Optimal Battery Aging: An Adaptive Weights Dynamic Programming Algorithm
We present an algorithm to handle the optimization over a long horizon of an electric microgrid including a battery energy storage system. While the battery is an important and costly component of the microgrid, its aging process is often not taken into account by the energy management system, mostl...
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| Veröffentlicht in: | Journal of optimization theory and applications Jg. 179; H. 3; S. 1043 - 1053 |
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| Sprache: | Englisch |
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01.12.2018
Springer Nature B.V Springer Verlag |
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| ISSN: | 0022-3239, 1573-2878 |
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| Abstract | We present an algorithm to handle the optimization over a long horizon of an electric microgrid including a battery energy storage system. While the battery is an important and costly component of the microgrid, its aging process is often not taken into account by the energy management system, mostly because of modeling and computing challenges. We address the computing aspect by a new approach combining dynamic programming, decomposition and relaxation techniques. We illustrate this adaptive weight’ method with numerical simulations for a toy microgrid model. Compared to a straightforward resolution by dynamic programming, our algorithm decreases the computing time by more than one order of magnitude, can be parallelized, and allows for online implementations. We believe that this approach can be used for other applications presenting fast and slow variables. |
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| AbstractList | We present an algorithm to handle the optimization over a long horizon of an electricmicrogrid including a battery energy storage system. While the battery is an important andcostly component of the microgrid, its aging process is often not taken into account by theEnergy Management System, mostly because of modeling and computing challenges. We addressthe computing aspect by a new approach combining dynamic programming, decomposition andrelaxation techniques. We illustrate this ’adaptive weight’ method with numerical simulationsfor a toy microgrid model. Compared to a straightforward resolution by dynamic programming,our algorithm decreases the computing time by more than one order of magnitude, can beparallelized, and allows for online implementations. We believe that this approach can be usedfor other applications presenting fast and slow variables. We present an algorithm to handle the optimization over a long horizon of an electric microgrid including a battery energy storage system. While the battery is an important and costly component of the microgrid, its aging process is often not taken into account by the energy management system, mostly because of modeling and computing challenges. We address the computing aspect by a new approach combining dynamic programming, decomposition and relaxation techniques. We illustrate this adaptive weight’ method with numerical simulations for a toy microgrid model. Compared to a straightforward resolution by dynamic programming, our algorithm decreases the computing time by more than one order of magnitude, can be parallelized, and allows for online implementations. We believe that this approach can be used for other applications presenting fast and slow variables. |
| Author | Martinon, Pierre Heymann, Benjamin |
| Author_xml | – sequence: 1 givenname: Benjamin orcidid: 0000-0002-0318-5333 surname: Heymann fullname: Heymann, Benjamin email: benjamin.heymann@polytechnique.edu organization: CMAP, Inria, Ecole Polytechnique, CNRS, Université Paris-Saclay, CMM, Universidad de Chile – sequence: 2 givenname: Pierre surname: Martinon fullname: Martinon, Pierre organization: CMAP, Inria, Ecole Polytechnique, CNRS, Université Paris-Saclay |
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| Cites_doi | 10.1109/ECC.2016.7810599 10.1109/TSTE.2011.2114901 10.1109/TSG.2012.2231440 10.1002/pip.480 10.1016/j.solener.2006.12.009 10.1007/s12667-016-0228-2 |
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| Copyright | Springer Science+Business Media, LLC, part of Springer Nature 2018 Journal of Optimization Theory and Applications is a copyright of Springer, (2018). All Rights Reserved. Distributed under a Creative Commons Attribution 4.0 International License |
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| DOI | 10.1007/s10957-018-1371-9 |
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| Keywords | Control 93A13 49L20 Energy management system 93C15 Aging 90C39 49M29 49M27 Dynamic programming Dynamic Programing Energy Management System |
