A novel nested stochastic dynamic programming (nSDP) and nested reinforcement learning (nRL) algorithm for multipurpose reservoir optimization

Blagoj Delipetrev, Andreja Jonoski, Dimitri P. Solomatine

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)

Abstract

In this article we present two novel multipurpose reservoir optimization algorithms named nested stochastic dynamic programming (nSDP) and nested reinforcement learning (nRL). Both algorithms are built as a combination of two algorithms; in the nSDP case it is (1) stochastic dynamic programming (SDP) and (2) nested optimal allocation algorithm (nOAA) and in the nRL case it is (1) reinforcement learning (RL) and (2) nOAA. The nOAA is implemented with linear and non-linear optimization. The main novel idea is to include a nOAA at each SDP and RL state transition, that decreases starting problem dimension and alleviates curse of dimensionality. Both nSDP and nRL can solve multi-objective optimization problems without significant computational expenses and algorithm complexity and can handle dense and irregular variable discretization. The two algorithms were coded in Java as a prototype application and on the Knezevo reservoir, located in the Republic of Macedonia. The nSDP and nRL optimal reservoir policies were compared with nested dynamic programming policies, and overall conclusion is that nRL is more powerful, but significantly more complex than nSDP.

Original languageEnglish
Pages (from-to)47-61
Number of pages15
JournalJournal of Hydroinformatics
Volume19
Issue number1
DOIs
Publication statusPublished - 1 Jan 2017

Keywords

  • Algorithm
  • Optimal reservoir operation
  • Reinforcement learning
  • Stochastic dynamic programming

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