Inertia Estimation in Power Systems using Energy Storage and System Identification Techniques

Tamrakar, Ujjwol; Guruwacharya, Nischal; Bhujel, Niranjan; Wilches-Bernal, Felipe; Hansen, Timothy M.; Tonkoski, Reinaldo

doi:10.1109/speedam48782.2020.9161919

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…The methods that are based on disturbances can be very accurate [5] provided that the on-set of the disturbance is correctly detected [6]- [9]. This can be challenging in low-inertia power systems, which show a "rich" dynamical behavior [10].…”

Section: B Literature Reviewmentioning

confidence: 99%

Inertia Estimation Through Covariance Matrix

Bizzarri

Giudice

Grillo

et al. 2024

IEEE Trans. Power Syst.

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This work presents a technique to estimate on-line the inertia of a power system based on ambient measurements. The proposed technique utilizes the covariance matrix of these measurements and solves an optimization problem that fits such measurements to the synchronous machine classical model. We show that the proposed technique is adequate to accurately estimate the actual inertia of synchronous machines and also the virtual inertia provided by the controllers of converter-interfaced generators that emulate the behavior of synchronous machines. We also show that the proposed approach is able to estimate the equivalent damping of the classical synchronous machine model. This feature is exploited to estimate the droop of gridfollowing converters, which has a similar effect of the swing equation equivalent damping. The technique is comprehensively tested on a modified version of the IEEE 39-bus system as well as on a dynamic 1479-bus model of the all-island Irish transmission system.

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Section: B Literature Reviewmentioning

confidence: 99%

Inertia Estimation Through Covariance Matrix

Bizzarri

Giudice

Grillo

et al. 2024

IEEE Trans. Power Syst.

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“…Other approaches address the COI's estimation [7]- [12], without discussing the regional inertia estimation. Likewise, some works tackle the regional inertia estimation to some extent classifying them by their distinct characteristics into the following categories: (i) disturbance methods [13]- [21]; (ii) probing signal methods [22], [23]; and (iii) ambient-signal methods [24]- [31]. As we are interested in regional inertia estimation, which entails larger systems, the occurrence of severe events is fairly greater than for a single machine, for instance, providing a reasonable amount of opportunities for estimation.…”

Section: A Literature Reviewmentioning

confidence: 99%

Assessment of the Center of Inertia and Regional Inertia with Load Contribution via a Fully Data-Driven Method

Lugnani¹,

Paternina²,

Dotta³

2022

Preprint

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This paper proposes a new comprehensive and fully data-driven methodology to estimate the center of inertia (COI) and the regional inertia, considering the displacement of the COI due to disturbances and load inertial contributions. The strategy uses the typicality-based data analysis (TDA) technique to detect the right pilot-bus that represents the COI. In the TDA, a compound of correlation and cosine similarities is implemented to approximate the actual distribution of the data and find the point (bus) closest to the mean which is elected as the pilot-bus. Then, the frequency response at the pilot-bus and the active power deviations are embedded into an autoregressive moving average exogenous input (ARMAX)-based approach to determine the regional inertia represented by an equivalent machine, whose inertia constant corresponds to the inertial contribution in the Region. The methodology is tested using the IEEE 68-bus benchmark test system and an adapted version with aggregated dynamical loads, corroborating the method effectiveness.

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“…During the preparation of the field demonstration, high-fidelity dynamic model of IFP's LB hydropower plant has been developed. The inertia constant used for this is 1.33 MW-s/MVA-estimated from the micro-phasor measurement unit (micro-PMU) data of December 2017 black start test, using the method described in [47]. Notice the lower inertia constant compared to what is typical for hydropower (2~4) [48].…”

Section: Flow-rate Dependent Inertia Constant In Synchronous Generato...mentioning

confidence: 99%

Enhancing Local Grid Resilience with Small Hydropower Hybrids: Proving the concept through demonstration, simulation, and analysis with Idaho Falls Power

Alam

Bhattarai

Hussain

et al. 2022

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Large hydropower, connected to the transmission system and typically possessing significant ability to balance grid frequency, has long been central to black start plans in regions where it is present. Small hydropower possesses most of the attributes required for black start but is often connected to distribution or sub-transmission systems and has less ability to balance grid frequency. Integrating energy storage such as batteries or ultracapacitors increases the combined asset's ability to balance frequency. This asset integration enables a bottom-up grid restoration paradigm in which critical electric loads on the local distribution system can be powered even when the regional transmission system is down. This report presents a field demonstration conducted with Idaho Falls Power that proved this concept. The contribution of energy storage in restoring the small hydro-dominated distribution grid and its operational sensitivity across different control settings are further analyzed using high-fidelity simulations. Readers will learn about technical details on the field demonstration setup, energy storage contribution to black start, detailed simulation steps of islanded distribution grid restoration, and usage of field demonstration measurements for transient model refinements. Collectively this report points to a great opportunity for small hydropower to enhance resilience of local electric grids. v vii Acronyms and AbbreviationsBPA Bonneville Power Administration CB City Bulb -Idaho Falls Power's 8.9 MVA City Bulb Plant (one horizontal Kaplan "bulb" unit) CT Current transformer ROR Run-of-river SoC State of charge USGS United States Geological Survey

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Inertia Estimation in Power Systems using Energy Storage and System Identification Techniques

Cited by 10 publications

References 8 publications

Inertia Estimation Through Covariance Matrix

Inertia Estimation Through Covariance Matrix

Assessment of the Center of Inertia and Regional Inertia with Load Contribution via a Fully Data-Driven Method

Enhancing Local Grid Resilience with Small Hydropower Hybrids: Proving the concept through demonstration, simulation, and analysis with Idaho Falls Power

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