Weighted cost emission dispatch optimization using GA-APO hybridization under priority sensitive scheduling for...

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Environmental Science

Background:

• Modern power utilities face complex scheduling challenges integrating economic fuel costs with environmental impacts.
• Traditional single-objective optimization tools struggle with non-linear fuel and emission characteristics of thermal units.

Purpose of the Study:

• To develop a robust two-stage optimization strategy for the economic-emission dispatch problem.
• To create a flexible weighted formulation for balancing cost savings and emission reduction.

Main Methods:

• A hybrid approach combining a Genetic Algorithm (GA) for broad search with Arctic Puffin Optimization (APO) for local refinement.
• Testing the proposed GA-APO method on a 24-hour schedule for a three-generator thermal power plant.

Main Results:

• The GA-APO method demonstrated satisfactory performance within operational limits across various conditions.
• Achieved up to 1.88% reduction in operating cost (cost-priority) compared to GA alone.
• Reduced emission consumption by approximately 0.21% (emission-priority) and cost per MWh by 0.68% (balanced).

Conclusions:

• The hybrid GA-APO strategy offers an effective solution for the economic-emission dispatch problem.
• The weighted formulation allows adaptable optimization towards cost, emission reduction, or a compromise.
• Validated effectiveness over traditional GA, providing significant economic and environmental benefits.