A multi-parameter programming based probabilistic energy flow calculation method for power-hydrogen coupling system

Abstract

The application relates to the field of comprehensive energy system analysis, and discloses a probability energy flow calculation method for an electricity-hydrogen coupling system based on multi-parameter programming, which specifically discloses the following: an electricity-hydrogen coupling system energy flow equation is constructed, and a multi-parameter programming model for solving the electricity-hydrogen coupling system energy flow equation is constructed, so that the optimal solution of the parameter programming model is equivalent to the solution of the electricity-hydrogen coupling system energy flow equation, thereby equivalently converting a non-linear energy flow calculation problem into a convex multi-parameter programming model. Then, a parameter quadratic approximation method is used to solve the multi-parameter programming model offline, and an energy flow analytical expression of the solution of the electricity-hydrogen coupling system energy flow equation with respect to the parameters is obtained, so that when the probability energy flow calculation is performed, only the real-time parameters need to be substituted into the energy flow analytical expression, and the probability energy flow of the electricity-hydrogen coupling system can be solved online, the repeated iterative solution of the non-linear energy flow equation is avoided, and the online calculation efficiency of the probability energy flow of the electricity-hydrogen coupling system is effectively improved.

Description

Technical Field

[0001] This application relates to the field of integrated energy system analysis, specifically to a probabilistic energy flow calculation method for an electric-hydrogen coupled system based on multi-parameter planning. Background Technology

[0002] The energy sector is a key area and main battleground for achieving carbon peaking and carbon neutrality goals. The electric-hydrogen coupling system (EHHC) is a new type of energy system that uses electricity and hydrogen as core energy carriers, enabling a high proportion of renewable energy supply and providing strong support for the low-carbon transformation of the energy system. On the one hand, in regions with abundant renewable energy resources, the EHHC system utilizes wind and solar power to electrolyze water to produce hydrogen, effectively reducing hydrogen production costs and enhancing the local consumption capacity of renewable energy. On the other hand, hydrogen turbines have the advantages of being clean, efficient, and having a fast response time, effectively supporting flexible peak shaving and frequency regulation in high-proportion renewable energy power systems.

[0003] Probabilistic energy flow calculations for electric-hydrogen coupled systems are fundamental for further system planning, operation, and analysis under uncertain conditions, and are therefore of great significance. The purpose of probabilistic energy flow calculations for electric-hydrogen coupled systems is to determine the probability distribution of all system state variables (such as voltage, phase angle, power, airflow, and air pressure) under uncertain boundary conditions. Traditional methods typically use Monte Carlo simulations to generate a massive number of boundary condition samples based on the probability distribution function of the boundary conditions. Then, under each boundary condition sample, nonlinear energy flow equations are solved iteratively to obtain the probabilistic energy flow distribution of the electric-hydrogen coupled system. However, traditional methods require a large number of repetitive iterative numerical calculations for the massive number of boundary condition samples, resulting in low computational efficiency. Summary of the Invention

[0004] This application provides a probabilistic energy flow calculation method for an electric-hydrogen coupled system based on multi-parameter planning, in order to solve the problem that traditional methods in the prior art require a large number of repetitive iterative numerical calculations for massive boundary condition samples, resulting in low computational efficiency.

[0005] Accordingly, this application also provides an electronic device and a computer-readable storage medium to ensure the implementation and application of the above methods.

[0006] To address the aforementioned technical problems, this application discloses a probabilistic energy flow calculation method for an electric-hydrogen coupling system based on multi-parameter programming, the method comprising:

[0007] Construct the energy flow equations for the electric-hydrogen coupled system;

[0008] Construct a multi-parameter programming model to solve the energy flow equation of the electric-hydrogen coupling system, so that the parameter optimal solution of the parameter programming model is equivalent to the solution of the energy flow equation of the electric-hydrogen coupling system;

[0009] The parametric quadratic approximation method is used to solve the multi-parameter programming model offline, and the analytical expression of energy flow of the solution of the energy flow equation of the electric-hydrogen coupling system with respect to the parameters and the corresponding parameter critical domain are obtained.

[0010] Based on the analytical expression of energy flow, the probabilistic energy flow of the electric-hydrogen coupled system is solved online.

[0011] This application also discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement one or more of the methods described in this application.

