Accelerated dual dynamic integer programming applied to short-term power generation scheduling

•Use of Dual Dynamic Integer Programming framework.•Inclusion of multiperiod stages and overlapping strategies to accelerate the method.•Solving the Short-Term Power Generation Scheduling problem.•Validation of the proposed scheme on the IEEE 118-bus system. The short-term generation scheduling (STG...

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Vydané v:	International journal of electrical power & energy systems Ročník 145; s. 108689
Hlavní autori:	Santos, Kenny Vinente dos, Colonetti, Bruno, Finardi, Erlon Cristian, Zavala, Victor M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.02.2023
Predmet:	Dual dynamic integer programming Mixed-integer linear programming Short-term generation scheduling Short-term generation scheduling Dual dynamic integer programming Mixed-integer linear programming
ISSN:	0142-0615
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Popis
Shrnutí:	•Use of Dual Dynamic Integer Programming framework.•Inclusion of multiperiod stages and overlapping strategies to accelerate the method.•Solving the Short-Term Power Generation Scheduling problem.•Validation of the proposed scheme on the IEEE 118-bus system. 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 proposed approach is significantly faster than standard DDiP and can deliver near-optimal solutions.
ISSN:	0142-0615
DOI:	10.1016/j.ijepes.2022.108689