Virtual power plant operation state analysis method and device based on dynamic model, medium and product

Abstract

The invention discloses a virtual power plant operation state analysis method and device based on a dynamic model, a medium and a product. The method comprises the following key steps: considering the characteristics of nonlinearity, time lag, volatility and the like of a system, and constructing a virtual power plant dynamic model which comprises a network dynamic model and an equipment dynamic model; aiming at the constructed dynamic model, the network dynamic model and the equipment dynamic model are solved by adopting a homotopy analysis method, and the whale optimization algorithm is combined to assist in searching optimal auxiliary parameters required by convergence of a solving result of the homotopy analysis method. According to the method, the operation state of the virtual power plant can be represented, the solving efficiency and precision of the dynamic model of the virtual power plant are improved, and the problems of capacity configuration, operation optimization, performance evaluation and the like are solved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular to a method, equipment, medium and product for analyzing the operating status of a virtual power plant based on a dynamic model. Background Art

[0002] As the proportion of renewable energy generation continues to increase, the main power supply of the power system will gradually shift from fossil energy to renewable energy. However, due to the strong randomness and volatility of new energy generation such as photovoltaic and wind power, this has greatly increased the uncertainty of the power generation side, bringing safety risks and great pressure on the balance of supply and demand to the power system. Virtual power plants can aggregate massive, dispersed, and diverse distributed resources through advanced communication and control technologies to form a flexibly controllable entity, providing an effective path to ensure the balance of supply and demand and safe and reliable operation of the power system.

[0003] Establishing a suitable mathematical model for a virtual power plant is the basis for monitoring the operating status of the system and analyzing the system performance. The existing modeling method of virtual power plants is mainly steady-state modeling, which establishes system equations to describe the overall operating characteristics of the system while ignoring short-term dynamic effects. The dynamic characteristics of different subsystems of virtual power plants vary greatly, so virtual power plant simulation relies on low-error, high-stability, and high-precision numerical calculation methods. The existing virtual power plant model solution methods mainly include numerical methods, analytical methods, and semi-analytical methods. The numerical method converts the continuous dynamic model into a discrete numerical model for solution through numerical approximation methods such as finite difference method, finite volume method, and finite element method; the analytical method solves the problem without approximation or discretization through mathematical derivation methods such as separation of variables, Laplace transform, and Fourier transform; the semi-analytical method is a solution method that combines analytical methods and numerical methods, and its essence is to approximate the solution of the model through Taylor series. However, with the increase in the proportion of renewable energy in virtual power plants, the volatility of power supply increases, and the existing modeling methods are difficult to collect and monitor real-time information of the system. In addition, the computational efficiency and solution accuracy of the existing model calculation methods need to be further optimized. Summary of the invention

[0004] The purpose of this application is to provide a method, equipment, medium and product for analyzing the operating status of a virtual power plant based on a dynamic model, which can characterize the operating status of the virtual power plant, while improving the efficiency and accuracy of solving the dynamic model of the virtual power plant, thereby solving problems such as capacity configuration, operation optimization, and performance evaluation.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for analyzing the operating status of a virtual power plant based on a dynamic model, comprising:

[0007] Based on the system characteristics of the virtual power plant, a dynamic model of the virtual power plant is constructed; the dynamic model of the virtual power plant includes a network dynamic model and an equipment dynamic model; the system characteristics include at least nonlinearity, time lag and volatility;

[0008] The network dynamic model and the device dynamic model are solved by homotopy analysis to obtain operation parameters;

[0009] Verifying the convergence of the operating parameters;

[0010] When the operating parameters converge, taking the operating parameters as the finally determined operating parameters;

[0011] When the operating parameters do not converge, the whale optimization algorithm is used to determine the optimal auxiliary parameters of the homotopy analysis method, and after the optimal auxiliary parameters replace the auxiliary parameters of the homotopy analysis method, the step of returning to execute the homotopy analysis method to solve the network dynamic model and the device dynamic model to obtain the operating parameters.