[0012] This application also discloses a computer-readable storage medium storing a computer program that, when executed by a processor, implements one or more of the methods described in this application.

[0013] In this application, an energy flow equation for an electric-hydrogen coupling system is constructed, and a multi-parameter programming model for solving this equation is built. The optimal solution of the parametric programming model is equivalent to the solution of the energy flow equation for the electric-hydrogen coupling system, thus transforming the nonlinear energy flow calculation problem into a convex multi-parameter programming model. Subsequently, a parametric quadratic approximation method is used to solve the multi-parameter programming model offline, obtaining the analytical expression of the energy flow of the electric-hydrogen coupling system's energy flow equation with respect to the parameters. Therefore, when performing probabilistic energy flow calculations, only real-time parameters need to be substituted into the analytical expression to achieve online solving of the probabilistic energy flow of the electric-hydrogen coupling system, avoiding repeated iterative solutions to the nonlinear energy flow equation and effectively improving the online computational efficiency of the probabilistic energy flow of the electric-hydrogen coupling system.

[0014] Additional aspects and advantages of this application will be set forth in the following description, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0015] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0016] Figure 1 A flowchart of a probabilistic energy flow calculation method for an electric-hydrogen coupling system based on multi-parameter planning, provided in an embodiment of this application;

[0017] Figure 2 This is a schematic diagram of the electro-hydrogen coupling system structure provided in an embodiment of this application;

[0018] Figure 3 A graph showing the probabilistic energy flow (airflow) results calculated by the method provided in the embodiments of this application;

[0019] Figure 4A graph showing the probabilistic energy flow (pressure) results calculated by the method provided in the embodiments of this application;

[0020] Figure 5 A comparison chart of calculation results between the method provided in this application embodiment and the conventional method;

[0021] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0022] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0023] Those skilled in the art will understand that, unless explicitly stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this application means the presence of features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or combinations thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0024] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0025] The solutions provided in this application can be executed by any electronic device, such as a terminal device or a server. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein. Regarding the technical problems existing in the prior art, the probabilistic energy flow calculation method for an electro-hydrogen coupling system based on multi-parameter planning provided in this application aims to solve at least one of the technical problems in the prior art.

[0026] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0027] This application provides a possible implementation method, such as... Figure 1 The diagram shows a flowchart of a probabilistic energy flow calculation method for an electro-hydrogen coupling system based on multi-parameter planning. This method can be executed by any electronic device, and optionally, it can be executed on a server or a terminal device.

[0028] like Figure 1 As shown, the method may include the following steps:

[0029] Step 101: Construct the energy flow equations for the electric-hydrogen coupling system; Solving the probabilistic energy flow of the electric-hydrogen coupling system using the energy flow equations is a nonlinear energy flow calculation problem.

[0030] Step 102: Construct a multi-parameter programming model to solve the energy flow equation of the electric-hydrogen coupling system, so that the optimal solution of the parameter programming model is equivalent to the solution of the energy flow equation of the electric-hydrogen coupling system; thus, the nonlinear energy flow calculation problem is equivalently transformed into a convex multi-parameter programming model.

[0031] Step 103: The multi-parameter programming model is solved offline using the parametric quadratic approximation method to obtain the analytical expression of energy flow of the solution of the energy flow equation of the electric-hydrogen coupling system with respect to the parameters and the corresponding parameter critical domain.

[0032] Step 104: Based on the analytical expression of energy flow, solve the probabilistic energy flow of the electric-hydrogen coupling system online.

[0033] After obtaining the offline analytical expression for energy flow, the probabilistic energy flow of the electric-hydrogen coupling system can be obtained by substituting the real-time obtained parameters into the analytical expression. In this embodiment, the real-time obtained parameters can be random parameters corresponding to samples generated by Monte Carlo simulation.

[0034] By analyzing the probabilistic energy flow of the electro-hydrogen coupling system, we can obtain the probability distributions of the grid voltage, phase angle, and power of the electro-hydrogen coupling system, as well as the probability distributions of the hydrogen grid gas flow and pressure.

