Hybrid genetic/simulated annealing approach to short-term multiple-fuel-constrained generation scheduling

1997 ◽  
Vol 12 (2) ◽  
pp. 776-784 ◽  
Author(s):  
K.P. Wong ◽  
Y.W. Wong
Keyword(s):  
Simulated Annealing ◽  
Short Term ◽  
Generation Scheduling

Three Gorges Cascade Hydropower System Joint Simulative Optimal Operation Research in Power Market

2011 ◽  
Vol 347-353 ◽  
pp. 1370-1373
Author(s):  
Jiao Zheng ◽  
Kan Yang ◽  
Ran Zhou ◽  
Yong Huai Hao ◽  
Guo Shuai Liu
Keyword(s):  
Power Market ◽  
Optimal Operation ◽  
Adaptive Genetic Algorithm ◽  
Three Gorges ◽  
Short Term ◽  
Roulette Wheel Selection ◽  
Generation Scheduling ◽  
Hydropower System ◽  
Selection Operator ◽  
Hydropower Stations

The short-term joint optimal operation simulation of Three Gorges cascade hydropower system aiming at maximum power generation benefit is proposed. And a new method for optimizing cascade hydropower station based on Adaptive Genetic Algorithm (AGA) with trigonometric selective operators is presented. In this paper, the practical optimal operation in power market is described. The temporal-spatial variation of flow between cascade hydropower stations is considered, and time of use (TOU) power price is also taken into account. Moreover, a contrast between Tangent-roulette selection operator and traditional one is made. To a certain degree, the results of simulative optimal operation based on several representative hydrographs show that Tangent-roulette wheel selection operator can find a more excellent solution, because the Tangent-roulette one can overcome the fitness requirements of non-negative. The research achievements also have an important reference for the compilation of daily generation scheduling of Three Gorges cascade hydropower system in the environment of power market.

Short Term Electric Load Forecasting with Back Propagation Neural Network and Simulated Annealing Genetic Algorithm

2015 ◽  
Vol 785 ◽  
pp. 14-18 ◽  
Author(s):  
Badar ul Islam ◽  
Zuhairi Baharudin ◽  
Perumal Nallagownden
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Forecast Accuracy ◽  
Back Propagation Neural Network ◽  
Search Performance ◽  
Training Algorithm ◽  
Short Term

Although, Back Propagation Neural Network are frequently implemented to forecast short-term electricity load, however, this training algorithm is criticized for its slow and improper convergence and poor generalization. There is a great need to explore the techniques that can overcome the above mentioned limitations to improve the forecast accuracy. In this paper, an improved BP neural network training algorithm is proposed that hybridizes simulated annealing and genetic algorithm (SA-GA). This hybrid approach leads to the integration of powerful local search capability of simulated annealing and near accurate global search performance of genetic algorithm. The proposed technique has shown better results in terms of load forecast accuracy and faster convergence. ISO New England data for the period of five years is employed to develop a case study that validates the efficacy of the proposed technique.

scholarly journals Hydrothermal systems generation scheduling using cultural algorithm

2009 ◽  
Vol 11 (1) ◽  
pp. 65-78 ◽  
Author(s):  
Xiaohui Yuan ◽  
Hao Nie ◽  
Yanbin Yuan ◽  
Anjun Su ◽  
Liang Wang
Keyword(s):  
Differential Evolution ◽  
Heuristic Search ◽  
Hydrothermal System ◽  
Selection Criteria ◽  
Optimization Methods ◽  
Hydrothermal Systems ◽  
Cultural Algorithm ◽  
Short Term ◽  
Generation Scheduling ◽  
Simple Selection

This paper proposes an enhanced cultural algorithm to solve the short-term generation scheduling of hydrothermal systems problem, in which differential evolution is embedded into a cultural algorithm and applies two knowledge sources to influence the variation operator of differential evolution and couples with simple selection criteria based on feasibility rules and heuristic search strategies to handle constraints in the cultural algorithm effectively. A test hydrothermal system is used to verify the feasibility and effectiveness of the proposed method. Results are compared with those of other optimization methods reported in the literature. It is shown that the proposed method is capable of yielding higher quality solutions.

scholarly journals The Improved Binary-Real Coded Shuffled Frog Leaping Algorithm for Solving Short-Term Hydropower Generation Scheduling Problem in Large Hydropower Station

2018 ◽  
Vol 2018 ◽  
pp. 1-29
Author(s):  
Zhe Yang ◽  
Kan Yang ◽  
Lyuwen Su ◽  
Hu Hu
Keyword(s):  
Cloud Model ◽  
Activation Mechanism ◽  
Hydropower Station ◽  
Utilization Rate ◽  
Search Performance ◽  
Integer Optimization ◽  
Shuffled Frog Leaping Algorithm ◽  
Short Term ◽  
Generation Scheduling ◽  
Shuffled Frog Leaping

The short-term hydro generation scheduling (STHGS) decomposed into unit commitment (UC) and economic load dispatch (ELD) subproblems is complicated problem with integer optimization, which has characteristics of high dimension, nonlinear and complex hydraulic and electrical constraints. In this study, the improved binary-real coded shuffled frog leaping algorithm (IBR-SFLA) is proposed to effectively solve UC and ELD subproblems, respectively. For IB-SFLA, the new grouping strategy is applied to overcome the grouping shortage of SFLA, and modified search strategies for each type of frog subpopulation based on normal cloud model (NCM) and chaotic theory are introduced to enhance search performance. The initialization strategy with chaos theory and adaptive frog activation mechanism are presented to strengthen performance of IR-SFLA on ELD subproblem. Furthermore, to solve ELD subproblem, the optimal economic operation table is formed using IR-SFLA and invoked from database. Moreover, reserve capacity supplement and repair, and minimum on and off time repairing strategies are applied to handle complex constraints in STHGS. Finally, the coupled external and internal model corresponding to UC and ELD subproblems is established and applied to solve STHGS problem in Three Gorges hydropower station. Simulation results obtained from IBR-SFLA are better than other compared algorithms with less water consumption. In conclusion, to solve STHGS optimization problem, the proposed IBR-SFLA presents outstanding performance on solution precision and convergence speed compared to traditional SFLA effectively and outperforms the rivals to get higher precision solution with improving the utilization rate of waterpower resources.

Short-Term Power Generation Scheduling via Robust Optimization†

2015 ◽  
pp. 1-12
Author(s):  
Chaoyue Zhao ◽  
Yonghong Chen ◽  
Yongpei Guan ◽  
Qianfan Wang ◽  
Xing Wang
Keyword(s):  
Power Generation ◽  
Robust Optimization ◽  
Short Term ◽  
Generation Scheduling
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