[0012] Optionally, the network dynamic model and the device dynamic model are solved by a homotopy analysis method to obtain operating parameters, including:

[0013] Performing standardized conversion on the network dynamic model and the device dynamic model to obtain a standard format model;

[0014] Setting a target equation based on the standard format model and selecting an initial guess solution;

[0015] According to the initial guess solution, the target equation is linearized to obtain a zero-order deformation equation;

[0016] Introducing the homotopy parameters into the zero-order deformation equation; the homotopy parameters include auxiliary parameters and auxiliary functions of the homotopy analysis method;

[0017] Performing Taylor series expansion on the zero-order deformation equation introducing the homotopy parameter to obtain a high-order deformation equation;

[0018] The high-order deformation equation is recursively solved to obtain the operating parameters.

[0019] Optionally, the high-order deformation equation is expressed as:

[0020]

[0021] Where L[*] represents the auxiliary linear operator, Ψ n-1 represents the weight function associated with order n-1, f n (x,y) represents the nth order approximate solution, f n-1(x,y) represents the approximate solution of the n-1th order, and h represents the auxiliary parameter of the homotopy analysis method; represents the convergence condition of the homotopy analysis method, where The value of the nonlinear residual converges to zero; represents the conjugate of the m-1th order approximate solution, H n represents the nonlinear residual, represents the n-1th order partial derivative of the embedded variable q, Γ(n) is the gamma function, F is an operator that depends on the specific form of the problem, Ψ n Represents the weight function related to the nth order, n represents the order, and x and y are independent variables.

[0022] Optionally, the whale optimization algorithm is used to determine the optimal auxiliary parameters of the homotopy analysis method, including:

[0023] Using the current auxiliary parameters of the homotopy analysis method as the initial target position of the whale optimization algorithm;

[0024] Position updating and prey searching are performed based on the initial target position until the convergence condition is reached, thereby obtaining the optimal auxiliary parameters of the homotopy analysis method.

[0025] Optionally, the convergence condition is expressed as:

[0026]

[0027] Where U f,ω represents the fitness of the ω-th position update and prey search, U f,ω-1 represents the fitness of the ω-1th position update and prey search, ε represents the preset accuracy threshold, and U f represents fitness, N represents the total number of position updates and prey searches, F(f(x,y)) represents the standardized transformation of the initial function f(x,y), and x and y are both independent variables.

[0028] Optionally, the network dynamic model includes: a power grid dynamic model, a gas grid dynamic model and a heat grid dynamic model.

[0029] Optionally, the equipment dynamic model includes: a cogeneration dynamic model, a wind turbine dynamic model, a photovoltaic generator dynamic model and an energy storage device dynamic model.

[0030] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the above-mentioned methods for analyzing the operating status of a virtual power plant based on a dynamic model.

[0031] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-mentioned methods for analyzing the operating status of a virtual power plant based on a dynamic model.

[0032] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any of the above-mentioned methods for analyzing the operating status of a virtual power plant based on a dynamic model.

[0033] According to the specific embodiments provided in this application, this application has the following technical effects:

[0034] The present application provides a method, equipment, medium and product for analyzing the operating status of a virtual power plant based on a dynamic model. The operating parameters are obtained by solving the network dynamic model and the equipment dynamic model using the homology analysis method. On the basis of not meeting the convergence conditions, the whale optimization algorithm is used to determine the optimal auxiliary parameters of the homology analysis method, and the auxiliary parameters of the homology analysis method are replaced by the optimal auxiliary parameters to redetermine the operating parameters. This can characterize the operating status of the virtual power plant, while improving the efficiency and accuracy of solving the dynamic model of the virtual power plant, thereby solving problems such as capacity configuration, operation optimization, and performance evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A schematic diagram of a flow chart of a method for analyzing the operating status of a virtual power plant based on a dynamic model provided in one embodiment of the present application;

[0037] Figure 2 A framework diagram of an implementation of a method for analyzing the operating status of a virtual power plant based on a dynamic model provided in an embodiment of the present application;

[0038] Figure 3 A detailed flow chart of a method for analyzing the operating status of a virtual power plant based on a dynamic model provided in one embodiment of the present application;

[0039] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0041] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0042] In an exemplary embodiment, a method for analyzing the operating status of a virtual power plant based on a dynamic model is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to a server as an example for explanation. Figure 1 As shown, the virtual power plant operation status analysis method based on the dynamic model provided in this application includes:

[0043] Step 100: Based on the system characteristics of the virtual power plant, a dynamic model of the virtual power plant is constructed; the dynamic model of the virtual power plant includes a network dynamic model and an equipment dynamic model; the system characteristics include at least nonlinearity, time lag and volatility.

