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Partial shading on photovoltaic (PV) modules significantly affects their productivity. Therefore, different PV reconfiguration approaches were employed as solutions for distributing the shade across the array. This paper proposes an advanced four-square (FS) sudoku technique for configuring the 9 × 9 total-cross-tied (TCT) PV array via moving the modules physically for one time to enhance the harvested maximum power under all possible shading patterns. The proposed FS is validated under different shading classes, including deterministic and random shading patterns. The performance of the proposed strategy is assessed versus six different approaches representing the literature, including basic sudoku (SDU), optimal sudoku (OSDU), chaos map (CMP), skyscraper (Sky-S), odd-even prime (OEP), and ancient chinese magic square (ACMS). The assessment process is conducted using the global maximum power point, execution ratio (ER), fill factor (FF), percentage power losses (% P L), power enhancement (% P H), and array output power-voltage (P-V) characteristic. Moreover, an extensive analysis of the cost and the energy saving during a day is performed to investigate the reliability of the proposed FS strategy. Furthermore, a critical evaluation is performed among the FS and recent dynamic reconfiguration approaches, including particle swarm optimizer (PSO), harris hawk optimization algorithm (HHO), genetic algorithm (GA), butterfly optimization algorithm (BOA), coyote optimization algorithm (COA) and artificial ecosystem-based optimization (AEO). The results of this paper reveal that the rearranged scheme-based FS approach can disperse different categories of shading efficiently with a high generated energy from the entire system and high revenue compared to TCT and other state-of-the-art arrangements with achieving smooth uni-peak P-V characteristics. • A novel static reconfiguration approach using four square sudoku (FS) is proposed. • FS is proposed as a universal scheme for dispersing several shade patterns. • FS is evaluated versus several static and dynamic reconfiguration approaches. • FS reliability is assessed by determining the energy savings over a day. • FS enhanced the power by 38.016% in the existence of random failed modules. [ABSTRACT FROM AUTHOR]

The effect of fin design parameters on the performance of plate-fin heat exchanges was investigated in the paper, in which an improved algorithm combing a Kriging response surface and multi-objective genetic algorithm was used. An ideal gas was adopted as the working fluid and ε -NTU method was utilized to determine the heat transfer and pressure drop. The fin height h , fin space s , fin thickness t and interrupted length l of serrated fin and channel inlet Reynolds number are firstly optimized, while the j factor, f factor and JF factor are optimization goals. The results show that when the inlet channel flow is in the laminar flow ( Re < 1000), it is beneficial to trade off the j factor and f factor. Furthermore, the total heat flow rate, total annual cost and number of entropy production units of plate-fin heat exchanges are optimized with the specified mass flow rate under given space by multi-objective optimization. Results obtained from the first two objectives and three objectives show that the fin design parameters are very similar except that the latter interrupted length is smaller slightly. A comparison between the results obtained by the previous approaches and the proposed algorithm shows that under the same effectiveness, the annual cost of the proposed algorithm is about 10% lower than the previous ones and it is faster to be converged. Therefore, this study demonstrated that the proposed algorithm is able to optimize the fin design parameter of serrated fin and the obtained results are beneficial to guide the design of plate-fin heat exchanges. [ABSTRACT FROM AUTHOR]

Throughout the recent decade, numerous investigations were done on Braysson cycles that were yielded in various output power and thermal efficiency analyses. Throughout the current research, the dimensionless power density and thermal efficiency are optimized using NSGA algorithm and finite time thermodynamic analysis. Compares optimum outcomes gained in this paper by executing LINMAP, TOPSIS and Fuzzy Bellman–Zadeh decision makers with relevant outcomes gained in previous works. Furthermore, it was shown that the FUZZY decision-maker provides better solutions in comparison with other implemented decision makers. Finally, analysis of deviation was carried out on the basis of the MAPE technique and it was obtained that the average deviation of results generated by the aforementioned decision maker were 0.015% and 0.03% for the dimensionless power density and thermal efficiency, respectively. The highest deviations of outcomes gained by used decision makers are 0.12% and 0.04% for the thermal efficiency and dimensionless power density, correspondingly. Outcomes of this study may be useful for any further design of Braysson engines. Moreover, optimized results of this research are the basis of further research for comparison and validation. [ABSTRACT FROM AUTHOR]

Due to the increasing energy demands and global warming, energy quality management (EQM) for districts has been getting importance over the last few decades. The evaluation of the optimum energy systems for specific districts is an essential part of EQM. This paper presents a deep analysis of the optimum energy systems for a district sited in China. A multi-objective optimization approach based on Genetic Algorithm (GA) is proposed for the analysis. The optimization process aims to search for the suitable 3E (minimum economic cost and environmental burden as well as maximum efficiency) energy systems. Here, life cycle CO 2 equivalent (LCCO 2 ), life cycle cost (LCC) and exergy efficiency (EE) are set as optimization objectives. Then, the optimum energy systems for the Chinese case are presented. The final work is to investigate the effects of different energy parameters. The results show the optimum energy systems might vary significantly depending on some parameters. [ABSTRACT FROM AUTHOR]

