Nested Algorithms for Optimal Reservoir Operation and Their Embedding in a Decision Support Platform

Reservoir operation is a multi-objective optimization problem traditionally solved with dynamic programming (DP) and stochastic dynamic programming (SDP) algorithms. The thesis presents novel algorithms for optimal reservoir operation named nested DP (nDP), nested SDP (nSDP), nested reinforcement learning (nRL) and their multi-objective (MO) variants correspondingly MOnDP, MOnSDP and MOnRL. The novel idea is to include a nested optimization algorithm into each state transition that reduces the initial problem dimension and alleviates the curse of dimensionality. These algorithms can solve multi-objective optimization problems, without significantly increasing the algorithm complexity, the computational expenses and can handle dense and irregular variable discretization. All algorithms are coded in Java and tested on the case study of Knezevo reservoir in the Republic of Macedonia. Nested optimization algorithms are embedded in a cloud application platform for water resources modeling and optimization. The platform is available 24X7, accessible from everywhere, scalable, distributed, interoperable, and it creates a real-time multiuser collaboration platform. This thesis contributes with new and more powerful algorithms for optimal reservoir operation and cloud application platform. All source code is available for public use and can be used by researchers and practitioners to advance the mentioned areas further.