Abstract
Applying Simulated Annealing For Optimal Operation of Multi-Reservoir Systems
Highlights
For a multi reservoir system, where the number of reservoirs is large, the conventional modeling by Dynamic Programming (DP) or classical Stochastic Dynamic Programming (SDP) presents difficulty, due to the curse of dimensionality inherent in the model solution
Simulated Annealing (SA) can be considered as a new method in optimization of multi-reservoir systems, especially in real large system, even with nonlinear complex objective function
Optimization and exploitation management of a multi-reservoir system is such an important issue that must be considered by planning staff
Summary
For a multi reservoir system, where the number of reservoirs is large, the conventional modeling by Dynamic Programming (DP) or classical Stochastic Dynamic Programming (SDP) presents difficulty, due to the curse of dimensionality inherent in the model solution. Simulated Annealing (SA), proposed by Kirkpatrick et al, is a randomized search method for optimization. It tries to improve a solution by walking randomly in the space of possible solutions and gradually adjusting a parameter called “temperature”. The random walk is almost unbiased and it converges to essentially the uniform distribution over the whole space of solutions. Each step of the random walk is more likely to move towards solutions with a better objective value and the distribution is more and more biased towards the optimal solutions[1]. The sequence of temperatures and lengths of time for which they are maintained is called the annealing schedule in analogy with statistical mechanics
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