Control-Centric Living Laboratory for Management of Distributed Energy Resources
Variability and uncertainty of renewable distributed generation increase power grid complexity, necessitating the development of advanced control strategies. demonstrates a real-world testbed and the implementation of control strategies on it to mitigate the challenges associated with variability an...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Open Access Journal of Power and Energy |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9956809/ |
| _version_ | 1797351678456889344 |
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| author | Roshan L. Kini David Raker Roan Martin-Hayden Robert G. Lutes Srinivas Katipamula Randy Ellingson Michael J. Heben Raghav Khanna |
| author_facet | Roshan L. Kini David Raker Roan Martin-Hayden Robert G. Lutes Srinivas Katipamula Randy Ellingson Michael J. Heben Raghav Khanna |
| author_sort | Roshan L. Kini |
| collection | DOAJ |
| description | Variability and uncertainty of renewable distributed generation increase power grid complexity, necessitating the development of advanced control strategies. demonstrates a real-world testbed and the implementation of control strategies on it to mitigate the challenges associated with variability and uncertainty of renewable distributed generation. This control-centric testbed includes 4.6 MW of controllable building loads, a 1 MW solar array, and a 125 kW / 130 kWh battery energy storage system (BESS). The capabilities of the testbed are illustrated by highlighting the implementation of three specific scenarios relevant to future smart grid infrastructures. In the first scenario, photovoltaic output variability is mitigated with the BESS using adaptive moving average and adaptive state of charge control methods. The second and third scenarios demonstrate peak load management and load following control to manage uncertainty using the Intelligent Load Control (ILC) algorithm. The ILC modifies controllable loads using a prioritization matrix and an analytical hierarchy process. The three scenarios all operate at a different time-constant, and are each effectively addressed, demonstrating the versatility and flexibility of the presented testbed. This demonstrated ability to rapidly test the efficacy of alternate control algorithms on a real system is crucial to the maturation of future smart-grid. |
| first_indexed | 2024-03-08T13:04:01Z |
| format | Article |
| id | doaj.art-3293f355107c405fa82bead5498a29ff |
| institution | Directory Open Access Journal |
| issn | 2687-7910 |
| language | English |
| last_indexed | 2024-03-08T13:04:01Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Access Journal of Power and Energy |
| spelling | doaj.art-3293f355107c405fa82bead5498a29ff2024-01-19T00:01:31ZengIEEEIEEE Open Access Journal of Power and Energy2687-79102023-01-0110486010.1109/OAJPE.2022.32236569956809Control-Centric Living Laboratory for Management of Distributed Energy ResourcesRoshan L. Kini0https://orcid.org/0000-0002-9347-2714David Raker1Roan Martin-Hayden2Robert G. Lutes3Srinivas Katipamula4https://orcid.org/0000-0002-7092-6517Randy Ellingson5https://orcid.org/0000-0001-9520-6586Michael J. Heben6https://orcid.org/0000-0002-3788-3471Raghav Khanna7https://orcid.org/0000-0001-9654-1626Pacific Northwest National Laboratory, Richland, WA, USAPacific Northwest National Laboratory, Richland, WA, USADepartment of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH, USAPacific Northwest National Laboratory, Richland, WA, USAPacific Northwest National Laboratory, Richland, WA, USAWright Center for Photovoltaics Innovation and Commercialization, The University of Toledo, Toledo, OH, USAWright Center for Photovoltaics Innovation and Commercialization, The University of Toledo, Toledo, OH, USADepartment of Electrical Engineering and Computer Science, The University of Toledo, Toledo, OH, USAVariability and uncertainty of renewable distributed generation increase power grid complexity, necessitating the development of advanced control strategies. demonstrates a real-world testbed and the implementation of control strategies on it to mitigate the challenges associated with variability and uncertainty of renewable distributed generation. This control-centric testbed includes 4.6 MW of controllable building loads, a 1 MW solar array, and a 125 kW / 130 kWh battery energy storage system (BESS). The capabilities of the testbed are illustrated by highlighting the implementation of three specific scenarios relevant to future smart grid infrastructures. In the first scenario, photovoltaic output variability is mitigated with the BESS using adaptive moving average and adaptive state of charge control methods. The second and third scenarios demonstrate peak load management and load following control to manage uncertainty using the Intelligent Load Control (ILC) algorithm. The ILC modifies controllable loads using a prioritization matrix and an analytical hierarchy process. The three scenarios all operate at a different time-constant, and are each effectively addressed, demonstrating the versatility and flexibility of the presented testbed. This demonstrated ability to rapidly test the efficacy of alternate control algorithms on a real system is crucial to the maturation of future smart-grid.https://ieeexplore.ieee.org/document/9956809/Smart grid testbeddistributed energy resources (DERs)intelligent load control (ILC)photovoltaic (PV) variability mitigationbattery energy storage system (BESS)peak load management |
| spellingShingle | Roshan L. Kini David Raker Roan Martin-Hayden Robert G. Lutes Srinivas Katipamula Randy Ellingson Michael J. Heben Raghav Khanna Control-Centric Living Laboratory for Management of Distributed Energy Resources IEEE Open Access Journal of Power and Energy Smart grid testbed distributed energy resources (DERs) intelligent load control (ILC) photovoltaic (PV) variability mitigation battery energy storage system (BESS) peak load management |
| title | Control-Centric Living Laboratory for Management of Distributed Energy Resources |
| title_full | Control-Centric Living Laboratory for Management of Distributed Energy Resources |
| title_fullStr | Control-Centric Living Laboratory for Management of Distributed Energy Resources |
| title_full_unstemmed | Control-Centric Living Laboratory for Management of Distributed Energy Resources |
| title_short | Control-Centric Living Laboratory for Management of Distributed Energy Resources |
| title_sort | control centric living laboratory for management of distributed energy resources |
| topic | Smart grid testbed distributed energy resources (DERs) intelligent load control (ILC) photovoltaic (PV) variability mitigation battery energy storage system (BESS) peak load management |
| url | https://ieeexplore.ieee.org/document/9956809/ |
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