State-of-the-Art for Modelling Reservoir Inflows and Management Optimization

Abstract

A multi-purpose reservoir operation requires a good decision from the operator in order to maximize the value of water. Therefore, a good inflows modelling will be very helpful in providing a better optimization solution. By then, perplexing in the selection of the most preferable solution might happen to the operator. A comprehensive review of different computational intelligent models which applied in reservoir inflows modelling and management optimization is presented in this paper. The aim of this study is to review, compare and summarize their attempts along with difficulties in dealing with the water management problem. The benefits derived from such comparison are used to improve the performance of the existing models for future work. Study showed that models based evolutionary algorithm revealing a great potential in the management of reservoir operation. However more research about the most recent self-optimization modelling application needs to be revised.

Appendices

Appendix A. Basic Algorithm of GA

Basic Algorithm of GA

Produce an initial population of individuals

Evaluate the fitness of all individuals

while termination condition not met do

Select fitter individuals for reproduction and produce new individuals

(crossover and mutation)

Evaluate fitness of new individuals

Generate a new population by inserting some new “good” individuals and

by erasing some old “bad” individuals

end while

Appendix B. Standard Algorithm of ABC

Standard Algorithm of ABC

place each employed bee on a random position in the search space

while stopping criterion not met do

for all employed bees do

if steps on same position == limit then

choose random position in search space

else

try improve position

if better position found then

change position

reset steps on same position

end if

end if

end if

for all onlooker bees do

choose position of employed bee

try improve position

end for

end while