Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet

High renewable energy penetration worsens systems instability. Balancing consumption energy and generation output energy reduces this instability. This paper introduces coordination control to coordinate the flow of electricity between MG buses and to stabilize the system under variable load, genera...

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Vydané v:	International transactions on electrical energy systems Ročník 2023; s. 1 - 33
Hlavní autori:	Jasim, Ali M., Jasim, Basil H., Alhasnawi, Bilal Naji, Flah, Aymen, Kraiem, Habib
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Hoboken Hindawi 2023 John Wiley & Sons, Inc Wiley
Predmet:	Adaptive systems Algorithms Alternative energy Artificial intelligence Control stability Cost analysis Data transmission Demand side management Design Direct current Distributed generation Electric converters Electric power demand Electrical loads Electricity Energy consumption Energy Internet Energy resources Energy storage Flow stability Fuzzy logic Genetic algorithms Heuristic methods Incremental conductance Industrial plant emissions Internet of Things Load shifting Maximum power Neural networks Optimization techniques Particle swarm optimization Peak demand Peak load Photovoltaics Real time Renewable energy Renewable resources Search algorithms Solar oscillations Solar power Systems stability Turbines Voltage converters (DC to DC) Wind power Wind turbines
ISSN:	2050-7038, 2050-7038
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Abstract	High renewable energy penetration worsens systems instability. Balancing consumption energy and generation output energy reduces this instability. This paper introduces coordination control to coordinate the flow of electricity between MG buses and to stabilize the system under variable load, generation conditions. The adopted MG regulates the bidirectional DC/AC main converter using digital proportional resonant controllers in a synchronous reference frame. A maximum power point tracker-based boost DC/DC converter enables the wind turbine and solar photovoltaic to harvest maximum power. Traditional methods such as perturb and observe and incremental conductance maximum power trackers cannot solve nonlinearity and inaccurate responses. This work provides a hybrid maximum power tracker strategy to modify the responses of standard maximum power point techniques based on particle swarm optimization-trained adaptive neuro-fuzzy inference system (ANFIS-PSO) to achieve quick and maximum solar power with minimal oscillation tracking. Concerning the management system, this paper adopts a recent meta-heuristic algorithms-based DSM program to modify consumers’ electricity use by shifting the load appliances to off-peak demand periods. The adopted algorithms for DSM are sparrow search algorithm (SSA), binary orientation search algorithm (BSOA), and cockroach algorithm (CA). Finally, based on energy Internet technology, ThingSpeak cloud-based MATLAB is adopted to gather and display real-time data streams and generate graphical analyses. The simulation results reveal that the recommended coordinating control produces quick grid frequency responsiveness and zero steady-state errors. The optimal demand management program minimizes peak energy consumption from 5.2 kWh to 4.6 kWh. All DSM methods cost 439.1 $ per month, compared to 484.4 $ for the nonscheduling load profile.
AbstractList	High renewable energy penetration worsens systems instability. Balancing consumption energy and generation output energy reduces this instability. This paper introduces coordination control to coordinate the flow of electricity between MG buses and to stabilize the system under variable load, generation conditions. The adopted MG regulates the bidirectional DC/AC main converter using digital proportional resonant controllers in a synchronous reference frame. A maximum power point tracker-based boost DC/DC converter enables the wind turbine and solar photovoltaic to harvest maximum power. Traditional methods such as perturb and observe and incremental conductance maximum power trackers cannot solve nonlinearity and inaccurate responses. This work provides a hybrid maximum power tracker strategy to modify the responses of standard maximum power point techniques based on particle swarm optimization-trained adaptive neuro-fuzzy inference system (ANFIS-PSO) to achieve quick and maximum solar power with minimal oscillation tracking. Concerning the management system, this paper adopts a recent meta-heuristic algorithms-based DSM program to modify consumers’ electricity use by shifting the load appliances to off-peak demand periods. The adopted algorithms for DSM are sparrow search algorithm (SSA), binary orientation search algorithm (BSOA), and cockroach algorithm (CA). Finally, based on energy Internet technology, ThingSpeak cloud-based MATLAB is adopted to gather and display real-time data streams and generate graphical analyses. The simulation results reveal that the recommended coordinating control produces quick grid frequency responsiveness and zero steady-state errors. The optimal demand management program minimizes peak energy consumption from 5.2 kWh to 4.6 kWh. All DSM methods cost 439.1 $ per month, compared to 484.4 $ for the nonscheduling load profile.
Author	Kraiem, Habib Jasim, Ali M. Flah, Aymen Alhasnawi, Bilal Naji Jasim, Basil H.
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Copyright	Copyright © 2023 Ali M. Jasim et al. Copyright © 2023 Ali M. Jasim et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0
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SubjectTerms	Adaptive systems Algorithms Alternative energy Artificial intelligence Control stability Cost analysis Data transmission Demand side management Design Direct current Distributed generation Electric converters Electric power demand Electrical loads Electricity Energy consumption Energy Internet Energy resources Energy storage Flow stability Fuzzy logic Genetic algorithms Heuristic methods Incremental conductance Industrial plant emissions Internet of Things Load shifting Maximum power Neural networks Optimization techniques Particle swarm optimization Peak demand Peak load Photovoltaics Real time Renewable energy Renewable resources Search algorithms Solar oscillations Solar power Systems stability Turbines Voltage converters (DC to DC) Wind power Wind turbines
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Title	Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet
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