	US 12,228,895 B2
	Optimization controller for distributed energy resources
Jorge Elizondo Martinez, Somerville, MA (US); Seth Calvert Drew, Somerville, MA (US); Trudie Wang, Oakland, CA (US); and Shuyang Li, Oakland, CA (US)
Assigned to Discovery Energy, LLC, Kohler, WI (US)
Filed by Heila Technologies, Inc., Somerville, MA (US)
Filed on Dec. 30, 2021, as Appl. No. 17/566,537.
Claims priority of provisional application 63/131,968, filed on Dec. 30, 2020.
Prior Publication US 2022/0215486 A1, Jul. 7, 2022
Int. Cl. G05B 13/04 (2006.01); G06Q 50/06 (2012.01); H02J 3/00 (2006.01); H02J 3/38 (2006.01)

CPC G05B 13/048 (2013.01) [G05B 13/041 (2013.01); G05B 13/042 (2013.01); G06Q 50/06 (2013.01); H02J 3/004 (2020.01); H02J 3/381 (2013.01); H02J 2203/10 (2020.01); H02J 2203/20 (2020.01)]

20 Claims

1. A method of controlling a distributed energy resource, the method comprising:

obtaining a model of a distributed energy resource or a system of distributed energy resources;

determining a first trajectory of desired power output or a state of the distributed energy resource over the course of a first prediction horizon by minimizing a cost function associated with the model, the first prediction horizon having a first temporal length and a first plurality of set points;

determining a second trajectory of desired power output or a state of the distributed energy resource over the course of a second prediction horizon by minimizing a cost function associated with the model, the second prediction horizon having a second temporal length and a second plurality of set points; and

constraining the second trajectory as a function of the first plurality of set points or states,

wherein the first temporal length is greater than the second temporal length,

wherein a time interval between sampling times in the first trajectory is greater than the time interval between sampling times in the second trajectory.