Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization
In this paper, Chaotic Aquila Optimization has been proposed for the solution of the demand response program of a grid-connected residential microgrid (GCRMG) system. Here, the main objective is to optimize the scheduling pattern of connected appliances of the building such that overall user cost ar...
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| Published in | Sustainability Vol. 15; no. 2; p. 1484 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
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01.01.2023
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| Online Access | Get full text |
| ISSN | 2071-1050 2071-1050 |
| DOI | 10.3390/su15021484 |
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| Abstract | In this paper, Chaotic Aquila Optimization has been proposed for the solution of the demand response program of a grid-connected residential microgrid (GCRMG) system. Here, the main objective is to optimize the scheduling pattern of connected appliances of the building such that overall user cost are minimized under the dynamic price rate of electricity. The GCRMG model considered for analysis is equipped with a fuel cell, combined heat and power (CHP), and a battery storage system. It has to control and schedule the thermostatically controlled deferrable and interruptible appliances of the building optimally. A multipowered residential microgrid system with distinct load demand for appliances and dynamic electricity price makes the objective function complex and highly constrained in nature, which is difficult to solve efficiently. For the solution of such a complex highly constrained optimization problem, both Chaotic Aquila Optimization (CAO) and Aquila optimization (AO) algorithms are implemented, and their performance is analyzed separately. Obtained simulation results in terms of optimal load scheduling and corresponding user cost reveal the better searching and constrained handling capability of AO. In addition, experimental results show that a sinusoidal map significantly improves the performances of AO. Comparison of results with other reported methods are also made, which supports the claim of superiority of the proposed approach. |
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| AbstractList | In this paper, Chaotic Aquila Optimization has been proposed for the solution of the demand response program of a grid-connected residential microgrid (GCRMG) system. Here, the main objective is to optimize the scheduling pattern of connected appliances of the building such that overall user cost are minimized under the dynamic price rate of electricity. The GCRMG model considered for analysis is equipped with a fuel cell, combined heat and power (CHP), and a battery storage system. It has to control and schedule the thermostatically controlled deferrable and interruptible appliances of the building optimally. A multipowered residential microgrid system with distinct load demand for appliances and dynamic electricity price makes the objective function complex and highly constrained in nature, which is difficult to solve efficiently. For the solution of such a complex highly constrained optimization problem, both Chaotic Aquila Optimization (CAO) and Aquila optimization (AO) algorithms are implemented, and their performance is analyzed separately. Obtained simulation results in terms of optimal load scheduling and corresponding user cost reveal the better searching and constrained handling capability of AO. In addition, experimental results show that a sinusoidal map significantly improves the performances of AO. Comparison of results with other reported methods are also made, which supports the claim of superiority of the proposed approach. |
| Author | Kujur, Sushmita Salkuti, Surender Reddy Dubey, Hari Mohan |
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| CitedBy_id | crossref_primary_10_1049_gtd2_12968 crossref_primary_10_1016_j_enbuild_2024_115067 crossref_primary_10_1080_15325008_2023_2239224 crossref_primary_10_1038_s41598_024_72952_5 crossref_primary_10_1080_15435075_2024_2413891 crossref_primary_10_1016_j_isci_2025_112121 crossref_primary_10_1155_2024_3035524 crossref_primary_10_1007_s11831_023_09945_6 crossref_primary_10_1016_j_geits_2025_100263 crossref_primary_10_1049_gtd2_13118 crossref_primary_10_1038_s41598_025_85175_z crossref_primary_10_1080_15567036_2023_2268571 |
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| SubjectTerms | Algorithms Carbon Consumers Demand side management Design optimization Electricity Energy consumption Energy management Energy resources Energy storage Industrial plant emissions Linear programming Operating costs Profits Scheduling Simulation Supply & demand Sustainability Tariffs |
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