An economic model predictive control-based flexible power point tracking strategy for photovoltaic power generation
In a high solar-power-penetration power grid, photovoltaic (PV) power generation requires to run in a flexible power point tracking (FPPT) mode. However, traditional hierarchical control-based FPPT strategy ignores the dynamic economic performance of PV power generation. To address this issue, an ad...
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| Veröffentlicht in: | Energy (Oxford) Jg. 283; S. 128993 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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15.11.2023
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| ISSN: | 0360-5442 |
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| Abstract | In a high solar-power-penetration power grid, photovoltaic (PV) power generation requires to run in a flexible power point tracking (FPPT) mode. However, traditional hierarchical control-based FPPT strategy ignores the dynamic economic performance of PV power generation. To address this issue, an advanced FPPT strategy based on economic model predictive control (EMPC) is proposed to achieve higher dynamic economic performance. This strategy integrates the PV voltage reference calculation, PV voltage control, and pulse width modulation into one optimal control framework, utilizing the economic indices of the PV power generation system (PVPGS) as the cost function to achieve its economic optimization and power tracking. Due to the strong nonlinearity in the PVPGS, the EMPC optimization problem is non-convex, leading to a local optimum. A mixed integer nonlinear programming algorithm is developed, which utilizes a finite number of converter switching states for obtaining the global optimum. Simulations demonstrate that the EMPC-based FPPT strategy enhances the dynamic economic performance compared to the hierarchical control-based FPPT strategy.
•An economic model predictive control strategy is proposed for PV power generation.•A converter-based model for the PV power generation system is constructed.•An efficient algorithm is proposed to solve online non-convex optimization problem.•The proposed scheme enhances dynamic economic performance for PV power generation. |
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| AbstractList | In a high solar-power-penetration power grid, photovoltaic (PV) power generation requires to run in a flexible power point tracking (FPPT) mode. However, traditional hierarchical control-based FPPT strategy ignores the dynamic economic performance of PV power generation. To address this issue, an advanced FPPT strategy based on economic model predictive control (EMPC) is proposed to achieve higher dynamic economic performance. This strategy integrates the PV voltage reference calculation, PV voltage control, and pulse width modulation into one optimal control framework, utilizing the economic indices of the PV power generation system (PVPGS) as the cost function to achieve its economic optimization and power tracking. Due to the strong nonlinearity in the PVPGS, the EMPC optimization problem is non-convex, leading to a local optimum. A mixed integer nonlinear programming algorithm is developed, which utilizes a finite number of converter switching states for obtaining the global optimum. Simulations demonstrate that the EMPC-based FPPT strategy enhances the dynamic economic performance compared to the hierarchical control-based FPPT strategy. In a high solar-power-penetration power grid, photovoltaic (PV) power generation requires to run in a flexible power point tracking (FPPT) mode. However, traditional hierarchical control-based FPPT strategy ignores the dynamic economic performance of PV power generation. To address this issue, an advanced FPPT strategy based on economic model predictive control (EMPC) is proposed to achieve higher dynamic economic performance. This strategy integrates the PV voltage reference calculation, PV voltage control, and pulse width modulation into one optimal control framework, utilizing the economic indices of the PV power generation system (PVPGS) as the cost function to achieve its economic optimization and power tracking. Due to the strong nonlinearity in the PVPGS, the EMPC optimization problem is non-convex, leading to a local optimum. A mixed integer nonlinear programming algorithm is developed, which utilizes a finite number of converter switching states for obtaining the global optimum. Simulations demonstrate that the EMPC-based FPPT strategy enhances the dynamic economic performance compared to the hierarchical control-based FPPT strategy. •An economic model predictive control strategy is proposed for PV power generation.•A converter-based model for the PV power generation system is constructed.•An efficient algorithm is proposed to solve online non-convex optimization problem.•The proposed scheme enhances dynamic economic performance for PV power generation. |
| ArticleNumber | 128993 |
| Author | Liu, Xiangjie Ma, Lele Lee, Kwang Y. Kong, Xiaobing Zhu, Zheng |
| Author_xml | – sequence: 1 givenname: Xiangjie orcidid: 0000-0002-9116-518X surname: Liu fullname: Liu, Xiangjie organization: The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China – sequence: 2 givenname: Zheng orcidid: 0009-0004-5104-7578 surname: Zhu fullname: Zhu, Zheng organization: The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China – sequence: 3 givenname: Xiaobing surname: Kong fullname: Kong, Xiaobing organization: The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China – sequence: 4 givenname: Lele surname: Ma fullname: Ma, Lele email: malele@ncepu.edu.cn organization: The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China – sequence: 5 givenname: Kwang Y. surname: Lee fullname: Lee, Kwang Y. organization: Department of Electrical and Computer Engineering, Baylor University, Waco, 76798, TX, USA |
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| Cites_doi | 10.1016/j.solener.2017.02.017 10.1016/j.ijepes.2021.107075 10.1016/j.apenergy.2011.06.024 10.1016/j.rser.2017.07.043 10.1109/TIE.2020.2998743 10.1016/j.disopt.2006.10.011 10.1016/j.solener.2017.12.016 10.1016/j.solener.2019.01.028 10.1016/j.renene.2019.12.131 10.1016/j.energy.2021.121646 10.1016/j.solener.2018.12.074 10.1109/TPEL.2018.2869172 10.1109/TPEL.2017.2724544 10.1109/TIE.2017.2779412 10.1109/TPEL.2017.2764321 10.1016/j.energy.2019.115881 10.1109/TPEL.2020.2970447 10.1109/TPEL.2021.3049275 10.1016/j.energy.2022.126278 10.1016/j.renene.2021.12.011 10.1016/j.energy.2016.07.024 10.1016/j.rser.2022.113046 10.1109/TSTE.2021.3132057 10.1016/j.ijepes.2020.106732 10.1016/j.energy.2020.118592 10.1109/TPEL.2018.2883320 10.1016/j.ijepes.2020.106645 10.1109/TII.2012.2192282 10.1109/TIE.2006.878328 10.1016/j.solener.2013.09.001 |
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| SubjectTerms | algorithms econometric models Economic model predictive control economic performance electric potential difference Flexible power point tracking Photovoltaic power generation power generation solar energy system optimization |
| Title | An economic model predictive control-based flexible power point tracking strategy for photovoltaic power generation |
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