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Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization

Author

Listed:
  • Sushmita Kujur

    (Department of Electrical Engineering, BIT Sindri, Dhanbad 828123, India)

  • Hari Mohan Dubey

    (Department of Electrical Engineering, BIT Sindri, Dhanbad 828123, India)

  • Surender Reddy Salkuti

    (Department of Railroad and Electrical Engineering, Woosong University, Daejeon 34606, Republic of Korea)

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.

Suggested Citation

  • Sushmita Kujur & Hari Mohan Dubey & Surender Reddy Salkuti, 2023. "Demand Response Management of a Residential Microgrid Using Chaotic Aquila Optimization," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1484-:d:1033632
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    References listed on IDEAS

    as
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