[0044] Step 101: Use homology analysis to solve the network dynamic model and the device dynamic model to obtain operating parameters.

[0045] Step 102: Verify the convergence of the operating parameters.

[0046] Step 103: When the operating parameters converge, the operating parameters are used as the final operating parameters.

[0047] Step 104: When the operating parameters do not converge, the whale optimization algorithm is used to determine the optimal auxiliary parameters of the homotopy analysis method, and the optimal auxiliary parameters are used to replace the auxiliary parameters of the homotopy analysis method, and then the execution returns to step 101.

[0048] In another exemplary embodiment of the present application, the network dynamic model constructed in the above step 100 may include: a power grid dynamic model, a gas grid dynamic model and a heat grid dynamic model.

[0049] (1) The established power grid dynamic model is as follows:

[0050]

[0051] In the formula, C e , G e , R e , Le are the branch capacitance, ground conductance, resistance and inductance per unit length, U and I are the branch voltage and current, t is the time variable, and x is the space variable.

[0052] (2) The established gas network dynamic model is as follows:

[0053]

[0054] Where P g , g 、v g and c g are gas pressure, gas density, gas flow rate and gas sound speed respectively, f p , D p are the pipeline friction coefficient and pipeline diameter respectively, g is the acceleration of gravity, and α is the pipeline inclination angle.

[0055] (3) The established dynamic model of the heating network is as follows:

[0056]

[0057] Where, T p and T amb are the water temperature in the pipe and the pipe environment temperature, A p is the cross-sectional area, λ p is the heat loss coefficient, m p is the water flow mass, ρ w 、c w are the density and specific heat of water respectively.

[0058] In another exemplary embodiment of the present application, the power plant mainly includes equipment such as cogeneration units, wind turbines, photovoltaic units, and energy storage equipment. Based on this, the equipment dynamic model constructed in the above step 100 includes: a cogeneration dynamic model, a wind turbine dynamic model, a photovoltaic unit dynamic model, and an energy storage device dynamic model. Among them:

[0059] (1) The established cogeneration dynamic model is as follows:

[0060]

[0061] In the formula, q b (*),q f (*) are the fuel quantity instruction and the actual fuel quantity entering the furnace at time *, T f 、T T are the dynamic time of the pulverizing system and the steam turbine respectively, τ is the pure delay time, p D (*), p T (*), p H(*) are respectively the drum pressure, main steam pressure and intermediate pressure cylinder exhaust pressure at *, C D is the heat storage capacity of the drum, μ T (*), μ H (*) are respectively the opening of the turbine high pressure regulating valve and the heating extraction steam regulating valve at the time *, K B , K D , K T are the power gain coefficients of the boiler, steam drum, and steam turbine respectively, P(*) is the output power of the unit at time *, σ is the work percentage of the high-pressure cylinder and the medium-pressure cylinder of the steam turbine, K LP is the work coefficient of the low-pressure cylinder of the turbine, K w 、m w 、T out (*), T in (*) are the specific heat capacity, mass flow rate, outlet temperature at time * and inlet temperature at time * of circulating water, respectively. C H is the reheater coefficient, a and b are the temperature and pressure coefficients of saturated water respectively.

[0062] (2) The established dynamic model of wind turbine is as follows:

[0063]

[0064] In the formula, H eq is the inertia time constant of the wind turbine, ω r is the rotor speed, P m is the mechanical power, P o is the output electromagnetic power.

[0065] (3) The established dynamic model of the photovoltaic generator set is as follows:

[0066]

[0067] Where: P PV (*) is the real-time power generation of the photovoltaic unit at the moment *, P st is the rated power, η PV is the performance coefficient, I S (*) and I Sref are the real-time average solar radiation intensity at * and the solar radiation intensity under standard test conditions, κ PV is the power temperature coefficient, T PV (*) and T ref They are respectively the real-time temperature of the photovoltaic cell at the moment * and the temperature under standard test conditions.

[0068] (4) The established dynamic model of energy storage equipment is as follows:

[0069]

[0070] Where E is the power of the energy storage system, P c is the charging power, P d is the discharge power, η c is the charging efficiency, η d is the discharge efficiency.

