Multivariate flexible resource scheduling method, system and terminal based on holomorphic embedding algorithm

Abstract

The application discloses a kind of based on the multi-element flexible resource scheduling method, system and terminal of holomorphic embedding algorithm, method includes, the kind of acquisition multi-element flexible resource and the parameter of each kind flexible resource belongs to power grid;Based on holomorphic embedding algorithm, the tide flow equation including multi-element flexible resource is constructed;Determine optimization target and constraint condition, linear programming model is constructed based on the tide flow equation;Solve linear programming model, obtain the coefficient of each variable series each order term, generate the operation instruction value of multi-element flexible resource.The application uses holomorphic embedding method to describe power flow constraint, so that the model has the constraint ability of considering tide flow, voltage, power;Relax high-order term linear, combined with optimization control target and flexible resource, power grid topology operation constraint, establish linear programming model, facilitate to use mature optimization solver to improve the convergence speed of algorithm, improve the feasibility of multi-element flexible resource control instruction.

Description

Technical Field

[0001] This invention belongs to the field of digital control technology, and specifically relates to a multi-dimensional flexible resource scheduling method, system and terminal based on a fully embedded algorithm. Background Technology

[0002] Driven by the "dual carbon" goals, my country's installed capacity of new energy sources such as wind and solar power has doubled. The strong random fluctuations of these new energy sources have greatly increased the pressure on power system peak shaving and absorption, requiring more abundant regulation resources to ensure the safe, economical, and stable operation of the power system. The continuous expansion of the scale of diverse and flexible resources such as user-side electric vehicles, distributed wind and solar power, distributed energy storage, and user-side combined cooling, heating, and power (CCHP) units has brought massive potential regulation resources to the power system. How to aggregate and utilize these small-capacity, widely distributed, and diverse flexible resources to improve the safety and economy of power system operation is an urgent problem to be solved.

[0003] In existing technologies, linear programming models of multiple flexible resources are generally established from an energy perspective, ignoring power flow constraints and simplifying the power network into a topological description of energy reachability at the source and load ends. Alternatively, linear power flow is used to establish a linear relationship between active power and node voltage phase angle to achieve optimal resource scheduling. Existing scheduling methods are simple and intuitive to implement and easy to solve using linear programming solvers. However, they cannot accurately consider power flow constraints and voltage limit constraints in the power system, require certain preconditions for use, have weak descriptive ability of the power system, resulting in poor usability of the optimized scheduling results and failure to fully utilize the control effectiveness of multiple flexible resources.

[0004] The multivariate flexible resource scheduling method based on the fully embedded algorithm uses recursion to describe the nonlinear power flow constraints of the power system. It does not require prior specification of the initial value of the recursive iteration, and the convergence effect of the recursion is satisfactory. However, the power flow constraints of the fully embedded algorithm are still nonlinear and cannot be solved using mature linear programming solvers. If only the 0th and 1st order expansions of the embedding factors are used to describe the power flow constraints, although a linear programming model can be constructed, ignoring higher-order terms will reduce the accuracy of the model. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-source flexible resource scheduling method, system, and terminal based on a fully embedded algorithm. When establishing the optimization model, a fully embedded method is used to describe power flow constraints, enabling the scheduling model to consider power flow, voltage, and power constraints. The McCormick envelope relaxation method is used to linearly relax the higher-order terms of the fully embedded method. Combined with the optimization control objective and the operational constraints of flexible resources and grid topology, a linear programming model is established. This facilitates the use of mature optimization solvers to improve the algorithm's convergence speed and enhances the feasibility of multi-source flexible resource control commands, thereby fully regulating multi-source flexible resources to serve the economical and safe operation of the new power system.

[0006] To achieve the above objectives, the solution of the present invention is:

[0007] A multivariate flexible resource scheduling method based on a fully pure embedding algorithm includes,

[0008] Obtain the types of diverse flexible resources and the parameters of the power grid to which each type of flexible resource belongs;

[0009] Based on the fully embedded algorithm, a power flow equation containing multiple flexible resources is constructed; wherein, the power flow equation uses the coefficient components of each order term of each variable series in the fully embedded algorithm as power flow constraints.