[0035] In this embodiment, an energy flow equation for an electric-hydrogen coupling system is constructed, and a multi-parameter programming model for solving this equation is also constructed. The optimal solution of the parameter programming model is equivalent to the solution of the energy flow equation for the electric-hydrogen coupling system, thus transforming the nonlinear energy flow calculation problem into a convex multi-parameter programming model. Subsequently, a parametric quadratic approximation method is used to solve the multi-parameter programming model offline, obtaining the analytical expression of the energy flow of the electric-hydrogen coupling system's energy flow equation with respect to the parameters. Therefore, when performing probabilistic energy flow calculations, only the real-time parameters need to be substituted into the analytical expression to achieve online solving of the probabilistic energy flow of the electric-hydrogen coupling system, avoiding repeated iterative solutions to the nonlinear energy flow equation and effectively improving the online calculation efficiency of the probabilistic energy flow of the electric-hydrogen coupling system.

[0036] In an optional embodiment, the energy flow equations of the electric-hydrogen coupled system include the linearized AC power flow equations of the power grid, the nonlinear energy flow equations of the hydrogen grid, and the power grid-hydrogen grid coupling equations.

[0037] In an optional embodiment, the AC power flow equations for grid linearization include active power balance equations and reactive power balance equations.

[0038] The active power balance equation is:

[0039]

[0040] The reactive power balance equation is:

[0041]

[0042] In the formula, i and j represent the starting bus and the terminal bus of line (i,j), respectively, and P i and Q i G represents the injected active power and reactive power of bus i, respectively. ij and B ij Let B represent the real and imaginary parts of the elements in the i-th row and j-th column of the nodal admittance matrix, respectively. i ' j V represents the imaginary part of the element in the i-th row and j-th column of the nodal admittance matrix without considering ground admittance. j and θj N represents the voltage magnitude and phase angle of bus j, respectively. e ρ represents the set of all buses in the power grid. i This represents the volatility of the active and reactive power injection amounts at bus i.

[0043] In an optional embodiment, the nonlinear energy flow equations of the hydrogen network include the gas flow equations of the hydrogen network branches, the gas flow balance equations of the hydrogen network nodes, and the gas pressure equations of the hydrogen network constant pressure nodes.

[0044] The hydrogen network branches mainly consist of hydrogen pipelines and compressors. The steady-state gas flow in the hydrogen pipelines needs to satisfy the classic Weymouth gas flow model. The compressor's operating modes can be mainly divided into constant relative pressure rise ratio and constant absolute pressure rise value. This application's embodiment considers the compressor model with constant absolute pressure rise. Therefore, the hydrogen network branch gas flow equations consider both the Weymouth gas flow model of the hydrogen pipelines and the absolute pressure rise model of the compressors. The hydrogen network branch gas flow equations are as follows:

[0045]

[0046] In the formula, m and n represent the starting node and ending node of the branch (m,n), respectively, and π m and π n Let C represent the squared air pressure values ​​at nodes m and n, respectively. mn f represents the pipe friction coefficient of branch (m,n). mn δ represents the airflow through branch (m,n). mn The absolute pressure boost of the compressor on branch (m,n) is represented by L, and the set of hydrogen network branches is represented by L.

[0047] The gas flow balance equation for the hydrogen network nodes is:

[0048]

[0049] In the formula, q m This represents the amount of airflow injected at node m. and Let N represent the set of all branches from which airflow leaves node m and the set of all branches from which airflow enters node m, respectively. h Let ρ represent the set of all nodes within the hydrogen network. m This represents the volatility of the gas flow injection at node m;

[0050] In a hydrogen network, each node must satisfy the hydrogen network node airflow balance equation.

[0051] The pressure at constant-pressure nodes in a hydrogen network is typically controlled to a fixed value. The pressure equation for constant-pressure nodes in a hydrogen network is as follows:

[0052]

[0053] In the formula, Π m Let m represent the squared pressure value of the constant pressure node m, and S represent the set of all constant pressure nodes.

[0054] In an optional embodiment, based on the coupling of the power grid and the hydrogen grid via a gas turbine, the power grid-hydrogen grid coupling equation is:

[0055]

[0056] In the formula, N h-e μ represents the set of nodes in the hydrogen grid where the hydrogen turbine is located. m This represents the hydrogen-to-electric conversion coefficient.

[0057] In summary, equations (1)-(6) constitute the energy flow equations for the electro-hydrogen coupling system.

