CN117895574A

CN117895574A - Method and device for determining regulation strategy, storage medium and electronic equipment

Info

Publication number: CN117895574A
Application number: CN202311286366.5A
Authority: CN
Inventors: 王方雨; 周文斌; 陈艳霞; 于希娟; 王海云; 杨莉萍; 张雨璇; 郑凯元; 徐鹏; 汪伟; 张再驰; 陈茜; 姚艺迪; 李鑫明; 闻宇
Original assignee: State Grid Corp of China SGCC; North China Electric Power University; State Grid Beijing Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; North China Electric Power University; State Grid Beijing Electric Power Co Ltd
Priority date: 2023-09-28
Filing date: 2023-09-28
Publication date: 2024-04-16

Abstract

The embodiment of the application provides a method and a device for determining a regulation strategy, a storage medium and electronic equipment, wherein the method comprises the following steps: determining the operation information of the target power distribution network in the load peak period according to the load prediction curve, and processing the target power distribution network by using a target processing mode to obtain a first comprehensive load model; under the condition that an objective function and constraint conditions for preventive control are acquired, a power flow model is built according to a first comprehensive load model, the objective function and the constraint conditions, first processing is carried out on operation information through the power flow model to obtain a first regulation strategy, a cluster scheduling model is built based on the first regulation strategy and the adjustable power under the condition that the adjustable power corresponding to an electric automobile cluster in a target power distribution network is acquired, and second processing is carried out on the operation information according to the cluster scheduling model to obtain a second regulation strategy; and performing preventive control on the target power distribution network based on the first regulation strategy and/or the second regulation strategy.

Description

Method and device for determining regulation strategy, storage medium and electronic equipment

Technical Field

The embodiment of the application relates to the field of communication, in particular to a method and a device for determining a regulation strategy, a storage medium and electronic equipment.

Background

With the continuous advancement of energy revolution and the large-scale access of new energy, the novel power system presents the characteristics of double high of high-proportion renewable energy and high-proportion power electronic equipment and the characteristics of double-sided random of random supply side and random demand side, which brings great challenges to the safe and stable operation of the novel power system.

Along with the construction of a novel power system, the demand side flexible resources represented by the electric automobile clusters are increased, become power grid side friendly interactive resources, and play an important role in peak clipping and valley filling of the power system. The existing research mostly applies the flexible resource on the demand side to the regulation and control of the power distribution network level so as to ensure the operation safety of the system and promote the capacity of absorbing new energy, and the research is focused on the frequency modulation and the voltage regulation of the power distribution network level, so that the influence of the controllable load with such a large amount on the power transmission network is not considered. The prevention control of the power transmission network layer also simply equivalent the power distribution network to static load, and the influence of the load change of the power distribution network on the transient stability of the power transmission network is not considered.

The problem that the electric automobile clusters cannot be prevented from participating in the electric power peak regulation scene in the prior art is not effectively solved.

Disclosure of Invention

The embodiment of the application provides a method and a device for determining a regulation strategy, a storage medium and electronic equipment, which are used for at least solving the problem that an electric automobile cluster cannot be prevented from participating in a power peak regulation scene in the related technology.

According to an embodiment of the present application, there is provided a method for determining a regulation strategy, including: determining the operation information of a target power distribution network in a load peak period according to a load prediction curve, and processing the target power distribution network by using a target processing mode to obtain a first comprehensive load model; under the condition of acquiring an objective function and a constraint condition for preventive control, constructing a power flow model according to the first comprehensive load model, the objective function and the constraint condition, and executing first processing on the operation information through the power flow model to obtain a first regulation strategy corresponding to the target power distribution network, wherein the objective function is used for indicating a function corresponding to the minimum total regulation cost of the target power distribution network; under the condition that the adjustable power corresponding to the electric automobile cluster in the target power distribution network is acquired, a cluster scheduling model is built based on the first regulation strategy and the adjustable power, and a second process is executed on the operation information according to the cluster scheduling model, so that a second regulation strategy corresponding to the target power distribution network is obtained; and performing preventive control on the target power distribution network based on the first regulation strategy and/or the second regulation strategy.

In an exemplary embodiment, after the target power distribution network is processed by using a target processing manner to obtain the first comprehensive load model, the method further includes: and identifying disturbance data in the operation information, and acquiring a plurality of distribution parameters of a target distribution network grid-connected point corresponding to the disturbance data, wherein the plurality of distribution parameters at least comprise: the voltage amplitude corresponding to the grid-connected point, the voltage phase angle corresponding to the grid-connected point, the active power corresponding to the grid-connected point and the reactive power corresponding to the grid-connected point; inputting the voltage amplitude and the voltage phase angle to the first comprehensive load model to obtain first response power and second response power; determining a first error of the first response power and the active power and a second error of the second response power and the reactive power, and performing error correction on the first comprehensive load model through the first error and the second error.

In one exemplary embodiment, obtaining an objective function for performing preventive control includes: determining the number of generators present in the target power distribution network and determining the number of loads present in the target power distribution network; determining a first objective sub-function according to the number of generators and a first regulation formula, and determining a second objective sub-function according to the number of loads and a second regulation formula, wherein the first regulation formula is used for determining generator regulation cost corresponding to a power distribution network, and the first regulation formula is used for determining load regulation cost corresponding to the power distribution network; an objective function for performing preventive control is determined based on the first objective subfunction and/or the second objective subfunction.

In an exemplary embodiment, the first regulation formula is: Wherein f ₁ (x) is the generator adjustment cost, S _g is the generator set, P _Gi is the i-th generator active output, a _i is the i-th generator active output adjustment cost coefficient, and i is a positive integer; the second regulation formula is as follows: v where f ₂ (x) is the total adjustment cost of the load; s _l is a load set, P _Li is the ith load active power, and b _i is a compensation cost coefficient for cutting the ith load active power.