[0010] The optimization objective is determined based on the characteristic quantities of the optimal operating state that the power dispatching system needs to achieve, which requires the participation of multiple flexible resources. The constraints are determined based on the limiting conditions of multiple flexible operating states and ensuring the safety of system operation. A linear programming model is constructed based on the power flow equation.

[0011] Solve the linear programming model to obtain the coefficients of each term of each variable series. Based on the coefficients of each term of each variable series, generate the running instruction values ​​for multiple flexible resources.

[0012] Among them, a power flow equation incorporating diverse and flexible resources is constructed based on a fully embedded algorithm, including:

[0013] The variables x(s) in the power flow equation containing multiple flexible resources are expressed as the power series expansion of the coefficient x[n] multiplied by the embedding factor α to the power of n, where x[n] and α are complex numbers;

[0014] Expand the equations in the fully embedded format using series expansion with respect to the embedding factor α. By comparing the coefficients of each series, recursively calculate the required variable series expression. Here, when the embedding factor α is 0, it is defined as the initial value of the recursion; when the embedding factor α is 1, it is defined as the target value of the variable series.

[0015] Specifically, based on the coefficients of each order term of the aforementioned variable series, the operating instruction values ​​for the diverse flexible resources are generated, including:

[0016] By adding the coefficients of each order term, the required approximate value of the variable is obtained, thus yielding the operating instruction value for various types of diverse and flexible resources.

[0017] A multi-variable flexible resource scheduling system based on a fully embedded algorithm includes,

[0018] The front-end data acquisition module is configured to acquire the types of diverse flexible resources and the parameters of the power grid to which each type of flexible resource belongs;

[0019] The fully embedded algorithm construction module is configured to construct a power flow equation containing multiple flexible resources based on the fully embedded algorithm; wherein, the power flow equation uses the coefficient components of each order term of each variable series in the fully embedded algorithm as power flow constraints.

[0020] The linear programming model building and solution module is configured to determine the optimization objective based on the characteristic quantities of the optimal operating state that the power dispatching system requiring the participation of multiple flexible resources needs to achieve, determine the constraints based on the limiting conditions of multiple flexible operating states and ensuring system operation safety, and construct a linear programming model based on the power flow equations; and...

[0021] The result generation module is configured to solve the linear programming model, obtain the coefficients of each order of the series of variables, and generate the running instruction values ​​of the multi-variable flexible resources based on the coefficients of each order of the series of variables.

[0022] Among them, a power flow equation incorporating diverse and flexible resources is constructed based on a fully embedded algorithm, including:

[0023] The variables x(s) in the power flow equation containing multiple flexible resources are expressed as the power series expansion of the coefficient x[n] multiplied by the embedding factor α to the power of n, where x[n] and α are complex numbers;

[0024] Expand the equations in the fully embedded format using series expansion with respect to the embedding factor α. By comparing the coefficients of each series, recursively calculate the required variable series expression. Here, when the embedding factor α is 0, it is defined as the initial value of the recursion; when the embedding factor α is 1, it is defined as the target value of the variable series.

[0025] Specifically, based on the coefficients of each order term of the aforementioned variable series, the operating instruction values ​​for the diverse flexible resources are generated, including:

[0026] By adding the coefficients of each order term, the required approximate value of the variable is obtained, thus yielding the operating instruction value for various types of diverse and flexible resources.

[0027] A terminal includes a processor and a storage medium; the storage medium is used to store instructions.

[0028] The processor is configured to operate according to the instructions to execute the steps of the multi-variable flexible resource scheduling method based on the fully embedded algorithm as described above.

[0029] A computer-readable storage medium storing a computer program; when executed by a processor, the computer program implements the steps of the multi-variable flexible resource scheduling method based on the fully embedded algorithm as described above.

