2016
DOI: 10.1109/tnnls.2015.2428611
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Storage Free Smart Energy Management for Frequency Control in a Diesel-PV-Fuel Cell-Based Hybrid AC Microgrid

Abstract: This paper proposes a novel, smart energy management scheme for a microgrid, consisting of a diesel generator and power electronic converter interfaced renewable energy-based generators, such as photovoltaic (PV) and fuel cell, for frequency regulation without any storage. In the proposed strategy, output of the PV is controlled in coordination with other generators using neurofuzzy controller, either only for transient frequency regulation or for both transient and steady-state frequency regulation, depending… Show more

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Cited by 84 publications
(41 citation statements)
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References 29 publications
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“…Controls which are uniform with respect to initial conditions take the form of feedback laws, leading to the aforementioned second family of optimization-based charging strategies, which are designed to achieve and maintain the desired operating conditions by comparing them with the actual operating conditions. In the field of smart energy and EV charging, most feedback laws are based on artificial intelligence: the authors in [14] consider a fuzzy logic-based autonomous controller for EV charging, while in [24] an evolutionary learning framework is developed for dynamic energy management of a smart microgrid, and in [25] a neurofuzzy controller is used for frequency regulation in microgrids with fuel cells. Feedback solutions are not free of challenges: the main problem, as compared to open-loop strategies, is that, since the current operating conditions must be compared with the desired operating conditions, one has to define the desired operating conditions.…”
Section: A Related Workmentioning
confidence: 99%
“…Controls which are uniform with respect to initial conditions take the form of feedback laws, leading to the aforementioned second family of optimization-based charging strategies, which are designed to achieve and maintain the desired operating conditions by comparing them with the actual operating conditions. In the field of smart energy and EV charging, most feedback laws are based on artificial intelligence: the authors in [14] consider a fuzzy logic-based autonomous controller for EV charging, while in [24] an evolutionary learning framework is developed for dynamic energy management of a smart microgrid, and in [25] a neurofuzzy controller is used for frequency regulation in microgrids with fuel cells. Feedback solutions are not free of challenges: the main problem, as compared to open-loop strategies, is that, since the current operating conditions must be compared with the desired operating conditions, one has to define the desired operating conditions.…”
Section: A Related Workmentioning
confidence: 99%
“…where U is the fuel utilization, q in are, respectively, (8) and (9). The restriction of current in actual practice is described by (10) [10].…”
Section: Fuel Cell Modelmentioning
confidence: 99%
“…An AC MG is the standard choice for MG designers [5][6][7] due to the flexibility to transform AC voltage level into other levels, in addition to the majority of the loads being AC type. Nowadays, due to the increase of using DC loads, DC MGs have been created due to their advantages in terms of efficiency and cost reduction [8][9][10].…”
Section: Introductionmentioning
confidence: 99%