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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 mixedinteger 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 Bilevel decomposition; used in order to reduce the computational burden to solve mathematical timedependent 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 subproblems. 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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Rolling Horizon AIMMS +  redirect page 