[0058] In an optional embodiment, the multi-parameter programming model is as follows:

[0059]

[0060] ρ∈Θ0(10)

[0061] In the formula, V and θ represent vectors composed of the voltage magnitude and phase angle of the power grid bus, respectively; ρ represents all ρ i and ρ m The vector formed by these parameters serves as the parameters of the multi-parameter programming model; x represents all state variables f. mn q m Vi and θ i The vector formed by these parameters serves as the optimization variables for the multi-parameter programming model; E(x) represents the objective function of the multi-parameter programming model; z(ρ) represents the function of the optimal value of the multi-parameter programming with respect to parameter ρ; Θ0 represents the feasible region of parameter ρ; Ω(V,θ) represents q, characterized by the power flow equations and the power grid-hydrogen grid coupling equations. m The feasible domain.

[0062] It is not difficult to find that the optimality condition of the proposed multi-parameter convex programming is equivalent to the original nonlinear energy flow equations (1)-(6) of the electric-hydrogen coupling system. Therefore, the optimal solution of the proposed parameter programming model is equivalent to the solution of the nonlinear energy flow equations of the electric-hydrogen coupling system.

[0063] In an optional embodiment, a parametric quadratic approximation method is used to solve the multi-parameter programming model offline, obtaining the analytical expression of energy flow of the solution to the energy flow equation of the electric-hydrogen coupling system with respect to the parameters and the corresponding parameter critical domain, including:

[0064] 1) Set the initial set of candidate parameter critical regions as R = {Θ0}, and the initial set of optimal parameter critical regions as... The initial parameters are optimal, x(ρ) = 0, and the convergence threshold of the quadratic approximation of the parameters is ε. max ;

[0065] If set R is not empty, then perform steps 2) to 4):

[0066] 2) Select any critical region of parameters within set R and label it Θ. Find the geometric center of Θ as ρ. c Solving a multi-parameter programming model with parameter ρ = ρ c The optimal solution at time x c ;

[0067] 3) In x = x c To construct a parametric quadratic programming model, a second Taylor approximation is performed on the nonlinear objective function E(x) of the multi-parameter programming model:

[0068]

[0069]

[0070] ρ∈Θ(14)

[0071] In the formula, H and U represent E(x) at x = x c The Hessian matrix and Jacobian matrix at z QP (ρ) represents the optimal value of the parametric quadratic programming model as a function of the parameter ρ, E(x) c ) represents the objective function of a multi-parameter programming model at x = x c The value at;

[0072] Solving the parametric quadratic programming model yields the optimal parametric solution x. QP (ρ) and the corresponding set of parameter critical regions R'={Θ1,…,Θ K}, and use x QP (ρ) Update x(ρ);

[0073] 4) For any parameter critical region Θ in set R' k Find Θ k vertex set For Θ k each vertex Solving convex optimization problems:

[0074]

[0075] Calculation error If ε>ε max , will Θ k Divide into two sub-parameter critical regions and add these two parameter critical regions to the set R; if ε≤ε max , will Θk Remove from set R and move into set R. * ;

[0076] If the set R is empty, then using the final obtained x(ρ) and the corresponding set of the critical domains of the optimal parameters R * An analytical expression for the energy flow of the solution to the energy flow equation of an electric-hydrogen coupled system with respect to the parameters.

[0077] In an optional embodiment, the probabilistic energy flow of the electric-hydrogen coupling system is solved online based on the analytical expression of energy flow, including:

[0078] a. Based on the probability distribution function satisfied by the random parameters, N sets of random parameter samples {ρ1,…,ρ2} are randomly generated using the Monte Carlo method. N The random parameters include power and airflow fluctuation rate, which serve as the input boundary conditions for probabilistic energy flow calculations.

[0079] b. Given N sets of random parameter samples {ρ1,…,ρ N Substitute the values ​​into the analytical expression for energy flow and solve for the probabilistic energy flow of the electric-hydrogen coupling system corresponding to the N sets of random parameter samples.

[0080] c. Based on the N sets of probabilistic energy flows obtained from the electric-hydrogen coupling system, the probabilistic energy flow distribution of the electric-hydrogen coupling system can be obtained, that is, the probability distribution functions of system state variables such as grid voltage, phase angle, power, and hydrogen grid gas flow and pressure.

