Optimization of steel production scheduling with complex time-sensitive electricity cost
•Scheduling models can be extended with minimum-cost network flow for energy-cost optimization.•A large monolithic formulation can be avoided with the bi-level heuristic algorithm.•Bi-level heuristic can solve industrial size problems. Energy-intensive industries can take advantage of process flexib...
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Veröffentlicht in: | Computers & chemical engineering 2015-05, Vol.76, p.117-136 |
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creator | Hadera, Hubert Harjunkoski, Iiro Sand, Guido Grossmann, Ignacio E. Engell, Sebastian |
description | •Scheduling models can be extended with minimum-cost network flow for energy-cost optimization.•A large monolithic formulation can be avoided with the bi-level heuristic algorithm.•Bi-level heuristic can solve industrial size problems.
Energy-intensive industries can take advantage of process flexibility to reduce operating costs by optimal scheduling of production tasks. In this study, we develop an MILP formulation to extend a continuous-time model with energy-awareness to optimize the daily production schedules and the electricity purchase including the load commitment problem. The sources of electricity that are considered are purchase on volatile markets, time-of-use and base load contracts, as well as onsite generation. The possibility to sell electricity back to the grid is also included. The model is applied to the melt shop section of a stainless steel plant. Due to the large-scale nature of the combinatorial problem, we propose a bi-level heuristic algorithm to tackle instances of industrial size. Case studies show that the potential impact of high prices in the day-ahead markets of electricity can be mitigated by jointly optimizing the production schedule and the associated net electricity consumption cost. |
doi_str_mv | 10.1016/j.compchemeng.2015.02.004 |
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Energy-intensive industries can take advantage of process flexibility to reduce operating costs by optimal scheduling of production tasks. In this study, we develop an MILP formulation to extend a continuous-time model with energy-awareness to optimize the daily production schedules and the electricity purchase including the load commitment problem. The sources of electricity that are considered are purchase on volatile markets, time-of-use and base load contracts, as well as onsite generation. The possibility to sell electricity back to the grid is also included. The model is applied to the melt shop section of a stainless steel plant. Due to the large-scale nature of the combinatorial problem, we propose a bi-level heuristic algorithm to tackle instances of industrial size. Case studies show that the potential impact of high prices in the day-ahead markets of electricity can be mitigated by jointly optimizing the production schedule and the associated net electricity consumption cost.</description><identifier>ISSN: 0098-1354</identifier><identifier>EISSN: 1873-4375</identifier><identifier>DOI: 10.1016/j.compchemeng.2015.02.004</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Computer simulation ; Continuous-time models ; Demand-side management ; Electric potential ; Electricity ; Energy optimization ; Marketing ; Markets ; Mathematical models ; Optimization ; Schedules ; Scheduling ; Steel plant</subject><ispartof>Computers & chemical engineering, 2015-05, Vol.76, p.117-136</ispartof><rights>2015 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c442t-d4e56dba55e9e969cf3d5b8959aa08cf21ed6ecec8b0b447943d4ffc0d0aa25f3</citedby><cites>FETCH-LOGICAL-c442t-d4e56dba55e9e969cf3d5b8959aa08cf21ed6ecec8b0b447943d4ffc0d0aa25f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.compchemeng.2015.02.004$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Hadera, Hubert</creatorcontrib><creatorcontrib>Harjunkoski, Iiro</creatorcontrib><creatorcontrib>Sand, Guido</creatorcontrib><creatorcontrib>Grossmann, Ignacio E.</creatorcontrib><creatorcontrib>Engell, Sebastian</creatorcontrib><title>Optimization of steel production scheduling with complex time-sensitive electricity cost</title><title>Computers & chemical engineering</title><description>•Scheduling models can be extended with minimum-cost network flow for energy-cost optimization.•A large monolithic formulation can be avoided with the bi-level heuristic algorithm.•Bi-level heuristic can solve industrial size problems.
