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19 pages, 4422 KiB

Open AccessArticle

A Decentralized Passive Islanding Detection Method Based on the Variations of Estimated Droop Characteristics

by Juan Roberto Lopez, Luis Ibarra, Pedro Ponce and Arturo Molina

Energies 2021, 14(22), 7759; https://doi.org/10.3390/en14227759 - 19 Nov 2021

Cited by 4 | Viewed by 1314

A microgrid including distributed generators can operate connected to the main electrical network or in an isolated manner, referred to as island operation. The transition between both states can occur voluntarily, but a disconnection can also happen unexpectedly. The associated transients can [...] Read more.

A microgrid including distributed generators can operate connected to the main electrical network or in an isolated manner, referred to as island operation. The transition between both states can occur voluntarily, but a disconnection can also happen unexpectedly. The associated transients can be harmful to the grid, and compensating actions must be triggered to avoid service interruption, preserve power quality, and minimize the possibility of faults; island detection methods are essential to this end. Such techniques typically depend on communication networks or on the introduction of minor electrical disturbances to identify and broadcast unexpected islanding events. However, local energy resources are distributed, variable, and are expected to be integrated in a plug-and-play manner; then, conventional island detection strategies can be ineffective as they rely on specific infrastructure. To overcome those problems, this work proposes a straightforward, distributed island detection technique only relying on local electrical measurements, available at the output of each generating unit. The proposed method is based on the estimated power-frequency ratio, associated with the stiffness of the grid. A “stiffness change” effectively reveals island operating conditions, discards heavy load variations, and enables independent (distributed) operation. The proposal was validated through digital simulations and an experimental test-bed. Results showed that the proposed technique can effectively detect island operation at each generating unit interacting in the microgrid. Moreover, it was about three times faster than other reported techniques. Full article

(This article belongs to the Special Issue Microgrids: Islanding & Operation)

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28 pages, 5409 KiB

Open AccessArticle

Black Start Restoration of Islanded Droop-Controlled Microgrids

by Ogbonnaya Bassey and Karen L. Butler-Purry

Energies 2020, 13(22), 5996; https://doi.org/10.3390/en13225996 - 17 Nov 2020

Cited by 9 | Viewed by 2502

Abstract

The electricity grid faces the possibility of outages due to extreme weather events, cyber-attack, and unexpected events. When these unwanted events occur, it is desired that electricity be restored as soon as possible to meet the power demands of critical loads. The microgrid [...] Read more.

The electricity grid faces the possibility of outages due to extreme weather events, cyber-attack, and unexpected events. When these unwanted events occur, it is desired that electricity be restored as soon as possible to meet the power demands of critical loads. The microgrid approach to power restoration holds a lot of promise, since microgrids can operate in island mode. This paper presents a novel sequential restoration methodology for microgrid black start. The microgrid architecture considered is assumed to be operating in multi-master mode. The master distributed generators (DGs) are coordinated to operate together through droop control. Several operational constraints are formulated and linearized to realize a mixed-integer linear programming (MILP) problem. The method is studied on an islanded microgrid based on a modified IEEE 13 node test feeder. Detailed transient simulation in PSCAD was used to verify the accuracy of the restoration methodology. The developed restoration method can maximize the energy restored while ensuring good voltage and frequency regulation, and ensure that power scheduling mismatch is shared in the desired proportion. Full article

(This article belongs to the Special Issue Microgrids: Islanding & Operation)

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12 pages, 463 KiB

Open AccessArticle

Power Loss-Aware Transactive Microgrid Coalitions under Uncertainty

by Mohammad Sadeghi, Shahram Mollahasani and Melike Erol-Kantarci

Energies 2020, 13(21), 5782; https://doi.org/10.3390/en13215782 - 04 Nov 2020

Cited by 6 | Viewed by 1513

Abstract

Peer-to-peer energy trading within microgrid (MG) communities emerges as a key enabler of the future transactive distribution system and the transactive electricity market. Energy trading within MGs refers to the idea that the surplus energy of one MG can be used to satisfy [...] Read more.

Peer-to-peer energy trading within microgrid (MG) communities emerges as a key enabler of the future transactive distribution system and the transactive electricity market. Energy trading within MGs refers to the idea that the surplus energy of one MG can be used to satisfy the demand of another MG or a group of MGs that form an MG community. These communities can be dynamically established through time, based on the variations of demand and supply of the interconnected MGs. In many modern MGs, Electric Vehicles (EVs) have been considered as a viable storage option due to their ease of use (plug-and-play) and their growing adoption rates by drivers. On the other hand, the dynamic nature of EVs escalates the uncertainty in the transactive distribution system. In this paper, we study the problem of energy trading among MGs and EVs with the aim of power loss minimization where there is uncertainty. We propose a novel Bayesian Coalition Game (BCG) based algorithm, which allows the MGs and EVs to reduce the overall power loss by allowing them to form coalitions intelligently. The proposed scheme is compared with a conventional coalitional game theory-based approach and a Q-learning based approach. Our results show significant improvement over other compared techniques. Full article

(This article belongs to the Special Issue Microgrids: Islanding & Operation)

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