Model Predictive Control and Distributed Optimization in Smart Grid Applications

Philipp Braun (), Lars Grüne (), Christopher M. Kellett () and Karl Worthmann ()
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Philipp Braun: Australian National University
Lars Grüne: University of Bayreuth
Christopher M. Kellett: Australian National University
Karl Worthmann: Technische Universität Ilmenau

A chapter in Handbook of Smart Energy Systems, 2023, pp 1239-1263 from Springer

Abstract: 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.

Keywords: Smart grids; Model predictive control; Distributed optimization (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-97940-9_163

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DOI: 10.1007/978-3-030-97940-9_163

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