PittsburghV1, PascalFrancis, Corpus, bibRecord, 000954

Optimal multi-scale capacity planning for power-intensive continuous processes under time-sensitive electricity prices and demand uncertainty. Part II: Enhanced hybrid bi-level decomposition

Identifieur interne : 000954 ( PascalFrancis/Corpus ); précédent : 000953; suivant : 000955

Optimal multi-scale capacity planning for power-intensive continuous processes under time-sensitive electricity prices and demand uncertainty. Part II: Enhanced hybrid bi-level decomposition

Auteurs : Sumit Mitra ; Jose M. Pinto ; Ignacio E. Grossmann

Source :

Computers & chemical engineering [ 0098-1354 ] ; 2014.

RBID : Pascal:14-0165507

Descripteurs français

Pascal (Inist)
- Planification optimale, Méthode échelle multiple, Capacité production, Gestion production, Réseau électrique, En continu, Temps continu, Offre et demande, Prix vente, Système incertain, Echelle grande, Programmation stochastique, Fonction pénalité, Programmation partiellement en nombres entiers, Problème mixte, Méthode partition, Programmation linéaire, Multiplicateur Lagrange, Investissement, Algorithme parallèle, Modélisation, Etude cas, Economies d'énergie, ., Réseau électrique intelligent.

English descriptors

KwdEn :
- Case study, Continuous process, Continuous time, Electrical network, Energy savings, Investment, Lagrange multiplier, Large scale, Linear programming, Mixed integer programming, Mixed problem, Modeling, Multiscale method, Optimal planning, Parallel algorithm, Partition method, Penalty function, Production capacity, Production management, Selling price, Smart grid, Stochastic programming, Supply demand balance, Uncertain system.

Abstract

We describe a hybrid bi-level decomposition scheme that addresses the challenge of solving a large-scale two-stage stochastic programming problem with mixed-integer recourse, which results from a multi-scale capacity planning problem as described in Part I of this paper series. The decomposition scheme combines bi-level decomposition with Benders decomposition, and relies on additional strengthening cuts from a Lagrangean-type relaxation and subset-type cuts from structure in the linking constraints between investment and operational variables. The application of the scheme with a parallel implementation to an industrial case study reduces the computational time by two orders of magnitude when compared with the time required for the solution of the full-space model.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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Format Inist (serveur)

NO :	PASCAL 14-0165507 INIST
ET :	Optimal multi-scale capacity planning for power-intensive continuous processes under time-sensitive electricity prices and demand uncertainty. Part II: Enhanced hybrid bi-level decomposition
AU :	MITRA (Sumit); PINTO (Jose M.); GROSSMANN (Ignacio E.)
AF :	Center for Advanced Process Decision-making, Department of Chemical Engineering, Carnegie Mellon University/Pittsburgh, PA 15213/Etats-Unis (1 aut., 3 aut.); Praxair, Inc./Danbury, CT 06810/Etats-Unis (2 aut.)
DT :	Publication en série; Niveau analytique
SO :	Computers & chemical engineering; ISSN 0098-1354; Coden CCENDW; Royaume-Uni; Da. 2014; Vol. 65; Pp. 102-111; Bibl. 1/4 p.
LA :	Anglais
EA :	We describe a hybrid bi-level decomposition scheme that addresses the challenge of solving a large-scale two-stage stochastic programming problem with mixed-integer recourse, which results from a multi-scale capacity planning problem as described in Part I of this paper series. The decomposition scheme combines bi-level decomposition with Benders decomposition, and relies on additional strengthening cuts from a Lagrangean-type relaxation and subset-type cuts from structure in the linking constraints between investment and operational variables. The application of the scheme with a parallel implementation to an industrial case study reduces the computational time by two orders of magnitude when compared with the time required for the solution of the full-space model.
CC :	001D01A13; 001D05I01H; 001D01A03; 001D06A; 230
FD :	Planification optimale; Méthode échelle multiple; Capacité production; Gestion production; Réseau électrique; En continu; Temps continu; Offre et demande; Prix vente; Système incertain; Echelle grande; Programmation stochastique; Fonction pénalité; Programmation partiellement en nombres entiers; Problème mixte; Méthode partition; Programmation linéaire; Multiplicateur Lagrange; Investissement; Algorithme parallèle; Modélisation; Etude cas; Economies d'énergie; .; Réseau électrique intelligent
ED :	Optimal planning; Multiscale method; Production capacity; Production management; Electrical network; Continuous process; Continuous time; Supply demand balance; Selling price; Uncertain system; Large scale; Stochastic programming; Penalty function; Mixed integer programming; Mixed problem; Partition method; Linear programming; Lagrange multiplier; Investment; Parallel algorithm; Modeling; Case study; Energy savings; Smart grid
SD :	Planificación óptima; Método escala múltiple; Capacidad producción; Gestión producción; Red eléctrica; En continuo; Tiempo continuo; Oferta y demanda; Precio venta; Sistema incierto; Escala grande; Programación estocástica; Función penalidad; Programación mixta entera; Problema mixto; Método partición; Programación lineal; Multiplicador Lagrange; Inversión; Algoritmo paralelo; Modelización; Estudio caso; Ahorros energía; Red eléctrica inteligente
LO :	INIST-16409.354000503228000100
ID :	14-0165507

