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Toward Multi-Layered MPC for Complex Electric Energy Systems

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Handbook of Model Predictive Control

Part of the book series: Control Engineering ((CONTRENGIN))

Abstract

This chapter formalizes a model predictive control (MPC) formulation for complex multi-temporal multi-spatial electric energy systems. It is motivated by the need for data-enabled decision-making in the changing industry in which different entities have the ability to predict uncertainties at different temporal and spatial granularity. It is explained that these needs arise because today’s industry practice relies on software tools which assume specific temporal and spatial decoupling. These no longer hold in the environment with highly intermittent resources and diverse decision makers. To account for temporal and spatial interdependencies, concepts of spatial and temporal lifting are utilized. These are partly motivated by a previously proposed Dynamic Monitoring and Decision Systems (DyMoNDS) framework for operating smart grids. These methods confirm the information exchange protocols required for near-optimal and highly reliable cost-effective and clean electricity services as proposed earlier. The use of DyMonDS tools is illustrated for efficient integration of temporally diverse generation and demand response. It is shown that this can be done while ensuring stable operation with minimal fast expensive storage.

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Acknowledgements

This material is based upon work supported by the Department of Energy under Air Force Contract No. FA8721-05-C-0002 and/or FA8702-15-D-0001. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Department of Energy. It is also supported in part by the National Institute of Standards and Technology (NIST) under the project of “Smart Grid in a Room Simulator” and PSERC project S-64 entitled “Monitoring and maintaining limits of area transfers with PMUs.”

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Authors and Affiliations

  1. Massachusetts Institute of Technology, Cambridge, MA, USA

    Marija Ilic, Rupamathi Jaddivada, Xia Miao & Nipun Popli

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  1. Marija Ilic
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  2. Rupamathi Jaddivada
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  3. Xia Miao
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  4. Nipun Popli
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Correspondence to Marija Ilic .

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  1. Independent Researcher, London, UK

    Saša V. Raković

  2. Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, USA

    William S. Levine

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Ilic, M., Jaddivada, R., Miao, X., Popli, N. (2019). Toward Multi-Layered MPC for Complex Electric Energy Systems. In: Raković, S., Levine, W. (eds) Handbook of Model Predictive Control. Control Engineering. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-77489-3_26

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  • DOI: https://doi.org/10.1007/978-3-319-77489-3_26

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  • Publisher Name: Birkhäuser, Cham

  • Print ISBN: 978-3-319-77488-6

  • Online ISBN: 978-3-319-77489-3

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