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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Vydané v:European journal of operational research Ročník 271; číslo 3; s. 1037 - 1054
Hlavní autori: Lara, Cristiana L., Mallapragada, Dharik S., Papageorgiou, Dimitri J., Venkatesh, Aranya, Grossmann, Ignacio E.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 16.12.2018
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ISSN:0377-2217, 1872-6860
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Abstract •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.
AbstractList •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.
Author Venkatesh, Aranya
Lara, Cristiana L.
Grossmann, Ignacio E.
Mallapragada, Dharik S.
Papageorgiou, Dimitri J.
Author_xml – sequence: 1
  givenname: Cristiana L.
  surname: Lara
  fullname: Lara, Cristiana L.
  organization: Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
– sequence: 2
  givenname: Dharik S.
  surname: Mallapragada
  fullname: Mallapragada, Dharik S.
  organization: Corporate Strategic Research, ExxonMobil Research and Engineering Company, 1545 Route 22 East, Annandale, NJ 08801, USA
– sequence: 3
  givenname: Dimitri J.
  surname: Papageorgiou
  fullname: Papageorgiou, Dimitri J.
  organization: Corporate Strategic Research, ExxonMobil Research and Engineering Company, 1545 Route 22 East, Annandale, NJ 08801, USA
– sequence: 4
  givenname: Aranya
  surname: Venkatesh
  fullname: Venkatesh, Aranya
  organization: Corporate Strategic Research, ExxonMobil Research and Engineering Company, 1545 Route 22 East, Annandale, NJ 08801, USA
– sequence: 5
  givenname: Ignacio E.
  orcidid: 0000-0002-7210-084X
  surname: Grossmann
  fullname: Grossmann, Ignacio E.
  email: grossmann@cmu.edu
  organization: Department of Chemical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
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SubjectTerms Large-scale optimization
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Strategic planning
Title Deterministic electric power infrastructure planning: Mixed-integer programming model and nested decomposition algorithm
URI https://dx.doi.org/10.1016/j.ejor.2018.05.039
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