Imitation Learning Based Fast Power System Production Cost Minimization Simulation
Production cost minimization (PCM) simulation is an important tool for long-term power system simulation and assessment. However, solving a PCM problem is always time-consuming for its numerous binary variables. Besides, as modern energy systems have various planning options, the slow solution speed...
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| Veröffentlicht in: | IEEE transactions on power systems 2023-05, Vol.38 (3), p.1-4 |
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| container_title | IEEE transactions on power systems |
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| creator | Hu, Qinran Guo, Zishan Li, Fangxing |
| description | Production cost minimization (PCM) simulation is an important tool for long-term power system simulation and assessment. However, solving a PCM problem is always time-consuming for its numerous binary variables. Besides, as modern energy systems have various planning options, the slow solution speed of PCM problems cannot satisfy the requirement of quick assessment of various plans. Most previous works on accelerating PCM problems ignore the importance of accurate solutions on proper assessment but only provide approximate solutions. Therefore, this work provides a fast PCM simulation method with optimality guarantee based on imitation learning. Compared with the popular open-source solver SCIP under default rules, the proposed method can find the optimal solution faster or provide smaller gap when the preset solving time limit hits. Simulation results show the effectiveness of the proposed method. |
| doi_str_mv | 10.1109/TPWRS.2023.3237398 |
| format | Article |
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However, solving a PCM problem is always time-consuming for its numerous binary variables. Besides, as modern energy systems have various planning options, the slow solution speed of PCM problems cannot satisfy the requirement of quick assessment of various plans. Most previous works on accelerating PCM problems ignore the importance of accurate solutions on proper assessment but only provide approximate solutions. Therefore, this work provides a fast PCM simulation method with optimality guarantee based on imitation learning. Compared with the popular open-source solver SCIP under default rules, the proposed method can find the optimal solution faster or provide smaller gap when the preset solving time limit hits. Simulation results show the effectiveness of the proposed method.</description><identifier>ISSN: 0885-8950</identifier><identifier>EISSN: 1558-0679</identifier><identifier>DOI: 10.1109/TPWRS.2023.3237398</identifier><identifier>CODEN: ITPSEG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Costs ; Fast production cost minimization simulation ; Generators ; imitation learning ; Input variables ; Learning ; Minimization ; Optimization ; Phase change materials ; power system planning ; Production costs ; Simulation ; Training ; Wind farms</subject><ispartof>IEEE transactions on power systems, 2023-05, Vol.38 (3), p.1-4</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Simulation results show the effectiveness of the proposed method.</description><subject>Costs</subject><subject>Fast production cost minimization simulation</subject><subject>Generators</subject><subject>imitation learning</subject><subject>Input variables</subject><subject>Learning</subject><subject>Minimization</subject><subject>Optimization</subject><subject>Phase change materials</subject><subject>power system planning</subject><subject>Production costs</subject><subject>Simulation</subject><subject>Training</subject><subject>Wind farms</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkE1Lw0AQhhdRsFb_gHgIeE6d3c1-5KjFaqFiaSsel20ykS1NUncTpP5606YHTzPwvs8MPITcUhhRCunDav65WI4YMD7ijCue6jMyoELoGKRKz8kAtBaxTgVckqsQNgAgu2BAFtPSNbZxdRXN0PrKVV_Rkw2YRxMbmmhe_6CPlvvQYBnNfZ232bE7rrvwzVWudL89vXRluz2u1-SisNuAN6c5JB-T59X4NZ69v0zHj7M444loYplZmSqLFjRnwjKFjCJmUoAUMsGC6oTStaW5prLIlZKFUJBmCWU8YYqt-ZDc93d3vv5uMTRmU7e-6l4apkGyzgajXYv1rczXIXgszM670vq9oWAO7szRnTm4Myd3HXTXQw4R_wFANVPA_wCKE2q-</recordid><startdate>20230501</startdate><enddate>20230501</enddate><creator>Hu, Qinran</creator><creator>Guo, Zishan</creator><creator>Li, Fangxing</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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However, solving a PCM problem is always time-consuming for its numerous binary variables. Besides, as modern energy systems have various planning options, the slow solution speed of PCM problems cannot satisfy the requirement of quick assessment of various plans. Most previous works on accelerating PCM problems ignore the importance of accurate solutions on proper assessment but only provide approximate solutions. Therefore, this work provides a fast PCM simulation method with optimality guarantee based on imitation learning. Compared with the popular open-source solver SCIP under default rules, the proposed method can find the optimal solution faster or provide smaller gap when the preset solving time limit hits. Simulation results show the effectiveness of the proposed method.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPWRS.2023.3237398</doi><tpages>4</tpages><orcidid>https://orcid.org/0000-0002-5398-5718</orcidid><orcidid>https://orcid.org/0000-0003-1060-7618</orcidid></addata></record> |
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| subjects | Costs Fast production cost minimization simulation Generators imitation learning Input variables Learning Minimization Optimization Phase change materials power system planning Production costs Simulation Training Wind farms |
| title | Imitation Learning Based Fast Power System Production Cost Minimization Simulation |
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