Dynamic Energy Management

Nicholas Moehle, Enzo Busseti (), Stephen Boyd () and Matt Wytock ()
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Nicholas Moehle: Stanford University
Enzo Busseti: Stanford University
Stephen Boyd: Stanford University
Matt Wytock: Gridmatic Inc.

A chapter in Large Scale Optimization in Supply Chains and Smart Manufacturing, 2019, pp 69-126 from Springer

Abstract: 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.

Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/978-3-030-22788-3_4

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