Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes

Direct current (DC) power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources (DERs) and the construction of advanced urban power grids. In this study, a typical topology analysis is performed fo...
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Autor*in:	Jianqiang Liu [verfasserIn] Xiaoguang Huang [verfasserIn] Zuyi Li [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Optimal power flow (OPF) medium-voltage direct current (MVDC) quadratically constrained quadratic programming (QCQP) model predictive control (MPC) particle swarm optimization (PSO)

Übergeordnetes Werk:	In: Journal of Modern Power Systems and Clean Energy - IEEE, 2016, 8(2020), 1, Seite 46-54
Übergeordnetes Werk:	volume:8 ; year:2020 ; number:1 ; pages:46-54

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.35833/MPCE.2018.000781

Katalog-ID:	DOAJ058543856

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520			\|a Direct current (DC) power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources (DERs) and the construction of advanced urban power grids. In this study, a typical topology analysis is performed for an advanced urban medium-voltage DC (MVDC) distribution network with DERs, including wind, photovoltaic, and electrical energy storage elements. Then, a multi-time scale optimal power flow (OPF) strategy is proposed for the MVDC network in different operation modes, including utility grid-connected and off-grid operation modes. In the utility grid-connected operation mode, the day-ahead optimization objective minimizes both the DER power curtailment and the network power loss. In addition, in the off-grid operation mode, the day-ahead optimization objective prioritizes the satisfaction of loads, and the DER power curtailment and the network power loss are minimized. A dynamic weighting method is employed to transform the multi-objective optimization problem into a quadratically constrained quadratic programming (QCQP) problem, which is solvable via standard methods. During intraday scheduling, the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery, and then seeks to minimize the DER power curtailment and the network power loss. Model predictive control (MPC) is used to correct deviations according to the results of ultra short-term load forecasting. Furthermore, an improved particle swarm optimization (PSO) algorithm is applied for global intraday optimization, which effectively increases the convergence rate to obtain solutions. MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient.
650		4	\|a Optimal power flow (OPF)
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spelling	10.35833/MPCE.2018.000781 doi (DE-627)DOAJ058543856 (DE-599)DOAJ30424e2980a540ceaa4e2e4b87db96c5 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Jianqiang Liu verfasserin aut Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Direct current (DC) power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources (DERs) and the construction of advanced urban power grids. In this study, a typical topology analysis is performed for an advanced urban medium-voltage DC (MVDC) distribution network with DERs, including wind, photovoltaic, and electrical energy storage elements. Then, a multi-time scale optimal power flow (OPF) strategy is proposed for the MVDC network in different operation modes, including utility grid-connected and off-grid operation modes. In the utility grid-connected operation mode, the day-ahead optimization objective minimizes both the DER power curtailment and the network power loss. In addition, in the off-grid operation mode, the day-ahead optimization objective prioritizes the satisfaction of loads, and the DER power curtailment and the network power loss are minimized. A dynamic weighting method is employed to transform the multi-objective optimization problem into a quadratically constrained quadratic programming (QCQP) problem, which is solvable via standard methods. During intraday scheduling, the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery, and then seeks to minimize the DER power curtailment and the network power loss. Model predictive control (MPC) is used to correct deviations according to the results of ultra short-term load forecasting. Furthermore, an improved particle swarm optimization (PSO) algorithm is applied for global intraday optimization, which effectively increases the convergence rate to obtain solutions. MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient. Optimal power flow (OPF) medium-voltage direct current (MVDC) quadratically constrained quadratic programming (QCQP) model predictive control (MPC) particle swarm optimization (PSO) Production of electric energy or power. Powerplants. Central stations Renewable energy sources Xiaoguang Huang verfasserin aut Zuyi Li verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 8(2020), 1, Seite 46-54 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:8 year:2020 number:1 pages:46-54 https://doi.org/10.35833/MPCE.2018.000781 kostenfrei https://doaj.org/article/30424e2980a540ceaa4e2e4b87db96c5 kostenfrei https://ieeexplore.ieee.org/document/8861470/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 1 46-54
