General Electric Company

Ge Aviation Systems Limited

General Electric Technology Gmbh

Ge Digital Holdings LLC

Reza Ghaemi

Eric Richard Westervelt

David Michael Lax

Mark Lawrence Darnell

Pierino Gianni Bonanni

Masoud Abbaszadeh

Chaitanya Ashok Baone

Naresh Acharya

Nicholas Race Visser

Benjamin Edward Beckmann

Yan Pan

James Ryan Reepmeyer

Khaled Yehia Bahei-Eldin

Owen Michael O'Keefe

Xuefei Li

Xing Wang

Mark Darnell

Russell Ramaker

Owen O'KEEFE

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.

Systems and methods for rapidly responding to commanded power profiles

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.

Method and system for determining a descent profile

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.

Distributed optimal control of an aircraft propulsion system

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.

Method and system for determining a climb profile

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.

Flight management via model-based iterative optimization

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).

Methods and systems for optimal guidance based on energy state approximation

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.

Efficient flight profiles with multiple RTA constraints

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.

Distributed ledger based control of large-scale, power grid energy resources

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.

Method and system for providing flexible reserve power for power grid

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.

Scalable flexibility control of distributed loads in a power grid

A method, medium, and system to receive a mathematical model representation of performance characteristics for an aircraft and an engine combination; perform a projection based model order reduction on the mathematical model representation; eliminate, based on the projected model, fast dynamics components of the mathematical model representation; determine a reduced order model, as a differential algebraic equation, wherein algebraic equations replace the fast dynamics; set a flight path angle and a throttle level angle as a control to minimize fuel consumption for the modeled aircraft and engine combination; discretize equations of motion for the modeled aircraft and engine combination and formulate optimization equations as a nonlinear programming problem; and determine an optimal open loop control that minimizes fuel consumption for the modeled aircraft and engine combination to climb to a prescribed cruise altitude and airspeed.

Growing community of inventors

Watervliet, NY, United States of America

Reza Ghaemi