Equivalent to represent inertial and primary frequency control effects of an external system

Weckesser, Tilman; Cutsem, Thierry Van

doi:10.1049/iet-gtd.2016.1641

Cited by 15 publications

(17 citation statements)

References 19 publications

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“…There is a clear trend that whenever the estimation error for η is above 15%, then where · denotes the vector infinity norm, i.e., the estimated inertia coefficientĤ tot provides a better characterization of the actual system behavior than the nominal one H tot , at least with the model (6), (21 dynamics may have on the behavior of the system frequency. Similar observations on the load voltage dynamics affecting the effective inertia of the system were made in [3], [28]. Given the complexity of the employed ENTSO-E model, we leave a detailed investigation of these aspects as well as a possible extension of the model (6), (21) to incorporate some of them for future work.…”

Section: Bg911190mentioning

confidence: 53%

“…Thus, the overall aggregated power system dynamics are given by (6) and (21) and the corresponding system parameters for the aggregated ENTSO-E system are given in Table I. Remark 4.1: As indicated in [28], the model (21) has proven to be sufficiently accurate for representing PFC effects provided predominantly by steam power plants. If a significant amount of other units, such as hydro or gas power plants, also contribute to PFC, then the model (21) should be modified to account for these dynamics.…”

Section: A Aggregated Power System Model Including Turbine-governor mentioning

confidence: 99%

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Online Estimation of Power System Inertia Using Dynamic Regressor Extension and Mixing

Schiffer

Aristidou²,

Ortega

2019

IEEE Trans. Power Syst.

105

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The increasing penetration of power-electronicinterfaced devices is expected to have a significant effect on the overall system inertia and a crucial impact on the system dynamics. In the future, the reduction of inertia will have drastic consequences on protection and real-time control and will play a crucial role in the system operation. Therefore, in a highly deregulated and uncertain environment, it is necessary for Transmission System Operators to be able to monitor the system inertia in real time. We address this problem by developing and validating an online inertia estimation algorithm. The estimator is derived using the recently proposed dynamic regressor and mixing procedure. The performance of the estimator is demonstrated via several test cases using the 1013-machine ENTSO-E dynamic model.Index Terms-Power system inertia, power system dynamics, power system stability, low-inertia systems, parameter estimation. J. Schiffer is with Brandenburgische

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Section: Bg911190mentioning

confidence: 53%

Section: A Aggregated Power System Model Including Turbine-governor mentioning

confidence: 99%

Online Estimation of Power System Inertia Using Dynamic Regressor Extension and Mixing

Schiffer

Aristidou²,

Ortega

2019

IEEE Trans. Power Syst.

105

View full text Add to dashboard Cite

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“…To this purpose, Dynamic Power Injectors (DPIs) as described in [16] are used to replace the external system and emulate its behavior in long-term dynamic simulations. DPIs are connected to all boundary buses of the buffer zone.…”

Section: B Equivalencing the External Systemmentioning

confidence: 99%

“…A simple approach to determine one set of parameters suitable for all connected DPIs was presented in [16]. …”

Section: B Equivalencing the External Systemmentioning

confidence: 99%

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A model reduction approach for simulation of long-term voltage and frequency dynamics

Weckesser

Franz²,

Grebe³

et al. 2017

2017 IEEE Manchester PowerTech

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Abstract-This paper presents a novel approach, which identifies a buffer zone around a given study system and replaces the external system with an equivalent. The aim is to obtain a reduced system, which is suitable for assessment of long-term voltage and frequency dynamics in the study system. First, the proposed methodology ensures that all components, which are impacting the voltage profile of the study system, are included in the buffer zone. For that purpose, the propagation of reactive power changes from the boundary of the study system into the external system are used in a modified depth-first search algorithm to identify the buffer zone. Second, the external system is replaced with equivalents accounting for inertia and primary frequency control effects. The proposed method was successfully tested on a modified version of the ENTSO-E Dynamic Study Model.

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Modelling of variable‐speed refrigeration for fast‐frequency control in low‐inertia systems

et al. 2020

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In modern power systems, shiftable loads contribute to the flexibility needed to increase robustness and ensure security. Thermal loads are among the most promising candidates for providing such service due to the large thermal storage time constants. This study demonstrates the use of variable-speed refrigeration (VSR) technology, based on brushless DC motors, for the fast-frequency response. First, the authors derive a detailed dynamic model of a single-phase VSR unit suitable for time-domain and small-signal stability analysis in low-inertia systems. For analysing dynamic interactions with the grid, they consider the aggregated response of multiple devices. However, the high computational cost involved in analysing large-scale systems leads to the need for reduced-order models. Thus, a set of reduced-order models is derived through transfer function fitting of data obtained from time-domain simulations of the detailed model. The modelling requirements and the accuracy versus computational complexity trade-off are discussed. Finally, the time-domain performance and frequency-domain analyses reveal substantial equivalence between the full-and suitable reduced-order models, allowing the application of simplified models in large-scale system studies.

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Equivalent to represent inertial and primary frequency control effects of an external system

Cited by 15 publications

References 19 publications

Online Estimation of Power System Inertia Using Dynamic Regressor Extension and Mixing

Online Estimation of Power System Inertia Using Dynamic Regressor Extension and Mixing

A model reduction approach for simulation of long-term voltage and frequency dynamics

Modelling of variable‐speed refrigeration for fast‐frequency control in low‐inertia systems

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