CN-121642998-B - Active instruction tracking and reactive voltage supporting coordinated control method for new energy base

CN121642998BCN 121642998 BCN121642998 BCN 121642998BCN-121642998-B

Abstract

The invention discloses a new energy base active instruction tracking and reactive voltage supporting coordination control method which comprises the steps of obtaining active power instructions and operating parameters of stations in a new energy base, constructing a new energy base grid-connected power flow model, determining optimization target variables, targets and constraint conditions according to the new energy base grid-connected power flow model, establishing a new energy base active instruction tracking and reactive voltage supporting coordination optimization model, solving the optimization model according to the new energy base active instruction tracking and reactive voltage supporting coordination optimization model by adopting a hybrid intelligent algorithm to obtain optimal active and reactive output coefficients of each station in the new energy base, and controlling active instruction tracking and reactive voltage supporting coordination of the new energy base according to the optimal active and reactive output coefficients.

Inventors

Nian Hang
YAO DONGXU
HE ZHEN

Assignees

浙江大学

Dates

Publication Date: 20260505
Application Date: 20260130

Claims (6)

1. The active command tracking and reactive voltage supporting coordinated control method for the new energy base is characterized by comprising the following steps of: Acquiring an active power instruction output by a new energy base grid-connected point and operating parameters of a station in the new energy base, and constructing a new energy base grid-connected power flow model; According to the new energy base grid-connected power flow model, taking active and reactive output coefficients of all stations in the new energy base as optimization target variables, taking active instruction tracking and maximizing reactive output as targets, and taking station capacity constraint, node voltage amplitude constraint, line power flow constraint and power balance constraint in the new energy base as constraint conditions, and establishing a new energy base active instruction tracking and reactive voltage support coordination optimization model; According to the active instruction tracking and reactive voltage supporting coordination optimization model of the new energy base, a hybrid intelligent algorithm is adopted to solve the optimization model, and the optimal active and reactive output coefficients of all stations in the new energy base are obtained; according to the optimal active and reactive power coefficients, active instruction tracking and reactive voltage supporting coordination of the new energy base are controlled; constructing a mixed algorithm framework of a fusion particle swarm algorithm and a genetic algorithm, wherein: In the early stage of iteration, a particle swarm algorithm updating stage is executed, the diversity of an initial population is increased through an intelligent diversity initialization strategy, and the initial population is generated by combining a history optimal solution, a physical heuristic method, latin hypercube sampling and random initialization; In the updating stage of the particle swarm algorithm, adopting self-adaptive inertial weight and combining with Lewy flight disturbance in the speed updating; in the operation stage of the genetic algorithm executed in the whole iterative process, the population is evolved through the self-adaptive crossover and self-adaptive mutation strategy, uniform crossover and large-range mutation are used in the early stage, and arithmetic crossover and small-range mutation are used in the later stage; After the genetic algorithm operation stage, performing elite local search on the current optimal solution, maintaining population diversity by calculating a population diversity index, and reinitializing part of individuals when the diversity is lower than a threshold value; the constraint condition is converted into a penalty term for the fitness function by processing the voltage constraint using a dynamic penalty function.
2. The new energy base active command tracking and reactive voltage supporting coordination control method according to claim 1, wherein the new energy base grid-connected point output active power command comprises a power grid scheduling command given by a power grid scheduling for 24 hours in future; The operation parameters of the stations in the new energy base comprise the types and the capacities of the stations in the new energy base, the current day wind power and the illumination condition in the new energy base, the geographic position parameters of the stations in the new energy base, the connection line parameters among the stations in the new energy base, and the maximum and the minimum voltage which can be born by the nodes of the stations in the new energy base, wherein the connection line parameters among the stations in the new energy base comprise the line impedance and the line allowable capacity.
3. The method for controlling active command tracking and reactive voltage support coordination of the new energy base according to claim 1 is characterized in that the new energy base grid-connected power flow model is used for grid-connected point output active command receiving, power calculation of all stations in the new energy base and operation conditions and operation modes of all stations in the new energy base.
4. The utility model provides a new energy base active command tracking and reactive voltage support coordinated control device which characterized in that includes: The acquisition modeling module is used for acquiring an active power instruction output by a new energy base grid-connected point and operating parameters of a station in the new energy base and constructing a new energy base grid-connected power flow model; The modeling module is used for establishing a new energy base active instruction tracking and reactive voltage supporting coordination optimization model by taking active and reactive output coefficients of all stations in the new energy base as optimization target variables, active instruction tracking and maximum reactive output as targets and taking station capacity constraint, node voltage amplitude constraint, line power flow constraint and power balance constraint in the new energy base as constraint conditions according to the new energy base grid-connected power flow model; The solving module is used for solving the optimizing model by adopting a hybrid intelligent algorithm according to the active instruction tracking and reactive voltage supporting coordination optimizing model of the new energy base so as to obtain the optimal active and reactive output coefficients of all stations in the new energy base; the control module is used for controlling the coordination of active instruction tracking and reactive voltage support of the new energy base according to the optimal active and reactive output coefficients; constructing a mixed algorithm framework of a fusion particle swarm algorithm and a genetic algorithm, wherein: In the early stage of iteration, a particle swarm algorithm updating stage is executed, the diversity of an initial population is increased through an intelligent diversity initialization strategy, and the initial population is generated by combining a history optimal solution, a physical heuristic method, latin hypercube sampling and random initialization; In the updating stage of the particle swarm algorithm, adopting self-adaptive inertial weight and combining with Lewy flight disturbance in the speed updating; in the operation stage of the genetic algorithm executed in the whole iterative process, the population is evolved through the self-adaptive crossover and self-adaptive mutation strategy, uniform crossover and large-range mutation are used in the early stage, and arithmetic crossover and small-range mutation are used in the later stage; After the genetic algorithm operation stage, performing elite local search on the current optimal solution, maintaining population diversity by calculating a population diversity index, and reinitializing part of individuals when the diversity is lower than a threshold value; the constraint condition is converted into a penalty term for the fitness function by processing the voltage constraint using a dynamic penalty function.
5. An electronic device, comprising: One or more processors; A memory for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-3.
6. A computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any of claims 1-3.

