Script:Rolling Horizon AIMMS

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General information

Description Solving the optimal configuration and operating strategy of an energy hub combining multiple energy sources for a whole year can become computationally demanding. Indeed the effort to solve a mixed-integer linear programming (MILP) problem grows dramatically with the number of integer variables. Rolling Horizon approach (RH) is part of the commonly employed decomposition methods; as the Bender decomposition, Lagrangean decomposition, and Bi-level decomposition; used in order to reduce the computational burden to solve mathematical time-dependent problem with high number of variables. Indeed rather than solving a complex problem considering all its time horizon frame, the problem is solved by planning intervals representing a smaller part of the horizon, allowing to reduce the size of the problem per interval, by breaking down one problem in easily solved sub-problems. From an existing AIMMS model, the implementation of a rolling horizon approach is done in two stages: 1) Systematic creation of a second model using Python script 'AIMMS_Control_Rolling_Horizon.py' (procedure described in documentation, section 2.1): 2) Manual tuning directly done on AIMMS file situated in 'MainProject -> ProjectName.ams' (procedure described in documentation, section 2.2).
Download URL https://bitbucket.org/empa_lbst/rolling_horizon_aimms/get/HEAD.zip
Authors Julien Marquant
Required software [[Required software::We assume you have AIMMS 4.0 or upward version installed. (if not you can download it from [1]). We assume you have Python 3.2 or upward version installed. (if not, we recommend the Anaconda distribution. You can download it from [2]).]]
Related publications
License type MIT License
Tags energy hub model, rolling horizon, computational time, milp


Documentation

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Documentation URL(s): https://bitbucket.org/empa_lbst/rolling_horizon_aimms/src/master/Guidelines_rolling_horizon.pdf
Documentation file: File:Guidelines rolling horizon.pdf
Documentation page: