Patent US10770897

accessing load reduction parameters for a load reduction event;

accessing energy consumption models for multiple systems involved in the load reduction event;

performing, based on the load reduction parameters and the energy consumption models, a plurality of simulations of load reduction events that simulate variations in control parameters used to control the multiple systems;

accessing a load reduction curve requested for the load reduction event;

optimizing, against the load reduction curve, the load reduction event by iteratively modifying the control parameters used in the plurality of simulations of load reduction events, the optimization against the load reduction curve comprises identifying, from among the plurality of simulations of load reduction events, a simulation that most closely matches the load reduction curve; and

outputting the optimal load reduction event with optimized control parameters.

accessing load reduction parameters for a load reduction event;

accessing energy consumption models for multiple systems involved in the load reduction event;

performing, based on the load reduction parameters and the energy consumption models, a plurality of simulations of load reduction events that simulate variations in control parameters used to control the multiple systems;

optimizing, against a load reduction curve, the load reduction event by iteratively modifying the control parameters used in the plurality of simulations of load reduction events; and

outputting the optimal load reduction event with optimized control parameters,

wherein optimizing the load reduction event by iteratively modifying the control parameters comprises: assigning a weighted value to load objective components that are defined by functions of the load reduction curve; and optimizing, based on the weighted values of each of the load objective components, the load reduction event,

wherein assigning a weighted value to the load objective components that are defined by functions of the load reduction curve comprises: assigning a first weighted value to a first load objective component that is derived from linear regression and directed to optimizing a total amount of load reduction achieved in the load reduction event; assigning a second weighted value to a second load objective component that is derived from linear regression and directed to capturing shed drift relative to the load reduction curve; and assigning a third weighted value to a third load objective component directed to optimizing an amount of noise around a linear estimate of the load reduction curve achieved in the load reduction event; and

 

wherein optimizing, based on the weighted values of each of the load objective components, the load reduction event comprises optimizing the load reduction event based on the first load objective component weighted according to the first weighted value, the second load objective component weighted according to the second weighted value, and the third load objective component weighted according to the third weighted value.