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Intelligent design and operation optimization allow energy systems to take advantage of the flexibility that multi-generation provides. This study proposes a basic solar-driven system integrated with thermal energy storage for round-the-clock energy harvesting. A modified configuration is then designed incorporating innovative multi-heat recovery approaches to increase the capacity and product diversity of the basic system. The modified system is able to cover vital urban utilities such as electricity, fresh water, cooling, and hydrogen throughout the day. To overcome the time-consuming procedure of dynamic techno-economic simulation as well as the limitation of commercial engineering equation solvers for tri-objective optimization, a deep learning approach is developed to reduce the computational complexity and improve the analysis accuracy. In this regard, the trained neural networks play an intermediary role in coupling the developed code with the MATLAB optimization toolbox. A comparison between the modified and conventional configurations indicates that implementing the multi-heat recovery approach results in a 29% increase in power generation while only increasing the overall system cost by 1.97%. From an economic perspective, the Sankey diagram depicts that the storage unit with a cost rate of 12.25 $/h accounts for 6.77% of the plant's cost rate, which enables the system to operate continuously. According to the sensitivity analysis and contour plots, the number of collectors significantly affects the total cost rate and fresh water production capacity while it has no tangible effect on the exergy efficiency. • A novel multigeneration system with thermal energy storage is designed. • A modified configuration with heat recovery is introduced to boost the capacity. • The comparative study indicates a 17.6% improvement in the power generation. • A dynamic simulation code is developed based on meteorological data. • A deep learning techno-economic optimization is applied to the system. [ABSTRACT FROM AUTHOR]

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 transient performance of solar thermal power plants is critical to the system design and optimization. This study numerically investigates the dynamic efficiencies of an open-loop air receiver and a thermal energy storage unit. One-dimensional dynamic models of the air receiver and thermal energy storage were developed using the Modelica language with a graphical user interface and the Dymola solver to provide comprehensive thermal simulation at low computational cost. An air receiver and thermal energy storage experimental platforms were built to validate the simulation models. The simulation results compare well with the experimental data, so the models can be used to predict the variations of air receiver and thermal energy storage efficiencies. The models were then combined into a receiver and thermal energy storage system model with control schemes. The schemes control the air receiver outlet air temperature at relatively stable values while the thermal energy storage automatically switches between charging, discharging and stand-by modes. [ABSTRACT FROM AUTHOR]