In one exemplary embodiment, the constraints include at least one of: the method comprises the following steps of a first constraint condition corresponding to equality constraint, a second constraint condition corresponding to stable operation constraint, a third constraint condition corresponding to equivalent constraint of a power distribution network and a fourth constraint condition corresponding to transient stability constraint.

In an exemplary embodiment, obtaining the adjustable power corresponding to the electric automobile cluster in the target power distribution network includes: establishing a quantization model of the electric automobile cluster in the target power distribution network; performing charge-discharge optimization on the quantized model by using preset charge-discharge power constraint and real-time electric quantity minimum constraint to obtain optimal charge-discharge power corresponding to the quantized model, wherein the charge-discharge optimization is used for indicating to solve the quantized model with the aim of maximizing the charge-discharge coefficient of the electric automobile cluster; and determining the optimal charge and discharge power as reference power, and determining the peak regulation capacity of the electric automobile cluster participating in centralized regulation by using the reference power to obtain the adjustable power.

In an exemplary embodiment, before the target power distribution network is prophylactically controlled based on the first regulation strategy and/or the second regulation strategy, the method further includes: under the condition that a set value of a load shedding step length corresponding to the target power distribution network is determined, acquiring a real-time value of an actual load shedding step length corresponding to the target power distribution network for preventive control; and under the condition that the real-time value is equal to the set value, determining the regulation strategy determined by using the prevention control as the current regulation strategy of the target power distribution network.

According to another embodiment of the present application, there is provided a regulation strategy determining apparatus, including: the first determining module is used for determining the operation information of the target power distribution network in the load peak period according to the load prediction curve, and processing the target power distribution network by using a target processing mode to obtain a first comprehensive load model; the first processing module is used for constructing a power flow model according to the first comprehensive load model, the target function and the constraint condition under the condition of acquiring the target function and the constraint condition for preventive control, and executing first processing on the operation information through the power flow model to obtain a first regulation strategy corresponding to the target power distribution network, wherein the target function is used for indicating a function corresponding to the minimum total regulation cost of the target power distribution network; the second processing module is used for constructing a cluster scheduling model based on the first regulation strategy and the adjustable power under the condition that the adjustable power corresponding to the electric automobile cluster in the target power distribution network is acquired, and executing second processing on the operation information according to the cluster scheduling model to obtain a second regulation strategy corresponding to the target power distribution network; and the control module is used for performing preventive control on the target power distribution network based on the first regulation strategy and/or the second regulation strategy.

According to a further embodiment of the application, there is also provided a computer readable storage medium having stored therein a computer program, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.

According to a further embodiment of the application there is also provided an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.

According to the application, the operation information of the target power distribution network in the load peak period is determined according to the load prediction curve, and the target power distribution network is processed by using a target processing mode to obtain a first comprehensive load model; under the condition that an objective function and constraint conditions for preventive control are acquired, a power flow model is built according to a first comprehensive load model, the objective function and the constraint conditions, first processing is carried out on operation information through the power flow model to obtain a first regulation strategy, a cluster scheduling model is built based on the first regulation strategy and the adjustable power under the condition that the adjustable power corresponding to an electric automobile cluster in a target power distribution network is acquired, and second processing is carried out on the operation information according to the cluster scheduling model to obtain a second regulation strategy; and performing preventive control on the target power distribution network based on the first regulation strategy and/or the second regulation strategy. . Therefore, the problem that the electric automobile clusters cannot be prevented from participating in the electric power peak shaving scene in the prior art can be solved, and the effect of cooperative prevention control of the electric automobile clusters in the electric power peak shaving scene is achieved.

Drawings

FIG. 1 is a block diagram of a hardware architecture of a computer terminal of a method for determining a regulation strategy according to an embodiment of the present application;

FIG. 2 is a flow chart of a determination of a regulatory strategy, as shown in FIG. 1, according to an embodiment of the present application;

Fig. 3 is a flowchart of a cooperative prevention control method in a scenario in which an electric vehicle cluster participates in power peak shaving according to an alternative embodiment of the present application;

fig. 4 is a schematic structural diagram of an equivalent model of a power distribution network considering electric vehicle clusters according to an alternative embodiment of the present application;

FIG. 5 is a diagram of a simulation system daily load prediction in accordance with an alternative embodiment of the present application;

FIG. 6 is a schematic diagram of overall regulation strategies for each distribution network at a large grid level given by simulation in accordance with an alternative embodiment of the present application;

fig. 7 is a strategy for adjusting the responses of each electric vehicle cluster and other demand sides in the distribution network 1 accessed by the node 23 according to an alternative embodiment of the present application;

FIG. 8 is a strategy for adjusting the response of each electric vehicle cluster and other demand sides in the distribution network 2 to which the node 24 is connected in accordance with an alternative embodiment of the present application;

fig. 9 is a block diagram of a configuration of a determination device of a regulation strategy according to an embodiment of the present application.

Detailed Description

Embodiments of the present application will be described in detail below with reference to the accompanying drawings in conjunction with the embodiments.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.

The method embodiments provided in the embodiments of the present application may be performed in a mobile terminal, a computer terminal or similar computing device. Taking the operation on a computer terminal as an example, fig. 1 is a block diagram of a hardware structure of a computer terminal of a method for determining a regulation policy according to an embodiment of the present application. As shown in fig. 1, the computer terminal may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA) and a memory 104 for storing data, wherein the computer terminal may further include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the computer terminal described above. For example, the computer terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to a method for determining a regulation policy in an embodiment of the present application, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, that is, implement the above-mentioned method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as a NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.