[0081] Through the above steps, the probabilistic energy flow distribution of the electric-hydrogen coupling system can be finally obtained. Since the method proposed in this embodiment has already obtained the analytical expression of the energy flow solution of the electric-hydrogen coupling system through multi-parameter programming in the offline stage, it is only necessary to substitute the random parameters corresponding to the samples generated by Monte Carlo simulation into the energy flow analytical expression to realize the online calculation of probabilistic energy flow. This avoids repeated iterative calculations of nonlinear energy flow equations and greatly improves the online calculation efficiency of probabilistic energy flow of the electric-hydrogen coupling system.

[0082] For example, an actual electro-hydrogen coupling system in a certain region is used as a case study to verify and analyze the method proposed in the embodiments of this application. The specific topology results of the electro-hydrogen coupling system are as follows: Figure 2 Specifically, this includes 39-node power grids (such as...) Figure 2 The buses numbered B1 to B39 shown in the diagram) and the 20-node hydrogen network (as shown in the diagram) Figure 2 (The hydrogen network nodes shown are numbered N1 to N20). In addition, Figure 2 The document also shows hydrogen turbines, coal-fired units, electric-hydrogen coupling buses, power transmission lines and power loads in the power grid, as well as hydrogen sources, compressors, hydrogen pipelines, electric-hydrogen coupling nodes, hydrogen loads and hydrogen turbine hydrogen loads in the hydrogen grid.

[0083] Using the actual load of the electric-hydrogen coupling system as the mean and 5% of the mean as the variance, a 1×10⁻⁶ random sample was generated using the Monte Carlo method. 6 Using the load scenario as the boundary condition for energy flow calculation, the method proposed in this case is used to calculate this 1×10 6 The probabilistic energy flow solution under the group load scenario is presented and compared with the traditional stochastic simulation solution method. Figure 3 The graph shows the probabilistic energy flow (airflow) results obtained by the proposed method. The horizontal axis represents the branch number, and the vertical axis represents the airflow (unit: km). 3 / h); Figure 4 The graph shows the probabilistic energy flow (pressure) results obtained by the proposed method. The horizontal axis represents the node number, and the vertical axis represents the pressure (unit: bar). Figure 5 A comparison chart of the calculation results of the proposed method and the stochastic simulation method is shown, respectively showing the probability density of the airflow through branch 3-4, the probability density of the airflow through branch 14-15, the probability density of the air pressure at node 4, and the probability density of the air pressure at branch node 20. In this chart, MPP represents the probabilistic energy flow calculation method of the electric-hydrogen coupling system based on multi-parameter planning proposed in this application embodiment (corresponding to the proposed method in Table 1), and NR-MCS represents the traditional Monte Carlo simulation method based on Newton's method (corresponding to the traditional method in Table 1).

[0084] according to Figure 5 It can be observed that the probabilistic energy flow results calculated by the method proposed in this application are basically consistent with the results obtained by stochastic simulation. The relative errors of the airflow obtained by the method in this application are all less than 3.5 × 10⁻⁶. -4 The relative error of air pressure is less than 3.5 × 10⁻⁶. -5 This demonstrates that the method proposed in the embodiments of this application has high computational accuracy.

[0085] Furthermore, the method proposed in this application embodiment was tested on seven electro-hydrogen coupling system examples and compared with traditional methods. As shown in Table 1, it can be found that the calculation error of the method proposed in this application embodiment on the seven test systems is less than 10. -3 The online computation efficiency of probabilistic energy flow is about 2-3 orders of magnitude higher than that of traditional methods, which shows that the method proposed in this application can significantly improve the computation efficiency of energy flow in the electric-hydrogen coupling system while ensuring computational accuracy.

[0086] Table 1 compares the computational efficiency of the proposed method with that of traditional methods.

[0087]

[0088]

