Optimization of power and heating systems based on a new hybrid algorithm

Abstract

A novel combination of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) is applied on a base case cogeneration optimization problem called the modified CGAM problem with two objective functions. The first objective function, the exergetic efficiency, should be maximized and the second one is the total cost rate that should be minimized. The effects of important parameters, such as equivalence ratio, emission, and unit cost of fuel are studied on the exergetic and economic performance of the system.

Keywords

• CGAM problem
• Exergoeconomic optimization
• Hybrid algorithms
• Particle swarm optimization
• Genetic algorithm