FIG. 2 is a flow chart of a determination of a regulatory strategy according to an embodiment of the application, as shown in FIG. 2, the flow comprising the steps of:

Step S202, determining the operation information of a target power distribution network in a load peak period according to a load prediction curve, and processing the target power distribution network by using a target processing mode to obtain a first comprehensive load model;

Step S204, under the condition of obtaining an objective function and a constraint condition for preventive control, constructing a power flow model according to the first comprehensive load model, the objective function and the constraint condition, and executing first processing on the operation information through the power flow model to obtain a first regulation strategy corresponding to the objective power distribution network, wherein the objective function is used for indicating a function corresponding to the minimum total regulation cost of the objective power distribution network;

Step S206, under the condition that the adjustable power corresponding to the electric automobile cluster in the target power distribution network is obtained, a cluster scheduling model is built based on the first regulation strategy and the adjustable power, and a second process is executed on the operation information according to the cluster scheduling model, so that a second regulation strategy corresponding to the target power distribution network is obtained;

Step S208, performing preventive control on the target power distribution network based on the first regulation strategy and/or the second regulation strategy.

Determining the operation information of the target power distribution network in the load peak period according to the load prediction curve, and processing the target power distribution network by using a target processing mode to obtain a first comprehensive load model; under the condition that an objective function and constraint conditions for preventive control are acquired, a power flow model is built according to a first comprehensive load model, the objective function and the constraint conditions, first processing is carried out on operation information through the power flow model to obtain a first regulation strategy, a cluster scheduling model is built based on the first regulation strategy and the adjustable power under the condition that the adjustable power corresponding to an electric automobile cluster in a target power distribution network is acquired, and second processing is carried out on the operation information according to the cluster scheduling model to obtain a second regulation strategy; and performing preventive control on the target power distribution network based on the first regulation strategy and/or the second regulation strategy. . Therefore, the problem that the electric automobile clusters cannot be prevented from participating in the electric power peak shaving scene in the prior art can be solved, and the effect of cooperative prevention control of the electric automobile clusters in the electric power peak shaving scene is achieved.

In an exemplary embodiment, the first regulation formula is: Wherein f ₁ (x) is the generator adjustment cost, S _g is the generator set, P _Gi is the i-th generator active output, a _i is the i-th generator active output adjustment cost coefficient, and i is a positive integer; the second regulation formula is as follows: v where f ₂ (x) is the total adjustment cost of the load; s _l is a load set, P _Li is the ith load active power, and b _i is a compensation cost coefficient for cutting the ith load active power. .

In order to better understand the process of the determining method of the regulation strategy, the implementation flow of the determining method of the regulation strategy is described below in conjunction with the optional embodiment, but is not limited to the technical scheme of the embodiment of the present application.

Aiming at the defects of the related technology, in order to realize the application of the response of the demand side in the transient stability prevention control of the large power grid, and improve the new energy consumption capability of the power system, an alternative embodiment of the application provides a collaborative prevention control method under the condition that an electric automobile cluster participates in the power peak regulation, and the power distribution network connected with each node of the large power grid and the load in the network are equivalent to a comprehensive dynamic load model, and the dominant parameters in the model are dynamically identified; secondly, taking the minimum total regulation cost as an objective function, taking constraint conditions such as equivalent power distribution network constraint, power flow constraint, transient stability constraint and the like into consideration, establishing a transient stability prevention control optimal power flow model, and solving; establishing a flexibility quantization model of the cluster electric automobile to obtain upper and lower spare capacity of the cluster electric automobile, namely an adjustable power upper limit and lower limit, and taking the upper and lower spare capacity of the cluster electric automobile as an adjustable power upper limit and lower limit constraint demand side resource to adjust flexibility load; based on a distribution network adjustment strategy given by preventive control and the adjustable power of the electric automobile clusters, a distribution network electric automobile cluster scheduling model taking the lowest total power consumption of the distribution network regulation as an objective function and comprehensively considering distribution network tide constraint, demand side response operation constraint and exchange power constraint is established, and the cooperative preventive control of the electric automobile clusters under the power peak regulation scene is realized.

Optionally, fig. 3 is a flowchart of a cooperative prevention control method in a scenario in which an electric automobile cluster participates in power peak shaving according to an alternative embodiment of the present application.

The method specifically comprises the following steps: inputting power grid operation data; matching electric automobile cluster quotations in a power grid; the power distribution network equivalence comprising electric automobile clusters; whether the transient stability analysis meets the margin requirement or not; if the corresponding preventive control strategy is directly determined; if not, adjusting through a preventive control model; according to the result of the adjustment of the prevention control model, determining the total load adjustment amount of the power distribution network of each node; and adjusting the equivalent of the distribution network comprising the electric automobile clusters according to the determined total load adjustment amount of the distribution network of each node and the distribution network electric automobile cluster scheduling model.

It should be noted that the above description is a breakthrough improvement on the existing power transmission network peak shaving prevention control method in combination with the new power system demand side response construction, and is different from the existing power transmission network prevention control method.

As an optional implementation manner, the above-mentioned collaborative prevention control method under the electric power peak regulation scene of the electric automobile cluster may adopt the following steps:

And step A, acquiring system operation information in a peak load period according to a daily load prediction curve. And collecting the response capacity and the price of the demand side reported by each user of the distribution network.

And B, according to the system peak operation disturbance data, adopting a total identification method to equate the power distribution network into a comprehensive load model (SLM).

