{"title":"通过实施两种不同的电池模型,采用随机方法确定双面光伏组件的最大功率点","authors":"Angela Najdoska, Goga Vladimir Cvetkovski","doi":"10.1108/compel-11-2023-0563","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>This paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>In this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. Based on the input electrical data for the analysed bifacial PV module as well as the mathematical model of the two PV cell presentations, the values for the current and the voltage at the maximum power point for a given solar irradiation and working temperature are determined by the algorithm for each solution in the population and generation.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>From the presented results and the performed analysis, it can be concluded that GA is quite appropriate for this purpose and gives adequate results for both models and for all working conditions. The two-diode model was found to be more suitable compared with the simplified model due to its complexity. Therefore, although the power difference for each of the scenarios for the two compared models does not differ significantly among the two models, it is in favour of the two-diode model. Which implicates that the for fast and simple calculation the simplified model can also do the job.</p><!--/ Abstract__block -->\n<h3>Practical implications</h3>\n<p>This approach can be very successfully applied in the design process of a PV plant to forecast the output characteristics of the PV system if there is enough information about the weather conditions for a given location. This procedure can be very helpful in the process of selection of right PV module and inverter for a given location.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>An optimisation technique using GA as an optimisation tool has been developed and successfully applied in the determination of the maximum power point for a bifacial PV module using to different models of solar cell. The results are compared with the analytically determined values as well as with the values given from the producer and they show good agreement.</p><!--/ Abstract__block -->","PeriodicalId":501376,"journal":{"name":"COMPEL","volume":"17 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Stochastic approach for maximum power point determination for bifacial PV modules by implementing two different cell models\",\"authors\":\"Angela Najdoska, Goga Vladimir Cvetkovski\",\"doi\":\"10.1108/compel-11-2023-0563\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3>Purpose</h3>\\n<p>This paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function.</p><!--/ Abstract__block -->\\n<h3>Design/methodology/approach</h3>\\n<p>In this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. 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引用次数: 0
摘要
目的 本文介绍了使用两种不同电池模型确定双面光伏(PV)系统最大功率点的方法。使用遗传算法 (GA) 作为优化工具,确定最佳功率点。本文的目的是找出在确定不同太阳辐照度下双面光伏系统的最大功率点时,两种分析模型中哪种结果更好。设计/方法/途径在这项研究工作中,通过使用两种不同的光伏电池模型,如简化光伏电池模型和双二极管光伏电池模型,来确定双面光伏组件的最大功率点。根据所分析的双面光伏模块的输入电气数据以及两种光伏电池模型的数学模型,在给定的太阳辐照度和工作温度下,通过算法确定群体和发电中每种解决方案的最大功率点的电流和电压值。由于其复杂性,双二极管模型比简化模型更适合。因此,尽管两种模型在每种情况下的功率差异不大,但双二极管模型更胜一筹。实际意义在光伏电站的设计过程中,如果有足够的给定地点的天气条件信息,这种方法可以非常成功地用于预测光伏系统的输出特性。原创性/价值使用 GA 作为优化工具开发了一种优化技术,并成功应用于确定双面光伏模块的最大功率点,该模块使用了不同型号的太阳能电池。研究结果与分析得出的数值以及生产商提供的数值进行了比较,结果表明两者具有良好的一致性。
Stochastic approach for maximum power point determination for bifacial PV modules by implementing two different cell models
Purpose
This paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function.
Design/methodology/approach
In this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. Based on the input electrical data for the analysed bifacial PV module as well as the mathematical model of the two PV cell presentations, the values for the current and the voltage at the maximum power point for a given solar irradiation and working temperature are determined by the algorithm for each solution in the population and generation.
Findings
From the presented results and the performed analysis, it can be concluded that GA is quite appropriate for this purpose and gives adequate results for both models and for all working conditions. The two-diode model was found to be more suitable compared with the simplified model due to its complexity. Therefore, although the power difference for each of the scenarios for the two compared models does not differ significantly among the two models, it is in favour of the two-diode model. Which implicates that the for fast and simple calculation the simplified model can also do the job.
Practical implications
This approach can be very successfully applied in the design process of a PV plant to forecast the output characteristics of the PV system if there is enough information about the weather conditions for a given location. This procedure can be very helpful in the process of selection of right PV module and inverter for a given location.
Originality/value
An optimisation technique using GA as an optimisation tool has been developed and successfully applied in the determination of the maximum power point for a bifacial PV module using to different models of solar cell. The results are compared with the analytically determined values as well as with the values given from the producer and they show good agreement.