Description

Active instruction tracking and reactive voltage supporting coordinated control method for new energy base Technical Field The application relates to the technical field of new energy grid-connected control, in particular to a method for active instruction tracking and reactive voltage supporting coordinated control of a new energy base. Background The new energy base represented by wind power and photovoltaic is accessed into the power grid in a large scale, and the intermittence and fluctuation of the output of the new energy base make the stable operation of the power grid face serious challenges. In order to meet the power supply requirement of the load side, the automatic power generation control system of the power grid needs to send frequent active scheduling instructions to the new energy resource base. However, the rapid adjustment of the active power can significantly change the grid power flow distribution, thereby inducing severe fluctuations in the grid-connected point voltage. Traditionally, the new energy station stabilizes the voltage through the power electronic reactive power compensation device in the operation station, but the passive response mode is difficult to cope with the dynamic voltage problem caused by the active dispatching instruction, neglects the reactive power regulation potential of the new energy station, and weakens the running safety and economy of the new energy base. Therefore, while active command tracking is achieved, the reactive voltage supporting capability of the new energy base itself needs to be further explored. How to coordinate the active power and the reactive power of each station in the new energy base so as to realize quick response to the active dispatching instruction and simultaneously improve the reactive voltage supporting capability to the greatest extent is the core of the coordination control of the active instruction tracking and the reactive voltage supporting of the new energy base. At present, the research on the aspect has several problems to be solved, namely (1) how to construct a collaborative optimization model capable of simultaneously and rapidly responding to the active command and the reactive voltage requirement of the power grid, (2) how to distribute the total active command received by the new energy base in the output of each station, and (3) how to rapidly and accurately solve the high-dimensional nonlinear optimization problem and generate an executable coordination control command on the premise of meeting various constraints. Most research methods adopt a strict tide computing method for the problem (1), although the method can accurately calculate the active and reactive power output of the node, but still adopts an open loop control mode, the active command is difficult to quickly respond and reactive power coordination control is realized, in the problem (2), the existing method often regards a new energy base as a whole, ignores the influence of internal impedance on active and reactive power, and generally adopts uniform power control for a new energy base internal site, so that the reactive power regulation potential of the new energy cannot be exerted, in the problem (3), the problem of high-dimensional nonlinear optimization is faced, and a single solving algorithm is still adopted at present, so that the problem of local optimum instead of global optimum exists, and the solving deviation is large. Disclosure of Invention In order to solve the problems, the embodiment of the application provides a new energy base active command tracking and reactive voltage supporting coordinated control method, which aims to solve the technical problem that a new energy base cannot quickly cooperate to respond to an active command and a reactive voltage requirement of a power grid in the related technology. According to a first aspect of an embodiment of the present application, there is provided a new energy base active instruction tracking and reactive voltage support coordination control method, including: Acquiring an active power instruction output by a new energy base grid-connected point and operating parameters of a station in the new energy base, and constructing a new energy base grid-connected power flow model; According to the new energy base grid-connected power flow model, taking active and reactive output coefficients of all stations in the new energy base as optimization target variables, taking active instruction tracking and maximizing reactive output as targets, and taking station capacity constraint, node voltage amplitude constraint, line power flow constraint and power balance constraint in the new energy base as constraint conditions, and establishing a new energy base active instruction tracking and reactive voltage support coordination optimization model; According to the active instruction tracking and reactive voltage supporting coordination optimization model of the new energy base, a hybrid intelligent