And C, performing system prevention control in the peak load period based on the equivalent distribution network. And taking the minimum total regulation cost as an objective function, taking the constraint of the equivalent power distribution network constraint, the load flow constraint, the transient stability constraint and the like into consideration, and establishing a transient stability prevention control optimal load flow model and solving. Meanwhile, in order to ensure the accuracy of the parameters of the comprehensive load model, in the process of prevention control, the load shedding step length of the power distribution network is set, namely, the load shedding load of the power distribution network does not exceed a set value , if the load of the power distribution network reaches the set value, the prevention control calculation is stopped, and the current prevention control result is given.

And D, establishing a flexibility quantization model of the clustered electric vehicles, obtaining upper and lower spare capacity of the clustered electric vehicles, namely, upper and lower adjustable power limits, and taking the upper and lower adjustable power limits as upper and lower adjustable power limits to restrict the up-regulation and down-regulation flexible load of resources on the demand side.

And E, based on a distribution network adjustment strategy given by preventive control and the adjustable power of the electric automobile clusters, establishing a distribution network electric automobile cluster scheduling model which is used for regulating and controlling the lowest total electricity consumption by the load of the distribution network and comprehensively considering the power flow constraint of the distribution network, the response operation constraint of the demand side and the exchange power constraint, and solving the power flow constraint.

F, under the result of the step E, re-performing the step B, namely, equating the power distribution network to a comprehensive load model, calculating the transient stability margin of the system in the peak period of the load by adopting a time domain simulation method, and ending the whole preventive control if the transient stability margin meets the requirement; and C-step E is performed again if the transient stability margin does not meet the requirement until the transient stability margin meets the requirement, and at the moment, a strategy for preventing, controlling and regulating the transient stability of the power transmission network in the exact load peak period is obtained.

Optionally, fig. 4 is a schematic structural diagram of an equivalent model of a power distribution network considering electric vehicle clusters according to an alternative embodiment of the present application;

on the basis of the power distribution network equivalent model of the electric automobile cluster, the step B specifically comprises the following steps:

Step B1: collecting voltage amplitude U, voltage phase angle, active power P and reactive power Q of a grid-connected point of a power distribution network in the disturbance occurrence process;

Step B2: the collected voltage amplitude U and voltage phase angle are used as input, the response powers Pl and Ql of the model are calculated by using the determined SLM load model, and the error between the model and the actual sampling power data is calculated.

As an alternative embodiment, the SLM load model is composed of a polynomial (ZIP) model and an induction motor model.

Optionally, the polynomial (ZIP) model is a static model, and is composed of a constant impedance (Z) model, a constant current (I) model and a constant power (P) model in a certain proportion. The actual active power and actual reactive power calculation model formula of the ZIP load is as follows:

Wherein, U ₀ is the initial voltage of the load point, U is the actual voltage of the load point, P _s0 is the initial active power of the ZIP load model, Q _s0 is the initial reactive power of the ZIP load model, a _p、b_p、c_p、a_q、b_q、c_q is the structural parameter of the ZIP model, and the following constraint conditions are satisfied:

Induction motor power calculation:

In the transient stability calculation of the power system, compared with the rotor transient process of the induction motor, the stator transient process of the induction motor is much faster, and the influence on the transient stability calculation result is negligible, so that the alternative embodiment of the application selects a common three-order motor model only considering the electromechanical transient as a dynamic load model in the comprehensive load model.

Wherein E' _x、E'_y is the x-axis component and the y-axis component of the transient electromotive force of the equivalent motor respectively; i _x、I_y is the x-axis and y-axis components of the equivalent motor end current respectively; synchronous rotational speed ω _B =2pi f, ω being the rotor rotational speed; rotor steady state reactance x=x _s+X_m; rotor transient reactance X' =x _s+X_mX_r/(X_m+X_r);T_j is the inertial time constant of the motor; the rotor winding time constant T' _d0＝(X_m+X_r)/(ω_BR_r);T₀ is the initial mechanical torque, A, B, C is the mechanical torque coefficient, and the three satisfy

Optionally, the current calculation formula is:

Wherein U _x、U_y is the x-axis component and the y-axis component of the motor terminal voltage of the motor respectively.

The induction motor power calculation formula is:

in order to realize the self-adaptive change of the rated capacity of the motor, the variable motor load rate K _L and the motor initial active power duty ratio K _pm are introduced.

When the system is disturbed, the transient electromotive force E' and the rotating speed omega of the induction motor cannot be suddenly changed, so that the steady state value is the initial value of dynamic solution. Setting the derivative of the time derivative in the formula (3) to zero, and solving simultaneously with the formula (4) can obtain:

compared with the Newton method and the implicit trapezoidal integration method, the method has the advantages of simple results, shorter iteration time and higher calculation accuracy. Therefore, the alternative embodiment of the application selects a 4-order Dragon-Kutta method to solve the dynamic equation of the induction motor, and the calculation process is as follows:

This further yields the iterative formula for E' _x、E'_y and ω:

In the formula, h is a manually set calculation step length.

From the above discussion of the integrated load model, it is known that there are R_s、X_s、R_r、X_r、X_m、T_j、K_L、k_pm、A、B、a_p、b_p、a_q、b_q 14 independent parameter variables to be determined.

Step B3: and carrying out iterative correction on the comprehensive load model parameters by adopting a PSO optimization algorithm, repeating the calculation process to minimize errors, and finally obtaining the SLM equivalent model parameters of the power distribution network.

Optionally, the PSO optimization algorithm specifically first randomly generates a group of particles, each of which is a potential solution of the search space, and then iteratively searches for an optimal solution.

