An Adaptive Local Learning-Based Methodology for Voltage Regulation in Distribution Networks With Dispersed Generation

Villacci, D.; Bontempi, Gianluca; Vaccaro, Alfredo

doi:10.1109/tpwrs.2006.876691

Cited by 76 publications

(40 citation statements)

References 16 publications

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“…Industrial load demands were simulated at 80% of their maximum values plus a Gaussian variation with standard deviation 15% of the nominal value. In addition to the original PV generators on buses 13,17,19,23, and 24; four more PV generators with capacity 1.2MW have been installed on buses 11, 28, 40, and 44, to model higher solar penetration. Figure 5 shows the reactive power compensation cost attained over the period 18:30-19:30 at 30-sec control intervals.…”

Section: Numerical Testsmentioning

confidence: 99%

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Stochastic reactive power management in microgrids with renewables

Kekatos

Wang

Conejo

et al. 2015

2015 IEEE Power &Amp; Energy Society General Meeting

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Abstract-Distribution microgrids are being challenged by reverse power flows and voltage fluctuations due to renewable generation, demand response, and electric vehicles. Advances in photovoltaic (PV) inverters offer new opportunities for reactive power management provided PV owners have the right investment incentives. In this context, reactive power compensation is considered here as an ancillary service. Accounting for the increasing time-variability of distributed generation and demand, a stochastic reactive power compensation scheme is developed. Given uncertain active power injections, an online reactive control scheme is devised. This scheme is distribution-free and relies solely on power injection data. Reactive injections are updated using the Lagrange multipliers of a second-order cone program. Numerical tests on an industrial 47-bus microgrid and the residential IEEE 123-bus feeder corroborate the reactive power management efficiency of the novel stochastic scheme over its deterministic alternative, as well as its capability to track variations in solar generation and household demand.

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Section: Numerical Testsmentioning

confidence: 99%

“…Closed-Form Minimizer for (13) Suppose a subgradient g t has been found. Upon completing the square, the optimization in (13) can be written aŝ q g t := arg min…”

Section: Stochastic Approximation Solvermentioning

confidence: 99%

Stochastic reactive power management in microgrids with renewables

Kekatos

Wang

Conejo

et al. 2015

2015 IEEE Power &Amp; Energy Society General Meeting

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“…Concerning the voltage drop issue, the authors in [12] outlined different methods of distributed and decentralized voltage control mentioning their advantages and shortcomings. A local learning-based methodology used for voltage regulation which depends on machine learning techniques has been thoroughly investigated in [13]. The authors in [14] used sensitivity theory for voltage regulation by controlling the reactive power of the power system, whereas the authors in [15] n load.…”

Section: Introductionmentioning

confidence: 99%

Voltage Regulation and Power Loss Minimization in Radial Distribution Systems via Reactive Power Injection and Distributed Generation Unit Placement

et al. 2018

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Distributed Generation (DG) has become an essential part of the smart grids due to the widespread integration of renewable energy sources. Reactive power compensation is still one of most important research topics in smart grids. DG units can be used for reactive power compensation purposes, therefore we can improve the voltage profile and minimize power losses in order to improve the power quality. In this paper two methods will be used to accomplish the mentioned tasks; the first technique depends on the reactive power demand change of the proposed network loads, whereas the second technique uses an algorithm to control DG units according to the measured voltage values in the feeders to generate the needed reactive power. Both methods were applied to different scenarios of DG unit positions and different reactive power values of loads. The chosen DG unit is made up of a Type-4 wind farm which could be used as a general unit where it is able to control reactive power generation in a wider range separately from active power. The simulation results show that using these two methods, the voltage profile could be improved, power losses reduced and the power factor increased according to the placement of DG units.

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“…A mixed integer linear programming approach has been implemented with embedded generation in [4][5] suggests an approach of dynamic adjustment of OLTC using DG with reactive power support for voltage profile improvement. A couple of methodologies have been explained using dynamic programming and fuzzy logic controllers where the system is divided into two parts such as substation capacitor and feeder capacitors.…”

mentioning

confidence: 99%

“…In some systems decisions are made on comparative cost of switching and line losses [7,4]. Some other techniques of NLP are used such as dynamic programming , supervised learning [5] and other sensitivity based calculations [6]. In these studies, effects such as optimal number of switching operations in presence of DGs and constraints are less explored while maintaining Volt/VAr standards.…”

mentioning

confidence: 99%