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

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.