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Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework

Author

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  • Khalid Alnowibet

    (Statistics and Operations Research Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

  • Andres Annuk

    (Chair of Energy Application Engineering, Institute of Technology, Estonian University of Life Sciences, 51006 Tartu, Estonia)

  • Udaya Dampage

    (Faculty of Engineering, Kotelawala Defence University, Kandawala Estate, Ratmalana 10390, Sri Lanka)

  • Mohamed A. Mohamed

    (Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt)

Abstract

During the last few years, attention has overwhelmingly focused on the integrated management of urban services and the demand of customers for locally-based supply. The rapid growth in developing smart measuring devices has made the underlying systems more observable and controllable. This exclusive feature has led the system designers to pursue the implementation of complex protocols to provide faster services based on data exchanges. On the other hand, the demands of consumers for locally-based supply could cause a disjunction and islanding behavior that demands to be dealt with by precise action. At first, keeping a centralization scheme was the main priority. However, the advent of distributed systems opened up new solutions. The operation of distributed systems requires the implementation of strong communication links to boost the existing infrastructure via smart control and supervision, which requires a foundation and effective investigations. Hence, necessary actions need to be taken to frustrate any disruptive penetrations into the system while simultaneously benefiting from the advantages of the proposed smart platform. This research addresses the detection of false data injection attacks (FDIA) in energy hub systems. Initially, a multi-hub system both in the presence of a microgrid (the interconnected smart energy hub-based microgrid system) and without it has been modeled for energy management in a way that allows them to cooperate toward providing energy with each other. Afterward, an FDIA is separately exerted to all three parts of the energy carrier including the thermal, water, and electric systems. In the absence of FDIA detection, the impact of FDIA is thoroughly illustrated on energy management, which considerably contributes to non-optimal operation. In the same vein, the intelligent priority selection based reinforcement learning (IPS-RL) method is proposed for FDIA detection. In order to model the uncertainty effects, the unscented transformation (UT) is applied in a stochastic framework. The results on the IEEE standard test system validate the system’s performance.

Suggested Citation

  • Khalid Alnowibet & Andres Annuk & Udaya Dampage & Mohamed A. Mohamed, 2021. "Effective Energy Management via False Data Detection Scheme for the Interconnected Smart Energy Hub–Microgrid System under Stochastic Framework," Sustainability, MDPI, vol. 13(21), pages 1-32, October.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11836-:d:665212
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    References listed on IDEAS

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    6. Abdulaziz Almalaq & Saleh Albadran & Mohamed A. Mohamed, 2023. "An Adoptive Miner-Misuse Based Online Anomaly Detection Approach in the Power System: An Optimum Reinforcement Learning Method," Mathematics, MDPI, vol. 11(4), pages 1-22, February.
    7. Hassan M. H. Farh & Abdullrahman A. Al-Shamma’a & Abdullah M. Al-Shaalan & Abdulaziz Alkuhayli & Abdullah M. Noman & Tarek Kandil, 2022. "Technical and Economic Evaluation for Off-Grid Hybrid Renewable Energy System Using Novel Bonobo Optimizer," Sustainability, MDPI, vol. 14(3), pages 1-18, January.
    8. Jian Xiao & Wei Hou, 2022. "Cost Estimation Process of Green Energy Production and Consumption Using Probability Learning Approach," Sustainability, MDPI, vol. 14(12), pages 1-14, June.
    9. Wei Hou & Rita Yi Man Li & Thanawan Sittihai, 2022. "Management Optimization of Electricity System with Sustainability Enhancement," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    10. Qunpeng Fan, 2022. "Management and Policy Modeling of the Market Using Artificial Intelligence," Sustainability, MDPI, vol. 14(14), pages 1-14, July.

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