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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2023/6615150 |
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| author | Ali M. Jasim Basil H. Jasim Bilal Naji Alhasnawi Aymen Flah Habib Kraiem |
| author_facet | Ali M. Jasim Basil H. Jasim Bilal Naji Alhasnawi Aymen Flah Habib Kraiem |
| author_sort | Ali M. Jasim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a35cd51559ac475bac3700970892dd9d |
| institution | OA Journals |
| issn | 2050-7038 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-a35cd51559ac475bac3700970892dd9d2025-08-20T02:20:32ZengWileyInternational Transactions on Electrical Energy Systems2050-70382023-01-01202310.1155/2023/6615150Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy InternetAli M. Jasim0Basil H. Jasim1Bilal Naji Alhasnawi2Aymen Flah3Habib Kraiem4Electrical Engineering DepartmentElectrical Engineering DepartmentDepartment of Electricity TechniquesProcessesDepartment of Electrical EngineeringHigh 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.http://dx.doi.org/10.1155/2023/6615150 |
| spellingShingle | Ali M. Jasim Basil H. Jasim Bilal Naji Alhasnawi Aymen Flah Habib Kraiem Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet International Transactions on Electrical Energy Systems |
| title | Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet |
| title_full | Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet |
| title_fullStr | Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet |
| title_full_unstemmed | Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet |
| title_short | Coordinated Control and Load Shifting-Based Demand Management of a Smart Microgrid Adopting Energy Internet |
| title_sort | coordinated control and load shifting based demand management of a smart microgrid adopting energy internet |
| url | http://dx.doi.org/10.1155/2023/6615150 |
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