[0030] After adopting the above scheme, the present invention first obtains the line topology, parameters, and voltage limits of each voltage node of the power transmission and distribution system; according to the control and operation characteristics of various flexible resources, the resources are represented as PQ or PV nodes; a power flow calculation model of the system is constructed using a fully embedded algorithm, and the system state variables under open-circuit conditions are selected as the initial recursive values; a power flow constraint model is constructed by selecting the components of each order of the recursive variables, and the nonlinear model is relaxed using the McCormick envelope method. The desired optimization objective is selected to form a linear programming model for multi-variable flexible resource scheduling; the scheduling instructions for multi-variable flexible resources can be obtained by solving the linear programming solver.

[0031] The beneficial effects of this invention are that, compared with the prior art, the multi-dimensional flexible resource scheduling method based on the fully embedded algorithm proposed in this invention realizes the optimized control and operation of multi-dimensional flexible resources, supporting the safe and economical operation of the power system. Using the fully embedded algorithm to describe the line power flow constraints allows for the consideration of more dimensions of line power flow constraint information during multi-dimensional flexible resource scheduling, improving the rationality of resource scheduling. Furthermore, the use of the McCormick envelope method for model linearization during power flow constraint construction ensures sufficient solution accuracy while reducing the difficulty of model construction, allowing for direct solution using mature planning solvers. This further improves the implementation accuracy and computational efficiency of multi-dimensional flexible resource scheduling instructions based on the fully embedded algorithm, avoiding non-convergence during numerical calculations.

[0032] This invention, as a multi-variable flexible resource scheduling method and system based on a fully embedded algorithm, can be widely applied in the fields of distribution network loss optimization and resource scheduling. Attached Figure Description

[0033] Figure 1 This is a flowchart of a multi-variable flexible resource scheduling method based on a fully embedded algorithm proposed in this invention;

[0034] Figure 2 This is an example of the implementation of the multi-variable flexible resource scheduling method based on the fully embedded algorithm in this invention.

[0035] Figure 3This invention presents a comparison of the computational performance of a multivariate flexible resource scheduling method based on a fully pure embedding algorithm using variables of order no higher than 1 with that using higher-order variables. Detailed Implementation

[0036] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0037] This invention provides a multi-dimensional flexible resource scheduling method based on a fully embedded algorithm, comprising:

[0038] Obtain the types of diverse flexible resources and the parameters of the power grid to which each type of flexible resource belongs;

[0039] Based on the fully embedded algorithm, a power flow equation containing multiple flexible resources is constructed; wherein, the power flow equation uses the coefficient components of each order term of each variable series in the fully embedded algorithm as power flow constraints.

[0040] The optimization objective is determined based on the characteristic quantities of the optimal operating state that the power dispatching system needs to achieve, which requires the participation of multiple flexible resources. The constraints are determined based on the limiting conditions of multiple flexible operating states and ensuring the safety of system operation. A linear programming model is constructed based on the power flow equation.

[0041] Solve the linear programming model to obtain the coefficients of each term of each variable series. Based on the coefficients of each term of each variable series, generate the running instruction values ​​for multiple flexible resources.

[0042] Among them, a power flow equation incorporating diverse and flexible resources is constructed based on a fully embedded algorithm, including:

[0043] The variables x(s) in the power flow equation containing multiple flexible resources are expressed as the power series expansion of the coefficient x[n] multiplied by the embedding factor α to the power of n, where x[n] and α are complex numbers;

[0044] Expand the equations in the fully embedded format using series expansion with respect to the embedding factor α. By comparing the coefficients of each series, recursively calculate the required variable series expression. Here, when the embedding factor α is 0, it is defined as the initial value of the recursion; when the embedding factor α is 1, it is defined as the target value of the variable series.

[0045] Specifically, based on the coefficients of each order term of the aforementioned variable series, the operating instruction values ​​for the diverse flexible resources are generated, including:

[0046] By adding the coefficients of each order term, the required approximate value of the variable is obtained, thus yielding the operating instruction value for various types of diverse and flexible resources.

