Search

The industrial sector is one of the biggest oil consumers in Cyprus, corresponding to 20% of the fuel consumption. A parabolic trough collector (PTC) system seems the best option to produce clean thermal energy at higher temperatures than those that can be achieved from the already widely used flat plate collectors on the island. This paper presents a simulation model built to investigate the performance of the first industrial PTC system in Cyprus, installed at the Cyprus biggest soft drinks factory. The simulation model is built in TRNSYS and is validated using data from the real PTC system installed at the factory. The results show a very good fitting between the operating parameters and the power output of the Solar Field (SF), Concrete Thermal Storage System (CTES), and the Steam Generator (SG). The average percentage relative error of the system's contribution to the process is less than 6.32% for the daily steam production and during a week did not exceed 6.45%. The novelty of this study is the development of a design tool that can be used by potential interested industries to identify the suitable system that fits their needs. All data are provided in the form of graphs and allow anyone to use as input data the thermal energy demand and required steam temperature of the industry to retrieve information about the size of a suitable system which satisfies these requirements depending on each case. The payback period for all cases examined varies from 2 to 6 years, depending on the size of the system. • Validated dynamic simulation model of a PTC system working under various operation modes for Industrial Process Heat application. • Evaluation of the operation modes and strategies developed to make the PTC system dispatchable. • A scaled up model for of the validated dynamic simulation model. • Development of a tool for potential interested industries in Cyprus for PTC systems. [ABSTRACT FROM AUTHOR]

• Alkali metal thermoelectric converter was combined with solar tower system. • For continuous power generation, thermal energy storage was utilized. • The impact of various solar tower parameters on the hybrid system was analyzed. • and the highest efficiency of 18% is obtained when the total heliostats area is 14,500 m 2. Solar tower plants (STPs) can foster reduce toxic emissions and a cleaner environment. Here, to step forward in this advance, this system is employed as a heat source for an alkali metal thermoelectric converter (AMTEC). Thermal energy storage (TES) is applied to stabilize the performance of the hybrid system along with solar irradiation fluctuations. The considered system is investigated on three different days for the humid subtropical climate of Bandar Abbas, Iran. This study's primary outcome and contribution is a novel simulation framework via TRNSYS to predict the performance of the system under fluctuations of solar irradiation at this site. Both TES and STP systems are defined in TRNSYS software as a new component type via utilizing the Fortran program. The effects of various parameters, including the total energy absorbed by the central receiver, the thermal capacity of TES, and the heliostats field area, are investigated on crucial output parameters of the AMTEC. The mass flow rate of the heat transfer fluid at several areas is then studied in different operating conditions. The optimum area for the heliostats (14,500 m 2), leading to the highest AMTEC efficiency (18%) is determined, which requests a TES tank volume of 28.13 m 3. This system is noticed to have its most remarkable performance in June and July. [ABSTRACT FROM AUTHOR]

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]