Addressing economic and emission dispatch challenges in combined heat and power systems with a multi-objective liver cancer algorithm for large-scale virtual power plants

The paper proposed a new efficient optimization technique called the Multiobjective Liver Cancer Algorithm (MOLCA) as a solution to the combined heat and power economic emission dispatch (CHPEED) problem. This subject seeks to optimize the arrangement of power and heat generation to minimize the total fuel cost (FC) and also emission pollution of various units in combined heat and power (CHP) systems while considering their operating constraints. The MOLCA method is an effective method that examines the best solution among several local optimal solutions obtained through various exploitation approaches. The proposed algorithm employs Random Opposition Based Learning (ROBL) to explore the search space intensively and a genetic operator with a high mutation rate, which makes the algorithm more powerful and avoids the situation where the system gets stuck in the regional optima. It also uses the Information Feedback Model (IFM), non-dominated sorting, and crowding distance in its process, which makes it significant. Two extensive test systems, 100 units, and 150 units, are being investigated to examine the efficacy of the planned MOLCA. The findings indicate that the proposed MOLCA exhibits superior effectiveness in delivering comparable outcomes compared to alternative optimization techniques for large-scale systems.