[0047] The following will combine Figure 1 This invention provides a preferred embodiment for optimizing the scheduling of various diverse and flexible resources within a power grid. The resource classification includes parameter acquisition based on a front-end acquisition module, equivalence of diverse and flexible resources, construction of a fully embedded algorithm, construction of a linear programming model, and result generation. Figure 1 As shown, the specific steps of the method are as follows:

[0048] Step 1: Obtain the parameters of various diverse and flexible resources and their respective power grids in the corresponding scenarios.

[0049] Specifically, the system's front-end acquisition unit acquires the regulation characteristics and operating output range of diverse flexible resources, the studied power grid topology, line parameters, node voltage limits, the access locations of diverse flexible resources in the power grid topology, and other resource regulation costs and operating limit parameters used to construct the optimization problem. The acquired data can be obtained through pre-entry or real-time acquisition methods.

[0050] Before performing optimized scheduling of diverse and flexible resources, the system obtains data through a front-end acquisition module. Parameters related to resource control characteristics, such as the resource's control range, response speed, response duration, etc., are declared by the resource during registration and confirmed through system testing. The cost value of a resource participating in control is given as a default value during registration and is updated by the resource after each invitation. The power grid topology, line parameters, and node voltage limits are provided by the power grid management system and can be imported into the system via E-file format. The access location of diverse and flexible resources in the power grid topology can be obtained by the power account number provided by the diverse and flexible resources that need to participate in system control, and through the account number and resource access relationship in the power grid marketing system.

[0051] Step 2: Setting up multiple flexible resource equivalent node types based on the fully embedded algorithm.

[0052] Based on the actual operating mode of flexible resources, the resources are equivalent to PQ nodes or PV nodes in the power system;

[0053] Common diverse and flexible resources include user-side generator sets, distributed photovoltaics, distributed wind power, user-side energy storage power stations, power and lighting, and controllable load resources. According to the operation and control mode of the resources, they are divided into constant active and reactive power nodes, i.e., PQ nodes; and constant active power and node voltage amplitude nodes, i.e., PV nodes.

[0054] Specifically, the operating mode of a resource is specified by the higher-level dispatch system and implemented by the local resource control system. For example, a typical load-type flexible resource can be equivalent to a PQ node based on its power consumption. For a power-type flexible resource, if it inputs power into the grid at a fixed power level, such as rooftop photovoltaics or distributed wind power, it is equivalent to a PQ node based on its power generation. If, while inputting power into the grid, the power-type flexible resource also has voltage support capability, such as combined cooling, heating, and power (CCHP) units or grid-connected energy storage, it is equivalent to a PV node based on its power generation and voltage setpoint.

[0055] Step 3: A multi-variable flexible resource algorithm format based on a fully embedded algorithm.

[0056] A fully embedded algorithm format for a power system with diverse and flexible resources is constructed, and the constant terms in each variable series are set as initial values ​​for recursion.

[0057] Specifically, the variables x(s) in the power flow equation, which includes multiple flexible resources, are expressed as power series expansions of coefficients x[n] multiplied by the embedding factor α to the power of n, where x[n] and α are complex numbers, and their forms are as follows:

[0058]

[0059] When α = 0, the coefficient x[0] is a known quantity, and its value needs to be given in the initial iteration. The recursive process using the holomorphic embedding algorithm can be expressed as:

[0060]

[0061] The above formula indicates that the coefficients of higher-order embedding factors can be recursively calculated from the coefficients of lower-order embedding factors, where... Let x(s) be the order of the power series expansion of x(s) when the recursion terminates.

[0062] There is a unique slack node in the system, and its voltage V is defined. s The value is a constant V sl For node i in PQ, its injected apparent power is defined as S. i Node voltage V i The line admittance Y of nodes i and k in PQ ik,r The ground-based self-admittance Y of node i i,sh For PV node j, define its injected active power P. j Injected reactive power Q j Voltage setting value Line admittance Y at nodes j and k jk,r The ground-based self-admittance Y of node j j,sh The superscript '*' of a variable indicates the conjugate of the corresponding variable, and its fully embedded format is:

[0063]

[0064] Expanding the equations in the fully embedded scheme using series expansion with respect to the embedding factor α, and recursively calculating the required variable series expression by comparing the coefficients of each series, we obtain the desired variable series expression. An embedding factor α of 0 is defined as the initial value for the recursion; an embedding factor α of 1 represents the target value of the variable series. For the initial recursion value, the power injection values ​​for the PQ node and PV node are defined as 0, and the voltage values ​​for the PV node and slack node are defined as the slack node's voltage setpoint V. sl .

