Abstract: A method for controlling a distributed power system is provided, the system including an aggregator communicatively coupled to a plurality of nodes, each of the plurality of nodes including an associated load. The method includes receiving, at the aggregator, a commanded power profile from an independent service operator, the commanded power profile including a commanded power deviation for the distributed power system, calculating, using the aggregator, a score for each of the plurality of nodes based on a current operating power of each node, selecting, using the aggregator, a subset of the plurality of nodes based on the calculated scores, and commanding, using the aggregator, each node in the subset to adjust its current power by a respective predetermined amount, the predetermined amounts determined based on the commanded power deviation.

Abstract: Provided is a system and method for determining the state estimate of a power grid by dividing the power grid into smaller sub-sections, generating state estimates for each sub-section, and then generating a consensus among the sub-sections. In one example, the method may include partitioning a section of the power distribution grid into a plurality of sub-sections based on loads distributed within the section of the power distribution grid, generating a plurality of state estimates for the plurality of sub-sections based on load distribution within the plurality of sub-sections and a Kalman Filter model, generating an aggregate state estimate for the section of the power distribution grid based on an aggregate of the plurality of state estimates and a boundary consensus between the plurality of sub-sections from a previous state estimation, and displaying data about the aggregate state estimate via a user interface.

Abstract: A method and system of operating a vehicle in a descent profile, the method comprising obtaining, at a controller module, a mathematical model of performance characteristics for an aircraft, generating an optimal guidance trajectory, and operating the aircraft in accordance with the optimal guidance trajectory prior to operating in a position-based guidance.

Abstract: A method for controlling a distributed power system is provided, the system including an aggregator communicatively coupled to a plurality of nodes, each of the plurality of nodes including an associated load. The method includes receiving, at the aggregator, a commanded power profile from an independent service operator, the commanded power profile including a commanded power deviation for the distributed power system, calculating, using the aggregator, a score for each of the plurality of nodes based on a current operating power of each node, selecting, using the aggregator, a subset of the plurality of nodes based on the calculated scores, and commanding, using the aggregator, each node in the subset to adjust its current power by a respective predetermined amount, the predetermined amounts determined based on the commanded power deviation.

Abstract: A method and system of determining or estimating a descent profile includes receiving a final approach fix for a target destination, repeatedly back-calculating a set of subsequent models, determining a descent profile based on the repeated back-calculating the set of subsequent models, and operating the aircraft in accordance with the descent profile.

Abstract: Some embodiments provide a distributed optimization framework and technology for the control of aggregated propulsion system components that iteratively operates until a commanded propulsion-related profile is produced by aggregated components of the propulsion system. Some embodiments use a distributed iterative solution in which each component solves a local optimization problem with local constraints and states, while using global variables that are based upon information from each other component. The global variables may be determined via a distributed transaction system using component-specific information obtained from each component at each iteration. The global variables may be broadcast to the components for each new iteration.

Abstract: A method for controlling a distributed power system is provided, the power system including an aggregator communicatively coupled to a plurality of nodes. The method includes receiving, at the aggregator, a specified aggregated power level, and at each of a plurality of sample times recurring at a regular interval, receiving, at the aggregator from each of the nodes, a condensed dataset, calculating, at the aggregator, a global value based on the specified aggregated power level, the condensed datasets, and a control prediction horizon, transmitting the global value to each of the nodes, solving, at each of the plurality of nodes, a local optimization problem based on the received global value and a local model prediction horizon for that node that is longer than the control prediction horizon, and controlling, at each of the plurality of nodes, a load based on the solved local optimization problem.

Abstract: A method and system of determining or predicting a climb profile for an aircraft, includes receiving, by a controller module, an initial climb profile defining a portion of a flight plan for the aircraft between takeoff and a cruise profile, and an altitude climb constraint defining at least one altitude limitation of the aircraft, determining, by the controller module, that the initial climb profile does not satisfy the altitude constraint, and determining an updated climb profile based on a set of subsequent climb models.

Abstract: A system, computer-readable medium, and a method including obtaining flight data for a specific aircraft for a prescribed flight; obtaining current sample measurements of at least one state or output of the specific aircraft; performing based on the obtained flight data, the current measurements or outputs, and a mathematical model accurately representing an actual operational performance of the specific aircraft and providing a predictive indication of a future performance of the specific aircraft, a control optimization to determine a cost-optimal control input for the prescribed flight; adjusting, in response to a consideration of actual operational characteristics of the specific aircraft, the optimized control input; and transmitting the adjusted optimized control input to the specific aircraft to operate the specific aircraft to minimize the direct operating cost for the prescribed flight.

Abstract: A system, computer-readable medium, and a method to operate a vehicle in a manner that minimizes a cost to travel from an origin to a destination that includes finding the input to a flight control system that minimizes direct operating cost. The approach described herein employs an energy state approximation (ESA).

