Paper/Poster/Presentation Title
Optimist: a Python library for Water System Optimal Operation and Analysis using SDDP
Keywords
Water Resources, Reservoir operation, Stochastic Dual Dynamic Programming, Cost Benefit analysis, Operational water management.
Location
Session D4: Water Resource Management and Planning - Modelling and Software for Improving Decisions and Engaging Stakeholders
Start Date
11-7-2016 2:50 PM
End Date
11-7-2016 3:10 PM
Abstract
Stochastic Dual Dynamic Programming (SDDP) is valuable tool in water management, employed for operational water management (i.e. suggesting effective release rules), and cost-benefit analysis evaluations. SDDP solves a multistage stochastic programming problem when uncertainty is a Markov process, and the system model is convex. SDDP can handle complex interconnected problem. Despite its potential, SDDP use is limited to few specialists.
We present Optimist, a python library for setting up an SDDP problem from components, i.e. elements and objectives, and their relationships. Elements presently developed are: streamflow input, reservoir, release, and river-reach. Objectives can be linear, used for energy production, and threshold, used for flood protection and environmental flow. Optimist largely simplifies the setting up a SDDP problem, and therefore it is dedicated to water resources management experts who want to use SDDP for their problems. Optimist is developed in Python using the Object Oriented Programming paradigm.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Optimist: a Python library for Water System Optimal Operation and Analysis using SDDP
Session D4: Water Resource Management and Planning - Modelling and Software for Improving Decisions and Engaging Stakeholders
Stochastic Dual Dynamic Programming (SDDP) is valuable tool in water management, employed for operational water management (i.e. suggesting effective release rules), and cost-benefit analysis evaluations. SDDP solves a multistage stochastic programming problem when uncertainty is a Markov process, and the system model is convex. SDDP can handle complex interconnected problem. Despite its potential, SDDP use is limited to few specialists.
We present Optimist, a python library for setting up an SDDP problem from components, i.e. elements and objectives, and their relationships. Elements presently developed are: streamflow input, reservoir, release, and river-reach. Objectives can be linear, used for energy production, and threshold, used for flood protection and environmental flow. Optimist largely simplifies the setting up a SDDP problem, and therefore it is dedicated to water resources management experts who want to use SDDP for their problems. Optimist is developed in Python using the Object Oriented Programming paradigm.
