Bio-inspired Novel Liver Cancer algorithm for solving large-scale combined heat and power economic dispatch problems

The potential of Combined Heat and Power (CHP) systems to enhance the economics and sustainability of the electricity system is garnering increasingly attention. The fact that these systems can have numerous generation units whose functions are controlled by intricate non-linear physics makes it challenging to determine how to operate them optimally. The complex interconnections within bulk power systems pose significant challenges in solving economic dispatch problems, particularly in large-scale Combined Heat and Power Economic Dispatch (CHPED) scenarios, which are difficult to address due to intricate thermal and electrical connections in cogeneration units. The current research work proposed a bio-inspired novel Liver Cancer Algorithm (LCA) to optimize a large-scale CHP economic dispatch system. The LCA algorithm employs genetic operators and a Random Opposition-Based Learning (ROBL) technique to effectively achieve a balance between local and global searches and thoroughly explore the search space. The mutation rate is adjusted based on the number of iterations, and the higher mutation rate facilitates the exploration of promising new locations and protects the algorithm from being trapped at a local minimum. Hence, a better optimum value can be achieved in less time. To investigate performance, the proposed method has been demonstrated on CHPED problems involving one medium and two different large-scale test systems, 48, 96, and 192 units, respectively, and the results were compared to other state-of-the-art powerful approaches. The experimental results indicated that the LCA algorithm surpasses other methods in solving medium and large-scale CHPED problems.