Review of existing reactive power requirements for variable generation

Ellis, Abraham; Nelson, Richard C.; Engeln, E. Von; MacDowell, Jason; Casey, Leo; Seymour, E.; Peter, W.; Williams, J. R.

doi:10.1109/pesgm.2012.6345555

Cited by 29 publications

(19 citation statements)

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“…Note that due to the high cost of storage deployment, researchers have proposed using storage for co-optimization additional goals along with energy arbitrage for financial feasibility [23]. Inverter reactive power output depends on its control design [24], [25] and can be governed by terminal voltage and/or active power measurements [21], [26]. The authors in [27] use energy storage for maintaining voltages at wind facilities.…”

Section: A Literature Reviewmentioning

confidence: 99%

Arbitrage With Power Factor Correction Using Energy Storage

Hashmi

Deka

Bušíć

et al. 2020

IEEE Trans. Power Syst.

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The importance of reactive power compensation for power factor (PF) correction will significantly increase with the large-scale integration of distributed generation interfaced via inverters producing only active power. In this work, we focus on co-optimizing energy storage for performing energy arbitrage as well as local power factor correction. The joint optimization problem is non-convex, but can be solved efficiently using a McCormick relaxation along with penalty-based schemes. Using numerical simulations on real data and realistic storage profiles, we show that energy storage can correct PF locally without reducing arbitrage profit. It is observed that active and reactive power control is largely decoupled in nature for performing arbitrage and PF correction (PFC). Furthermore, we consider a real-time implementation of the problem with uncertain load, renewable and pricing profiles. We develop a model predictive control based storage control policy using auto-regressive forecast for the uncertainty. We observe that PFC is primarily governed by the size of the converter and therefore, look-ahead in time in the online setting does not affect PFC noticeably. However, arbitrage profit are more sensitive to uncertainty for batteries with faster ramp rates compared to slow ramping batteries. TABLE I * Nomenclature ηch, ηdis Charging and discharging efficiency x i Battery charge change at time i b i Battery charge level at time i; b i = b i−1 + x i P i h Active power consumed by inelastic load Q i h Reactive power consumed by inelastic load P i r Active power generated by renewable source Q i r Reactive power output of renewable source P i Active power of inelastic load and renewable generation; P i = P i h − P i r Q i Reactive power output of inelastic load and renewable generation; Q i = Q i h − Q i r P i B Active power output of battery + converter P max B Maximum active power output of battery + converter P min B Minimum active power output of battery + converter Q i B Reactive power output of battery + converter S i B Instantaneous apparent power output of storage interfaced by converter; S i B = P i B + jQ i B S max B Maximum apparent power output of storage converter P i T Total active power seen by the grid; P i T = P i + P i B Q i T Total reactive power seen by the grid; Q i T = Q i + Q i B p i elec Price of electricity at time i

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Section: A Literature Reviewmentioning

confidence: 99%

Arbitrage With Power Factor Correction Using Energy Storage

Hashmi

Deka

Bušíć

et al. 2020

IEEE Trans. Power Syst.

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“…The solar power generation profile are computed based on the weather history in North California and the physical parameters of ten 5kW solar panels. The power factor is fixed as 0.90 lagging, which satisfies the regulation of many U.S. utilities [28] and IEEE standard [29]. Table I summarizes the average detection delay in eight distribution grids with 14 configurations.…”

Section: B Outage Detection In Systems With Dersmentioning

confidence: 99%

Urban distribution grid line outage identification

Liao

Weng

Tan

et al. 2016

2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)

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The growing integration of distributed energy resources (DERs) in urban distribution grids raises various reliability issues due to DER's uncertain and complex behaviors. With a large-scale DER penetration, traditional outage detection methods, which rely on customers making phone calls and smart meters' "last gasp" signals, will have limited performance, because the renewable generators can supply powers after line outages and many urban grids are mesh so line outages do not affect power supply. To address these drawbacks, we propose a data-driven outage monitoring approach based on the stochastic time series analysis from micro phasor measurement unit (µPMU). Specifically, we prove via power flow analysis that the dependency of time-series voltage measurements exhibits significant statistical changes after line outages. This makes the theory on optimal change-point detection suitable to identify line outages via µPMUs with fast and accurate sampling. However, existing change point detection methods require post-outage voltage distribution unknown in distribution systems. Therefore, we design a maximum likelihood-based method to directly learn the distribution parameters from µPMU data. We prove that the estimated parameters-based detection still achieves the optimal performance, making it extremely useful for distribution grid outage identifications. Simulation results show highly accurate outage identification in eight distribution grids with 14 configurations with and without DERs using µPMU data.

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“…However, several studies have found that this method can overestimate the actual reac tive power production capacity and that more accurate methods are required [4]- [6]. Existing industry standards specifying the role of wind-farms in reactive power support of the grid, and the testing of those control capabilities are varying and under development [7].…”

Section: Introductionmentioning

confidence: 99%

Reactive power limitation due to wind-farm collector networks

Martin

Hiskens

2015

2015 IEEE Eindhoven PowerTech

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Type-3 and Type-4 wind turbines are capable of contributing to the reactive power required by wind-farms for supporting grid voltages. However, characterizing the maximum reactive power capability of a wind-farm by summing the individ ual generator ratings does not account for the effects of voltage variations over the radial collector network and can significantly overestimate the total reactive power production capacity. This paper considers the reactive power produced by a wind-farm in response to a common reactive power set-point Q set that is broadcast to all wind turbines. Analysis shows that a sustained increase in Qset will result in the wind-farm delivering maximal reactive power. Several examples demonstrate that generator voltage limits can significantly curtail the reactive power output requested by the control strategy. This improved characterization of wind-farm reactive power production capabilities, which takes into account collector network voltages, will enable better design and operation of wind-farm reactive power resources, reducing the need for additional shunt capacitors and static synchronous compensators.Index Terms-Wind energy integration, reactive power, radial networks.

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Review of existing reactive power requirements for variable generation

Cited by 29 publications

References 0 publications

Arbitrage With Power Factor Correction Using Energy Storage

Arbitrage With Power Factor Correction Using Energy Storage

Urban distribution grid line outage identification

Reactive power limitation due to wind-farm collector networks

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