Systems of Energy, Food & Water (production and/or distribution) have strong interlinkages between them. These interlinkages represent the synergies and trade‑offs arising from an increasingly interconnected and complex world. In order to try to understand and analyze, not just the local behavior of one of this macro-systems, but the dependence between them, it is necessary to use advanced modeling and optimization methods.
Valeria Javalera-Rincón have disigned an architecture and and a methodology to deal with the interaction between systems (or sub-systems) in a distributed control architecture for Large Scale Systems (LSS). This approach combines ideas from Distributed Artificial Intelligence (DAI) and Reinforcement Learning (RL) in order to provide a controller interaction based on negotiation, cooperation and learning techniques. The aim of this methodology is to provide a general structure to perform optimal control in networked distributed environments, where multiple dependencies between subsystems are found.
Her work in IIASA will focuse on the implementation of this architecture in order to schedule a sustainable development of energy and agricultural industries under competition for land and water resources. The models can include stochastic parameters and can be extended to other groups of systems.
Funding: The Mexican National Council for Science and Technology (CONACYT)
Program: Advanced Systems Analysis and Ecosystems Services and Management Program
Dates: May 2017 - present
Last edited: 02 November 2017
International Institute for Applied Systems Analysis (IIASA)
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Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313