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    KCI등재 SCIE SCOPUS

    Multi-objective Optimal Allocation of TCSC for Power Systems with Wind Power Considering Load Randomness

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    https://www.riss.kr/link?id=A108488937

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Optimum allocation of flexible AC transmission system (FACTS) can improve the power grids performances such as available transmission capacity (ATC) and voltage stability. Optimal allocation of FACTS with multiple optimization objectives for power systems comprise multiple random variables is still a challenging task to be solved. This paper derives a scenario generation method for systems containing multiple random variables first. Then, a thyristor-controlled series capacitor (TCSC) multi-objective optimal allocation model with ATC and voltage stability L index as an objective function is established. By adding the chaos initialization and the variable inertia weight setting, an improved multi-objective particle swarm optimization (MOPSO) algorithm is also proposed to easily solve the established model. Finally, based on the improved IEEE-30 bus system, the non-inferior solutions of multiple system scenarios are compared and analyzed. Simulation results show that the proposed scenario processing method, the TCSC multi-objective optimal allocation model and the improved MOPSO algorithm are effective in solving related problems.
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    Optimum allocation of flexible AC transmission system (FACTS) can improve the power grids performances such as available transmission capacity (ATC) and voltage stability. Optimal allocation of FACTS with multiple optimization objectives for power sys...

    Optimum allocation of flexible AC transmission system (FACTS) can improve the power grids performances such as available transmission capacity (ATC) and voltage stability. Optimal allocation of FACTS with multiple optimization objectives for power systems comprise multiple random variables is still a challenging task to be solved. This paper derives a scenario generation method for systems containing multiple random variables first. Then, a thyristor-controlled series capacitor (TCSC) multi-objective optimal allocation model with ATC and voltage stability L index as an objective function is established. By adding the chaos initialization and the variable inertia weight setting, an improved multi-objective particle swarm optimization (MOPSO) algorithm is also proposed to easily solve the established model. Finally, based on the improved IEEE-30 bus system, the non-inferior solutions of multiple system scenarios are compared and analyzed. Simulation results show that the proposed scenario processing method, the TCSC multi-objective optimal allocation model and the improved MOPSO algorithm are effective in solving related problems.

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    참고문헌 (Reference)

    1 Zhao S, "Wind power scenario reduction based on improved K-means clustering and SBR algorithm" 45 (45): 3947-3954, 2021

    2 Babu R, "Weak bus-constrained PMU placement for complete observability of a connected power network considering voltage stability indices" 5 (5): 1-14, 2020

    3 Salama HS, "Voltage stability indices: a comparison and a review" 98 : 2022

    4 Mohseni-Bonab SM, "Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties : a stochastic approach" 85 : 598-609, 2016

    5 Fan M, "Uncertainty evaluation algorithm in power system dynamic analysis with correlated renewable energy sources" 36 (36): 5602-5611, 2021

    6 Yang M, "Stochastic optimal reactive power dispatch in a power system considering wind power and load uncertainty" 48 (48): 134-141, 2020

    7 Ehsan A, "Scenario-based planning of active distribution systems under uncertainties of renewable generation and electricity demand" 5 (5): 56-62, 2019

    8 Gao H, "Scenario clustering based distributionally robust comprehensive optimization of active distribution network" 44 (44): 32-41, 2020

    9 Li Z, "Research on a composite voltage and current measurement device for HVDC networks" 69 (69): 8930-8941, 2021

    10 Khan MSU, "Reliability and economic feasibility analysis of parallel unity power factor rectifi er for wind turbine system" 14 (14): 1184-1192, 2020

    1 Zhao S, "Wind power scenario reduction based on improved K-means clustering and SBR algorithm" 45 (45): 3947-3954, 2021

    2 Babu R, "Weak bus-constrained PMU placement for complete observability of a connected power network considering voltage stability indices" 5 (5): 1-14, 2020

    3 Salama HS, "Voltage stability indices: a comparison and a review" 98 : 2022

    4 Mohseni-Bonab SM, "Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties : a stochastic approach" 85 : 598-609, 2016

