Interconnection-level primary frequency control by MBPSS with wind generation and evaluation of economic impacts

Huang, Jinan; Rimorov, Dmitry; Moeini, Ali; Kamwa, Innocent; Darvishi, Atena; Fardanesh, B.; Babaei, Saman

doi:10.1016/j.ijepes.2020.105867

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…While, the optimization of gains is achieved in the second step. The constraints for each band are defined to ensure the gain and time constant limitations, as in Equations (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29), very low band.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…The linearization of a nonlinear system was performed to analyze system stability and mitigate oscillations effectively. Huang et al (2020) [23] applied the MB-PSS to an interconnection power system with the implementation of wind energy. The researchers summarized that improved frequency stability together with the local and inter-area modes damping using MB-PSS.…”

Section: Introductionmentioning

confidence: 99%

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Transient Stability Enhancement of Grid Integrated Wind Energy Using Particle Swarm Optimization Based Multi-Band PSS4C

et al. 2022

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There is a growing need for stability enhancement in modern electrical networks integrated with wind energy, particularly due to different oscillation modes disturbances. Power system stabilizers (PSSs) are used to mitigate oscillations and improve the stability of the power system. This paper presents a comprehensive analysis of single-band PSS1A and multi-band PSS4C (MB-PSS4C) connected to the ST1A excitation system. An efficient approach in the selection of the parameters of MB-PSS4C using Particle Swarm Optimization (PSO) is proposed. A comparative investigation linked to the common Pattern Search and Simplex Search, from previous work, has been conducted to gauge the efficacy of PSO. The generator's transient stability with considerations of relative power angle (power angle differences), speed deviation, and active power of synchronous generators (SGs) are analyzed. Different wind penetration levels ranging from 36MW to 108 MW are integrated into the network and are investigated. Results demonstrate that PSO-MB-PSS4C connected to ST1A stabilizes the system effectively with reduced settling time while mitigating the peak power angle differences of SGs. Reduced settling time of 1.35 s is observed for wind penetration of 48 MW, with 43.31 ͦ peak power angle; while for high wind penetration of 108 MW, the settling time is around 8 s with peak power angle of 43.9 ͦ .

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transient Stability Enhancement of Grid Integrated Wind Energy Using Particle Swarm Optimization Based Multi-Band PSS4C

et al. 2022

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“…After the wind farm is established, the observed power is in the form of active power and reactive power. The voltage profile is also observed at each bus so that there is no overvoltage or undervoltage after the wind farm is installed losses or losses are observed after the installation of the wind farm, whether the presence of losses can decrease or not, transient stability includes voltage and frequency stability [24]- [26] in the existing plant observed before and after the installation of the wind farm whether it is still in a stable condition or not. In this study, Timor system modelling, transient analysis, and load flow will be carried out with needs before and after the interconnection of a 2 x 11 MW wind farm.…”

Section: Introductionmentioning

confidence: 99%

Grid impact analysis on wind power plant interconnection in strengthening electricity systems

Senen,

Kurniawan,

Dini

et al. 2024

IJEECS

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The Timor system is one of the large systems in the East Nusa Tenggara region. Based on the general plan for electricity supply for 2021-2030, there is a plan to interconnect a 2x11 MW wind power plant. The addition of wind power plants will pose a considerable threat to the system due to the intermittency nature of renewable energy plants. Therefore, a comprehensive grid impact study is needed to convince network managers that adding wind farms will not cause disruptions to the system either locally or in general and is expected to strengthen the electricity system. The power flow simulation results, installing a 2x11 MW wind farm on the Timor system can improve voltage quality and reduce losses on both 70 and 150 kV systems. For transient stability, the frequency value on the Timor system still meets the grid code requirements. In addition, the simulation results of the intermittency impact of the wind power plant output show that the Timor system is still in a stable condition. The stability of the rotor angle of the existing power plant when the transient stability simulation is carried out shows that it is still in a balanced condition.

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“…Many existing models and techniques for security control in recent literature are based on restorative actions aimed to restore the system from the unstable to the normal state. Cases of implementation of primary and secondary frequency controls have been are presented in [21][22][23][24]. Models based on virtual power plant (VPP) application were proposed in [25,26]; synthetic inertia techniques were developed in [27,28]; and fast frequency response (FFR) control methods using backup generators were proposed in [29,30].…”

Section: Introductionmentioning

confidence: 99%

An Online Security Prediction and Control Framework for Modern Power Grids

2021

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The proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation.

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Interconnection-level primary frequency control by MBPSS with wind generation and evaluation of economic impacts

Cited by 6 publications

References 12 publications

Transient Stability Enhancement of Grid Integrated Wind Energy Using Particle Swarm Optimization Based Multi-Band PSS4C

Transient Stability Enhancement of Grid Integrated Wind Energy Using Particle Swarm Optimization Based Multi-Band PSS4C

Grid impact analysis on wind power plant interconnection in strengthening electricity systems

An Online Security Prediction and Control Framework for Modern Power Grids

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