Multi-objective optimization of coal-fired power plants using differential evolution
The design trade-offs between thermodynamics and economics of thermal systems needs the aid of multi-objective optimization techniques.The investment costs are usually conflicting with the plant thermodynamic performance.In this paper,an enhanced differential evolution with diversity-preserving and density-adjusting mechanisms and a newly-proposed algorithm for searching the decision space frontier in a single run were realized,to investigate the multi-objective optimization of large-scale supercritical coal-fired plants.The uncertainties of cost functions are discussed by analyzing the sensitivity of decision space frontier with some significant parameters involved in cost functions.Comparisons between the cost minimum designs and a real industrial design based on exergoeconomic analysis presented how the system was improved.It is concluded that a reduction of the cost of electricity with a minimum 2%and a maximum 4%can be achieved and,more importantly and accordingly,the efficiency could be increased by up to two percentage points.The largest uncertainty comes from the temperature-related and reheat-related cost coefficients of the steam generator.More reliable data on the price prediction of future advanced materials should be used to obtain more accurate fronts of the objective space.
Multi-objective Optimization Pareto Front Cost-effective Design Uncertainty Coal-fired Power Plant
Ligang Wang Yongping Yang Chaqing Dong Tatiana Morosuk George Tsatsaronis
North China Electric Power University,Beijing,China Technische Universit(a)t Berlin,Berlin,Germany
国际会议
桂林
英文
1-18
2013-07-16（万方平台首次上网日期，不代表论文的发表时间）