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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Main Authors: Wenyan Qian, Siyuan Cao, Yuanshi Zhang, Qinran Hu, Hengyu Li, Yang Li
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Energy Research
Subjects:
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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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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