• Energy Research

Identifying the parameters of a triple-diode electrical circuit structure in PV modules is a critical issue, and it has been regarded as an important research area. Accordingly, in this study, a differential evolution algorithm (DEA) is hybridized with an electromagnetism-like algorithm (EMA) in the mutation stage to enhance the reliability and efficiency of the DEA. A new formula is presented to adapt the control parameters (mutation factor and crossover rate) of the DEA. Seven different experimental data sets are used to improve the performance of the proposed differential evolution with an integrated mutation per iteration algorithm (DEIMA). The results of the proposed PV modeling method are evaluated with other state-of-the-art approaches. According to different statistical criteria, the DEIMA demonstrates superiority in terms of root mean square error and main bias error by at least 5.4% and 10%, respectively, as compared to other methods. Furthermore, the DEIMA has an average execution time of 27.69 s, which is less than that of the other methods.

Identifying the parameters of a triple-diode electrical circuit structure in PV modules is a critical issue, and it has been regarded as an important research area. Accordingly, in this study, a differential evolution algorithm (DEA) is hybridized with an electromagnetism-like algorithm (EMA) in the mutation stage to enhance the reliability and efficiency of the DEA. A new formula is presented to adapt the control parameters (mutation factor and crossover rate) of the DEA. Seven different experimental data sets are used to improve the performance of the proposed differential evolution with an integrated mutation per iteration algorithm (DEIMA). The results of the proposed PV modeling method are evaluated with other state-of-the-art approaches. According to different statistical criteria, the DEIMA demonstrates superiority in terms of root mean square error and main bias error by at least 5.4% and 10%, respectively, as compared to other methods. Furthermore, the DEIMA has an average execution time of 27.69 s, which is less than that of the other methods.

This paper proposes different evolutionary algorithms, such as differential evolution and electromagnetism-like algorithms, to extract the five parameters of a single-diode photovoltaic module's model. Hybrid evolutionary algorithms are proposed with integrated and adaptive mutation per iteration schemes. In addition, a new formula to adjust the mutation scaling factor and crossover rate for each generation is proposed. Analyses are performed based on experimental data points under different weather conditions to explain the robustness and reliability of the proposed methods. Results show that the proposed hybrid algorithms, namely, evolutionary algorithm with integrated mutation per iteration and evolutionary algorithm with adaptive mutation per iteration, exhibit better performance than electromagnetism-like algorithm and other methods in terms of accuracy, CPU execution time, and convergence. The proposed hybrid algorithms offer a root mean square error, mean bias error, coefficient of determination and CPU execution time around 0.062, 0.006 and 0.992, and less than 20 s respectively. Furthermore, the feasibility of the proposed methods is validated by comparing the obtained results with those of other methods under various statistical errors. As a conclusion, the proposed hybrid algorithms offer root mean square error and mean bias error less than other methods by 14% at least.

This paper proposes different evolutionary algorithms, such as differential evolution and electromagnetism-like algorithms, to extract the five parameters of a single-diode photovoltaic module's model. Hybrid evolutionary algorithms are proposed with integrated and adaptive mutation per iteration schemes. In addition, a new formula to adjust the mutation scaling factor and crossover rate for each generation is proposed. Analyses are performed based on experimental data points under different weather conditions to explain the robustness and reliability of the proposed methods. Results show that the proposed hybrid algorithms, namely, evolutionary algorithm with integrated mutation per iteration and evolutionary algorithm with adaptive mutation per iteration, exhibit better performance than electromagnetism-like algorithm and other methods in terms of accuracy, CPU execution time, and convergence. The proposed hybrid algorithms offer a root mean square error, mean bias error, coefficient of determination and CPU execution time around 0.062, 0.006 and 0.992, and less than 20 s respectively. Furthermore, the feasibility of the proposed methods is validated by comparing the obtained results with those of other methods under various statistical errors. As a conclusion, the proposed hybrid algorithms offer root mean square error and mean bias error less than other methods by 14% at least.