Links to Exploration step

Pascal:14-0165507

Le document en format XML

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<front><div type="abstract" xml:lang="en">We describe a hybrid bi-level decomposition scheme that addresses the challenge of solving a large-scale two-stage stochastic programming problem with mixed-integer recourse, which results from a multi-scale capacity planning problem as described in Part I of this paper series. The decomposition scheme combines bi-level decomposition with Benders decomposition, and relies on additional strengthening cuts from a Lagrangean-type relaxation and subset-type cuts from structure in the linking constraints between investment and operational variables. The application of the scheme with a parallel implementation to an industrial case study reduces the computational time by two orders of magnitude when compared with the time required for the solution of the full-space model.</div>
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<s5>08</s5>
</fC03>
<fC03 i1="04" i2="X" l="FRE"><s0>Gestion production</s0>
<s5>09</s5>
</fC03>
<fC03 i1="04" i2="X" l="ENG"><s0>Production management</s0>
<s5>09</s5>
</fC03>
<fC03 i1="04" i2="X" l="SPA"><s0>Gestión producción</s0>
<s5>09</s5>
</fC03>
<fC03 i1="05" i2="X" l="FRE"><s0>Réseau électrique</s0>
<s5>10</s5>
</fC03>
<fC03 i1="05" i2="X" l="ENG"><s0>Electrical network</s0>
<s5>10</s5>
</fC03>
<fC03 i1="05" i2="X" l="SPA"><s0>Red eléctrica</s0>
<s5>10</s5>
</fC03>
<fC03 i1="06" i2="X" l="FRE"><s0>En continu</s0>
<s5>11</s5>
</fC03>
<fC03 i1="06" i2="X" l="ENG"><s0>Continuous process</s0>
<s5>11</s5>
</fC03>
<fC03 i1="06" i2="X" l="SPA"><s0>En continuo</s0>
<s5>11</s5>
</fC03>
<fC03 i1="07" i2="X" l="FRE"><s0>Temps continu</s0>
<s5>12</s5>
</fC03>
<fC03 i1="07" i2="X" l="ENG"><s0>Continuous time</s0>
<s5>12</s5>
</fC03>
<fC03 i1="07" i2="X" l="SPA"><s0>Tiempo continuo</s0>
<s5>12</s5>
</fC03>
<fC03 i1="08" i2="X" l="FRE"><s0>Offre et demande</s0>
<s5>13</s5>
</fC03>
<fC03 i1="08" i2="X" l="ENG"><s0>Supply demand balance</s0>
<s5>13</s5>
</fC03>
<fC03 i1="08" i2="X" l="SPA"><s0>Oferta y demanda</s0>
<s5>13</s5>
</fC03>
<fC03 i1="09" i2="X" l="FRE"><s0>Prix vente</s0>
<s5>14</s5>
</fC03>
<fC03 i1="09" i2="X" l="ENG"><s0>Selling price</s0>
<s5>14</s5>
</fC03>
<fC03 i1="09" i2="X" l="SPA"><s0>Precio venta</s0>
<s5>14</s5>
</fC03>
<fC03 i1="10" i2="X" l="FRE"><s0>Système incertain</s0>
<s5>15</s5>
</fC03>
<fC03 i1="10" i2="X" l="ENG"><s0>Uncertain system</s0>
<s5>15</s5>
</fC03>
<fC03 i1="10" i2="X" l="SPA"><s0>Sistema incierto</s0>
<s5>15</s5>
</fC03>
<fC03 i1="11" i2="X" l="FRE"><s0>Echelle grande</s0>
<s5>16</s5>
</fC03>
<fC03 i1="11" i2="X" l="ENG"><s0>Large scale</s0>
<s5>16</s5>
</fC03>
<fC03 i1="11" i2="X" l="SPA"><s0>Escala grande</s0>
<s5>16</s5>
</fC03>
<fC03 i1="12" i2="X" l="FRE"><s0>Programmation stochastique</s0>
<s5>17</s5>
</fC03>
<fC03 i1="12" i2="X" l="ENG"><s0>Stochastic programming</s0>
<s5>17</s5>
</fC03>
<fC03 i1="12" i2="X" l="SPA"><s0>Programación estocástica</s0>
<s5>17</s5>
</fC03>
<fC03 i1="13" i2="X" l="FRE"><s0>Fonction pénalité</s0>
<s5>18</s5>
</fC03>
<fC03 i1="13" i2="X" l="ENG"><s0>Penalty function</s0>
