Multiple objective optimization based on particle swarm algorithm for MMC-MTDC system
Multi-terminal high voltage DC (MTDC) network is an effective technology to integrate large-scale offshore wind energy sources into conventional AC grids and improve the stability and flexibility of the power system. In this paper, firstly, an analytical model of a general applicable MTDC system int...
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Frontiers Media S.A.
2022-09-01
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Series: | Frontiers in Energy Research |
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Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1030259/full |
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author | Wenyan Qian Siyuan Cao Yuanshi Zhang Qinran Hu Hengyu Li Yang Li |
author_facet | Wenyan Qian Siyuan Cao Yuanshi Zhang Qinran Hu Hengyu Li Yang Li |
author_sort | Wenyan Qian |
collection | DOAJ |
description | Multi-terminal high voltage DC (MTDC) network is an effective technology to integrate large-scale offshore wind energy sources into conventional AC grids and improve the stability and flexibility of the power system. In this paper, firstly, an analytical model of a general applicable MTDC system integrated with several isolated AC grids is established. Then, an improved AC-DC power flow algorithm is used to eliminate the additional DC slack bus or droop bus iteration (SBI/DBI) step of the conventional AC-DC sequential power flow. A multi-objective optimal power flow (MOPF) algorithm is proposed to minimize two optimization targets, i.e., overall active power loss and generation costs of the system. To increase the degree of freedom, adaptive droop control is used in the proposed optimization algorithm in which the voltage references and droop coefficients of the modular multilevel converters (MMCs) are control variables. A multiple objective particle swarm optimization (MOPSO) method is applied to solve the MOPF problem and achieve the Pareto front. A technique for order of preference by similarity to ideal solution (TOPSIS) is incorporated in the decision analysis section and helps the decision maker to identify the best compromise solution. |
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issn | 2296-598X |
language | English |
last_indexed | 2024-04-12T04:21:40Z |
publishDate | 2022-09-01 |
publisher | Frontiers Media S.A. |
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spelling | doaj.art-d90128858aed422896def8ca68361a0c2022-12-22T03:48:13ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2022-09-011010.3389/fenrg.2022.10302591030259Multiple objective optimization based on particle swarm algorithm for MMC-MTDC systemWenyan Qian0Siyuan Cao1Yuanshi Zhang2Qinran Hu3Hengyu Li4Yang Li5School of Electrical Engineering, Southeast University, Nanjing, ChinaNanyang Technological University, Singapore, SingaporeSchool of Electrical Engineering, Southeast University, Nanjing, ChinaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaSchool of Engineering, University of British Columbia, Kelowna, BC, CanadaSchool of Electrical Engineering, Southeast University, Nanjing, ChinaMulti-terminal high voltage DC (MTDC) network is an effective technology to integrate large-scale offshore wind energy sources into conventional AC grids and improve the stability and flexibility of the power system. In this paper, firstly, an analytical model of a general applicable MTDC system integrated with several isolated AC grids is established. Then, an improved AC-DC power flow algorithm is used to eliminate the additional DC slack bus or droop bus iteration (SBI/DBI) step of the conventional AC-DC sequential power flow. A multi-objective optimal power flow (MOPF) algorithm is proposed to minimize two optimization targets, i.e., overall active power loss and generation costs of the system. To increase the degree of freedom, adaptive droop control is used in the proposed optimization algorithm in which the voltage references and droop coefficients of the modular multilevel converters (MMCs) are control variables. A multiple objective particle swarm optimization (MOPSO) method is applied to solve the MOPF problem and achieve the Pareto front. A technique for order of preference by similarity to ideal solution (TOPSIS) is incorporated in the decision analysis section and helps the decision maker to identify the best compromise solution.https://www.frontiersin.org/articles/10.3389/fenrg.2022.1030259/fulladaptive droop controlmulti-objective optimal power flowMOPSOmodular multilevel converters (MMCs)MTDCsequential power flow |
spellingShingle | Wenyan Qian Siyuan Cao Yuanshi Zhang Qinran Hu Hengyu Li Yang Li Multiple objective optimization based on particle swarm algorithm for MMC-MTDC system Frontiers in Energy Research adaptive droop control multi-objective optimal power flow MOPSO modular multilevel converters (MMCs) MTDC sequential power flow |
title | Multiple objective optimization based on particle swarm algorithm for MMC-MTDC system |
title_full | Multiple objective optimization based on particle swarm algorithm for MMC-MTDC system |
title_fullStr | Multiple objective optimization based on particle swarm algorithm for MMC-MTDC system |
title_full_unstemmed | Multiple objective optimization based on particle swarm algorithm for MMC-MTDC system |
title_short | Multiple objective optimization based on particle swarm algorithm for MMC-MTDC system |
title_sort | multiple objective optimization based on particle swarm algorithm for mmc mtdc system |
topic | adaptive droop control multi-objective optimal power flow MOPSO modular multilevel converters (MMCs) MTDC sequential power flow |
url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.1030259/full |
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