Abstract In this paper, an improved differential evolution with adaptive mutation per iteration algorithm (DEAM) is proposed for extracting PV module’s model parameters. DEAM utilizes the attraction–repulsion concept which is used in the electromagnetism to boost the mutation operation of the original differential evolution (DE). Furthermore, a new formula to adjust the mutation scaling factor and crossover rate for each generation is proposed. The proposed method has been validated by experimental data and other previous methods. The results of the proposed method show a high agreement between the experimental and simulated I – V characteristics. The average root mean square error, mean bias error, coefficient of determination and CPU-execution time of the proposed method are 1.744%, 0.158%, 99.21% and 18.5975 s respectively. According to the results, the proposed method offers better performance than other methods in terms of accuracy, CPU-execution time and convergence.

Abstract In this paper, an improved differential evolution with adaptive mutation per iteration algorithm (DEAM) is proposed for extracting PV module’s model parameters. DEAM utilizes the attraction–repulsion concept which is used in the electromagnetism to boost the mutation operation of the original differential evolution (DE). Furthermore, a new formula to adjust the mutation scaling factor and crossover rate for each generation is proposed. The proposed method has been validated by experimental data and other previous methods. The results of the proposed method show a high agreement between the experimental and simulated I – V characteristics. The average root mean square error, mean bias error, coefficient of determination and CPU-execution time of the proposed method are 1.744%, 0.158%, 99.21% and 18.5975 s respectively. According to the results, the proposed method offers better performance than other methods in terms of accuracy, CPU-execution time and convergence.

Abstract In this paper, a differential evolution based multiobjective optimization algorithm is proposed to optimally size a photovoltaic water pumping system (PVPS). Three weighted individual objectives are aggregated by a single function to optimize the configuration of PVPS. Loss of load probability, life cycle cost and the volume of excess water are considered as three individual objective functions. The proposed pumping system is supposed to provide a daily water volume of 30 m3 with a static head of 20 m. The complexity of the initializing of the weights for each individual objective function is overcome by testing a wide range sets of weights. The performance of the system is tested based on hourly meteorological data. The performance results of the proposed system show that the loss of load probability and the average hourly water flow rate over a year time are around 0.5% and 3.297 m3/h, respectively. The life cycle cost, water deficit, and cost of water unit of the system are 9911 USD, 55.015 m3, and 0.045 USD/m3, respectively.

Abstract In this paper, a differential evolution based multiobjective optimization algorithm is proposed to optimally size a photovoltaic water pumping system (PVPS). Three weighted individual objectives are aggregated by a single function to optimize the configuration of PVPS. Loss of load probability, life cycle cost and the volume of excess water are considered as three individual objective functions. The proposed pumping system is supposed to provide a daily water volume of 30 m3 with a static head of 20 m. The complexity of the initializing of the weights for each individual objective function is overcome by testing a wide range sets of weights. The performance of the system is tested based on hourly meteorological data. The performance results of the proposed system show that the loss of load probability and the average hourly water flow rate over a year time are around 0.5% and 3.297 m3/h, respectively. The life cycle cost, water deficit, and cost of water unit of the system are 9911 USD, 55.015 m3, and 0.045 USD/m3, respectively.

Abstract In this paper, a differential evolution based multi-objective optimization algorithm is proposed to optimally size a photovoltaic water pumping system (PVPS). Non-dominated sorting and crowding distance concepts are used to increase the elitism and diversity of the proposed algorithm. The proposed objective function is composed of technical and economic objectives. Loss of load probability is used as a technical objective, whereas life cycle cost is considered as an economic objective. The proposed PVPS is designed to provide a daily water demand of 30 m 3 with a 20 m static head and a drawdown level. The optimal configuration of the system is selected from an optimal Pareto set of configurations to achieve balance between reliability, cost, and excess water of the system. The performance of the system is tested using hourly metorological data for one year time. Results show that the loss of load probability of the proposed system is around 0.5%. The life cycle cost, water deficit, and cost of water unit of the system are 9910 USD, 55 m 3 , and 0.045 USD/m 3 , respectively.