Dynamic Energy Management

  • Nicholas Moehle
  • Enzo BussetiEmail author
  • Stephen Boyd
  • Matt Wytock
Part of the Springer Optimization and Its Applications book series (SOIA, volume 149)


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.



This research was partly supported by MISO energy; we especially thank Alan Hoyt and DeWayne Johnsonbaugh of MISO for many useful discussions.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nicholas Moehle
    • 1
  • Enzo Busseti
    • 2
    Email author
  • Stephen Boyd
    • 3
  • Matt Wytock
    • 4
  1. 1.Department of Mechanical EngineeringStanford UniversityStanfordUSA
  2. 2.Department of Management Science and EngineeringStanford UniversityStanfordUSA
  3. 3.Department of Electrical EngineeringStanford UniversityStanfordUSA
  4. 4.Gridmatic Inc.San JoseUSA

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