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Optimizing dynamic economic dispatch through an enhanced Cheetah-inspired algorithm for integrated renewable energy

Karthik Nagarajan1, Arul Rajagopalan2, Mohit Bajaj3,4,5,6

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The Enhanced Cheetah Optimizer Algorithm (ECOA) effectively reduces operational costs in dynamic economic dispatch (DED) by integrating renewable energy and demand-side management (DSM). ECOA demonstrates superior performance compared to existing algorithms, enhancing power system economic efficiency.

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Area of Science:

  • Electrical Engineering
  • Optimization Algorithms
  • Power Systems

Background:

  • Dynamic Economic Dispatch (DED) is complex due to integrating renewable energy sources (solar, wind), pumped-storage hydroelectric units, and demand-side management (DSM).
  • Existing optimization algorithms may not fully address the multi-objective nature and dynamic complexities of modern power systems.
  • The variability of non-conventional energy sources necessitates advanced management strategies for grid stability and economic efficiency.

Purpose of the Study:

  • To introduce the Enhanced Cheetah Optimizer Algorithm (ECOA) for solving the DED problem.
  • To evaluate the impact of Demand-Side Management (DSM) on operational costs and power system efficiency.
  • To assess ECOA's performance against established algorithms like COA and Grey Wolf Optimizer.

Main Methods:

  • Development and implementation of the Enhanced Cheetah Optimizer Algorithm (ECOA).
  • Integration of solar, wind, and thermal energy sources with pumped-storage hydroelectric units.
  • Simulation and analysis of the DED problem with and without DSM implementation on two test systems.

Main Results:

  • ECOA achieved operational cost savings of 0.24% (10-unit system) and 0.43% (20-unit system) with DSM.
  • The algorithm demonstrated superior performance and cost reduction compared to COA and Grey Wolf Optimizer.
  • DSM implementation was shown to significantly minimize costs and enhance economic efficiency.

Conclusions:

  • ECOA is a reliable and adaptable algorithm for multi-objective energy management in microgrids, especially with demand response.
  • The algorithm can effectively solve multi-objective dynamic optimal power flow problems with integrated renewable energy and electric vehicles.
  • DSM plays a crucial role in improving the economic efficiency of power systems through cost reduction.