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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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 |
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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 |
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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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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Qian, Wenyan Cao, Siyuan Zhang, Yuanshi Hu, Qinran Li, Hengyu Li, Yang |
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Article |
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Qian, Wenyan Cao, Siyuan Zhang, Yuanshi Hu, Qinran Li, Hengyu Li, Yang |
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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 |
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2023 |
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https://hdl.handle.net/10356/164519 |
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1757048198609764352 |