allfields_unstemmed	10.35833/MPCE.2018.000781 doi (DE-627)DOAJ058543856 (DE-599)DOAJ30424e2980a540ceaa4e2e4b87db96c5 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Jianqiang Liu verfasserin aut Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Direct current (DC) power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources (DERs) and the construction of advanced urban power grids. In this study, a typical topology analysis is performed for an advanced urban medium-voltage DC (MVDC) distribution network with DERs, including wind, photovoltaic, and electrical energy storage elements. Then, a multi-time scale optimal power flow (OPF) strategy is proposed for the MVDC network in different operation modes, including utility grid-connected and off-grid operation modes. In the utility grid-connected operation mode, the day-ahead optimization objective minimizes both the DER power curtailment and the network power loss. In addition, in the off-grid operation mode, the day-ahead optimization objective prioritizes the satisfaction of loads, and the DER power curtailment and the network power loss are minimized. A dynamic weighting method is employed to transform the multi-objective optimization problem into a quadratically constrained quadratic programming (QCQP) problem, which is solvable via standard methods. During intraday scheduling, the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery, and then seeks to minimize the DER power curtailment and the network power loss. Model predictive control (MPC) is used to correct deviations according to the results of ultra short-term load forecasting. Furthermore, an improved particle swarm optimization (PSO) algorithm is applied for global intraday optimization, which effectively increases the convergence rate to obtain solutions. MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient. Optimal power flow (OPF) medium-voltage direct current (MVDC) quadratically constrained quadratic programming (QCQP) model predictive control (MPC) particle swarm optimization (PSO) Production of electric energy or power. Powerplants. Central stations Renewable energy sources Xiaoguang Huang verfasserin aut Zuyi Li verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 8(2020), 1, Seite 46-54 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:8 year:2020 number:1 pages:46-54 https://doi.org/10.35833/MPCE.2018.000781 kostenfrei https://doaj.org/article/30424e2980a540ceaa4e2e4b87db96c5 kostenfrei https://ieeexplore.ieee.org/document/8861470/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 1 46-54
allfieldsGer	10.35833/MPCE.2018.000781 doi (DE-627)DOAJ058543856 (DE-599)DOAJ30424e2980a540ceaa4e2e4b87db96c5 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Jianqiang Liu verfasserin aut Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Direct current (DC) power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources (DERs) and the construction of advanced urban power grids. In this study, a typical topology analysis is performed for an advanced urban medium-voltage DC (MVDC) distribution network with DERs, including wind, photovoltaic, and electrical energy storage elements. Then, a multi-time scale optimal power flow (OPF) strategy is proposed for the MVDC network in different operation modes, including utility grid-connected and off-grid operation modes. In the utility grid-connected operation mode, the day-ahead optimization objective minimizes both the DER power curtailment and the network power loss. In addition, in the off-grid operation mode, the day-ahead optimization objective prioritizes the satisfaction of loads, and the DER power curtailment and the network power loss are minimized. A dynamic weighting method is employed to transform the multi-objective optimization problem into a quadratically constrained quadratic programming (QCQP) problem, which is solvable via standard methods. During intraday scheduling, the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery, and then seeks to minimize the DER power curtailment and the network power loss. Model predictive control (MPC) is used to correct deviations according to the results of ultra short-term load forecasting. Furthermore, an improved particle swarm optimization (PSO) algorithm is applied for global intraday optimization, which effectively increases the convergence rate to obtain solutions. MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient. Optimal power flow (OPF) medium-voltage direct current (MVDC) quadratically constrained quadratic programming (QCQP) model predictive control (MPC) particle swarm optimization (PSO) Production of electric energy or power. Powerplants. Central stations Renewable energy sources Xiaoguang Huang verfasserin aut Zuyi Li verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 8(2020), 1, Seite 46-54 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:8 year:2020 number:1 pages:46-54 https://doi.org/10.35833/MPCE.2018.000781 kostenfrei https://doaj.org/article/30424e2980a540ceaa4e2e4b87db96c5 kostenfrei https://ieeexplore.ieee.org/document/8861470/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 1 46-54
