An enhanced differential evolution algorithm for daily optimal hydro generation scheduling

Authors:
Xiaohui Yuan;Yongchuan Zhang;Liang Wang;Yanbin Yuan
Affiliations:
School of Hydropower & Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China;School of Hydropower & Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China;School of Hydropower & Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China;School of Resource & Environmental Engineering, Wuhan University of Technology, Wuhan, 430070, China
Venue:
Computers & Mathematics with Applications
Year:
2008

Citing 3
Cited 3

Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
A constraint handling approach for the differential evolution algorithm

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Chaotic sequences to improve the performance of evolutionary algorithms

IEEE Transactions on Evolutionary Computation

An adaptive hybrid differential evolution algorithm for dynamic economic dispatch with valve-point effects

Expert Systems with Applications: An International Journal
Utilization of SOMA and differential evolution for robust stabilization of chaotic Logistic equation

Computers & Mathematics with Applications
Chaotic differential evolution methods for dynamic economic dispatch with valve-point effects

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.09

Visualization

Abstract

The daily optimal hydro generation scheduling problem (DOHGSB) is a complicated nonlinear dynamic constrained optimization problem, which plays an important role in the economic operation of electric power systems. This paper proposes a new enhanced differential evolution algorithm to solve DOHGSB. In the proposed method, chaos theory was applied to obtain self-adaptive parameter settings in differential evolution (DE). In order to handle constraints effectively, three simple feasibility-based selection comparison techniques embedded into DE are devised to guide the process toward the feasible region of the search space. The feasibility of the proposed method is demonstrated for the daily generation scheduling of a hydro system with four interconnected cascade hydro plants, and the test results are compared with those obtained by the conjugate gradient and two-phase neural network method in terms of solution quality. The simulation results show that the proposed method is able to obtain higher quality solutions.