[0071] In another exemplary embodiment of the present application, based on the above-mentioned models, in this embodiment, the dynamic parameters in each model, such as U, I, P, are solved by combining the homology analysis method and the whale optimization algorithm. g , g 、v g Etc. Among them, the homotopy analysis method is rooted in homotopy mapping, so it is necessary to create a continuous transformation. Based on this, the implementation process of step 101 provided above in this application includes:

[0072] Step 1: Standardize and transform the network dynamic model and the device dynamic model to obtain a standard format model. The standard format model obtained by considering the binary partial differential equation can be expressed as:

[0073] F[f(x,y)]=0.

[0074] Where F is an operator, which can be linear or nonlinear. f(x,y) is the initial function. f0(x,y) is the initial guess solution of the initial function f(x,y).

[0075] Step 2: Set the target equation based on the standard format model and select the initial guess solution.

[0076] Step 3: Based on the initial guess solution, linearize the target equation to obtain the zero-order deformation equation.

[0077] Step 4: Introduce homotopy parameters into the zero-order deformation equation. Homotopy parameters include auxiliary parameters and auxiliary functions of the homotopy analysis method. Among them, the zero-order deformation equation with the introduction of homotopy parameters is expressed as:

[0078] (1-q)L[Φ(x,y;q)-f0(x,y)]=qhB(x,y)F[Φ(x,y;q)].

[0079] When q=0 and q=1, we have:

[0080]

[0081] Where L is an auxiliary linear operator, q is an embedded variable, and q∈[0,1], h is an auxiliary parameter, Φ(x,y;q) is the mapping of the function f(x,y), and B(x,y) is an auxiliary function. When q changes from 0 to 1, Φ(x,y;q) gradually approaches the exact solution f(x,y) from the initial guess solution f0(x,y).

[0082] For example: in the dynamic model of the power grid, the dynamic parameters are U and I, and their independent variables are x and t. Then U(x, t) and I(x, t) correspond to the initial function f(x, y) in the analysis method, and Φ(U, I; q) corresponds to the mapping function Φ(x, y; q) in the analysis method, that is, Φ(U, I; q) is the mapping of the initial functions U(x, t) and I(x, t).

[0083] Introducing this into the solution process of the homotopy analysis method can approach the exact solution.

[0084] Other subsystems are similar.

[0085] Step 5: Perform Taylor series expansion on the zero-order deformation equation with homotopy parameters introduced to obtain a high-order deformation equation. Where: For Φ(x, y; q) in the above zero-order deformation equation, perform Taylor series expansion based on homotopy parameters, and we have:

[0086] Based on Taylor expansion theorem, Φ(x,y;q) is expanded into a power series of q, and we have:

[0087]

[0088] In the formula, represents the nth-order partial derivative of q, f n (x,y) represents the nth order approximate solution, where n represents the order.

[0089] If the auxiliary parameter h and the auxiliary function B(x,y) are selected to appropriate values, when q=1, the formula (1-q)L[Φ(x,y;q)-f0(x,y)]=qhB(x,y)F[Φ(x,y;q)] can converge, thus obtaining a series solution, which is expressed as:

[0090]

[0091] Define the vector:

[0092] f n ={f n (x, y)}, n = 0, 1, 2, ..., m. m represents the recursive order.

[0093] Step 6: Recursively solve the higher-order deformation equation to obtain the operating parameters. For example, based on the zero-order deformation equation with homology parameters, calculate the n-th order derivative of q, set q = 0, and then divide by n! The obtained higher-order deformation equation is expressed as:

[0094]

[0095] Where L[*] represents the auxiliary linear operator, Ψ n-1 represents the weight function associated with order n-1, f n (x,y) represents the nth order approximate solution, f n-1 (x,y) represents the approximate solution of the n-1th order, and h represents the auxiliary parameter of the homotopy analysis method. represents the convergence condition of the homotopy analysis method, where The nonlinear residual value converges to zero. represents the conjugate of the m-1th order approximate solution, H n represents the nonlinear residual, represents the n-1th order partial derivative of q, Γ(n) is the gamma function, F is an operator that depends on the specific form of the problem, Ψ n Represents the weight function related to the nth order, n represents the order, and x and y are independent variables.

[0096] The high-order deformation equation essentially transforms the target equation into an infinite number of linear sub-problems composed of high-order deformation equations, and takes the sum of the solutions of the first few sub-problems to approximate the exact solution.

