thermal plant with storage. A continuous and smooth model is also developed for natural convection inside the storage tank. The limitations of the model are discussed and perspectives on the modeling of natural convection for optimization models are given. • Orthogonal collocation is suggested for the 1D storage tank model discretization. • The proposed model runs faster than the multinode model for the same accuracy. • Orthogonal collocation on finite elements is suggested for optimization studies. • The storage tank model is validated with real plant data. • A continuous and smooth model for natural convection is developed. [ABSTRACT FROM AUTHOR]

The capability to dynamically plan, predict, and control indoor conditioning allows to adapt to individual preferences of inhabitants and enables demand side management. While former mainly improves thermal comfort of inhabitants so does latter unlock ecological and financial opportunities mostly for energy utilities. Commonly, dynamic indoor conditioning is based on piece-wise constant indoor temperature constraints. This paper's contribution is the presentation of additional constraints: in particular ones expressed relative to the nominal behavior of a hydronic heating system. This allows to simultaneously harness the relevant process variables in particular during the pre-loading and post-loading phases of a load reduction. The findings are based on data sets acquired on 10 inhabited, residential buildings in Stockholm over a whole year. One of the findings is that building models need to be adaptable if predictive control is applied in practice. This adaptability is assured by a novel concept, i.e. a so called model manager on which the control is relying for the selection of the most accurate model. Centralized optimal control of buildings connected to a district heating network is challenging in practice due to a high computational load. In order to reduce it, the herein presented method elaborates plans only every hour instead of at every control step for optimal control. Since these plans cannot be optimal due to the lack of regular update a hitherto unknown cascaded control logic has been developed that corrects planning errors and other disturbances. Capabilities are demonstrated and compared to conventional controllers in dynamic simulations of a multi-zoned building. The herein presented method is to our knowledge the first to provide all flexibility desired by energy utilities and inhabitants alike through harnessing the consequences of transitions. [ABSTRACT FROM AUTHOR]