Single and Multi-Objective Optimization of a Cogeneration System Using Hybrid Algorithms

Current design and operation of energy systems must consider the efficient utilization of energy resources, reduced environmental harms, and sustainable development. Many techniques for energy systems analysis and optimization have thus been developed worldwide. To evaluate different methodologies,...

Full description

Saved in:
Bibliographic Details
Published in:Heat transfer engineering 2009-03, Vol.30 (4), p.261-271
Main Authors: Padilha, Ricardo S., Santos, Hugo F. L., Colaço, Marcelo J., Cruz, Manuel E.
Format: Article
Language:English
Subjects:
Applied sciences
Energy
Energy resources
Energy. Thermal use of fuels
Exact sciences and technology
Installations for energy generation and conversion: thermal and electrical energy
Optimization algorithms
Other installations: mhd power plants, fuel cell plants, incineration plants, etc
Thermodynamics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Current design and operation of energy systems must consider the efficient utilization of energy resources, reduced environmental harms, and sustainable development. Many techniques for energy systems analysis and optimization have thus been developed worldwide. To evaluate different methodologies, the benchmark CGAM problem was proposed, which consisted of the optimization of a cogeneration system with explicit physical, thermodynamic, and economic models. The original CGAM problem was formulated as a single objective optimization problem, where the objective function was the sum of the purchased equipment, maintenance and operation, and fuel consumption costs. However, in real-life applications, costs must be analyzed individually: for example, one might increase equipment costs but save in fuel consumption for the entire system life. In this paper, single- and multi-objective hybrid optimizations of the CGAM system are performed. A hybrid optimization algorithm combines the strengths of deterministic and heuristic methods. Usually, it employs a heuristic method to locate a region where the global extreme point lies, and then switches to a deterministic method to get to the exact point faster. The objective functions are the fuel consumption cost rate and the total capital investment. Thus, a Pareto front is obtained for all non-dominated solutions, from which the final decision can be made considering appropriate scenarios.
ISSN:0145-7632
1521-0537
DOI:10.1080/01457630802375048