[0065] like Figure 2 The algorithm execution case shown is a three-node system, namely ballast node 0, PQ node 1, and PV node 2. The three nodes form a ring network through three lines, and the admittance parameters of the three lines are known. It is necessary to use the fully embedded format to construct the relationship between the voltage of the three nodes and the injected power of each node.

[0066] Defining the voltage at the slack node as a constant real number, we can obtain the formula for calculating the first-order term of the variable x(s):

[0067]

[0068] Where G is the real part of the node admittance matrix, B is the imaginary part of the node admittance matrix, the subscript r indicates the real part of the variable, the subscript i indicates the imaginary part of the variable, and the number components of each stage of the PQ node and the PV node are linear functions of the active and reactive power of the PQ node and the active and voltage amplitude of the PV node.

[0069] For higher-order terms of variable x(s), the following recursive formula is used for calculation:

[0070]

[0071] Where q>1 represents the order of the embedding factor, and N represents the number of nodes in the power system. When the recursive algorithm converges to a feasible solution, the coefficients of the m-th order terms obtained by recursive calculation satisfy |x[m]|<|x[m-1]| with the coefficients of their neighboring m-1 order terms. Furthermore, the coefficients of higher-order terms will converge to 0 quickly. Therefore, in the calculation, only the 4th order term of the embedding factor α needs to be selected to meet the calculation accuracy requirements.

[0072] Step 4: Set optimization objectives and variable constraints, and construct a linear model of system power flow constraints using the coefficient components of each order of the series of variables in the fully embedded algorithm. Combine the optimization objectives and constraints to construct a linear programming model for the multivariate flexible resource scheduling problem.

[0073] Specifically, in step 4, based on the multivariate flexible resource scheduling linear programming model using the fully embedded algorithm, the optimization objective and constraints of the optimization algorithm are selected, and combined with the system power flow constraints formed by the fully embedded algorithm, the optimization model is as follows:

[0074]

[0075] stf i (x)≤ε i 1≤i≤M

[0076] Where g(x) is the objective function value, which is set according to the optimization objective; f i (x) is the constraint expression, ε i Here, i represents the constraint limit, M represents the constraint number, and M represents the number of constraints.

[0077] Specifically, step 4 includes:

[0078] Step 4.1, the optimization objectives include the characteristic quantities of the optimal operating state that the power dispatching system needs to achieve, which require the participation of multiple flexible resources, such as: minimizing system network loss, minimizing the cost of dispatching with multiple flexible resources, and achieving the optimal operating state of multiple flexible resources; the example selected is minimizing system network loss.

[0079] Step 4.2, the constraints include limiting the multiple flexible operating states and the constraints to ensure the safe operation of the system, such as the operating limits of multiple flexible resources and node voltage constraints. In the example, the active power limits and voltage limits of node 1 and node 2 are selected.

[0080] For the recursive form of the higher-order terms of variable x(s), there exists a nonlinear term resulting from the multiplication of two variables. The original expression needs to be relaxed using the McCormick envelope method. The method for relaxing nonlinear terms is as follows:

[0081]

[0082]

[0083] Replace the original nonlinear term with the relaxed variable z. This allows for the linearization of the iterative format. In the formula, variable L represents the lower bound of the variable, and variable U represents the lower bound of the variable. The upper and lower bounds of the variable are obtained by the relationship that the coefficient of the variable is not greater than the coefficient of the adjacent lower-order term during the recursive calculation process.