<s5>18</s5>
</fC03>
<fC03 i1="13" i2="X" l="SPA"><s0>Función penalidad</s0>
<s5>18</s5>
</fC03>
<fC03 i1="14" i2="X" l="FRE"><s0>Programmation partiellement en nombres entiers</s0>
<s5>19</s5>
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<s5>19</s5>
</fC03>
<fC03 i1="14" i2="X" l="SPA"><s0>Programación mixta entera</s0>
<s5>19</s5>
</fC03>
<fC03 i1="15" i2="X" l="FRE"><s0>Problème mixte</s0>
<s5>20</s5>
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<s5>20</s5>
</fC03>
<fC03 i1="15" i2="X" l="SPA"><s0>Problema mixto</s0>
<s5>20</s5>
</fC03>
<fC03 i1="16" i2="X" l="FRE"><s0>Méthode partition</s0>
<s5>21</s5>
</fC03>
<fC03 i1="16" i2="X" l="ENG"><s0>Partition method</s0>
<s5>21</s5>
</fC03>
<fC03 i1="16" i2="X" l="SPA"><s0>Método partición</s0>
<s5>21</s5>
</fC03>
<fC03 i1="17" i2="X" l="FRE"><s0>Programmation linéaire</s0>
<s5>22</s5>
</fC03>
<fC03 i1="17" i2="X" l="ENG"><s0>Linear programming</s0>
<s5>22</s5>
</fC03>
<fC03 i1="17" i2="X" l="SPA"><s0>Programación lineal</s0>
<s5>22</s5>
</fC03>
<fC03 i1="18" i2="X" l="FRE"><s0>Multiplicateur Lagrange</s0>
<s5>23</s5>
</fC03>
<fC03 i1="18" i2="X" l="ENG"><s0>Lagrange multiplier</s0>
<s5>23</s5>
</fC03>
<fC03 i1="18" i2="X" l="SPA"><s0>Multiplicador Lagrange</s0>
<s5>23</s5>
</fC03>
<fC03 i1="19" i2="X" l="FRE"><s0>Investissement</s0>
<s5>24</s5>
</fC03>
<fC03 i1="19" i2="X" l="ENG"><s0>Investment</s0>
<s5>24</s5>
</fC03>
<fC03 i1="19" i2="X" l="SPA"><s0>Inversión</s0>
<s5>24</s5>
</fC03>
<fC03 i1="20" i2="X" l="FRE"><s0>Algorithme parallèle</s0>
<s5>25</s5>
</fC03>
<fC03 i1="20" i2="X" l="ENG"><s0>Parallel algorithm</s0>
<s5>25</s5>
</fC03>
<fC03 i1="20" i2="X" l="SPA"><s0>Algoritmo paralelo</s0>
<s5>25</s5>
</fC03>
<fC03 i1="21" i2="X" l="FRE"><s0>Modélisation</s0>
<s5>26</s5>
</fC03>
<fC03 i1="21" i2="X" l="ENG"><s0>Modeling</s0>
<s5>26</s5>
</fC03>
<fC03 i1="21" i2="X" l="SPA"><s0>Modelización</s0>
<s5>26</s5>
</fC03>
<fC03 i1="22" i2="X" l="FRE"><s0>Etude cas</s0>
<s5>27</s5>
</fC03>
<fC03 i1="22" i2="X" l="ENG"><s0>Case study</s0>
<s5>27</s5>
</fC03>
<fC03 i1="22" i2="X" l="SPA"><s0>Estudio caso</s0>
<s5>27</s5>
</fC03>
<fC03 i1="23" i2="X" l="FRE"><s0>Economies d'énergie</s0>
<s5>41</s5>
</fC03>
<fC03 i1="23" i2="X" l="ENG"><s0>Energy savings</s0>
<s5>41</s5>
</fC03>
<fC03 i1="23" i2="X" l="SPA"><s0>Ahorros energía</s0>
<s5>41</s5>
</fC03>
<fC03 i1="24" i2="X" l="FRE"><s0>.</s0>
<s4>INC</s4>
<s5>82</s5>
</fC03>
<fC03 i1="25" i2="X" l="FRE"><s0>Réseau électrique intelligent</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="25" i2="X" l="ENG"><s0>Smart grid</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fC03 i1="25" i2="X" l="SPA"><s0>Red eléctrica inteligente</s0>
<s4>CD</s4>
<s5>96</s5>
</fC03>
<fN21><s1>209</s1>
</fN21>
<fN44 i1="01"><s1>OTO</s1>
</fN44>
<fN82><s1>OTO</s1>
</fN82>
</pA>
</standard>
<server><NO>PASCAL 14-0165507 INIST</NO>
<ET>Optimal multi-scale capacity planning for power-intensive continuous processes under time-sensitive electricity prices and demand uncertainty. Part II: Enhanced hybrid bi-level decomposition</ET>
<AU>MITRA (Sumit); PINTO (Jose M.); GROSSMANN (Ignacio E.)</AU>
