49. optimal distributed nonlinear battery control

Department: Electrical & Computer Engineering
Research Institute Affiliation: Agile - Sustainable Power and Energy Center (SPEC)
Faculty Advisor(s): Tajana S. Rosing

Primary Student
Name: Michael Henry Ostertag
Email: mosterta@ucsd.edu
Phone: 425-213-4513
Grad Year: 2020

Student Collaborators
Sinan Akyurek, aakyurek@eng.ucsd.edu

Energy storage plays a more important role than ever before, due to the transition to smart grid along with higher penetration of renewable resources. In this paper, we describe our optimal nonlinear battery control algorithm that can handle multiple batteries connected to the grid in a distributed and costoptimal fashion, while maintaining low complexity of O(N2). In contrast to the state-of-the-art, we use a high accuracy nonlinear battery model with 2% error. We present three distributed solutions: 1) Circular negotiation ring, providing convergence rates independent of number of batteries, 2) Mean circular negotiation ring, converging very quickly for a low number of batteries, 3) Bisection method has a convergence rate independent of battery capacities. We compare our algorithm to the state-ofthe-art and show that we can decrease the utility cost of an actual building by up to 50% compared to the batteryless case, by 30% over the load-following heuristic and by 60% over a state-of-the art optimal control algorithm designed using a linear battery model. For a constant load profile, optimal linear control incurs costs higher by 150% for MPC

Industry Application Area(s)
Control Systems | Energy/Clean technology

Related Links:

  1. http://ieeexplore.ieee.org/document/7797556/

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