Abstract: Methods, systems, and apparatuses for generating efficient flight profiles in a variety of aircraft are disclosed. A method includes receiving required time of arrival (RTA) constraints for the aerial vehicle. The RTA constraints include a required time of arrival for a waypoint. The method also includes inputting the RTA constraints into a problem configured to generate flight plans based on the required time of arrival for the waypoint of the RTA constraints. The problem includes an altitude variable and a speed variable. The method also includes generating, as a solution to the problem, a flight plan by varying the altitude variable and the speed variable in order to reduce operating cost of the aerial vehicle based at least in part on the RTA constraints, and providing the flight plan to at least one computing system of the aerial vehicle. The flight plan includes a route traversing the waypoint.

Abstract: Some embodiments may provide a distributed optimization technology for the control of aggregation of distributed flexibility resource nodes (e.g., associated with distributed energy resources) that operates iteratively until a commanded power profile is produced by aggregated loads. Some embodiments use a distributed iterative solution in which each node solves a local optimization problem with local constraints and states, while using global qualities (e.g., associated with a Lagrange multiplier) that are based upon information from each other node. The global qualities may be determined via a secure, distributed transaction ledger (e.g., associated with blockchain) using DER-specific information obtained in a condensed form (e.g., a scalar or vector) from each node at each iteration. The global qualities may be broadcast to the nodes for each new iteration. Embodiments may provide an iterative, distributed solution to the network optimization problem of aggregated load power tracking.

Abstract: An optimization-based method and system is disclosed to enable heterogeneous loads and distributed energy resources (DERs) to participate in grid ancillary services, such as spinning and non-spinning reserves, and ramping reserves. The method includes receiving inputs for decision parameters for optimizing an objective for obtaining flexible reserve power, solving the objective for obtaining flexible reserve power, determining a reserve power schedule for a prediction horizon for providing flexible reserve power based on the objective, generating a service bid based on the reserve power schedule for the power grid; and when the service bid is accepted, providing flexible reserve power to the power grid based on the service bid.

Abstract: A method and system of determining or estimating a descent profile includes receiving a final approach fix for a target destination, repeatedly back-calculating a set of subsequent models, determining a descent profile based on the repeated back-calculating the set of subsequent models, and operating the aircraft in accordance with the descent profile.

Abstract: A method and system of determining or predicting a climb profile for an aircraft, includes receiving, by a controller module, an initial climb profile defining a portion of a flight plan for the aircraft between takeoff and a cruise profile, and an altitude climb constraint defining at least one altitude limitation of the aircraft, determining, by the controller module, that the initial climb profile does not satisfy the altitude constraint, and determining an updated climb profile based on a set of subsequent climb models.

Abstract: Some embodiments provide a distributed optimization framework and technology for the control of aggregated propulsion system components that iteratively operates until a commanded propulsion-related profile is produced by aggregated components of the propulsion system. Some embodiments use a distributed iterative solution in which each component solves a local optimization problem with local constraints and states, while using global variables that are based upon information from each other component. The global variables may be determined via a distributed transaction system using component-specific information obtained from each component at each iteration. The global variables may be broadcast to the components for each new iteration.

Abstract: Some embodiments may provide a distributed optimization technology for the control of aggregation of distributed flexibility resource nodes (e.g., associated with distributed energy resources) that operates iteratively until a commanded power profile is produced by aggregated loads. Some embodiments use a distributed iterative solution in which each node solves a local optimization problem with local constraints and states, while using global qualities (e.g., associated with a Lagrange multiplier) that are based upon information from each other node. The global qualities may be determined via a secure, distributed transaction ledger (e.g., associated with blockchain) using DER-specific information obtained in a condensed form (e.g., a scalar or vector) from each node at each iteration. The global qualities may be broadcast to the nodes for each new iteration. Embodiments may provide an iterative, distributed solution to the network optimization problem of aggregated load power tracking.

Abstract: The technology described herein is generally directed towards a distributed optimization technology for the control of aggregation of distributed flexibility resource nodes that operates iteratively until a commanded power profile is produced by aggregated loads. The technology uses a distributed iterative solution in which each node solves a local optimization problem with local constraints and states, while using a global Lagrange multiplier that is based upon information from each other node. The global Lagrange multiplier is determined at an aggregation level using load-specific information that is obtained in a condensed form (e.g., a scalar) from each node at each iteration. The global Lagrange multiplier is broadcasted to the nodes for each new iteration. The technology provides an iterative, distributed solution to the network optimization problem of power tracking of aggregated loads.

Abstract: Methods, systems, and apparatuses for generating efficient flight profiles in a variety of aircraft are disclosed. A method can include receiving required time of arrival (RTA) constraints for the aerial vehicle. The RTA constraints include a required time of arrival for a waypoint. The method also includes inputting the RTA constraints into a problem configured to generate flight plans based on the required time of arrival for the waypoint of the RTA constraints. The problem can include an altitude variable and a speed variable. The method also includes generating, as a solution to the problem, a flight plan by varying the altitude variable and the speed variable in order to reduce operating cost of the aerial vehicle based at least in part on the RTA constraints, and providing the flight plan to at least one computing system of the aerial vehicle. The flight plan includes a route traversing the waypoint.

Abstract: A system, computer-readable medium, and a method to operate a vehicle in a manner that minimizes a cost to travel from an origin to a destination that includes finding the input to a flight control system that minimizes direct operating cost. The approach described herein employs an energy state approximation (ESA).