[0097] In another exemplary embodiment of the present application, based on the above description, the auxiliary parameter h plays an important role in satisfying convergence in the solution process of the homotopy analysis method. Based on this, in order to improve the accuracy and efficiency of solving the dynamic equations of the virtual power plant, the process of seeking the optimal auxiliary parameter h through the whale optimization algorithm in the present application includes:

[0098] Step 1: Use the current auxiliary parameters of the homotopy analysis method as the initial target position of the whale optimization algorithm. The whale optimization algorithm assumes that the initial target position is the optimal position, and other whales shrink around this target. The mathematical model of this process is expressed as:

[0100] D=|CX * (ω)-X(ω)|

[0101] X(ω+1)=X * (ω)-AD

[0102] A=2θ·r-θ

[0103] C=2·r

[0104]

[0105] Where D is the distance between the whale and its prey, ω is the number of iterations, and X * (ω) is the current optimal position, X(ω) is the position of the ωth iteration, X(ω+1) is the position of the ω+1th iteration, A is a random number in [-2,2], C is a random number in [0,2], r is a random number in [0,1], θ is the convergence factor, which decreases from 2 to 0 as the number of iterations increases, ω max is the maximum number of iterations.

[0106] Step 2: Update the position and search for prey based on the initial target position until the convergence condition is reached, and the optimal auxiliary parameters of the homotopy analysis method are obtained. This process can be described as:

[0107] (1) Location update:

[0108] Whales approach the target location by surrounding the prey and swimming in a circle and blowing bubbles to drive the prey. The mathematical model of spiral motion is expressed as:

[0109]

[0110] In the formula, is the distance between the whale and the target position, δ is the constant of the logarithmic spiral state, and l is a random number in [-1,1].

[0111] Random selection of two types of exercise:

[0112]

[0113] Usually choose p i =50%, so that the two methods can be carried out in coordination.

[0114] (2) Prey search:

[0115] When the whale group searches for prey, A is used as the dividing line and the position iteration update method is selected:

[0116]

[0117] Where, X rand is a random reference whale position.

[0118] (3) Update auxiliary parameters:

[0119] At each iteration of the whale position update, a new solution is obtained for each round, which is used to update the auxiliary parameter h in the homotopy analysis method (i.e., the zero-order deformation equation that introduces the homotopy parameter). The convergence condition is expressed as:

[0120]

[0121] Where U f,ω represents the fitness of the ω-th position update and prey search, U f,ω-1 represents the fitness of the ω-1th position update and prey search, and ε represents the preset accuracy threshold. f represents the fitness, which is used to evaluate the quality of the solution. N represents the total number of position updates and prey searches.

[0122] When the accuracy meets the condition, the iteration stops, otherwise it continues.

[0123] Based on the above description, the implementation architecture and process of the virtual power plant operation status analysis method based on the dynamic model provided in this application can be found in Figure 2 and Figure 3 By solving the dynamic model of the virtual power plant (i.e., the network dynamic model and the equipment dynamic model), the dispatching status of the electricity, gas, and heat networks, such as the power flow within the power grid, the pressure distribution of the gas network, the flow rate of the heat network, and other operating parameters, can be obtained. The equipment dispatching status can be obtained, such as the output power of each unit, the fuel consumption of the cogeneration unit, the wind speed and solar radiation intensity fluctuations of the wind power / photovoltaic unit, and the energy storage status of the energy storage equipment.

[0124] According to the operating parameters obtained by solving the network dynamic model and the equipment dynamic model, problems such as capacity configuration, operation optimization, and performance evaluation can be solved. For example, in a large-scale power grid system, a virtual power plant can participate in the grid dispatch as an independent control unit, obtain power distribution data through a dynamic model, optimize the output of various resources, reduce dispatch uncertainty and reduce the dispatch pressure of power plants, and optimize the dispatch of the power system; as the proportion of new energy such as wind energy and solar energy increases, the volatility of the power system increases. Through real-time monitoring of the dynamic response of new energy through dynamic models, optimizing the grid connection strategy, and reasonably configuring energy storage capacity, the impact of grid fluctuations on system stability can be reduced, and support for new energy grid connection can be optimized; wind power and photovoltaic output have strong randomness and uncertainty. Using dynamic models combined with weather forecast data to simulate the fluctuations in wind and solar output and their impact on the power grid, analyze the role of energy storage and peak-shaving resources, and evaluate the system's ability to cope with load fluctuations.