    5 Fan M, "Uncertainty evaluation algorithm in power system dynamic analysis with correlated renewable energy sources" 36 (36): 5602-5611, 2021

    6 Yang M, "Stochastic optimal reactive power dispatch in a power system considering wind power and load uncertainty" 48 (48): 134-141, 2020

    7 Ehsan A, "Scenario-based planning of active distribution systems under uncertainties of renewable generation and electricity demand" 5 (5): 56-62, 2019

    8 Gao H, "Scenario clustering based distributionally robust comprehensive optimization of active distribution network" 44 (44): 32-41, 2020

    9 Li Z, "Research on a composite voltage and current measurement device for HVDC networks" 69 (69): 8930-8941, 2021

    10 Khan MSU, "Reliability and economic feasibility analysis of parallel unity power factor rectifi er for wind turbine system" 14 (14): 1184-1192, 2020

    11 Das S, "Qualitative assessment of power swing for enhancing security of distance relay in a TCSCcompensated line" 36 (36): 223-234, 2021

    12 Xin S, "Probabilistic available transfer capability assessment in power systems with wind power integration" 14 (14): 1912-1920, 2020

    13 Kenfack-Sadem C, "Potential of wind energy in Cameroon based on Weibull, normal, and lognormal distribution" 12 : 761-786, 2021

    14 Alcahuaman H, "Optimized reactive power capability of wind power plants with tap-changing transformers" 12 (12): 1935-1946, 2021

    15 Zhang T, "Optimization sheduling of regional integrated energy systems based on electric-thermalgas intergrated demand response" 49 (49): 52-61, 2021

    16 Biswas PP, "Optimal reactive power dispatch with uncertainties in load demand and renewable energy sources adopting scenario-based approach" 75 : 616-632, 2019

    17 Duman S, "Optimal power flow with stochastic wind power and FACTS devices : a modified hybrid PSOGSA with chaotic maps approach" 32 : 8463-8492, 2020

    18 Ebrahimi H, "Optimal planning in active distribution networks considering nonlinear loads using the MOPSO algorithm in the TOPSIS framework" 30 (30): 1-17, 2020

    19 Roy NB, "Optimal allocation of active and reactive power of dispatchable distributed generators in a droop controlled islanded microgrid considering renewable generation and load demand uncertainties" 27 (27): 1-20, 2021

    20 Zhang J, "Multi-objective economicenvironmental dispatch for power system with wind power and small runoff hydropower" 45 (45): 38-45, 2021

    21 Zhang J, "Multi-objective dispatching adopting chaos particle swarm optimization cooperated with interior point method" 45 (45): 613-621, 2021

    22 Zhuo Z, "Key technologies and developing challenges of power system with high proportion of renewable energy" 45 (45): 171-191, 2021

    23 Kou X, "Interval optimization for available transfer capability evaluation considering wind power uncertainty" 11 (11): 250-259, 2020

    24 Hang J, "Energy storage capacity configuration of isolated microgrid based on monte carlo simulation and spetrum analysis" 44 (44): 1622-1629, 2020

    25 Kamel M, "Development and application of a new voltage stability index for on-line monitoring and shedding" 33 (33): 1231-1241, 2018

    26 Karuppasamypandiyan M, "Day ahead dynamic available transfer capability evaluation incorporating probabilistic transmission capacity margins in presence of wind generators" 31 (31): e12693-, 2021

    27 Lai J, "Coordinated control of voltage unbalance compensation in islanded microgrid based on particle swarm optimization algorithm" 44 (44): 121-129, 2020

    28 Li J, "Coordinated and optimal scheduling of inter-regional interconnection system based on source and load status" 44 (44): 26-33, 2020

    29 Shokrian M, "Application of a multi objective multi-leader particle swarm optimization algorithm on NLP and MINLP problems" 60 : 57-75, 2014

    30 Rim C, "A niching chaos optimization algorithm for multimodal optimization" 22 : 621-633, 2018

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