operations optimization
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2021 ◽  
Author(s):  
Thayna d. Oliveira ◽  
Sasha Madar ◽  
Cedric Y. Justin ◽  
Dimitri N. Mavris

Author(s):  
Michelle A. Girts ◽  
Robert L. Knight

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2688 ◽  
Author(s):  
Milad Hooshyar ◽  
S. Jamshid Mousavi ◽  
Masoud Mahootchi ◽  
Kumaraswamy Ponnambalam

Stochastic dynamic programming (SDP) is a widely-used method for reservoir operations optimization under uncertainty but suffers from the dual curses of dimensionality and modeling. Reinforcement learning (RL), a simulation-based stochastic optimization approach, can nullify the curse of modeling that arises from the need for calculating a very large transition probability matrix. RL mitigates the curse of the dimensionality problem, but cannot solve it completely as it remains computationally intensive in complex multi-reservoir systems. This paper presents a multi-agent RL approach combined with an aggregation/decomposition (AD-RL) method for reducing the curse of dimensionality in multi-reservoir operation optimization problems. In this model, each reservoir is individually managed by a specific operator (agent) while co-operating with other agents systematically on finding a near-optimal operating policy for the whole system. Each agent makes a decision (release) based on its current state and the feedback it receives from the states of all upstream and downstream reservoirs. The method, along with an efficient artificial neural network-based robust procedure for the task of tuning Q-learning parameters, has been applied to a real-world five-reservoir problem, i.e., the Parambikulam–Aliyar Project (PAP) in India. We demonstrate that the proposed AD-RL approach helps to derive operating policies that are better than or comparable with the policies obtained by other stochastic optimization methods with less computational burden.


2020 ◽  
Vol 141 ◽  
pp. 106296 ◽  
Author(s):  
Baicheng Yan ◽  
Xiaoning Zhu ◽  
Der-Horng Lee ◽  
Jian Gang Jin ◽  
Li Wang

2019 ◽  
Vol 84 ◽  
pp. 154-163 ◽  
Author(s):  
Mengyao Yuan ◽  
Holger Teichgraeber ◽  
Jennifer Wilcox ◽  
Adam R. Brandt

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