A comparative study of multi-agent control approaches for optimization of central cooling systems without significant storage
This paper presents the application of a multi-agent control methodology to large chiller plants. The approach, originally conceived to automate controller design and reduce engineering costs in building energy systems, consisted of a multi-agent simulation framework with distributed consensus-based...
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| Published in: | Science & technology for the built environment Vol. 26; no. 8; pp. 1065 - 1081 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Taylor & Francis
13.09.2020
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| ISSN: | 2374-4731, 2374-474X |
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| Abstract | This paper presents the application of a multi-agent control methodology to large chiller plants. The approach, originally conceived to automate controller design and reduce engineering costs in building energy systems, consisted of a multi-agent simulation framework with distributed consensus-based optimization algorithms. To adapt the approach to this scenario, agents representing physical components of a cooling plant were developed and incorporated in the framework along with two alternative optimization methods: centralized, parallel optimization with a genetic algorithm (GA), and a combination of the GA with a quasi-newton method to handle non-linear equality constraints associated to physical component behavior. An existing cooling plant was utilized as case study to simulate the performance of the methods under different operating conditions. The results demonstrated the difficulty of the consensus-based algorithms to find optimal solutions. The GAs, on the other hand, showed that significant energy savings can be achieved through the implementation of multi-agent control with algorithms capable of handling non-convex objective functions and a combination of discrete and continuous variables, which are characteristic of central cooling systems. |
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| AbstractList | This paper presents the application of a multi-agent control methodology to large chiller plants. The approach, originally conceived to automate controller design and reduce engineering costs in building energy systems, consisted of a multi-agent simulation framework with distributed consensus-based optimization algorithms. To adapt the approach to this scenario, agents representing physical components of a cooling plant were developed and incorporated in the framework along with two alternative optimization methods: centralized, parallel optimization with a genetic algorithm (GA), and a combination of the GA with a quasi-newton method to handle non-linear equality constraints associated to physical component behavior. An existing cooling plant was utilized as case study to simulate the performance of the methods under different operating conditions. The results demonstrated the difficulty of the consensus-based algorithms to find optimal solutions. The GAs, on the other hand, showed that significant energy savings can be achieved through the implementation of multi-agent control with algorithms capable of handling non-convex objective functions and a combination of discrete and continuous variables, which are characteristic of central cooling systems. |
| Author | Jaramillo, Rita C. Horton, W. Travis Braun, James E. |
| Author_xml | – sequence: 1 givenname: Rita C. surname: Jaramillo fullname: Jaramillo, Rita C. email: rita_cristi@yahoo.com organization: School of Mechanical Engineering, Purdue University – sequence: 2 givenname: James E. surname: Braun fullname: Braun, James E. organization: School of Mechanical Engineering, Purdue University – sequence: 3 givenname: W. Travis surname: Horton fullname: Horton, W. Travis organization: School of Mechanical Engineering, Purdue University |
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| Cites_doi | 10.1109/IECON.2010.5675530 10.1016/j.enbuild.2012.06.028 10.1016/j.autcon.2011.11.012 10.1016/j.buildenv.2004.08.011 10.1016/j.energy.2014.06.102 10.1080/10789669.2008.10391034 10.1016/j.enbuild.2016.05.040 10.1080/10789669.2005.10391148 10.1016/j.enconman.2004.06.011 10.1016/j.enbuild.2012.01.007 10.1090/S0025-5718-1967-0224273-2 10.1109/ICSMC.2011.6083659 10.4304/jsw.9.2.389-397 10.1109/CDC.2011.6161076 10.1016/j.apenergy.2010.07.036 10.1016/j.enbuild.2012.10.025 10.1016/j.ejor.2011.09.008 10.1016/j.applthermaleng.2017.11.037 10.1016/j.proeng.2017.10.058 10.1109/TDC.2012.6281687 10.1109/ACC.2016.7525271 10.1109/UPEC.2013.6714910 10.1016/j.buildenv.2016.10.011 10.1080/10789669.2012.682693 10.1016/S0378-7788(01)00085-8 10.1109/Allerton.2012.6483202 |
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| Title | A comparative study of multi-agent control approaches for optimization of central cooling systems without significant storage |
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