Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm

•Mixed-integer model optimizes generation investment and hourly operation decisions.•Aggregation and time sampling for reducing dimension of spatial and temporal scales.•Extension Nested Benders Decomposition for large instances of multi-period model.•Model and solution method used in Texas case stu...

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Vydáno v:European journal of operational research Ročník 271; číslo 3; s. 1037 - 1054
Hlavní autoři: Lara, Cristiana L., Mallapragada, Dharik S., Papageorgiou, Dimitri J., Venkatesh, Aranya, Grossmann, Ignacio E.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 16.12.2018
Témata:
Large-scale optimization
OR in energy
Strategic planning
OR in energy
Strategic planning
Large-scale optimization
ISSN:0377-2217, 1872-6860
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Shrnutí:•Mixed-integer model optimizes generation investment and hourly operation decisions.•Aggregation and time sampling for reducing dimension of spatial and temporal scales.•Extension Nested Benders Decomposition for large instances of multi-period model.•Model and solution method used in Texas case study with large computational savings. This paper addresses the long-term planning of electric power infrastructures considering high renewable penetration. To capture the intermittency of these sources, we propose a deterministic multi-scale Mixed-Integer Linear Programming (MILP) formulation that simultaneously considers annual generation investment decisions and hourly operational decisions. We adopt judicious approximations and aggregations to improve its tractability. Moreover, to overcome the computational challenges of treating hourly operational decisions within a monolithic multi-year planning horizon, we propose a decomposition algorithm based on Nested Benders Decomposition for multi-period MILP problems to allow the solution of larger instances. Our decomposition adapts previous nested Benders methods by handling integer and continuous state variables, although at the expense of losing its finite convergence property due to potential duality gap. We apply the proposed modeling framework to a case study in the Electric Reliability Council of Texas (ERCOT) region, and demonstrate massive computational savings from our decomposition.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2018.05.039