Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm

2016 ◽  
Vol 351 ◽  
pp. 48-66 ◽  
Author(s):  
B.Y. Qu ◽  
J.J. Liang ◽  
Y.S. Zhu ◽  
Z.Y. Wang ◽  
P.N. Suganthan
Keyword(s):  
Evolutionary Algorithm ◽  
Wind Power ◽  
Multi Objective ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Dynamic Economic Emission Dispatch Using Multi-Objective Hybrid Evolutionary Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2154-2158
Author(s):  
Lei Zhang ◽  
Jun Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Power Plants ◽  
Test System ◽  
Selection Strategy ◽  
Hybrid Evolutionary Algorithm ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Output Constraints ◽  
Emission Dispatch ◽  
Economic Emission Dispatch

Dynamic economic emission dispatch (DEED) is an important optimization task for power plants. The problem is a highly constrained multi-objective optimization problem involving conflicting objectives with both equality and inequality constraints. This paper introduces two objective functions of DEED model: the lowest generation cost and the smallest carbon emissions with power balance constraints, unit output constraints and unit ramp rate limits. Then the paper presents a multi-objective hybrid evolutionary algorithm (MHEA) to solve the DEED model. The MHEA is a hybrid optimization algorithm based on orthogonal initialization, improved differential operation with migration strategy, parameter adaptive control, multi-objective selection strategy and analytic hierarchy process based fuzzy technique (AFT). Numerical results of one test system demonstrate the capabilities of the proposed approach. Compared with other classical methods, the proposed approach gets better result.

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