Script:Rolling Horizon AIMMS
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|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).|
|Required software||[[Required software::We assume you have AIMMS 4.0 or upward version installed. (if not you can download it from ). We assume you have Python 3.2 or upward version installed. (if not, we recommend the Anaconda distribution. You can download it from ).]]|
|License type||MIT License|
|Tags||energy hub model, rolling horizon, computational time, milp|
|Documentation file:||File:Guidelines rolling horizon.pdf|