Thus, when applied to the iterative correction of the above-described integrated load model parameters, it can be performed by the following example, assuming that m particles form a community in a D-dimensional target search space, where the position of the i-th particle is denoted as x _i＝(x_i1,x_i2,…,x_iD), i=1, 2, …, m; speed is denoted v _i＝(v_i1,v_i2,…,v_iD). The optimal position searched so far for the ith particle is p _i＝(p_i1,p_i2,…,p_iD), and the optimal position of the whole particle group is p _g＝(p_g1,p_g2,…,p_gD). The PSO algorithm basic formula is:

wherein i=1, 2, …, m, d=1, 2, …, D; w is a non-negative number, called the inertia factor, and acceleration factors c1 and c2 are non-negative constants, respectively adjusting the maximum step size of the individual best particles and the global best particles flight. If too small, the particles may be away from the target area, and if too large, the particles may be caused to fly off suddenly toward the target area, or fly over the target area. Suitable c1 and c2 can accelerate convergence and are not prone to falling into local optimum, usually letting c1=c2=2; r1 and r2 are random numbers between [0,1 ]. The speed of the particles in each dimension of flight does not exceed the maximum speed v _max set by the algorithm.

And forming each particle by 14 independent model parameters to be determined, substituting the voltage phase angle and the amplitude collected by combining sampling into the response power calculation formula, calculating the response power under the current voltage condition, performing difference with the actual sampling power to obtain a model calculation error, iterating the particle swarm through PSO, enabling the error to be smaller than a set value, ending PSO calculation, and outputting the current model parameters.

Optionally, the step C specifically includes the following steps:

Step C1: and (5) performing system prevention control in the peak load period based on the equivalent distribution network. Alternatively, FIG. 5 is a graph of a simulation system daily load prediction in accordance with an alternative embodiment of the present application, with peak hours as indicated by the circles in the daily load prediction curve of FIG. 5.

Specifically, the preventive control objective function includes two parts: generator regulation cost and load regulation cost. The specific expression is as follows:

Where f ₁ (x) is the generator adjustment cost and f ₂ (x) is the total load adjustment cost; s _G is a generator set, and S _L is a load set; p _Gi is the i-th generator active power output, P _Li is the i-th load active power, wherein the i-th load active power comprises the equivalent load of the power distribution network; a _i is the active output adjustment cost coefficient of the ith generator; b _i is the compensation cost coefficient that cuts the i-th load active.

Step C2: the preventive control constraints include:

(1) Equation constraint:

Node injection power balance equation:

Wherein: p _Ni、Q_Ni is the node active and reactive injection power; p _Di、Q_Di is the node active and reactive output power; v _i、V_j is the node voltage amplitude; θ _ij is the node voltage phase angle difference; g _ij、B_ij is the real and imaginary parts of the node admittance matrix; s _n is a node set.

(2) Steady operation constraints

Wherein: The upper limit and the lower limit of the active output of the generator are set; the upper limit and the lower limit of reactive output of the generator are indicated by the letter ''; v _i ^min、V_i ^max is the upper and lower limits of the node voltage; the upper limit and the lower limit of line tide constraint are indicated by the letter,/> ; s _l is a line set.

(3) Equivalent constraint of power distribution network

The distribution network is connected to the main network, and the exchange power constraint between the main network and the distribution network needs to be considered, and the constraint is generally determined by the transmission capacity of the transformer.

Wherein is the upper and lower limits of the power exchanged between the kth power distribution network and the main network respectively.

Not all loads of the distribution network can participate in demand response, and the adjustable load quantity is determined according to the load quantity reported by each user main body, so that the equivalent power adjustment quantity of the comprehensive load model of the distribution network is required to be in an allowable range.

/>

Where Δp _k is the k-th power distribution network adjustment load, and is the k-th power distribution network maximum adjustable load.

(4) Transient stability constraints

Wherein: x is a state variable; y is algebraic variable; lambda is the control variable.

Alternatively, the embodiment of the application can solve the nonlinear algebraic differential equation in the transient stability constraint by adopting a time domain simulation method. The time domain simulation method forms a full system model of each element model of the electric power system according to the topological relation among the elements, takes a steady-state working condition or a tide solution as an initial value, solves a numerical solution under disturbance, namely gradually solves a change curve of system state quantity and algebraic quantity along with time, and judges the transient stability of the system according to the relative swing angle among the generator rotors.

In the process of prevention control, setting a load shedding step length of the power distribution network, namely, if the load shedding of the power distribution network does not exceed a set value and if the load shedding of the power distribution network reaches the set value, stopping the prevention control calculation, and giving a current prevention control result.

Optionally, the step D specifically includes the following steps:

And D1, establishing a flexibility quantization model of the clustered electric vehicles. And (3) carrying out charge and discharge optimization with the aim of maximizing the charge and discharge coefficient of the electric automobile cluster, obtaining optimal charge and discharge power by considering the constraint of charge and discharge power and the minimum constraint of real-time electric quantity, and calculating the peak regulation capacity of the EV cluster participating in centralized regulation and control by taking the optimal charge and discharge power as reference power.

(1) EV charge/discharge power constraint

-P_min≤P(t)≤P_max； (22)

i_c(t)+i_d(t)≤1； (23)

Wherein: p _c (t) and P _d (t) are respectively the charging power and the discharging power of the electric automobile in the period of t; i _c (t) and i _d (t) are charge and discharge state 0-1 variables, respectively, i _c (t) =0 indicates that EV is in a non-charge state, and i _c (t) =1 indicates that EV is in a charge state; i _d (t) =0 indicates that EV is in a non-discharge state, and i _d (t) =1 indicates that EV is in a discharge state; And/> is the charge efficiency and discharge efficiency, respectively; p _max and P _min are maximum charge power and maximum discharge power, respectively.

