Autonomous dynamic economic dispatch with limited fuel and renewable energy sources using marine predators optimizer

Area of Science:

• Power Systems Engineering
• Optimization Algorithms
• Sustainable Energy

Background:

• Power systems face challenges with fuel constraints and integrating renewable energy sources (RES).
• Dynamic Economic Emission Dispatch (DEED) is crucial for efficient power system operation.
• Ensuring grid stability and reliability during fuel shortages is a significant concern.

Purpose of the Study:

• To propose a Dynamic Economic Emission Dispatch (DEED) model that incorporates Renewable Energy Sources (RES) and Dynamic Generation Capacity (DGC).
• To evaluate the performance of the Marine Predators Algorithm (MPA), Walrus Optimization Algorithm (WaOA), and Particle Swarm Optimization (PSO) for the proposed DEED model.
• To assess the effectiveness of the DEED approach in managing limited fuel conditions and enhancing power system security.

Main Methods:

• Development of a DEED model integrating RES (PV and wind) and DGC.
• Implementation and comparison of MPA, WaOA, and PSO for optimizing the DEED model.
• Testing the proposed strategies on a 10-unit power system under various operating scenarios, including fuel shortage conditions.

Main Results:

• The MPA demonstrated superior performance, significantly reducing generation costs and improving result consistency compared to WaOA and PSO.
• The DEED approach with DGC and RES achieved notable reductions in fuel costs and emissions, especially under fuel shortage scenarios.
• Renewable energy sources alone were insufficient to meet load demands during unpredictable fuel shortages, highlighting the need for DGC.

Conclusions:

• Integrating DGC and RES into the DEED framework is effective for reducing operational costs and emissions.
• The proposed DEED model enhances power system security and reliability, particularly during fuel scarcity.
• The findings support the development of sustainable and intelligent future power grids.