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Dynamic Energy Management

In: Large Scale Optimization in Supply Chains and Smart Manufacturing

Author

Listed:
  • Nicholas Moehle

    (Stanford University)

  • Enzo Busseti

    (Stanford University)

  • Stephen Boyd

    (Stanford University)

  • Matt Wytock

    (Gridmatic Inc.)

Abstract

We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optimizing power flows in a static network, and then proceed to the case of optimizing dynamic power flows, i.e., power flows that change with time over a horizon. We leverage this to develop a real-time control strategy, model predictive control, which at each time step solves a dynamic power flow optimization problem, using forecasts of future quantities such as demands, capacities, or prices, to choose the current power flow values. Finally, we consider a useful extension of model predictive control that explicitly accounts for uncertainty in the forecasts. We mirror our framework with an object-oriented software implementation, an open-source Python library for planning and controlling power flows at any scale. We demonstrate our method with various examples. Appendices give more detail about the package, and describe some basic but very effective methods for constructing forecasts from historical data.

Suggested Citation

  • Nicholas Moehle & Enzo Busseti & Stephen Boyd & Matt Wytock, 2019. "Dynamic Energy Management," Springer Optimization and Its Applications, in: Jesús M. Velásquez-Bermúdez & Marzieh Khakifirooz & Mahdi Fathi (ed.), Large Scale Optimization in Supply Chains and Smart Manufacturing, pages 69-126, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-22788-3_4
    DOI: 10.1007/978-3-030-22788-3_4
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    Cited by:

    1. Jiayu Cheng & Dongliang Duan & Xiang Cheng & Liuqing Yang & Shuguang Cui, 2020. "Probabilistic Microgrid Energy Management with Interval Predictions," Energies, MDPI, vol. 13(12), pages 1-23, June.
    2. Guillermo Angeris & Akshay Agrawal & Alex Evans & Tarun Chitra & Stephen Boyd, 2021. "Constant Function Market Makers: Multi-Asset Trades via Convex Optimization," Papers 2107.12484, arXiv.org.
    3. Jiayu Cheng & Dongliang Duan & Xiang Cheng & Liuqing Yang & Shuguang Cui, 2021. "Adaptive Control for Energy Exchange with Probabilistic Interval Predictors in Isolated Microgrids," Energies, MDPI, vol. 14(2), pages 1-23, January.
    4. Shane Barratt & Stephen Boyd, 2020. "Multi-Period Liability Clearing via Convex Optimal Control," Papers 2005.09066, arXiv.org.

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