(2) Real-time electric-quantity minimum constraint of EV

EV is temporarily unregulated until the charge is lower than the bottom-up charge E _base; to make the EV reach the desired power E _exp before off-grid, the real-time power of the EV should have the lowest constraint E _min after accessing the grid to during off-grid, namely:

(3) Objective function

And (3) carrying out charge or charge-discharge optimization with the aim of maximizing the charge-discharge coefficient of the clustered electric vehicles to obtain optimal charge-discharge power, and calculating EV cluster standby capacity participating in centralized regulation and control by taking the optimal charge-discharge power as reference power. The optimization objective function is as follows:

M_j＝M_jd-M_jc； (26)

M_d＝P_d×π_d； (27)

M_c＝P_c×π_c； (28)

Wherein M _agg is the total charging and discharging coefficient of N EVs which are intensively regulated; m _j is the charge-discharge coefficient after the j-th EV participates in centralized regulation; m _jd is the discharge coefficient of j EVs after participating in centralized scheduling; m _jc is the charging coefficient of j EV participated in centralized dispatching, pi _c、π_d is the charging and discharging weight factors respectively, and N is the total amount of clustered electric vehicles; p _d is a discharge power matrix when EV receives centralized scheduling; p _c is a discharge power matrix when EV receives centralized scheduling; And the EV is not subjected to a centralized scheduling charge-discharge power matrix, namely the power condition of network access and charging is implemented.

(4) EV spare capacity modeling

And solving the objective function and the constraint by adopting an optimization algorithm to obtain the final optimized power of the EV, and carrying the final optimized power into formulas (31), (32) and (33) to obtain the spare capacity of each EV.

Wherein: e (k) and E ₀ are the electric quantity of the electric automobile at the moment k and the initial moment; gamma (k) denotes a kth period EV on-line state, gamma (k) =1 denotes on-line, and gamma (k) =0 denotes off-line. P _cu (k) and P _cd (k) are upper standby capability and lower standby capability, respectively; p (k) is the optimal charge-discharge power of the kth period EV; p _max -P (k) and-P _min +P (k) are maximum allowable charge and discharge power in the kth period, and are represented as power boundaries; e (k) -E _min (k+1) is the maximum dischargeable amount in the kth period; (E (k) -E _min (k+1)/Deltat+P (k)) is the dischargeable volume space of the current EV, and represents the electric quantity boundary.

In a subsequent planning model, the spare capacity of the electric automobile is used as the upper and lower power limit of the adjustable power of the flexible resource at the demand side to carry out power distribution network planning:

P_t ^down,max/min＝P_cu(t)； (33)

P_t ^up,max/min＝P_cd(t)； (34)

optionally, the step E specifically includes the following steps:

e1, carrying out power distribution network demand side response scheduling by taking the spare capacity of the electric automobile as the upper and lower limits of adjustable power in reference formulas (34) and (35);

E2, under the condition that the total load adjustment quantity is determined, establishing a power distribution network demand side response scheduling model which regulates and controls the minimum total electricity consumption by the power distribution network and comprehensively considers power flow constraint, demand side response operation constraint and exchange power constraint of the power distribution network:

distribution network total load adjustment amount:

In the formula, n is the adjustable load quantity of the distribution network, deltaP _i is the load adjustment quantity of each i-th adjustable load, and DeltaP is the total load adjustment quantity of each distribution network given by the large power grid prevention control.

Node power balancing:

Wherein, P _buy,t and Q _buy,t respectively represent active power and reactive power exchanged between the power distribution network and the upper level in the period t; a ^old represents a node association matrix of the existing branch of the power distribution network; Respectively representing the active power and the reactive power of the existing branch of the power distribution network in the period t; p _load,t and Q _load,t represent active and reactive loads, respectively, at time period t; p _down,t and P _up,t represent the down-regulated load power and the up-regulated load power, respectively, at the period t.

Branch tidal current constraint:

Wherein r (j) represents a first node set taking j as a tail node in the power distribution network; s (j) represents a tail node set taking j as a head node in the power distribution network; p _ij,t、Q_ij,t respectively represents active power and reactive power of a power distribution network branch ij in a period t; p _j,t、Q_j,t respectively represents active injection power and reactive injection power of a node j in a period t; r _ij、x_ij represents the resistance and reactance of branch ij, respectively; u _i,t、I_ij,t represents the voltage at node i and the current on branch ij, respectively, for period t.

As an alternative implementation, in practical application, based on the original data of the new england 10 machine 39 node system and its tide results, a power distribution network including two electric automobile clusters and two other flexible loads is added at the node 23, and a power distribution network including two electric automobile clusters and one other flexible load is added at the node 24. Overall system load prediction graph as shown in fig. 5, fig. 5 is a simulation system daily load prediction graph according to an alternative embodiment of the present application. The preventive control is performed during peak load periods. The result of the prevention control of the large grid level is shown in fig. 6. FIG. 6 is a schematic diagram of overall regulation strategies for each distribution network at a large grid level given by simulation in accordance with an alternative embodiment of the present application; in the figure, ps23 and Pm23 are respectively the equivalent static load and the equivalent motor load of the power distribution network 1 at the node 23, and Ps24 and Pm24 are respectively the equivalent static load and the equivalent motor load of the power distribution network 2 at the node 24. Based on the load shedding requirements of the power distribution network given by the large power grid, power distribution network optimization is carried out, and fig. 7 and 8 are respectively response load regulation strategies of each electric automobile cluster and other demand sides of the power distribution network 1 and the power distribution network 2. Fig. 7 is a strategy for adjusting the responses of each electric vehicle cluster and other demand sides in the distribution network 1 accessed by the node 23 according to an alternative embodiment of the present application; fig. 8 is a strategy for adjusting the responses of clusters of electric vehicles and other demand sides in the distribution network 2 to which the node 24 is connected according to an alternative embodiment of the present application.

