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A photovoltaic-thermoelectric (PV-TE) system is modeled theoretically and demonstrated experimentally. Mathematical formulations of the system are proposed according to energy conservation and finite-time thermodynamics, which can be applicable to analyze and optimize the system that consists of commercial photovoltaic (PV) cells and thermoelectric generators (TEGs). The genetic algorithm is introduced to maximize the system's power by optimizing multiple parameters. In Yan'an City, which is located in the northern Shaanxi Province in northwest China, the actual solar irradiance is recorded for almost a full year and the proposed model is verified. Theoretical and experimental results are analyzed and compared. It is found that there are several independent structure parameters in the PV-TE systems and they should be optimized simultaneously to maximize the performance. The PV-TE system's maximum theoretical average power of 0.343 W is 2.69 % greater than the solo PV cell's maximum theoretical power of 0.334 W. The measured average power of the system of 0.317 W is 0.32 % more than that of a solo PV cell of 0.316 W. The duration of strong lighting in Yan'an City reaches 8 h every day for nearly one year and the irradiation intensity can approach 1000 W/m2. The solar radiance is weakly affected by the seasonal changes of four seasons but is greatly affected by rainy days and cloud thickness. The research findings can serve as a helpful guide for the specific implementation of the PV-TE system and exploitation of solar energy resources in northwest China. • The novel model of a tandem commercial PV-TE system is established. • The genetic algorithm optimizes parameters to maximize the power of the system. • The maximum power of the system is 2.69 % higher than that of a solo PV cell. • The experiment power of the system is 0.32 % higher than that of a solo PV cell. [ABSTRACT FROM AUTHOR]

To promote the continuous operating of solar energy systems and increase the proportion of renewable energy consumption, solar devices are often integrated with conventional energy systems to reduce greenhouse gas emissions. In this study, a solar-natural gas coupling system is constructed, utilizing solar photovoltaics, thermal collectors, and high-efficiency energy conversion devices. After modelling the energy system, the operating modes are illustrated considering working sequences of water-cooled devices. A modified genetic algorithm, which incorporates reverse learning, controlled elite, and dynamic crowding distance theory, is constructed to optimize the configurations of the proposed energy system, with the aim of minimizing carbon and economic prices of the products. The performance of the energy system is compared with different algorithms and cases, and the results demonstrate that the proposed algorithm addresses the limitations of conventional methods, albeit with increased optimization times. Moreover, the proposed operating mode (Case 1) exhibits a stable performance, leading to a higher environmental and economic performance compared to conventional modes, with carbon and economic prices of 0.242 kg/kWh and 0.067 $/kWh, respectively. This study provides new insights into carbon footprints and economic performance evaluation of high-efficiency energy systems, offering valuable directions for future research in this field. [Display omitted] • A solar-natural gas coupling system to fulfill building multiple demands. • Comparisons of fours cases considering operating sequences of two types of chillers. • Modified genetic algorithm using reverse learning, elite controlled, dynamic crowding distance theory. • Carbon and economic prices of the products are illustrated. [ABSTRACT FROM AUTHOR]

We proposed a dual-ejector-based regenerative dual–pressure organic flash cycle (DEj–RDPOFC) with higher performance than the RDPOFC. The effects of high and low pressures and the entrainment ratio of the ejector on the DEj–RDPOFC performance were analyzed from the viewpoints of thermodynamics and thermoeconomics. An exergy-flow distribution diagram revealed why the DEj–RDPOFC outperformed the RDPOFC. Next, the performances of the DEj–RDPOFC and dual–pressure organic Rankine cycle (DORC) were compared under the same boundary conditions. Finally, the performance of the DEj–RDPOFC was optimized using the nondominated sorting genetic algorithm (NSGA-II). According to the results, the power output of the high-pressure (HP) turbine of the DEj–RDPOFC can be increased by replacing the HP throttling valve (TV) with an ejector (which increases the working fluid dryness of the HP vapor separator and the mass flow rate of the HP turbine) and the low-pressure (LP) TV with an ejector (which increases the HP turbine expansion ratio). The DEj–RDPOFC achieves a higher maximum net power output (W net) and a lower corresponding levelized cost of electricity (LCOE) than the DORC. Under the optimum condition, the W net and LCOE of the DEj–RDPOFC are 9.71 % higher and 6.59 % lower, respectively, than those of the RDPOFC. • The effect of the HP and LP ejector on the performance of the regenerative DPOFC (RDPOFC) are analyzed. • The performance of the RDPOFC assisted by ejectors (DEj–RDPOFC) is compared with the dual-pressure ORC. • The DEj–RDPOFC is optimized using a multi-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper explores the influence of different turbocharging modes and multi-parameter coordinated control on the performance of compression-ignition (CI) aviation piston engine (APE). Firstly, based on a constructed one-dimensional thermodynamic model of a CI APE, the study investigates the effects of different turbocharging modes and combinations of high and low-pressure stage variable geometry turbines (VGT) on the operational performance of the engine. Subsequently, a novel stepwise approximate multi-objective optimization algorithm is proposed, combining backpropagation neural networks and the non-dominated sorting genetic algorithm II. This algorithm evaluates the influence of multiple control parameters on a two-stage turbocharged engine's performance, achieving a balance between fuel economy and reliability. The research shows that equipping CI APEs with twin VGT for supercharging can notably enhance engine performance, enabling the achievement of favorable power recovery objectives at an altitude of 8000 m. The proposed optimization algorithm exhibits strong predictability and reliability, substantially accelerating the computation speed and reducing the data volume by approximately 95%. At an engine speed of 3887 rpm, compared to the unoptimized conditions, the brake specific fuel consumption of the best scenario at altitudes of 2000 m, 4000 m, 6000 m, and 8000 m is reduced by 7.1%, 7.2%, 8.6%, and 6.9%, respectively. • Influences of different turbocharging modes on the performance of a CI APE are investigated. • A novel stepwise approximate multi-objective optimization method based on a hybrid model is proposed. • Explored the altitude-varying control strategy under coordinated multi-parameter optimization. • Increased the altitude ceiling for the CI APE, reaching 82% power recovery target at 8000 m. [ABSTRACT FROM AUTHOR]