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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2012.14926 |
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| _version_ | 1866929565334503424 |
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| author | Fallahi, Farnaz Yildirim, Murat Lin, Jeremy Wang, Caisheng |
| author_facet | Fallahi, Farnaz Yildirim, Murat Lin, Jeremy Wang, Caisheng |
| contents | Industrial sensor data provides significant insights into the failure risks of microgrid generation assets. In traditional applications, these sensor-driven risks are used to generate alerts that initiate maintenance actions without considering their impact on operational aspects. The focus of this paper is to propose a framework that i) builds a seamless integration between sensor data and operational & maintenance drivers, and ii) demonstrates the value of this integration for improving multiple aspects of microgrid operations. The proposed framework offers an integrated stochastic optimization model that jointly optimizes operations and maintenance in a multi-microgrid setting. Maintenance decisions identify optimal crew routing, opportunistic maintenance, and repair schedules as a function of dynamically evolving sensor-driven predictions on asset life. Operational decisions identify commitment and generation from a fleet of distributed energy resources, storage, load management, as well as power transactions with the main grid and neighboring microgrids. Operational uncertainty from renewable generation, demand, and market prices are explicitly modeled through scenarios in the optimization model. We use the structure of the model to develop a decomposition-based solution algorithm to ensure computational scalability. The proposed model provides significant improvements in reliability and enhances a range of operational outcomes, including costs, renewables, generation availability, and resilience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2012_14926 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Predictive Multi-Microgrid Generation Maintenance: Formulation and Impact on Operations & Resilience Fallahi, Farnaz Yildirim, Murat Lin, Jeremy Wang, Caisheng Systems and Control Industrial sensor data provides significant insights into the failure risks of microgrid generation assets. In traditional applications, these sensor-driven risks are used to generate alerts that initiate maintenance actions without considering their impact on operational aspects. The focus of this paper is to propose a framework that i) builds a seamless integration between sensor data and operational & maintenance drivers, and ii) demonstrates the value of this integration for improving multiple aspects of microgrid operations. The proposed framework offers an integrated stochastic optimization model that jointly optimizes operations and maintenance in a multi-microgrid setting. Maintenance decisions identify optimal crew routing, opportunistic maintenance, and repair schedules as a function of dynamically evolving sensor-driven predictions on asset life. Operational decisions identify commitment and generation from a fleet of distributed energy resources, storage, load management, as well as power transactions with the main grid and neighboring microgrids. Operational uncertainty from renewable generation, demand, and market prices are explicitly modeled through scenarios in the optimization model. We use the structure of the model to develop a decomposition-based solution algorithm to ensure computational scalability. The proposed model provides significant improvements in reliability and enhances a range of operational outcomes, including costs, renewables, generation availability, and resilience. |
| title | Predictive Multi-Microgrid Generation Maintenance: Formulation and Impact on Operations & Resilience |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2012.14926 |