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Script:Rolling Horizon AIMMS
Authors Julien Marquant +
Description Solving the optimal configuration and oper
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).
described in documentation, section 2.2).  +
Documentation URL https://bitbucket.org/empa_lbst/rolling_horizon_aimms/src/master/Guidelines_rolling_horizon.pdf  +
Documentation file File:Guidelines rolling horizon.pdf +
Download URL https://bitbucket.org/empa_lbst/rolling_horizon_aimms/get/HEAD.zip  +
License type MIT License  +
Tags Energy hub model + , Rolling horizon + , Computational time + , Milp +
Categories Scripts , Modules2
Modification date
This property is a special property in this wiki.
19 December 2016 10:33:44  +
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