<AF>Center for Advanced Process Decision-making, Department of Chemical Engineering, Carnegie Mellon University/Pittsburgh, PA 15213/Etats-Unis (1 aut., 3 aut.); Praxair, Inc./Danbury, CT 06810/Etats-Unis (2 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>Computers & chemical engineering; ISSN 0098-1354; Coden CCENDW; Royaume-Uni; Da. 2014; Vol. 65; Pp. 102-111; Bibl. 1/4 p.</SO>
<LA>Anglais</LA>
<EA>We describe a hybrid bi-level decomposition scheme that addresses the challenge of solving a large-scale two-stage stochastic programming problem with mixed-integer recourse, which results from a multi-scale capacity planning problem as described in Part I of this paper series. The decomposition scheme combines bi-level decomposition with Benders decomposition, and relies on additional strengthening cuts from a Lagrangean-type relaxation and subset-type cuts from structure in the linking constraints between investment and operational variables. The application of the scheme with a parallel implementation to an industrial case study reduces the computational time by two orders of magnitude when compared with the time required for the solution of the full-space model.</EA>
<CC>001D01A13; 001D05I01H; 001D01A03; 001D06A; 230</CC>
<FD>Planification optimale; Méthode échelle multiple; Capacité production; Gestion production; Réseau électrique; En continu; Temps continu; Offre et demande; Prix vente; Système incertain; Echelle grande; Programmation stochastique; Fonction pénalité; Programmation partiellement en nombres entiers; Problème mixte; Méthode partition; Programmation linéaire; Multiplicateur Lagrange; Investissement; Algorithme parallèle; Modélisation; Etude cas; Economies d'énergie; .; Réseau électrique intelligent</FD>
<ED>Optimal planning; Multiscale method; Production capacity; Production management; Electrical network; Continuous process; Continuous time; Supply demand balance; Selling price; Uncertain system; Large scale; Stochastic programming; Penalty function; Mixed integer programming; Mixed problem; Partition method; Linear programming; Lagrange multiplier; Investment; Parallel algorithm; Modeling; Case study; Energy savings; Smart grid</ED>
<SD>Planificación óptima; Método escala múltiple; Capacidad producción; Gestión producción; Red eléctrica; En continuo; Tiempo continuo; Oferta y demanda; Precio venta; Sistema incierto; Escala grande; Programación estocástica; Función penalidad; Programación mixta entera; Problema mixto; Método partición; Programación lineal; Multiplicador Lagrange; Inversión; Algoritmo paralelo; Modelización; Estudio caso; Ahorros energía; Red eléctrica inteligente</SD>
<LO>INIST-16409.354000503228000100</LO>
<ID>14-0165507</ID>
</server>
</inist>
</record>

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Optimal multi-scale capacity planning for power-intensive continuous processes under time-sensitive electricity prices and demand uncertainty. Part II: Enhanced hybrid bi-level decomposition

Optimal multi-scale capacity planning for power-intensive continuous processes under time-sensitive electricity prices and demand uncertainty. Part II: Enhanced hybrid bi-level decomposition

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