Energy-intensive industries can take advantage of process flexibility to reduce operating costs by optimal scheduling of production tasks. In this study, we develop an MILP formulation to extend a continuous-time model with energy-awareness to optimize the daily production schedules and the electricity purchase including the load commitment problem. The sources of electricity that are considered are purchase on volatile markets, time-of-use and base load contracts, as well as onsite generation. The possibility to sell electricity back to the grid is also included. The model is applied to the melt shop section of a stainless steel plant. Due to the large-scale nature of the combinatorial problem, we propose a bi-level heuristic algorithm to tackle instances of industrial size. Case studies show that the potential impact of high prices in the day-ahead markets of electricity can be mitigated by jointly optimizing the production schedule and the associated net electricity consumption cost.</description><subject>Computer simulation</subject><subject>Continuous-time models</subject><subject>Demand-side management</subject><subject>Electric potential</subject><subject>Electricity</subject><subject>Energy optimization</subject><subject>Marketing</subject><subject>Markets</subject><subject>Mathematical models</subject><subject>Optimization</subject><subject>Schedules</subject><subject>Scheduling</subject><subject>Steel plant</subject><issn>0098-1354</issn><issn>1873-4375</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><recordid>eNqNkEtPwzAQhC0EEqXwH8KNS8I6sRP7iCpeUqVeQOJmOfamdZUXsVMev5605cCR00qr-WZ3hpBrCgkFmt9uE9M1vdlgg-06SYHyBNIEgJ2QGRVFFrOs4KdkBiBFTDPOzsmF91sASJkQM_K26oNr3LcOrmujrop8QKyjfujsaA47P5nbsXbtOvpwYRPt79X4GU0Yxh5b74LbYYQ1mjA448LXJPHhkpxVuvZ49Tvn5PXh_mXxFC9Xj8-Lu2VsGEtDbBny3Jaac5Qoc2mqzPJSSC61BmGqlKLN0aARJZSMFZJlllWVAQtap7zK5uTm6Du9_D6iD6px3mBd6xa70StaFJCBEMAmqTxKzdB5P2Cl-sE1evhSFNS-TbVVf9pU-zYVpAoO7OLI4pRl53BQ3jhsDVo3TMGV7dw_XH4AwY2Heg</recordid><startdate>20150508</startdate><enddate>20150508</enddate><creator>Hadera, Hubert</creator><creator>Harjunkoski, Iiro</creator><creator>Sand, Guido</creator><creator>Grossmann, Ignacio E.</creator><creator>Engell, Sebastian</creator><general>Elsevier Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7U5</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20150508</creationdate><title>Optimization of steel production scheduling with complex time-sensitive electricity cost</title><author>Hadera, Hubert ; Harjunkoski, Iiro ; Sand, Guido ; Grossmann, Ignacio E. ; Engell, Sebastian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c442t-d4e56dba55e9e969cf3d5b8959aa08cf21ed6ecec8b0b447943d4ffc0d0aa25f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Computer simulation</topic><topic>Continuous-time models</topic><topic>Demand-side management</topic><topic>Electric potential</topic><topic>Electricity</topic><topic>Energy optimization</topic><topic>Marketing</topic><topic>Markets</topic><topic>Mathematical models</topic><topic>Optimization</topic><topic>Schedules</topic><topic>Scheduling</topic><topic>Steel plant</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hadera, Hubert</creatorcontrib><creatorcontrib>Harjunkoski, Iiro</creatorcontrib><creatorcontrib>Sand, Guido</creatorcontrib><creatorcontrib>Grossmann, Ignacio E.</creatorcontrib><creatorcontrib>Engell, Sebastian</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers & chemical engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hadera, Hubert</au><au>Harjunkoski, Iiro</au><au>Sand, Guido</au><au>Grossmann, Ignacio E.</au><au>Engell, Sebastian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimization of steel production scheduling with complex time-sensitive electricity cost</atitle><jtitle>Computers & chemical engineering</jtitle><date>2015-05-08</date><risdate>2015</risdate><volume>76</volume><spage>117</spage><epage>136</epage><pages>117-136</pages><issn>0098-1354</issn><eissn>1873-4375</eissn><abstract>•Scheduling models can be extended with minimum-cost network flow for energy-cost optimization.•A large monolithic formulation can be avoided with the bi-level heuristic algorithm.•Bi-level heuristic can solve industrial size problems.
Energy-intensive industries can take advantage of process flexibility to reduce operating costs by optimal scheduling of production tasks. In this study, we develop an MILP formulation to extend a continuous-time model with energy-awareness to optimize the daily production schedules and the electricity purchase including the load commitment problem. The sources of electricity that are considered are purchase on volatile markets, time-of-use and base load contracts, as well as onsite generation. The possibility to sell electricity back to the grid is also included. The model is applied to the melt shop section of a stainless steel plant. Due to the large-scale nature of the combinatorial problem, we propose a bi-level heuristic algorithm to tackle instances of industrial size. Case studies show that the potential impact of high prices in the day-ahead markets of electricity can be mitigated by jointly optimizing the production schedule and the associated net electricity consumption cost.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.compchemeng.2015.02.004</doi><tpages>20</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Computer simulation Continuous-time models Demand-side management Electric potential Electricity Energy optimization Marketing Markets Mathematical models Optimization Schedules Scheduling Steel plant |
title | Optimization of steel production scheduling with complex time-sensitive electricity cost |
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