[0089] Based on the same principles as the methods shown in the embodiments of this application, embodiments of this application also provide an electronic device, which may include, but is not limited to: a processor and a memory; the memory for storing computer programs; and the processor for executing the probabilistic energy flow calculation method for an electric-hydrogen coupled system based on multi-parameter programming shown in any optional embodiment of this application by calling the computer program. Compared with the prior art, the probabilistic energy flow calculation method for an electric-hydrogen coupled system based on multi-parameter programming provided in this application constructs the energy flow equation of the electric-hydrogen coupled system and constructs a multi-parameter programming model for solving the energy flow equation of the electric-hydrogen coupled system, making the optimal solution of the parameter programming model equivalent to the solution of the energy flow equation of the electric-hydrogen coupled system, thereby transforming the nonlinear energy flow calculation problem into a convex multi-parameter programming model. Subsequently, a parametric quadratic approximation method was used to solve the multi-parameter programming model offline, obtaining the analytical expression of energy flow in the solution of the energy flow equation of the electric-hydrogen coupling system with respect to the parameters. Therefore, when performing probabilistic energy flow calculation, it is only necessary to substitute the real-time parameters into the analytical expression of energy flow to realize the online solution of the probabilistic energy flow of the electric-hydrogen coupling system, avoiding repeated iterative solutions to the nonlinear energy flow equation, thereby effectively improving the online calculation efficiency of the probabilistic energy flow of the electric-hydrogen coupling system.

[0090] In an alternative embodiment, an electronic device, such as Figure 6 As shown, Figure 6 The illustrated electronic device 600 can be a server, including a processor 601 and a memory 603. The processor 601 and the memory 603 are connected, for example, via a bus 602. Optionally, the electronic device 600 may also include a transceiver 604. It should be noted that in practical applications, the transceiver 604 is not limited to one type, and the structure of this electronic device 600 does not constitute a limitation on the embodiments of this application.

[0091] Processor 601 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 601 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0092] Bus 602 may include a pathway for transmitting information between the aforementioned components. Bus 602 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 602 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0093] The memory 603 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0094] The memory 603 stores application code that executes the scheme of this application, and its execution is controlled by the processor 601. The processor 601 executes the application code stored in the memory 603 to implement the content shown in the foregoing method embodiments.

[0095] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 6 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0096] The server provided in this application can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein.

[0097] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0098] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0099] It should be noted that the computer-readable storage medium described above in this application can also be a computer-readable signal medium or a combination of computer-readable storage media and computer-readable storage media. Computer-readable storage media can be, for example,—but not limited to—electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0100] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0101] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods shown in the above embodiments.

[0102] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the probabilistic energy flow calculation method for an electro-hydrogen coupling system based on multi-parameter planning provided in the various alternative implementations described above.

[0103] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0105] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A multi-parameter-programming-based probabilistic energy flow calculation method for an electricity-hydrogen coupled system, characterized in that, The method comprises: constructing an electric-hydrogen coupling system energy flow equation, the electric-hydrogen coupling system energy flow equation comprising a linearized alternating current flow equation of an electric network, a nonlinear energy flow equation of a hydrogen network and an electric network-hydrogen network coupling equation, the linearized alternating current flow equation of the electric network comprising an active power balance equation and a reactive power balance equation; the active power balance equation is: the reactive power balance equation is: where i and j represent the starting bus and the terminal bus of line (i, j) respectively, P i and Q i represent the active power and the reactive power injected at bus i, respectively, G ij and B ij represent the real part and the imaginary part of the element of the bus admittance matrix in the i-th row and the j-th column, B' ij represents the imaginary part of the element of the bus admittance matrix in the i-th row and the j-th column without considering the ground admittance, V j and θ j represent the voltage amplitude and the phase angle of bus j, respectively, N e represents the set of all buses of the power grid, p i represents the volatility of the active and reactive power injection at bus i; the nonlinear energy flow equation of the hydrogen network comprises a hydrogen network branch gas flow equation, a hydrogen network node gas flow balance equation and a hydrogen network constant pressure node gas pressure equation; the hydrogen network branch gas flow equation considers a Weymouth gas flow model of a hydrogen pipeline and an absolute boost model of a compressor, and the hydrogen network branch gas flow equation is: where m and n represent the start node and end node of branch (m, n), respectively, and m and π n represent the square pressure value of node m and node n, respectively, C mn represents the pipe friction coefficient of branch (m, n), f mn represents the gas flow through branch (m, n), δ mn represents the absolute boost of the gas compressor on branch (m, n), and L represents the set of hydrogen network branches. the hydrogen network node gas flow balance equation is: where q m denotes the flow injection amount of node m, and denote the set of all outflow branches of node m and the set of all inflow branches of node m, respectively, N h denotes the set of all nodes in the hydrogen network, p m denotes the fluctuation rate of the flow injection amount of node m; the hydrogen network constant pressure node gas pressure equation is: wherein Π m denotes the square barometric pressure value of the constant pressure node m, and S denotes the set of all constant pressure nodes; based on the coupling of the electric network and the hydrogen network through a gas turbine, the electric network-hydrogen network coupling equation is: In the formula, N h-e denotes a set of hydrogen grid nodes in which the hydrogen combustion turbine is located, μ m denotes a hydrogen-electric conversion factor; constructing a multi-parameter programming model for solving the electric-hydrogen coupling system energy flow equation, so that the optimal solution of the parameter programming model is equivalent to the solution of the electric-hydrogen coupling system energy flow equation, and the multi-parameter programming model is: ρ∈Θ0 In the formula, V and θ represent vectors composed of the voltage magnitude and phase angle of the power grid bus, respectively; ρ represents all ρ i and ρ m The vector formed by these parameters serves as the parameters of the multi-parameter programming model; x represents all state variables f. mn q m Vi and θ i The vector formed by these parameters serves as the optimization variables for the multi-parameter programming model; E(x) represents the objective function of the multi-parameter programming model; z(ρ) represents the function of the optimal value of the multi-parameter programming with respect to parameter ρ; Θ0 represents the feasible region of parameter ρ; Ω(V,θ) represents q, characterized by the power flow equations and the power grid-hydrogen grid coupling equations. m The feasible domain; offline solving the multi-parameter programming model by using a parameter quadratic approximation method to obtain an energy flow analytical expression of the solution of the electric-hydrogen coupling system energy flow equation with respect to parameters and a corresponding parameter critical region; based on the energy flow analytical expression, online solving a probabilistic energy flow of an electric-hydrogen coupling system.

