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: Qian, Wenyan, Cao, Siyuan, Zhang, Yuanshi, Hu, Qinran, Li, Hengyu, Li, Yang
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/164519
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1645192023-01-30T07:01:31Z Multiple objective optimization based on particle swarm algorithm for MMC-MTDC system Qian, Wenyan Cao, Siyuan Zhang, Yuanshi Hu, Qinran Li, Hengyu Li, Yang School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Adaptive Droop Control Sequential Power Flow 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. Published version This research is supported by the National Natural Science Foundation of China (51907026), Natural Science Foundation of Jiangsu Province, China (BK20190361), Key Research and Development Program of Jiangsu Province, China (BE2020081-2). 2023-01-30T07:01:31Z 2023-01-30T07:01:31Z 2022 Journal Article Qian, W., Cao, S., Zhang, Y., Hu, Q., Li, H. & Li, Y. (2022). Multiple objective optimization based on particle swarm algorithm for MMC-MTDC system. Frontiers in Energy Research, 10, 1030259-. https://dx.doi.org/10.3389/fenrg.2022.1030259 2296-598X https://hdl.handle.net/10356/164519 10.3389/fenrg.2022.1030259 2-s2.0-85139945418 10 1030259 en Frontiers in Energy Research © 2022 Qian, Cao, Zhang, Hu, Li and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Adaptive Droop Control
Sequential Power Flow
spellingShingle Engineering::Electrical and electronic engineering
Adaptive Droop Control
Sequential Power Flow
Qian, Wenyan
Cao, Siyuan
Zhang, Yuanshi
Hu, Qinran
Li, Hengyu
Li, Yang
Multiple objective optimization based on particle swarm algorithm for MMC-MTDC system
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.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Qian, Wenyan
Cao, Siyuan
Zhang, Yuanshi
Hu, Qinran
Li, Hengyu
Li, Yang
format Article
author Qian, Wenyan
Cao, Siyuan
Zhang, Yuanshi
Hu, Qinran
Li, Hengyu
Li, Yang
author_sort Qian, Wenyan
title 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_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_sort multiple objective optimization based on particle swarm algorithm for mmc-mtdc system
publishDate 2023
url https://hdl.handle.net/10356/164519
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