Microgrid Resilience Enhancement with Sensor Network-Based Monitoring and Risk Assessment Involving Uncertain Data

Tangxiao Yuan (), Kossigan Roland Assilevi (), Kondo Hloindo Adjallah, Ayité Sénah A. Ajavon and Huifen Wang
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Tangxiao Yuan: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Kossigan Roland Assilevi: Centre d’Excellence Régional pour la Maîtrise de l’Electricité (CERME), Université de Lomé, Lomé 01 BP 1515, Togo
Kondo Hloindo Adjallah: LCOMS, Université de Lorraine, 57070 Metz, France
Ayité Sénah A. Ajavon: Centre d’Excellence Régional pour la Maîtrise de l’Electricité (CERME), Université de Lomé, Lomé 01 BP 1515, Togo
Huifen Wang: School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Energies, 2024, vol. 17, issue 23, 1-32

Abstract: This paper focuses on enhancing the resilience of microgrids—localized power systems that integrate multiple energy sources—against challenges such as natural disasters, technological obstacles, and human errors. It begins by defining the specific connotation of microgrid resilience and then proposes an innovative solution centered on the use of advanced sensor technology to continuously monitor the microgrid and its operational environment, ensuring accurate and timely data collection under dynamic conditions. Subsequently, a decision risk assessment framework is constructed, integrating data quality evaluation and operational risk considerations, to drive strategy optimization through in-depth data analysis. At the application level, this framework is successfully applied to two critical decision-making scenarios: the first is to optimize the power allocation strategy between solar energy and the auxiliary grid, aiming to maximize cost efficiency and minimize power outage losses; the second is to develop low-risk maintenance plans based on the predicted failure probabilities of microgrid components with uncertain information. Both decision processes skillfully utilize Monte Carlo simulation and multi-objective genetic algorithms to effectively manage the uncertainty risks in the decision-making process, thereby significantly enhancing the overall resilience of the microgrid.

Keywords: microgrid; resilience; sensor network; risk; decision making (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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