Optimal Operation of the Hybrid Electricity Generation System Using Multiverse Optimization Algorithm.

Area of Science:

• Energy Systems Engineering
• Optimization Algorithms
• Operations Research

Background:

• Pakistan experiences significant load shedding and rising energy prices attributed to mismanagement and dependence on costly fuel sources.
• The national energy sector faces challenges in meeting demand due to inefficient energy production systems.

Purpose of the Study:

• To develop a robust dataset of energy production solutions for Pakistan's 95 systems.
• To identify strategies for mitigating load shedding and reducing energy costs.
• To evaluate the effectiveness of an improved multiverse optimizer in energy system optimization.

Main Methods:

• A linear programming model was formulated for 95 energy production systems in Pakistan.
• An improved multiverse optimizer was employed to generate a dataset of 100,000 potential solutions.
• The generated solutions were analyzed for energy demand fulfillment (9587 MW to 27208 MW) and system reliability.
• Case studies based on reported electricity shortfalls were addressed.

Main Results:

• The study generated a comprehensive dataset offering diverse solutions to meet national energy demands.
• The proposed solutions demonstrate the feasibility of maintaining energy supply even with partial system failures.
• Graphical analysis visually supports the findings and the effectiveness of the proposed optimization approach.
• The improved multiverse optimizer outperformed state-of-the-art algorithms in achieving lower power generation costs.

Conclusions:

• The developed dataset provides practical and cost-effective solutions to significantly reduce load shedding in Pakistan.
• The improved multiverse optimizer is a superior tool for optimizing energy systems, offering lower costs and enhanced reliability.
• Implementing the proposed solutions can alleviate the ongoing energy crisis in Pakistan.