Through the embodiment, the following beneficial effects can be achieved:

(1) According to the collaborative prevention control method under the electric power peak regulation scene of the electric automobile cluster, which is provided by the alternative embodiment of the application, the dynamic equivalence of the distribution network is a comprehensive load model (SLM) unlike the previous method of taking the equivalent value of the distribution network as a static PQ load model or selecting a typical value of the comprehensive load model, the load removal step length of the distribution network is set in the prevention control process, the main distribution network interacts, and the parameters of the comprehensive load model are dynamically adjusted in real time, so that a more accurate prevention control strategy is obtained;

(2) In the process of preventing, controlling and regulating the power transmission network, the optional embodiment of the application considers the influence of a large number of power distribution network demand side responses on the stability of the power transmission network, brings the demand side responses represented by the electric automobile clusters into the transient stability prevention control of the power transmission network in the peak period, improves the flexibility of the system and the economical efficiency of the prevention control, fully plays the peak clipping and valley filling functions of the demand side responses, and improves the capacity of the system for absorbing new energy.

From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present application.

The embodiment also provides a device for determining a regulation strategy, which is used for implementing the above embodiment and the preferred implementation manner, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

Fig. 9 is a block diagram of a determining apparatus for a regulation strategy according to an embodiment of the present application, as shown in fig. 9, the apparatus includes:

A first determining module 92, configured to determine, according to a load prediction curve, operation information of a target power distribution network in a load peak period, and process the target power distribution network by using a target processing manner, so as to obtain a first comprehensive load model;

the first processing module 94 is configured to construct a power flow model according to the first comprehensive load model, the objective function, and the constraint condition when an objective function and the constraint condition for performing preventive control are acquired, and perform a first process on the operation information through the power flow model to obtain a first regulation strategy corresponding to the target power distribution network, where the objective function is used to indicate a function corresponding to a minimum total regulation cost of the target power distribution network;

The second processing module 96 constructs a cluster scheduling model based on the first regulation strategy and the adjustable power under the condition that the adjustable power corresponding to the electric automobile cluster in the target power distribution network is acquired, and executes second processing on the operation information according to the cluster scheduling model to obtain a second regulation strategy corresponding to the target power distribution network;

The control module 98 is configured to perform preventive control on the target power distribution network based on the first regulation policy and/or the second regulation policy.

By the device, the operation information of the target power distribution network in the load peak period is determined according to the load prediction curve, and the target power distribution network is processed by using a target processing mode to obtain a first comprehensive load model; under the condition that an objective function and constraint conditions for preventive control are acquired, a power flow model is built according to a first comprehensive load model, the objective function and the constraint conditions, first processing is carried out on operation information through the power flow model to obtain a first regulation strategy, a cluster scheduling model is built based on the first regulation strategy and the adjustable power under the condition that the adjustable power corresponding to an electric automobile cluster in a target power distribution network is acquired, and second processing is carried out on the operation information according to the cluster scheduling model to obtain a second regulation strategy; and performing preventive control on the target power distribution network based on the first regulation strategy and/or the second regulation strategy. . Therefore, the problem that the electric automobile clusters cannot be prevented from participating in the electric power peak shaving scene in the prior art can be solved, and the effect of cooperative prevention control of the electric automobile clusters in the electric power peak shaving scene is achieved.

In an exemplary embodiment, the above apparatus further includes: the correction module is used for processing the target power distribution network by using a target processing mode, recognizing disturbance data in the operation information after a first comprehensive load model is obtained, and acquiring a plurality of power distribution parameters of a target power distribution network grid-connected point corresponding to the disturbance data, wherein the plurality of power distribution parameters at least comprise: the voltage amplitude corresponding to the grid-connected point, the voltage phase angle corresponding to the grid-connected point, the active power corresponding to the grid-connected point and the reactive power corresponding to the grid-connected point; inputting the voltage amplitude and the voltage phase angle to the first comprehensive load model to obtain first response power and second response power; determining a first error of the first response power and the active power and a second error of the second response power and the reactive power, and performing error correction on the first comprehensive load model through the first error and the second error.

In an exemplary embodiment, the second processing module is further configured to establish a quantization model of the electric automobile cluster in the target power distribution network; performing charge-discharge optimization on the quantized model by using preset charge-discharge power constraint and real-time electric quantity minimum constraint to obtain optimal charge-discharge power corresponding to the quantized model, wherein the charge-discharge optimization is used for indicating to solve the quantized model with the aim of maximizing the charge-discharge coefficient of the electric automobile cluster; and determining the optimal charge and discharge power as reference power, and determining the peak regulation capacity of the electric automobile cluster participating in centralized regulation by using the reference power to obtain the adjustable power.

In an exemplary embodiment, the above apparatus further includes: the second determining module is used for acquiring a real-time value of an actual load shedding step length corresponding to the target power distribution network for preventive control under the condition of determining a set value of the load shedding step length corresponding to the target power distribution network before preventive control is carried out on the target power distribution network based on the first regulating strategy and/or the second regulating strategy; and under the condition that the real-time value is equal to the set value, determining the regulation strategy determined by using the prevention control as the current regulation strategy of the target power distribution network.

It should be noted that each of the above modules may be implemented by software or hardware, and for the latter, it may be implemented by, but not limited to: the modules are all located in the same processor; or the above modules may be located in different processors in any combination.

Embodiments of the present application also provide a computer readable storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.

In one exemplary embodiment, the computer readable storage medium may include, but is not limited to: a usb disk, a read-only memory (ROM), a random access memory (Random Access Memory RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.

An embodiment of the application also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.

In an exemplary embodiment, the electronic device may further include a transmission device connected to the processor, and an input/output device connected to the processor.

