Application of SCE-UA Approach to Economic Load Dispatch of Hydrothermal Generation System

2013 ◽  
Vol 448-453 ◽  
pp. 4296-4303
Author(s):  
Jia Rui Dong ◽  
Chui Yong Zheng ◽  
Guang Yuan Kan ◽  
Zhi Jia Li ◽  
Min Zhao

The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network and water transport delay, which make the problem of finding global optimum difficult using standard optimization methods. This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using shuffled complex evolution (SCE-UA) method. The proposed method introduces the new concept of competitive evolution and complex shuffling, which ensure that the information on the parameter space gained by each of individual complexes is shared throughout the entire population. This conducts an efficient search of the parameter space. In this study, the hydrothermal scheduling is formulated as an objective problem that maximizes the social welfare. Penalty function is proposed to handle the equality, inequality constraints especially active power balance constraint and ramp rate constraints. The simulation results reveal that SCE-UA effectively overcomes the premature phenomenon and improves the global convergence and optimization searching capability. It is a relatively consistent, effective and efficient optimization method in solving the short-term hydrothermal scheduling problem.

2017 ◽  
Vol 11 (1) ◽  
pp. 23-37 ◽  
Author(s):  
Chen Gonggui ◽  
Huang Shanwai ◽  
Sun Zhi

This study proposes a novel chaotic quantum-behaved particle swarm optimization (CQPSO) algorithm for solving short-term hydrothermal scheduling problem with a set of equality and inequality constraints. In the proposed method, chaotic local search technique is employed to enhance the local search capability and convergence rate of the algorithm. In addition, a novel constraint handling strategy is presented to deal with the complicated equality constrains and then ensures the feasibility and effectiveness of solution. A system including four hydro plants coupled hydraulically and three thermal plants has been tested by the proposed algorithm. The results are compared with particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) and other population-based artificial intelligence algorithms considered. Comparison results reveal that the proposed method can cope with short-term hydrothermal scheduling problem and outperforms other evolutionary methods in the literature.


2021 ◽  
Vol 20 (1) ◽  
pp. 15-20
Author(s):  
Mohamed ahmed Ayoub

In this study, the Imperialist Competitive Algorithm (ICA) is proposed to solve a multi-chain Short-Term Hydrothermal Scheduling problem (STHTS). It aims to minimize the generation cost of the thermal plants while satisfying the thermal and hydro plants constraints. In order to evaluate the effectiveness of the ICA, it has been tested on a system having a hydro plant with four-cascaded reservoir and a thermal plant. The results are compared with that obtained by other techniques. The ICA has the good convergence and the better results.


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