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| References | Heymann, B.: Mathematical contributions for the optimization and regulation of electricity production. PhD Thesis (2016). https://hal.archives-ouvertes.fr/tel-01416404 Bonnans, J.F., Martinon, P., Giorgi, D., Grélard, V., Heymann, B., Jinyan, L., Maindrault, S., Tissot, O.: Bocop—a collection of examples. Technical report (2016). http://bocop.saclay.inria.fr RiffonneauYBachaSBarruelFPloixSOptimal power flow management for grid connected PV systems with batteriesIEEE Trans. Sustain. Energy20112330932010.1109/TSTE.2011.2114901 Bonnans, J.F., Giorgi, D., Heymann, B., Martinon, P., Tissot, O.: BocopHJB 1.0.1—user guide. Technical Report RT-0467, INRIA (2015). https://hal.inria.fr/hal-01192610 Heymann, B., Bonnans, J.F., Silva, F., Jimenez, G.: A Stochastic continuous time model for microgrid energy management. In: ECC2016. Aalborg, Denmark (2016) Haessig, P., Multon, B., Ben Ahmed, H., Lascaud, S., Jamy, L.: Aging-aware NaS battery model in a stochastic wind-storage simulation framework. In: PowerTech (POWERTECH), 2013 IEEE Grenoble, pp. 1–6. IEEE (2013) Palma-BehnkeRBenavidesCLanasFSeverinoBReyesLLlanosJSáezDA microgrid energy management system based on the rolling horizon strategyIEEE Trans. Smart Grid201342996100610.1109/TSG.2012.2231440 HeymannBBonnansJFMartinonPContinuous optimal control approaches to microgrid energy managementEnergy Syst.201891597710.1007/s12667-016-0228-2 GuaschDSilvestreSDynamic battery model for photovoltaic applicationsProg. Photovolt. Res. Appl.200311319320610.1002/pip.480 SvobodaVOperating conditions of batteries in off-grid renewable energy systemsSolar Energy200781111409142510.1016/j.solener.2006.12.009 B Heymann (1371_CR5) 2018; 9 V Svoboda (1371_CR7) 2007; 81 1371_CR10 D Guasch (1371_CR4) 2003; 11 1371_CR6 1371_CR8 1371_CR9 Y Riffonneau (1371_CR2) 2011; 2 R Palma-Behnke (1371_CR3) 2013; 4 1371_CR1 |
| References_xml | – reference: SvobodaVOperating conditions of batteries in off-grid renewable energy systemsSolar Energy200781111409142510.1016/j.solener.2006.12.009 – reference: HeymannBBonnansJFMartinonPContinuous optimal control approaches to microgrid energy managementEnergy Syst.201891597710.1007/s12667-016-0228-2 – reference: Bonnans, J.F., Giorgi, D., Heymann, B., Martinon, P., Tissot, O.: BocopHJB 1.0.1—user guide. Technical Report RT-0467, INRIA (2015). https://hal.inria.fr/hal-01192610 – reference: RiffonneauYBachaSBarruelFPloixSOptimal power flow management for grid connected PV systems with batteriesIEEE Trans. Sustain. Energy20112330932010.1109/TSTE.2011.2114901 – reference: GuaschDSilvestreSDynamic battery model for photovoltaic applicationsProg. Photovolt. Res. Appl.200311319320610.1002/pip.480 – reference: Heymann, B., Bonnans, J.F., Silva, F., Jimenez, G.: A Stochastic continuous time model for microgrid energy management. In: ECC2016. Aalborg, Denmark (2016) – reference: Bonnans, J.F., Martinon, P., Giorgi, D., Grélard, V., Heymann, B., Jinyan, L., Maindrault, S., Tissot, O.: Bocop—a collection of examples. Technical report (2016). http://bocop.saclay.inria.fr/ – reference: Haessig, P., Multon, B., Ben Ahmed, H., Lascaud, S., Jamy, L.: Aging-aware NaS battery model in a stochastic wind-storage simulation framework. In: PowerTech (POWERTECH), 2013 IEEE Grenoble, pp. 1–6. IEEE (2013) – reference: Heymann, B.: Mathematical contributions for the optimization and regulation of electricity production. PhD Thesis (2016). https://hal.archives-ouvertes.fr/tel-01416404 – reference: Palma-BehnkeRBenavidesCLanasFSeverinoBReyesLLlanosJSáezDA microgrid energy management system based on the rolling horizon strategyIEEE Trans. Smart Grid201342996100610.1109/TSG.2012.2231440 – ident: 1371_CR1 – ident: 1371_CR6 doi: 10.1109/ECC.2016.7810599 – volume: 2 start-page: 309 issue: 3 year: 2011 ident: 1371_CR2 publication-title: IEEE Trans. Sustain. Energy doi: 10.1109/TSTE.2011.2114901 – volume: 4 start-page: 996 issue: 2 year: 2013 ident: 1371_CR3 publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2012.2231440 – volume: 11 start-page: 193 issue: 3 year: 2003 ident: 1371_CR4 publication-title: Prog. Photovolt. Res. Appl. doi: 10.1002/pip.480 – ident: 1371_CR9 – ident: 1371_CR8 – ident: 1371_CR10 – volume: 81 start-page: 1409 issue: 11 year: 2007 ident: 1371_CR7 publication-title: Solar Energy doi: 10.1016/j.solener.2006.12.009 – volume: 9 start-page: 59 issue: 1 year: 2018 ident: 1371_CR5 publication-title: Energy Syst. doi: 10.1007/s12667-016-0228-2 |
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| SubjectTerms | Adaptive algorithms Algorithms Applications of Mathematics Batteries Calculus of Variations and Optimal Control; Optimization Computer simulation Computing time Distributed generation Dynamic programming Energy management Energy storage Engineering Mathematical models Mathematics Mathematics and Statistics Operations Research/Decision Theory Optimization Optimization and Control Parallel processing Technical Note Theory of Computation Weight |
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| Title | Optimal Battery Aging: An Adaptive Weights Dynamic Programming Algorithm |
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