2. The multi-parametric programming based probabilistic energy flow calculation method for electro-hydrogen coupled systems according to claim 1, characterized in that, The offline solving of the multi-parameter programming model by using the parameter quadratic approximation method to obtain the energy flow analytical expression of the solution of the electric-hydrogen coupling system energy flow equation with respect to parameters and the corresponding parameter critical region comprises: Set the initial candidate parameter critical region set as R = {Θ0}, and the initial optimal parameter critical region set as The initial parameter optimal solution x(ρ) = 0, and the convergence threshold of the parameter quadratic approximation is ε max ; if the set R is not an empty set, the following steps are performed: Select any one parameter critical region in set R and mark it as Θ, find the geometric center of Θ as ρ c , solve the optimal solution of the multi-parameter programming model when parameter ρ = ρ c is x c ; At x = x c The nonlinear objective function E(x) of the multiparameter programming model is twice Taylor approximated at x = x c to construct a quadratic programming model of parameters: ρ∈Θ where H and U represent the Hessian and Jacobian matrices of E(x) at x = x c , respectively, and z QP (ρ) represents the function of the optimal value of the parametric quadratic programming model with respect to the parameter p, E(x c ) represents the value of the objective function of the multi-parametric programming model at x = x c . solving the parameterized quadratic programming model to obtain a parameter optimal solution x QP (p) and the corresponding parameter critical region set R' = {Θ1,..., Θ K} and updating x(p) with x QP (p). For any parameter critical region Θ in set R' k Find Θ k vertex set For Θ k each vertex Solving convex optimization problems: Computing error If ε > ε max , split Θ k into two sub-parameter critical regions and add these two parameter critical regions to the set R; if ε < ε max , split Θ k from the set R and move into the set R * ; If the set R is empty, the final x(ρ) and the corresponding optimal parameter critical region set R * The energy flow analytical expression of the solution of the energy flow equation of the electric-hydrogen coupling system with respect to the parameters.

3. The multi-parametric programming based probabilistic energy flow calculation method for electro-hydrogen coupled systems according to claim 1, wherein, The online solving of the probabilistic energy flow of the electric-hydrogen coupling system based on the energy flow analytical expression comprises: Based on the probability distribution function satisfied by the random parameters, N groups of random parameter samples {ρ1, …, ρ N} are randomly generated by Monte Carlo method as the input boundary conditions of the probabilistic energy flow calculation; the random parameters include power and air flow fluctuation rate; The N sets of random parameter samples {ρ1,…,ρ N Substitute the values ​​into the analytical expression for energy flow to obtain the probabilistic energy flow of the electric-hydrogen coupling system corresponding to the N sets of random parameter samples.

4. An electronic device, comprising: The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1-3.

5. A computer readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1-3.

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