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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]

• An innovative slim decentralised plug-and-play tank is proposed for DHW production. • Model calibration is performed by experimental data in charge/discharge process. • Retrofit solution cuts required annual energy in half of which 82% is RES quota. • Optimised charging scheme reduces the total annual electrical energy by 5.2 %. • A degree increment in draw-off temperature increases annual CO 2 emission by 103 kg. The present study deals with a multi-objective analysis of an innovative decentralised system to produce and store domestic hot water (DHW), emphasising on the combined effects of the technological aspect, control strategy and user's behaviour. The proposed system, by relying on thermal energy storage, decouples energy production and demand while shaves peaks in the energy demand and, at the same time, provides more autonomy to users through local storages. To identify subtle interactions in components of DHW system, dynamic simulations are carried out by establishing a coupled TRNSYS-MATLAB code, calibrated and validated by experimental measurements. The energy analysis implies that the proposed system cuts the required annual electrical energy in half, of which up to 82% of needed primary energy is supplied from renewable sources, compared to previous electrical-decentralised system. The optimisation of the results through applying control strategies indicates that adopting a three-time charging scheme is advantageous in terms of providing a more stable temperature profile as well as a higher hot water temperature. Compared to an available-by-demand operation, this scheme reduces the required total annual electricity by 5.2 % and enhances total thermal loss from components up to 4.0%. Furthermore, a sensitivity analysis on the results emphasises the striking role of the user behaviour in electrical energy consumption either via draw-off temperature or adjusting the pre-defined temperature for activation of the built-in auxiliary heater. [ABSTRACT FROM AUTHOR]

This study presents the optimal design of a multi-source renewable energy combined cooling, heating, and power (M−RCCHP) system integrated with energy storage units for an apartment community from techno-economic perspectives using response surface methodology (RSM) and dynamic evaluation. The proposed M−RCCHP system is consisted of photovoltaic/thermal (PV/T) collectors, wind turbines (WTs), fuel cell subsystem, heat pump (HP), while the battery banks and hydrogen storage tank are the energy storage subsystems. The behavior of the system is examined by a dynamic approach using the TRNSYS simulation platform. Besides, the main design variables of the M−RCCHP system are selected to find the foremost combination of them using the design of experiments (DOE) technique. The number of PV/T panels, number of WT, fuel cell power, HP cooling capacity and HP heating capacity were considered as decision variables. Overall electricity consumption (OEC) and boiler fuel consumption (BFC) as indicators of primary energy consumption of the system and predicted average vote (PMV) as an index of thermal comfort of the system and life cycle cost (LCC) as economic criteria as 4 objective functions were selected for optimization. The optimization results showed that the M−RCCHP system with 225 PV/T collectors, 15 wt, fuel cells with a power of 74.96 kW, HP cooling capacity of 50.25 kW and HP heating capacity of 20 kW achieved the best techno-economic performance during the period. The results of the system modeling showed that the fuel cell subsystem, PV/T collectors and WTs with the maximum power of 75 kW, 52 kW and 24 kW have the largest contribution in electricity production. The optimization results showed that the system cycle cost (LCC) is 323,004.96 $ per year, the system comfort coefficient (PMV) is 0.115 per year, the boiler fuel consumption is 41,067.88 cubic meters per year, and the total electricity consumption is −317,194.94 kWh per year, as well as the amount The optimal cooling and heating capacities of HP were calculated as 50.25 and 20 kW, respectively. And finally, the error percentage of the optimization method was reported as 6.53 %. [ABSTRACT FROM AUTHOR]