allfieldsSound	10.35833/MPCE.2018.000781 doi (DE-627)DOAJ058543856 (DE-599)DOAJ30424e2980a540ceaa4e2e4b87db96c5 DE-627 ger DE-627 rakwb eng TK1001-1841 TJ807-830 Jianqiang Liu verfasserin aut Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Direct current (DC) power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources (DERs) and the construction of advanced urban power grids. In this study, a typical topology analysis is performed for an advanced urban medium-voltage DC (MVDC) distribution network with DERs, including wind, photovoltaic, and electrical energy storage elements. Then, a multi-time scale optimal power flow (OPF) strategy is proposed for the MVDC network in different operation modes, including utility grid-connected and off-grid operation modes. In the utility grid-connected operation mode, the day-ahead optimization objective minimizes both the DER power curtailment and the network power loss. In addition, in the off-grid operation mode, the day-ahead optimization objective prioritizes the satisfaction of loads, and the DER power curtailment and the network power loss are minimized. A dynamic weighting method is employed to transform the multi-objective optimization problem into a quadratically constrained quadratic programming (QCQP) problem, which is solvable via standard methods. During intraday scheduling, the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery, and then seeks to minimize the DER power curtailment and the network power loss. Model predictive control (MPC) is used to correct deviations according to the results of ultra short-term load forecasting. Furthermore, an improved particle swarm optimization (PSO) algorithm is applied for global intraday optimization, which effectively increases the convergence rate to obtain solutions. MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient. Optimal power flow (OPF) medium-voltage direct current (MVDC) quadratically constrained quadratic programming (QCQP) model predictive control (MPC) particle swarm optimization (PSO) Production of electric energy or power. Powerplants. Central stations Renewable energy sources Xiaoguang Huang verfasserin aut Zuyi Li verfasserin aut In Journal of Modern Power Systems and Clean Energy IEEE, 2016 8(2020), 1, Seite 46-54 (DE-627)75682821X (DE-600)2727912-1 21965420 nnns volume:8 year:2020 number:1 pages:46-54 https://doi.org/10.35833/MPCE.2018.000781 kostenfrei https://doaj.org/article/30424e2980a540ceaa4e2e4b87db96c5 kostenfrei https://ieeexplore.ieee.org/document/8861470/ kostenfrei https://doaj.org/toc/2196-5420 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 1 46-54
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callnumber-first	T - Technology
author	Jianqiang Liu
spellingShingle	Jianqiang Liu misc TK1001-1841 misc TJ807-830 misc Optimal power flow (OPF) misc medium-voltage direct current (MVDC) misc quadratically constrained quadratic programming (QCQP) misc model predictive control (MPC) misc particle swarm optimization (PSO) misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes
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topic_title	TK1001-1841 TJ807-830 Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes Optimal power flow (OPF) medium-voltage direct current (MVDC) quadratically constrained quadratic programming (QCQP) model predictive control (MPC) particle swarm optimization (PSO)
topic	misc TK1001-1841 misc TJ807-830 misc Optimal power flow (OPF) misc medium-voltage direct current (MVDC) misc quadratically constrained quadratic programming (QCQP) misc model predictive control (MPC) misc particle swarm optimization (PSO) misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources
topic_unstemmed	misc TK1001-1841 misc TJ807-830 misc Optimal power flow (OPF) misc medium-voltage direct current (MVDC) misc quadratically constrained quadratic programming (QCQP) misc model predictive control (MPC) misc particle swarm optimization (PSO) misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources
topic_browse	misc TK1001-1841 misc TJ807-830 misc Optimal power flow (OPF) misc medium-voltage direct current (MVDC) misc quadratically constrained quadratic programming (QCQP) misc model predictive control (MPC) misc particle swarm optimization (PSO) misc Production of electric energy or power. Powerplants. Central stations misc Renewable energy sources
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title	Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes
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title_sort	multi-time scale optimal power flow strategy for medium-voltage dc power grid considering different operation modes
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title_auth	Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes
abstract	Direct current (DC) power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources (DERs) and the construction of advanced urban power grids. In this study, a typical topology analysis is performed for an advanced urban medium-voltage DC (MVDC) distribution network with DERs, including wind, photovoltaic, and electrical energy storage elements. Then, a multi-time scale optimal power flow (OPF) strategy is proposed for the MVDC network in different operation modes, including utility grid-connected and off-grid operation modes. In the utility grid-connected operation mode, the day-ahead optimization objective minimizes both the DER power curtailment and the network power loss. In addition, in the off-grid operation mode, the day-ahead optimization objective prioritizes the satisfaction of loads, and the DER power curtailment and the network power loss are minimized. A dynamic weighting method is employed to transform the multi-objective optimization problem into a quadratically constrained quadratic programming (QCQP) problem, which is solvable via standard methods. During intraday scheduling, the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery, and then seeks to minimize the DER power curtailment and the network power loss. Model predictive control (MPC) is used to correct deviations according to the results of ultra short-term load forecasting. Furthermore, an improved particle swarm optimization (PSO) algorithm is applied for global intraday optimization, which effectively increases the convergence rate to obtain solutions. MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient.
