Water management problems generally involve conflicting and non-commensurable objectives. Assuming a centralized perspective at the system-level, the set of Pareto-optimal alternatives represents the ideal solution of most of the problems. Yet, in typical real-world applications, only a few primary objectives are explicitly considered, taking precedence over all other concerns. These remaining concerns are then internalized as static constraints within the problem's formulation. This approach yields to solutions that fail to explore the full set of objectives tradeoffs. In this paper, we propose a novel method, called direct policy conditioning (DPC), that combines direct policy search, multi-objective evolutionary algorithms, and input variable selection to design dynamic constraints that change according to the current system conditions. The method is demonstrated for the management problem of the Conowingo Dam, located within the Lower Susquehanna River, USA. The DPC method is used to identify environmental protection mechanisms and is contrasted with traditional static constraints de fining minimum environmental flow requirements. Results show that the DPC method identifies a set of dynamically constrained control policies that overcome the current alternatives based on the minimum environmental flow constraint, in terms of environmental protection but also of the primary objectives.

Improving the Protection of Aquatic Ecosystems by Dynamically Constraining Reservoir Operation Via Direct Policy Conditioning

GIULIANI, MATTEO;CASTELLETTI, ANDREA FRANCESCO;
2014-01-01

Abstract

Water management problems generally involve conflicting and non-commensurable objectives. Assuming a centralized perspective at the system-level, the set of Pareto-optimal alternatives represents the ideal solution of most of the problems. Yet, in typical real-world applications, only a few primary objectives are explicitly considered, taking precedence over all other concerns. These remaining concerns are then internalized as static constraints within the problem's formulation. This approach yields to solutions that fail to explore the full set of objectives tradeoffs. In this paper, we propose a novel method, called direct policy conditioning (DPC), that combines direct policy search, multi-objective evolutionary algorithms, and input variable selection to design dynamic constraints that change according to the current system conditions. The method is demonstrated for the management problem of the Conowingo Dam, located within the Lower Susquehanna River, USA. The DPC method is used to identify environmental protection mechanisms and is contrasted with traditional static constraints de fining minimum environmental flow requirements. Results show that the DPC method identifies a set of dynamically constrained control policies that overcome the current alternatives based on the minimum environmental flow constraint, in terms of environmental protection but also of the primary objectives.
Proceedings of 19th World Congress The International Federation of Automatic Control
9783902823625
AUT
File in questo prodotto:
File Dimensione Formato  
1964.pdf

accesso aperto

: Publisher’s version
Dimensione 802.04 kB
Formato Adobe PDF
802.04 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/962471
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact