Multi-objective evolutionary method for multi-area dynamic emission/economic dispatch considering energy storage and...

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Smart Grid Technology

Background:

• The multi-area economic/emission dispatch (MAEED) problem in smart grids aims to reduce operating costs and emissions from thermal units.
• Integrating renewable energy (RE) and energy storage (ES) systems is crucial for environmental sustainability and emission reduction.
• Conventional economic dispatch (ED) methods struggle with multi-objective optimization, non-linear constraints, and lack speed and accuracy.

Purpose of the Study:

• To address the multi-objective multi-area dynamic economic/emission dispatch (MADEED) problem with complex constraints like prohibited operating zones (POZs), valve point effect (VPE), and transmission losses.
• To minimize both operating costs and emission objectives simultaneously.
• To evaluate the effectiveness of a novel hybrid optimization approach.

Main Methods:

• A hybrid Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) is proposed to leverage the strengths of both algorithms.
• The hybrid PSO-WOA method is applied to solve the multi-objective MADEED problem, incorporating constraints such as RE units, ES systems, POZs, VPE, transmission losses, ramp restrictions, and tie-line capacity.
• The proposed method is tested on a 10-generator network across two scenarios and compared against original PSO and WOA techniques.

Main Results:

• The hybrid PSO-WOA method demonstrates superior efficiency and performance in solving the MADEED problem compared to individual PSO and WOA.
• Simulations confirm that integrating system constraints leads to legitimate system operation and dependable output.
• The proposed approach achieved an approximate 3% reduction in the overall cost function value compared to other methods.

Conclusions:

• The hybrid PSO-WOA algorithm is an effective tool for solving the complex multi-objective MADEED problem in smart grids.
• The integration of RE and ES systems, along with handling various operational constraints, is successfully managed by the proposed method.
• The study validates the enhanced performance and cost-saving benefits of the hybrid optimization technique for smart grid operations.