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Accelerated dual dynamic integer programming applied to short-term power generation scheduling
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DOI：10.1016/j.ijepes.2022.108689.png)
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The short-term generation scheduling (STGS) problem defines which units must operate and how much power they must deliver to satisfy the system demand over a planning horizon of up to two weeks. The problem is typically formulated as a large-scale mixed-integer linear programming problem, where off-the-shelf commercial solvers generally struggle to efficiently solve realistic instances of the STGS, mainly due to the large-scale of these models. Thus, decomposition approaches that break the model into smaller instances that are more easily handled are attractive alternatives to directly employing these solvers. This paper proposes a dual dynamic integer programming (DDiP) framework for solving the STGS problem efficiently. As in the standard DDiP approach, we use a nested Benders decomposition over the time horizon but introduce multiperiod stages and overlap strategies to accelerate the method. Simulations performed on the IEEE-118 system show that the pro-posed approach is significantly faster than standard DDiP and can deliver near-optimal solutions.
Keyword:
Dual dynamic integer programming
Short-term generation scheduling
Mixed -integer linear programming
期刊
I
IF:
5
论文数:
1.1W
被引数:
3.1W
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