Double-objective heat and power dispatch based on lexicographic optimization method and ε-constraint technique
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Double-objective heat and power dispatch based on lexicographic optimization method and ε-constraint technique
Thermal Power GenerationVol. 46, Issue 10, Pages: 81-87(2017)
作者机构:
1. 广西电网有限责任公司电力科学研究院
2. 广西大学电气工程学院
作者简介:
基金信息:
DOI:
CLC:TM73
Published:2017
稿件说明:
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LIANG Jie, LI Gang, WANG Jiannan. Double-objective heat and power dispatch based on lexicographic optimization method and ε-constraint technique[J]. Thermal power generation, 2017, 46(10): 81-87.
DOI:
LIANG Jie, LI Gang, WANG Jiannan. Double-objective heat and power dispatch based on lexicographic optimization method and ε-constraint technique[J]. Thermal power generation, 2017, 46(10): 81-87.DOI:
Double-objective heat and power dispatch based on lexicographic optimization method and ε-constraint technique
With the increasing attentions to environmental protection in China
during regional dispatch for thermal and power cogeneration
besides saving the operation costs
pollutant emission reduction has also drawn more and more attentions. Against this dual objective minimization problem of economy and environment
using the conventional ε-constraint method can not effectively optimize the revenue matrix
so the non-dominated solution can not be ensured. To solve this problem
this paper proposes to adopt the lexicographic optimization method
it updates the income matrix gradually based on the ε expectations formed in the previous optimization process so as to reduce the search scope. By introducing slack variables
the e sub-problems are transformed into a standard linear programming model
and the primal-dual interior point algorithm is used to solve it to improve the global search ability. In addition
the fuzzy decision technique is used to provide a quantitative solution for determining satisfactory solutions. Finally
two examples are analyzed by the proposed method and the weighting coefficient adjustment method. The results verifies that
both the quality of pareto solution and the computational speed of the proposed method are better.