Abstract:
The disclosure provides power distribution network reliability index calculation method based on mixed integer linear programming. The method includes: establishing a model for optimizing reliability indexes of a power distribution network based on a mixed integer linear programming model, wherein the model comprises an objective function and constraint conditions, the objective function is for minimizing a system average interruption duration index (SAIDI); solving the model based on the objective function and the constraint conditions to obtain reliability indexes of the power distribution network; and controlling operation of the power distribution network based on the reliability indexes.
Abstract:
The present disclosure provides a stability criterion for time-delay of cyber-physical power systems under distributed control, which relates to a field of cyber-physical power systems technologies. The method first establishes an cyber side model of the cyber-physical power systems under distributed control and a physical power grid model of the cyber-physical power systems under distributed control respectively; then establishes simultaneous equations of the cyber side model and the physical power grid model to establish an unified differential algebraic equation model of the cyber-physical power systems under distributed control, so as to obtain a time-delay characteristic equation expression of the cyber-physical power systems under distributed control; determines a time-delay of each node, and solving the time-delay characteristic equation expression to obtain a maximum characteristic root, and performing the stability criterion for the time-delay of the cyber-physical power systems under distributed control according to a real part of the maximum characteristic root.
Abstract:
A reactive power-voltage control method for integrated transmission and distribution networks is provided. The reactive power-voltage control method includes: establishing a reactive power-voltage control model for a power system consisting of a transmission network and a plurality of distribution networks; performing a second order cone relaxation on a non-convex constraint of the plurality of distribution network constraints to obtain the convex-relaxed reactive power-voltage control model; solving the convex-relaxed reactive power-voltage control model to acquire control variables of the transmission network and control variables of each distribution network; and controlling the transmission network based on the control variables of the transmission network and controlling each distribution network based on the control variables of the distribution network, so as to realize coordinated control of the power system.
Abstract:
A method for estimating a state of a combined heat and power system is provided. The method include: establishing an objective function; establishing constraints under a steady-state operating stage; converting the objective function and the constraints by utilizing a Lagrangian multiplier to obtain a Lagrange function; obtaining a steady-state estimation result of the combined heat and power system based on the Lagrange function; calculating an energy transmission delay produced by each pipe; establishing a dynamic constraint of each pipe based on the steady-state estimation result and the energy transmission delay; converting the objective function, the constraints, and the dynamic constraint by utilizing the Lagrangian multiplier to update the Lagrange function; obtaining a dynamic-state estimation result of the combined heat and power system during a dynamic-state operating stage of the combined heat and power system based on the updated Lagrange function.
Abstract:
A reactive power optimization method for integrated transmission and distribution networks related to a field of operation and control technology of an electric power system is provided. The reactive power optimization method includes: establishing a reactive power optimization model for a transmission and distribution network consisting of a transmission network and a plurality of distribution networks, in which the reactive power optimization model includes an objective function and a plurality of constraints; performing a second order cone relaxation on a non-convex constraint of a plurality of distribution network constraints of the plurality of constraints; and solving the reactive power optimization model by using a generalized Benders decomposition method so as to control each generator in the transmission network and each generator in the plurality of distribution networks.
Abstract:
The present disclosure provides a frequency control method for a micro-grid and a control device. The method includes: determining a middle parameter at iteration k; determining a local gradient parameter at iteration k according to the cost increment rate at iteration k, the frequency difference between iterations k and k+1, and communication coefficients; performing a quasi-Newton recursion according to the middle parameter and local gradient parameter to acquire a recursion value; determining the cost increment rate at iteration k+1 according to the recursion value; determining an adjustment value of an active power according to the cost increment rate at iteration k+1; adjusting the active power according to the adjustment value if the adjustment value satisfies a constraint condition and judging whether the difference is smaller than a predetermined threshold; executing k=k+1 if yes and stopping the frequency control if no.
Abstract:
The present disclosure relates to a method and an apparatus for controlling a voltage in a wind farm. The method includes: collecting measured values of parameters as initial values of the prediction values; inputting the initial values into a preset control model for optimizing a model predictive control; solving the preset control model to obtain a first solution sequence of the reactive power setting values of the wind turbines and a second solution sequence of the terminal voltage setting values of the static var generators; and sending first values in the first solution sequence to the wind turbines and first values in the second solution sequence to the static var generators, such that a voltage control in the wind farm is realized.
Abstract:
A method and a device for charging an electric vehicle in a power system are provided. The method includes: obtaining a first electric vehicle connected to the power system, and obtaining a rated charging power and a first charging requirement; determining a first charging period corresponding to the first electric vehicle; determining a forecast period, and obtaining a second electric vehicle to be connected to the power system; revising the first charging period to obtain a second charging period, and obtaining a second charging requirement and a maximum charging power; establishing a charging model, establishing a first constraint of the charging model, and establishing a second constraint of the charging model; and solving the charging model under the first constraint and the second constraint to obtain an optimal charging power so as to charge each first electric vehicle under the optimal charging power.
Abstract:
A security and economy coordinated automatic voltage control method based on a cooperative game theory is provided. The method includes: establishing a multi-objective reactive voltage optimizing model of a power system; resolving the multi-objective reactive voltage optimizing model into an economy model and a security model; solving the economy model and the security model based on the cooperative game theory to obtain the automatic voltage control instruction; and performing an automatic voltage control for the power system according to the automatic voltage control instruction.
Abstract:
A method and a device for navigating an electric vehicle in charging are provided. The method comprises: S1, obtaining a navigation area, wherein the navigation area comprises a plurality of charging stations; S2, receiving a charging request from an electric vehicle in the navigation area; S3, obtaining a plurality of first time periods according to the electric vehicle and the plurality of charging stations; S4, selecting a minimum first time period from the plurality of first time periods; and S5, navigating the electric vehicle to a charging station corresponding to the minimum first time period.