[0125] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 4As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store virtual power plant operation status analysis data based on a dynamic model. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a virtual power plant operation status analysis method based on a dynamic model is implemented.

[0126] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0127] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0128] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0129] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0130] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0131] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0132] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0133] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for analyzing the operating status of a virtual power plant based on a dynamic model, characterized in that: The virtual power plant operation status analysis method based on the dynamic model includes: Based on the system characteristics of the virtual power plant, a dynamic model of the virtual power plant is constructed; the dynamic model of the virtual power plant includes a network dynamic model and an equipment dynamic model; the system characteristics include at least nonlinearity, time lag and volatility; The network dynamic model and the device dynamic model are solved by homotopy analysis to obtain operation parameters; Verifying the convergence of the operating parameters; When the operating parameters converge, taking the operating parameters as the finally determined operating parameters; When the operating parameters do not converge, the whale optimization algorithm is used to determine the optimal auxiliary parameters of the homotopy analysis method, and after the optimal auxiliary parameters replace the auxiliary parameters of the homotopy analysis method, the step of returning to execute the homotopy analysis method to solve the network dynamic model and the device dynamic model to obtain the operating parameters.

2. The method for analyzing the operating status of a virtual power plant based on a dynamic model according to claim 1, characterized in that: The network dynamic model and the device dynamic model are solved by homotopy analysis to obtain operation parameters, including: Performing standardized conversion on the network dynamic model and the device dynamic model to obtain a standard format model; Setting a target equation based on the standard format model and selecting an initial guess solution; According to the initial guess solution, the target equation is linearized to obtain a zero-order deformation equation; Introducing the homotopy parameters into the zero-order deformation equation; the homotopy parameters include auxiliary parameters and auxiliary functions of the homotopy analysis method; Performing Taylor series expansion on the zero-order deformation equation introducing the homotopy parameter to obtain a high-order deformation equation; The high-order deformation equation is recursively solved to obtain the operating parameters.

3. The method for analyzing the operating status of a virtual power plant based on a dynamic model according to claim 2, characterized in that: The high-order deformation equation is expressed as: Where L[*] represents the auxiliary linear operator, Ψ n-1 represents the weight function associated with order n-1, f n (x,y) represents the nth order approximate solution, f n-1 (x,y) represents the approximate solution of the n-1th order, and h represents the auxiliary parameter of the homotopy analysis method; represents the convergence condition of the homotopy analysis method, where The value of the nonlinear residual converges to zero; represents the conjugate of the m-1th order approximate solution, H n represents the nonlinear residual, represents the n-1th order partial derivative of the embedded variable q, Γ(n) is the gamma function, F is an operator that depends on the specific form of the problem, Ψ n Represents the weight function related to the nth order, n represents the order, and x and y are independent variables.

4. The method for analyzing the operating status of a virtual power plant based on a dynamic model according to claim 1, characterized in that: The optimal auxiliary parameters of the homotopy analysis method are determined by using the whale optimization algorithm, including: Using the current auxiliary parameters of the homotopy analysis method as the initial target position of the whale optimization algorithm; Position updating and prey searching are performed based on the initial target position until the convergence condition is reached, thereby obtaining the optimal auxiliary parameters of the homotopy analysis method.

5. The method for analyzing the operating status of a virtual power plant based on a dynamic model according to claim 4 is characterized in that: The convergence condition is expressed as: Where U f,ω represents the fitness of the ω-th position update and prey search, U f,ω-1 represents the fitness of the ω-1th position update and prey search, ε represents the preset accuracy threshold, and U f represents fitness, N represents the total number of position updates and prey searches, F(f(x,y)) represents the standardized transformation of the initial function f(x,y), and x and y are both independent variables.

6. The method for analyzing the operating status of a virtual power plant based on a dynamic model according to claim 1, characterized in that: The network dynamic model includes: a power grid dynamic model, a gas grid dynamic model and a heat grid dynamic model.

7. The method for analyzing the operation status of a virtual power plant based on a dynamic model according to claim 1, characterized in that: The equipment dynamic model includes: a cogeneration dynamic model, a wind turbine dynamic model, a photovoltaic generator dynamic model and an energy storage equipment dynamic model.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the dynamic model-based virtual power plant operating status analysis method described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the virtual power plant operation status analysis method based on a dynamic model as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, it implements the virtual power plant operation status analysis method based on a dynamic model as described in any one of claims 1-7.

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