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An adaptive digital framework for energy management of complex multi-device systems

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Abstract

Energy Management Systems (EMS) refer to frameworks that control the energy generation, transmission and storage for multiple devices which are coupled together. These can range from nationwide grids, utility-scale systems, microgrids, data-centers to electric vehicles and can consist of renewable energy sources, fossil-fuel energy, transmission lines, batteries, generators, ultracapacitors and transformers, to name a few. The goals of such systems are typically to balance the load, guarantee the power supply for each device, maximize overall efficiency and to minimize overall losses. Remarkable increases in desktop computing have opened up the possibility for researchers and practitioners to construct and tailor simulation paradigms for their own specific system’s needs. Accordingly, the objective of this work is to develop a flexible and rapidly computable framework that researchers can easily alter and manipulate for their specific system. The approach taken in this work is to study a model problem, consisting of an energy supplier and a large number of strongly coupled devices with specific needs. The framework computes an energy balance for each device in the system and ascertains what the energy supplier must deliver or extract from the device to allow it to meet a specific target state while accounting for transmission losses. A digital-twin is created of such a system that is capable of running at extremely high speeds and which is coupled to a genetic-based machine-learning algorithm in order to optimize the operation of the supplier. Numerical examples are provided to illustrate the approach.

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Notes

  1. For very energy intensive data-centers, electricity can account for over 10 \(\%\) of the cost of ownership.

  2. APV systems can involve a variety of aspects, even utilizing pollinating insects, such as bees, to “solar grazing” systems.

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Acknowledgements

This work has been partially supported by the UC Berkeley College of Engineering and the USDA AI Institute for Next Generation Food Systems (AIFS), USDA award number 2020-67021- 32855.

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  1. Department of Mechanical Engineering, University of California, Berkeley, CA, 94720-1740, USA

    T. I. Zohdi

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Zohdi, T.I. An adaptive digital framework for energy management of complex multi-device systems. Comput Mech 70, 867–878 (2022). https://doi.org/10.1007/s00466-022-02212-8

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