An Autonomous Distributed Coordination Strategy for Sustainable Consumption in a Microgrid Based on a Bio-Inspired Approach
Distributed energy resources have demonstrated their potential to mitigate the limitations of large, centralized generation systems. This is achieved through the geographical distribution of generation sources that capitalize on the potential of their respective environments to satisfy local demand....
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Published in | Energies (Basel) Vol. 17; no. 3; p. 757 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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Abstract | Distributed energy resources have demonstrated their potential to mitigate the limitations of large, centralized generation systems. This is achieved through the geographical distribution of generation sources that capitalize on the potential of their respective environments to satisfy local demand. In a microgrid, the control problem is inherently distributed, rendering traditional control techniques inefficient due to the impracticality of central governance. Instead, coordination among its components is essential. The challenge involves enabling these components to operate under optimal conditions, such as charging batteries with surplus solar energy or deactivating controllable loads when market prices rise. Consequently, there is a pressing need for innovative distributed strategies like emergent control. Inspired by phenomena such as the environmentally responsive behavior of ants, emergent control involves decentralized coordination schemes. This paper introduces an emergent control strategy for microgrids, grounded in the response threshold model, to establish an autonomous distributed control approach. The results, utilizing our methodology, demonstrate seamless coordination among the diverse components of a microgrid. For instance, system resilience is evident in scenarios where, upon the failure of certain components, others commence operation. Moreover, in dynamic conditions, such as varying weather and economic factors, the microgrid adeptly adapts to meet demand fluctuations. Our emergent control scheme enhances response times, performance, and on/off delay times. In various test scenarios, Integrated Absolute Error (IAE) metrics of approximately 0.01% were achieved, indicating a negligible difference between supplied and demanded energy. Furthermore, our approach prioritizes the utilization of renewable sources, increasing their usage from 59.7% to 86.1%. This shift not only reduces reliance on the public grid but also leads to significant energy cost savings. |
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AbstractList | Distributed energy resources have demonstrated their potential to mitigate the limitations of large, centralized generation systems. This is achieved through the geographical distribution of generation sources that capitalize on the potential of their respective environments to satisfy local demand. In a microgrid, the control problem is inherently distributed, rendering traditional control techniques inefficient due to the impracticality of central governance. Instead, coordination among its components is essential. The challenge involves enabling these components to operate under optimal conditions, such as charging batteries with surplus solar energy or deactivating controllable loads when market prices rise. Consequently, there is a pressing need for innovative distributed strategies like emergent control. Inspired by phenomena such as the environmentally responsive behavior of ants, emergent control involves decentralized coordination schemes. This paper introduces an emergent control strategy for microgrids, grounded in the response threshold model, to establish an autonomous distributed control approach. The results, utilizing our methodology, demonstrate seamless coordination among the diverse components of a microgrid. For instance, system resilience is evident in scenarios where, upon the failure of certain components, others commence operation. Moreover, in dynamic conditions, such as varying weather and economic factors, the microgrid adeptly adapts to meet demand fluctuations. Our emergent control scheme enhances response times, performance, and on/off delay times. In various test scenarios, Integrated Absolute Error (IAE) metrics of approximately 0.01% were achieved, indicating a negligible difference between supplied and demanded energy. Furthermore, our approach prioritizes the utilization of renewable sources, increasing their usage from 59.7% to 86.1%. This shift not only reduces reliance on the public grid but also leads to significant energy cost savings. |
Audience | Academic |
Author | R-Moreno, María D García, Marcel Aguilar, Jose |
Author_xml | – sequence: 1 givenname: Marcel orcidid: 0000-0001-6912-5256 surname: García fullname: García, Marcel – sequence: 2 givenname: Jose orcidid: 0000-0003-4194-6882 surname: Aguilar fullname: Aguilar, Jose – sequence: 3 givenname: María D. orcidid: 0000-0002-7024-0427 surname: R-Moreno fullname: R-Moreno, María D. |
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Cites_doi | 10.1007/s11761-019-00266-w 10.1016/j.scs.2018.04.001 10.1016/j.ijepes.2018.02.031 10.1080/15325008.2021.1937390 10.1016/j.ref.2021.07.003 10.1006/bulm.1998.0041 10.1109/TCST.2017.2692739 10.1016/j.apenergy.2017.06.007 10.1109/TSG.2018.2879793 10.1016/j.rser.2019.02.032 10.1016/j.rser.2020.110248 10.1007/978-3-319-31232-3_94 10.1016/j.rser.2014.01.048 10.1016/j.ijepes.2019.04.040 10.1109/ACCESS.2019.2937639 10.1109/TSG.2019.2911129 10.1007/978-3-030-23593-2 10.1109/COMST.2020.3023963 10.1109/CSCI54926.2021.00351 10.1016/j.jpowsour.2018.11.084 10.3390/en15176182 10.1109/ICRERA.2016.7884455 10.1002/2050-7038.12885 10.1016/j.apenergy.2018.03.017 10.3390/en81011187 10.1109/ACCESS.2020.2966545 10.1109/EI2.2018.8581985 10.1007/978-981-15-4095-0 10.1109/TSG.2020.2971427 10.1109/SSCI50451.2021.9659540 10.1109/ACCESS.2020.3038735 |
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SubjectTerms | Alternative energy Analysis Batteries Building management systems Commodities industry Communication Consumers distributed control systems emergent control Energy consumption Energy industry Energy management Energy management systems Energy storage Game theory Literature reviews Optimization Power supply Renewable resources response threshold model Scheduling smart grid Solar energy Sustainable consumption |
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