A sizing optimization of a hybrid system consisting of photovoltaic (PV) panels, a backup source (microturbine or diesel), and a battery system minimizes the cost of energy production (COE), and a complete design of this optimized system supplying a small community with power in the Palestinian Territories is presented in this paper. A scenario that depends on a standalone PV, and another one that depends on a backup source alone were analyzed in this study. The optimization was achieved via the usage of genetic algorithm. The objective function minimizes the COE while covering the load demand with a specified value for the loss of load probability (LLP). The global warming emissions costs have been taken into account in this optimization analysis. Solar radiation data is firstly analyzed, and the tilt angle of the PV panels is then optimized. It was discovered that powering a small rural community using this hybrid system is cost-effective and extremely beneficial when compared to extending the utility grid to supply these remote areas, or just using conventional sources for this purpose. This hybrid system decreases both operating costs and the emission of pollutants. The hybrid system that realized these optimization purposes is the one constructed from a combination of these sources. [ABSTRACT FROM AUTHOR]

• The thermal economy model of combined cycle is investigated. • RBF-NSGA-Ⅱ optimization algorithm is proposed. • The functional relationship among exergy loss, cost and efficiency is established. This investigation proposed the RBF-NSGA-Ⅱ optimization algorithm and explored the thermal economy of different bottom combined cycles under 16 design parameters. The maximum efficiency, minimum exergy loss and minimum unit investment of different gas–water system combined cycles under various design factors were compared with traditional optimization, NSGA-Ⅱ optimization and RBF-NSGA-Ⅱ optimization. The results indicated that the combined cycle efficiency optimized by NSGA-Ⅱ and RBF-NSGA-Ⅱ increased by 25.2%, the exergy loss was reduced by 32%, and the specific investment was reduced by 38% compared with the traditional optimization. This investigation showed that the convergence speed of the newly proposed RBF-NSGA-Ⅱ optimization algorithm is increased by three times compared to the NSGA-Ⅱ optimization algorithm. Furthermore, through single-parameter sensitivity analysis, the relationship between compressor outlet pressure, gas turbine inlet temperature, pinch point temperature difference, approach point temperature difference, waste heat boiler pressure and combined cycle efficiency, exergy loss, and specific investment costs were investigated. The paper provided a new way for combined cycle design and guided the operation of combined cycle power plants. [ABSTRACT FROM AUTHOR]

• A new optimal mileage-based PV array reconfiguration (OMAR) is constructed. • The OMAR can maximize the total benefit instead of only the generation benefit. • The OMAR decomposition with two sub-problems reduces the optimization difficulty. • The swarm reinforcement learning is used to obtain high-quality optimums of OMAR. • The proposed method can obtain higher total benefit than 6 comparative algorithms. This paper constructs a new optimal mileage-based PV array reconfiguration (OMAR) in a PV power plant under partial shading conditions. It aims to maximize the power output of a PV power plant, and minimize the additional capacity and mileage payments resulting from the power fluctuation in a performance-based frequency regulation market. To reduce the optimization difficulty of OMAR, it is decomposed into two optimization sub-problems, including an upper-layer discrete optimization of PV array reconfiguration and a lower-layer continuous optimization of real-time generation scheduling. The upper-layer discrete optimization is addressed by the proposed swarm reinforcement learning (SRL), which can implement an efficient exploration and exploitation with multiple cooperative agents instead of a single learning agent. The rest lower-layer optimization is handled by the fast interior point method. The proposed method's effectiveness is thoroughly evaluated on the 10 × 10 total-cross-tied PV arrays under various partial shading conditions. Simulation results demonstrate that the proposed SRL can obtain a larger total benefit than genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA), harris hawks optimizer (HHO), butterfly optimization algorithm (BOA), and Q-learning, in which the benefit increment can reach from 2.12% (against PSO) to 10.62% (against Q-learning). [ABSTRACT FROM AUTHOR]

• A robust optimization framework is proposed for the dew point evaporative cooler. • Two optimization methods are developed using genetic algorithm. • A 2-D thermodynamic model is coupled with the optimization algorithms. • The optimization results from the two methods are discussed and compared. • The multi-to-single-objective optimization simplifies the optimization complexity. Dew point evaporative cooling is an essential alternative to conventional vapor compression chillers in air conditioning systems to reduce electricity consumption and carbon emission. In this paper, we propose a robust optimization framework of the dew point evaporative cooler towards favorable dew point effectiveness, cooling capacity and Coefficient of Performance (COP). Two optimization algorithms, i.e., multi-to-single-objective and multi-objective optimizations, are developed using genetic algorithm. Concurrently, a 2-D thermodynamic model is established for a counter-flow dew point evaporative cooler and coupled with the optimization algorithms. The model agrees well with the experimental tests on a cooler prototype, and the maximum discrepancy is within ±4.7%. The proposed optimization study is then carried out to investigate the ultimate objective functions and their corresponding decision variables. Key findings that have emerged from this study reveal that the multi-to-single-objective optimization is able to obtain appropriate objective functions according to predefined preference with less complexity and faster response, compared to a multi-objective optimization. The optimal channel length and working ratio are found to be constant in different scenarios, i.e., at 0.50 m and 0.40, respectively, hence they can be eliminated to reduce the number of decision variables. [ABSTRACT FROM AUTHOR]