Bilateral stochastic power grid dispatching method

    公开(公告)号:US11689024B2

    公开(公告)日:2023-06-27

    申请号:US17383658

    申请日:2021-07-23

    IPC分类号: H02J3/38 G05B19/042

    摘要: The disclosure relates to a two-side stochastic dispatching method for a power grid. By analyzing historical data of wind power, the Gaussian mixture distribution is fitted by software. For certain power system parameters, a two-side chance-constrained stochastic dispatching model is established. The hyperbolic tangent function is used to analyze and approximate cumulative distribution functions of random variables in the reserve demand constraint and the power flow constraint, to convert the two-side chance constraint into a deterministic constraint. The disclosure can have the advantage of using the hyperbolic tangent function to convert the two-side chance constraint containing risk levels and random variables into the solvable deterministic convex constraint, effectively improving the solution efficiency of the model, and providing decision makers with a more reasonable dispatching basis.

    Method for calculating control parameters of heating supply power of heating network

    公开(公告)号:US11262713B2

    公开(公告)日:2022-03-01

    申请号:US16842600

    申请日:2020-04-07

    摘要: A method for calculating control parameters of a heating supply power of a heating network, pertaining to the technical field of operation and control of a power system containing multiple types of energy. The method: establishing a heating network simulation model that simulates a thermal dynamic process of the heating network; starting an upward simulation based on the heating network simulation model to obtain first control parameters from a set of up adjustment amounts; starting a downward simulation based on the heating network simulation model, to obtain second control parameters from a set of down adjustment amounts.

    Random rolling scheduling method for power system based on Newton method

    公开(公告)号:US11043818B2

    公开(公告)日:2021-06-22

    申请号:US16542620

    申请日:2019-08-16

    IPC分类号: H02J3/38 G05B19/042

    摘要: The disclosure provides a stochastic look-ahead dispatch method for power system based on Newton method, belonging to power system dispatch technologies. The disclosure analyzes historical data of wind power output, and uses statistical or fitting software to perform Gaussian mixture model fitting. A dispatch model with chance constraints is established for system parameters. Newton method is to solve quantiles of random variables obeying Gaussian mixture model, so that chance constraints are transformed into deterministic linear constraints, thus transforming original problem to convex optimization problem with linear constraints. Finally, the model is solved to obtain look-ahead dispatch. The disclosure employs Newton method to transform chance constraints containing risk level and random variables into deterministic linear constraints, which effectively improves model solution efficiency, and provides reasonable dispatch for decision makers. The disclosure is employed to the dispatch of the power system including large-scale renewable energy grid-connected.

    Stochastic dynamical unit commitment method for power system based on solving quantiles via newton method

    公开(公告)号:US10923916B2

    公开(公告)日:2021-02-16

    申请号:US16542651

    申请日:2019-08-16

    IPC分类号: H02J3/38 G05B17/02

    摘要: The disclosure provides a stochastic dynamical unit commitment method for power system based on solving quantiles via Newton method, belonging to power system technologies. The method establishes a unit commitment model with chance constraints for power system parameters. Quantiles of random variables obeying mixed Gaussian distribution is solved by Newton method, and chance constraints are transformed into deterministic linear constraints, so that original problem is transformed into mixed integer linear optimization problem. Finally, the model is solved to obtain on-off strategy and active power plan of units. The disclosure employs Newton method to transform chance constraints containing risk level and random variables into deterministic mixed integer linear constraints, which effectively improves the model solution efficiency, eliminates conservative nature of conventional robust unit commitment, provides reasonable dispatch basis for decision makers. The disclosure is employed to the stochastic and dynamic unit commitment of the power system including large-scale renewable energy grid-connected.

    Method and device for estimating state of power system

    公开(公告)号:US10222815B2

    公开(公告)日:2019-03-05

    申请号:US15331876

    申请日:2016-10-23

    摘要: A method and a device for estimating a state of a power system are provided. The method includes: dividing the power system into a plurality of sub-systems; establishing a first linear model of the power system for a first stage; solving the first linear model by an alternating direction multiplier method to obtain the intermediate state variables of each sub-system; performing a nonlinear transformation at a second stage on the intermediate state variables to obtain intermediate measured values; establishing a second linear model of the power system for a third stage according to the intermediate measured values; and solving the third linear model by the alternating direction multiplier method to obtain the final state variables of each sub-system.

    Method, apparatus, and storage medium for controlling heating system

    公开(公告)号:US12104803B2

    公开(公告)日:2024-10-01

    申请号:US17152041

    申请日:2021-01-19

    摘要: The disclosure provides a method, an apparatus, and a storage medium for controlling heating system. The method includes: establishing an objective function and constraints for estimating system parameters of the heating system, in which the heating system includes nodes, pipelines and equivalent branches, the equivalent branch is configured to represent a heating resource or a heating load in the heating system, the system parameters include a resistance coefficient of each of the pipelines and equivalent branches, and a heat dissipation coefficient of each of the pipelines; solving the objective function based on the constraints to obtain the system parameters; modeling the heating system based on the obtained system parameters to obtain control parameters of the heating system; and controlling the heating system based on the control parameters.