Stochastic distribution system operation considering voltage regulation risks in the presence of PV generation

Agalgaonkar, Yashodhan P.; Pal, Bikash; Jabr, Rabih A.

doi:10.1109/pesgm.2016.7741153

Cited by 24 publications

(46 citation statements)

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“…Nodal power injections at time t should satisfy the instantaneous constraints (5b)-(5e). Further, the infinitely many variables are coupled across time via the objective function and the average constraints (5f) and (5g), hence challenging the solution of (5). A stochastic optimization approach for tackling (5) is pursued in the next section.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The apparent power capability for smart inverters is set to 1.3 times the nameplate capacity of the associated PV. Performance is evaluated in terms of the energy management cost and the instantaneous counterpart of the cost in (5). All algorithms were implemented using MATLAB and CVX on an Intel CPU @ 3.4 GHz (32 GB RAM) computer [41].…”

Section: Numerical Testsmentioning

confidence: 99%

“…Traditionally, distribution grid voltage regulation is performed via load-tap-changing transformers, capacitor banks, and voltage regulators [4]. This utility-owned equipment involves discrete control actions, and its lifespan is affected by frequent switching operations [5], [3]. Regulating voltage under increasing generation from distributed renewable sources would require even more frequent switching actions and perhaps further installations.…”

Section: Introductionmentioning

confidence: 99%

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Ergodic Energy Management Leveraging Resource Variability in Distribution Grids

Wang

Kekatos

Conejo

et al. 2016

IEEE Trans. Power Syst.

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Abstract-Contemporary electricity distribution systems are being challenged by the variability of renewable energy sources. Slow response times and long energy management periods cannot efficiently integrate intermittent renewable generation and demand. Yet stochasticity can be judiciously coupled with system flexibilities to enhance grid operation efficiency. Voltage magnitudes for instance can transiently exceed regulation limits, while smart inverters can be overloaded over short time intervals. To implement such a mode of operation, an ergodic energy management framework is developed here. Considering a distribution grid with distributed energy sources and a feedin tariff program, active power curtailment and reactive power compensation are formulated as a stochastic optimization problem. Tighter operational constraints are enforced in an average sense, while looser margins are enforced to be satisfied at all times. Stochastic dual subgradient solvers are developed based on exact and approximate grid models of varying complexity. Numerical tests on a real-world 56-bus distribution grid and the IEEE 123-bus test feeder relying on both grid models corroborate the advantages of the novel schemes over their deterministic alternatives.Index Terms-Energy management, reactive power compensation, active power curtailment, stochastic optimization, dual decomposition.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Numerical Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ergodic Energy Management Leveraging Resource Variability in Distribution Grids

Wang

Kekatos

Conejo

et al. 2016

IEEE Trans. Power Syst.

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“…Solar generation from residential photovoltaics (PVs) can fluctuate by up to 15% of their rating within one-minute intervals [2]. Utilityowned voltage control equipment, such as load-tap-changing transformers, capacitor banks, and step-voltage regulators, involves discrete control actions, and its lifespan is related to the number of switching operations [3]. Regulating voltage under increasing renewable generation may require more frequent Manuscript Color versions of one or more of the figures is this paper are available online at http://ieeexplore.ieee.org.…”

Section: Introductionmentioning

confidence: 99%

Designing Reactive Power Control Rules for Smart Inverters Using Support Vector Machines

Jalali

Kekatos

Gatsis

et al. 2020

IEEE Trans. Smart Grid

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Smart inverters have been advocated as a fastresponding mechanism for voltage regulation in distribution grids. Nevertheless, optimal inverter coordination can be computationally demanding, and preset local control rules are known to be subpar. Leveraging tools from machine learning, the design of customized inverter control rules is posed here as a multi-task learning problem. Each inverter control rule is modeled as a possibly nonlinear function of local and/or remote control inputs. Given the electric coupling, the function outputs interact to yield the feeder voltage profile. Using an approximate grid model, inverter rules are designed jointly to minimize a voltage deviation objective based on anticipated load and solar generation scenarios. Each control rule is described by a set of coefficients, one for each training scenario. To reduce the communication overhead between the grid operator and the inverters, we devise a voltage regulation objective that is shown to promote parsimonious descriptions for inverter control rules. Numerical tests using real-world data on a benchmark feeder demonstrate the advantages of the novel nonlinear rules and explore the trade-off between voltage regulation and sparsity in rule descriptions.

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“…In [13], the stochasticity present in the forecasts was dealt by setting up a robust reactive power optimization framework which was subsequently solved by an approach similar to Bender's decomposition, known as the column and constraint generation algorithm. A chance constrained approach to solve the stochastic VVC problem was considered in [14], whose solution algorithm was more or less similar to the gradient descent algorithm. However, the solutions required a posteriori validation through Monte Carlo trials because of the non-convex nature of the power flow equations.…”

Section: Introductionmentioning

confidence: 99%