Specific examples in this embodiment may refer to the examples described in the foregoing embodiments and the exemplary implementation, and this embodiment is not described herein.

It will be appreciated by those skilled in the art that the modules or steps of the application described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.

The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for determining a regulatory strategy, comprising:

determining the operation information of a target power distribution network in a load peak period according to a load prediction curve, and processing the target power distribution network by using a target processing mode to obtain a first comprehensive load model;

under the condition of acquiring an objective function and a constraint condition for preventive control, constructing a power flow model according to the first comprehensive load model, the objective function and the constraint condition, and executing first processing on the operation information through the power flow model to obtain a first regulation strategy corresponding to the target power distribution network, wherein the objective function is used for indicating a function corresponding to the minimum total regulation cost of the target power distribution network;

under the condition that the adjustable power corresponding to the electric automobile cluster in the target power distribution network is acquired, a cluster scheduling model is built based on the first regulation strategy and the adjustable power, and a second process is executed on the operation information according to the cluster scheduling model, so that a second regulation strategy corresponding to the target power distribution network is obtained;

and performing preventive control on the target power distribution network based on the first regulation strategy and/or the second regulation strategy.

2. The method of claim 1, wherein after processing the target power distribution network using a target processing method to obtain a first integrated load model, the method further comprises:

and identifying disturbance data in the operation information, and acquiring a plurality of distribution parameters of a target distribution network grid-connected point corresponding to the disturbance data, wherein the plurality of distribution parameters at least comprise: the voltage amplitude corresponding to the grid-connected point, the voltage phase angle corresponding to the grid-connected point, the active power corresponding to the grid-connected point and the reactive power corresponding to the grid-connected point;

inputting the voltage amplitude and the voltage phase angle to the first comprehensive load model to obtain first response power and second response power;

Determining a first error of the first response power and the active power and a second error of the second response power and the reactive power, and performing error correction on the first comprehensive load model through the first error and the second error.

3. The method according to claim 1, wherein acquiring an objective function for performing preventive control includes:

Determining the number of generators present in the target power distribution network and determining the number of loads present in the target power distribution network;

Determining a first objective sub-function according to the number of generators and a first regulation formula, and determining a second objective sub-function according to the number of loads and a second regulation formula, wherein the first regulation formula is used for determining generator regulation cost corresponding to a power distribution network, and the first regulation formula is used for determining load regulation cost corresponding to the power distribution network;

An objective function for performing preventive control is determined based on the first objective subfunction and/or the second objective subfunction.

4. The method of claim 3, wherein the step of,

The first regulation formula is as follows: Wherein f ₁ (x) is the generator adjustment cost, S _g is the generator set, P _Gi is the i-th generator active output, a _i is the i-th generator active output adjustment cost coefficient, and i is a positive integer;

The second regulation formula is as follows: Wherein f ₂ (x) is the total adjustment cost of the load; s _l is a load set, P _Li is the ith load active power, and b _i is a compensation cost coefficient for cutting the ith load active power.

5. The method of claim 1, wherein the constraints include at least one of: the method comprises the following steps of a first constraint condition corresponding to equality constraint, a second constraint condition corresponding to stable operation constraint, a third constraint condition corresponding to equivalent constraint of a power distribution network and a fourth constraint condition corresponding to transient stability constraint.

6. The method of claim 1, wherein obtaining the adjustable power corresponding to the electric vehicle cluster in the target power distribution network comprises:

establishing a quantization model of the electric automobile cluster in the target power distribution network;

performing charge-discharge optimization on the quantized model by using preset charge-discharge power constraint and real-time electric quantity minimum constraint to obtain optimal charge-discharge power corresponding to the quantized model, wherein the charge-discharge optimization is used for indicating to solve the quantized model with the aim of maximizing the charge-discharge coefficient of the electric automobile cluster;

And determining the optimal charge and discharge power as reference power, and determining the peak regulation capacity of the electric automobile cluster participating in centralized regulation by using the reference power to obtain the adjustable power.

7. The method of claim 1, wherein prior to preventive control of the target power distribution network based on the first regulatory strategy and/or the second regulatory strategy, the method further comprises:

Under the condition that a set value of a load shedding step length corresponding to the target power distribution network is determined, acquiring a real-time value of an actual load shedding step length corresponding to the target power distribution network for preventive control;

And under the condition that the real-time value is equal to the set value, determining the regulation strategy determined by using the prevention control as the current regulation strategy of the target power distribution network.

8. A regulation strategy determining apparatus, comprising:

the first determining module is used for determining the operation information of the target power distribution network in the load peak period according to the load prediction curve, and processing the target power distribution network by using a target processing mode to obtain a first comprehensive load model;

The first processing module is used for constructing a power flow model according to the first comprehensive load model, the target function and the constraint condition under the condition of acquiring the target function and the constraint condition for preventive control, and executing first processing on the operation information through the power flow model to obtain a first regulation strategy corresponding to the target power distribution network, wherein the target function is used for indicating a function corresponding to the minimum total regulation cost of the target power distribution network;

The second processing module is used for constructing a cluster scheduling model based on the first regulation strategy and the adjustable power under the condition that the adjustable power corresponding to the electric automobile cluster in the target power distribution network is acquired, and executing second processing on the operation information according to the cluster scheduling model to obtain a second regulation strategy corresponding to the target power distribution network;

And the control module is used for performing preventive control on the target power distribution network based on the first regulation strategy and/or the second regulation strategy.

9. A computer readable storage medium, characterized in that a computer program is stored in the computer readable storage medium, wherein the computer program, when being executed by a processor, implements the steps of the method according to any of the claims 1 to 7.

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed.