Abstract: Building energy consumption is controlled by operating energy consumptive devices according to a control plan determined by: using a software building model to simulate building behavior over a simulation period in accordance with predicted circumstances and in accordance with multiple control plans; for at least one of the control plans, re-simulating building behavior a plurality of times each with a different perturbation imposed, with each perturbation corresponding to a different degree of participation in a grid market, to thereby determine an amount of participation in the grid market available to the building; resimulating building behavior a plurality of times imposing said determined participation amount as a constraint; and selecting an optimal control plan based on the last round of resimulations.

Abstract: Buildings or facilities containing energy consuming or energy generating devices may be optimized for efficient energy usage and distribution. Energy consumption or generation by a building or components may be controlled by a system comprising a building model for predicting behavior of the building given predicted future conditions and possible control inputs. An optimization component running an optimization algorithm in conjunction with the building model may evaluate the predicted building behavior in accordance with at least one criterion and determine a desired set of control inputs. Commercial building thermal mass may be harnessed to continuously and optimally integrate large commercial building HVAC operations with electric grid operations and markets in large metropolitan areas. The service may be deployed using scalable, automated, web-based technology.

Abstract: Building energy consumption is controlled by operating energy consumptive devices according to a control plan determined by: using a software building model to simulate building behavior over a simulation period in accordance with predicted circumstances and in accordance with multiple control plans; for at least one of the control plans, re-simulating building behavior a plurality of times each with a different perturbation imposed, with each perturbation corresponding to a different degree of participation in a grid market, to thereby determine an amount of participation in the grid market available to the building; resimulating building behavior a plurality of times imposing said determined participation amount as a constraint; and selecting an optimal control plan based on the last round of resimulations.

Abstract: Buildings or facilities containing energy consuming or energy generating devices may be optimized for efficient energy usage and distribution. Energy consumption or generation by a building or components may be controlled by a system comprising a building model for predicting behavior of the building given predicted future conditions and possible control inputs. An optimization component running an optimization algorithm in conjunction with the building model may evaluate the predicted building behavior in accordance with at least one criterion and determine a desired set of control inputs. Commercial building thermal mass may be harnessed to continuously and optimally integrate large commercial building HVAC operations with electric grid operations and markets in large metropolitan areas. The service may be deployed using scalable, automated, web-based technology.

Abstract: Deployment of commercial and industrial (“C&I”) facilities in wholesale markets may be optimized by unbundling attributes from the facilities and combining those attributes for deployment. Buildings within portfolios may include complimentary attributes, features, and capabilities that can be coordinated for synergistic effect. The optimization may include mathematically unbundling attributes of each facility that may be mathematically and optimally aggregated into synthetic resources that are matched for selecting particular markets. These synthetic resources may be deployed optimally in the selected markets.

Abstract: A building heating/cooling system energy optimization method for a building having a heating/cooling system includes the steps of providing a mathematical model of the heating/cooling system, obtaining real-time weather information, reading the input water temperature (IWT), the output water temperature (OWT) and the supply air temperature (SA) output to the building, periodically transferring the IWT, the OWT and the SA to an optimization system which is operative to analyze the real-time data in coordination with the mathematical model by assigning at least three selected values in a range surrounding and including the current values of each of the IWT, the OWT and the SA and calculating the efficiency profile of the components of the heating/cooling system for each of the selected values, then cooperatively optimizing and selecting those values calculated to provide the highest efficiency profile, then periodically resetting the system values to those selected by the optimization system.