An integrated binary metaheuristic approach in dynamic unit commitment and economic emission dispatch for hybrid...

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Renewable Energy Systems

Background:

• Growing demand for zero-emissions energy sources due to fossil fuel depletion and global warming.
• Challenges in integrating intermittent renewable energy sources (RES) into existing power grids.
• Limitations of traditional Unit Commitment (UC) and Combined Economic Emission Dispatch (CEED) methods with RES.

Purpose of the Study:

• To develop an efficient hybrid metaheuristic algorithm for solving the complex UC-CEED problem.
• To minimize fuel costs and harmful emissions in power systems with integrated RES.
• To improve the scheduling of conventional and renewable power generation units.

Main Methods:

• Proposed a novel hybrid algorithm: Crow Search Improved Binary Grey Wolf Optimization (CS-BIGWO).
• Integrated CS-BIGWO with enhanced lambda iteration for UC-CEED problem-solving.
• Utilized Levy-Flight Chaotic Whale Optimization Algorithm-optimized Extreme Learning Machines (LCWOA-ELM) for day-ahead RES forecasting.

Main Results:

• The CS-BIGWO algorithm demonstrated superior performance on standard mathematical functions.
• Tested on an IEEE-39 bus system, the proposed method achieved reductions in fuel cost and emissions.
• Achieved 0.1021% fuel cost and 0.7995% emission reduction (Case 1: no RES) and 0.12896% fuel cost and 0.772% emission reduction (Case 2: with RES).

Conclusions:

• The proposed CS-BIGWO integrated with enhanced lambda iteration effectively solves the UC-CEED problem for hybrid energy systems.
• The methodology proves superior to existing methods in reducing operational costs and environmental impact.
• Validates the potential of advanced metaheuristic algorithms for optimizing power system operations with renewable energy integration.