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Model Predictive Control and Distributed Optimization in Smart Grid Applications

In: Handbook of Smart Energy Systems

Author

Listed:
  • Philipp Braun

    (Australian National University)

  • Lars Grüne

    (University of Bayreuth)

  • Christopher M. Kellett

    (Australian National University)

  • Karl Worthmann

    (Technische Universität Ilmenau)

Abstract

In this chapter we summarize and outline ideas on model predictive control (MPC) and distributed optimization in the context of demand-side management and smart grids. In particular, taking the perspective of an energy provider and its customers, we illustrate how a dynamical system representing individual customers and how an overall systems can be modeled. We discuss how the degree of freedom in the overall system, in terms of local storage devices, for example, can be used in MPC algorithms to optimize the aggregated power demand profile of the overall network. In the last part of the chapter, we show how the underlying optimization problem in the MPC algorithm can be solved using distributed optimization algorithms to ensure plug-and-play capabilities of the overall controller design. While we focus on the presentation of main ideas in this chapter, references containing more detailed expositions of the individual topics are provided throughout the text.

Suggested Citation

  • Philipp Braun & Lars Grüne & Christopher M. Kellett & Karl Worthmann, 2023. "Model Predictive Control and Distributed Optimization in Smart Grid Applications," Springer Books, in: Michel Fathi & Enrico Zio & Panos M. Pardalos (ed.), Handbook of Smart Energy Systems, pages 1239-1263, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-97940-9_163
    DOI: 10.1007/978-3-030-97940-9_163
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