abstractGer	Direct current (DC) power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources (DERs) and the construction of advanced urban power grids. In this study, a typical topology analysis is performed for an advanced urban medium-voltage DC (MVDC) distribution network with DERs, including wind, photovoltaic, and electrical energy storage elements. Then, a multi-time scale optimal power flow (OPF) strategy is proposed for the MVDC network in different operation modes, including utility grid-connected and off-grid operation modes. In the utility grid-connected operation mode, the day-ahead optimization objective minimizes both the DER power curtailment and the network power loss. In addition, in the off-grid operation mode, the day-ahead optimization objective prioritizes the satisfaction of loads, and the DER power curtailment and the network power loss are minimized. A dynamic weighting method is employed to transform the multi-objective optimization problem into a quadratically constrained quadratic programming (QCQP) problem, which is solvable via standard methods. During intraday scheduling, the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery, and then seeks to minimize the DER power curtailment and the network power loss. Model predictive control (MPC) is used to correct deviations according to the results of ultra short-term load forecasting. Furthermore, an improved particle swarm optimization (PSO) algorithm is applied for global intraday optimization, which effectively increases the convergence rate to obtain solutions. MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient.
abstract_unstemmed	Direct current (DC) power grids based on flexible high-voltage DC technology have become a common solution of facilitating the large-scale integration of distributed energy resources (DERs) and the construction of advanced urban power grids. In this study, a typical topology analysis is performed for an advanced urban medium-voltage DC (MVDC) distribution network with DERs, including wind, photovoltaic, and electrical energy storage elements. Then, a multi-time scale optimal power flow (OPF) strategy is proposed for the MVDC network in different operation modes, including utility grid-connected and off-grid operation modes. In the utility grid-connected operation mode, the day-ahead optimization objective minimizes both the DER power curtailment and the network power loss. In addition, in the off-grid operation mode, the day-ahead optimization objective prioritizes the satisfaction of loads, and the DER power curtailment and the network power loss are minimized. A dynamic weighting method is employed to transform the multi-objective optimization problem into a quadratically constrained quadratic programming (QCQP) problem, which is solvable via standard methods. During intraday scheduling, the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery, and then seeks to minimize the DER power curtailment and the network power loss. Model predictive control (MPC) is used to correct deviations according to the results of ultra short-term load forecasting. Furthermore, an improved particle swarm optimization (PSO) algorithm is applied for global intraday optimization, which effectively increases the convergence rate to obtain solutions. MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient.
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title_short	Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes
url	https://doi.org/10.35833/MPCE.2018.000781 https://doaj.org/article/30424e2980a540ceaa4e2e4b87db96c5 https://ieeexplore.ieee.org/document/8861470/ https://doaj.org/toc/2196-5420
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doi_str	10.35833/MPCE.2018.000781
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up_date	2024-07-03T18:38:13.947Z
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During intraday scheduling, the optimization objective gives priority to ensure minimum deviation between the actual and predicted values of the state of charge of the battery, and then seeks to minimize the DER power curtailment and the network power loss. Model predictive control (MPC) is used to correct deviations according to the results of ultra short-term load forecasting. Furthermore, an improved particle swarm optimization (PSO) algorithm is applied for global intraday optimization, which effectively increases the convergence rate to obtain solutions. MATLAB simulation results indicate that the proposed optimization strategy is effective and efficient.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Optimal power flow (OPF)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">medium-voltage direct current (MVDC)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">quadratically constrained quadratic programming (QCQP)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">model predictive control (MPC)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">particle swarm optimization (PSO)</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Production of electric energy or power. Powerplants. 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Multi-time Scale Optimal Power Flow Strategy for Medium-voltage DC Power Grid Considering Different Operation Modes

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