Feeder Voltage Regulation with High-Penetration PV Using Advanced Inverters and a Distribution Management System: A Duke Energy Case Study

Palmintier, Bryan; Giraldez, Julieta; Gruchalla, Kenny; Gotseff, Peter; Nagarajan, Adarsh; Harris, Tom; Bugbee, Bruce; Baggu, Murali; Gantz, Jesse; Boardman, Ethan

doi:10.2172/1331479

Cited by 27 publications

(11 citation statements)

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“…We use the voltage violation rate of a feeder in reality (e.g. 0.1% [40]) to set the lower bound of parameters.…”

Section: Evolutionary Algorithm For the Jcc Problemmentioning

confidence: 99%

Distributionally Robust Joint Chance-Constrained Optimization for Networked Microgrids Considering Contingencies and Renewable Uncertainty

Ding,

Morstyn,

McCulloch

2021

Preprint

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In light of a reliable and resilient power system under extreme weather and natural disasters, networked microgrids integrating local renewable resources have been adopted extensively to supply demands when the main utility experiences blackouts. However, the stochastic nature of renewables and unpredictable contingencies are difficult to address with the deterministic energy management framework. The paper proposes a comprehensive distributionally robust joint chance-constrained (DR-JCC) framework that incorporates microgrid island, power flow, distributed batteries and voltage control constraints. All chance constraints are solved jointly and each one is assigned to an optimized violation rate. To highlight, the JCC problem with the optimized violation rates has been recognized to be NP-hard and challenging to be solved. This paper proposes a novel evolutionary algorithm which successfully tackles the problem and reduces the solution conservativeness (i.e. operation cost) by around 50% comparing with the baseline Bonferroni Approximation. Considering the imperfect solar power forecast, we construct three data-driven ambiguity sets to model uncertain forecast error distributions. The solution is thus robust for any distribution in sets with the shared moment and shape assumptions. The proposed method is validated by robustness tests based on those sets and firmly secures the solution robustness.

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“…We use the voltage violation rate of a feeder in reality (e.g. 0.1% [40]) to set the lower bound of parameters.…”

Section: Evolutionary Algorithm For the Jcc Problemmentioning

confidence: 99%

Distributionally Robust Joint Chance-Constrained Optimization for Networked Microgrids Considering Contingencies and Renewable Uncertainty

Ding,

Morstyn,

McCulloch

2021

Preprint

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“…Voltage control is the act of monitoring and managing the real and reactive voltage levels at generators, substations, and equipment (e.g., voltage transformers) along the power lines to provide real power to customers at regulated voltage levels [18]. Transmission and distribution operators have a number of tools at their disposal to use for voltage control [19], including dispatch of generation (for real and reactive power), adjustment of voltage transformers, and switching of reactive power compensators, some of which may occur automatically.…”

Section: 422voltage Control and Reactive Supply Servicesmentioning

confidence: 99%

Characterization of residential distributed energy resource potential to provide ancillary services

Holmberg¹,

Omar²

2018

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This report reviews a set of electric grid ancillary services and analyzes the technical potential for residential devices to provide those services. A set of common residential devices are evaluated against six indicators of capability: flexibility, availability, fast response, reserves, reactive power, and power level. Based on these indicators of capability, three scores are developed to express the potential of a device to provide frequency response, reserves and reactive power services. These scores are additionally used to estimate how many of each device might be required, in aggregation, to provide a meaningful level of each of these three ancillary services to the grid. This report also provides a review of some existing programs and pilot efforts where residential devices are being used to provide ancillary services. Lastly, the report provides insights on a continued path to increased integration of residential devices for ancillary services provision.

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“…A trivial way of using prefabricated settings or configuration settings based upon location‐specific grid characteristics gives an inferior performance. Many methods proposed in the literature [12]–[13] have implemented sequential optimisation methods which gave suboptimal results. A general optimisation algorithm has to be designed to find the optimal settings for smart inverter functions given any operating conditions, which could enable better performance of distribution system assets.…”

Section: Introductionmentioning

confidence: 99%

An intelligent methodology to improve distribution system operational parameters utilising smart inverter functionalities of PV sources

Verma

Eluvathiangal

2019

J. eng.

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Feeder Voltage Regulation with High-Penetration PV Using Advanced Inverters and a Distribution Management System: A Duke Energy Case Study

Cited by 27 publications

References 0 publications

Distributionally Robust Joint Chance-Constrained Optimization for Networked Microgrids Considering Contingencies and Renewable Uncertainty

Distributionally Robust Joint Chance-Constrained Optimization for Networked Microgrids Considering Contingencies and Renewable Uncertainty

Characterization of residential distributed energy resource potential to provide ancillary services

An intelligent methodology to improve distribution system operational parameters utilising smart inverter functionalities of PV sources

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