[0084] like Figure 3The comparison shown compares the calculation results using only the first-order component of the power series and considering the 20th-order power series. Given that the voltage of the slack node is 10.6kV, the reactive power of node 1 is 0.426MVar, and the voltage amplitude of node 2 is 10.2kV, the voltage difference between nodes 1 and 2 and the slack node is obtained by changing the injected active power of nodes 1 and 2 in the range of [-2,2]MW.

[0085] It can be seen that the calculation results of the two are similar in trend. The calculation results of the higher-order power series have higher calculation accuracy. With the goal of minimizing network loss, the resource scheduling algorithm shown in this patent can be used to obtain the minimum network loss of the example system when the active power of node 1 is 0.2kW and the active power of node 2 is -0.4kW, which is consistent with the actual simulation situation and verifies the effectiveness of the method shown in this patent.

[0086] Step 5: Generate the results of multi-variable flexible resource scheduling based on the fully embedded algorithm;

[0087] Solve the linear programming model to obtain the coefficients of each term in the series of each variable, generate the operation instruction values ​​of various multi-dimensional flexible resources, and issue the instruction values ​​to achieve closed-loop optimization and control of various multi-dimensional flexible resources.

[0088] Specifically, the linear programming model is solved using a linear programming solver to obtain the coefficients of each variable and each order term. The coefficients of each order term are added together to obtain the approximate values ​​of the required variables, and the operation command values ​​of various types of flexible resources are obtained. The command values ​​are then sent to the flexible resources through a remote scheduling interface to achieve closed-loop control of the flexible resources.

[0089] Based on the optimized scheduling results, it is possible to control various diverse and flexible resources.

[0090] This invention proposes a power flow constraint model based on the fundamental principle of fully embedded algorithms. It selects components of recursive variables at various orders to construct the model, relaxes the nonlinear model using the McCormick envelope method, selects the desired optimization objective, and forms a linear programming model for multivariate flexible resource scheduling. The model is then solved using a linear programming solver. Considering power flow constraints, this approach ensures sufficient solution accuracy while reducing the difficulty of model construction, facilitating the use of mature linear programming solvers, and improving the computational efficiency of the multivariate flexible resource scheduling method.

[0091] This invention also provides a multi-variable flexible resource scheduling system based on a fully embedded algorithm, comprising:

[0092] The front-end data acquisition module is configured to acquire the types of diverse flexible resources and the parameters of the power grid to which each type of flexible resource belongs;

[0093] The fully embedded algorithm construction module is configured to construct a power flow equation containing multiple flexible resources based on the fully embedded algorithm; wherein, the power flow equation uses the coefficient components of each order term of each variable series in the fully embedded algorithm as power flow constraints.

[0094] The linear programming model building and solution module is configured to determine the optimization objective based on the characteristic quantities of the optimal operating state that the power dispatching system requiring the participation of multiple flexible resources needs to achieve, determine the constraints based on the limiting conditions of multiple flexible operating states and ensuring system operation safety, and construct a linear programming model based on the power flow equations; and...

[0095] The result generation module is configured to solve the linear programming model, obtain the coefficients of each order of the series of variables, and generate the running instruction values ​​of the multi-variable flexible resources based on the coefficients of each order of the series of variables.

[0096] Among them, a power flow equation incorporating diverse and flexible resources is constructed based on a fully embedded algorithm, including:

[0097] The variables x(s) in the power flow equation containing multiple flexible resources are expressed as the power series expansion of the coefficient x[n] multiplied by the embedding factor α to the power of n, where x[n] and α are complex numbers;

[0098] Expand the equations in the fully embedded format using series expansion with respect to the embedding factor α. By comparing the coefficients of each series, recursively calculate the required variable series expression. Here, when the embedding factor α is 0, it is defined as the initial value of the recursion; when the embedding factor α is 1, it is defined as the target value of the variable series.

[0099] Specifically, based on the coefficients of each order term of the aforementioned variable series, the operating instruction values ​​for the diverse flexible resources are generated, including:

[0100] By adding the coefficients of each order term, the required approximate value of the variable is obtained, thus yielding the operating instruction value for various types of diverse and flexible resources.

[0101] As an embodiment of the multi-variable flexible resource scheduling system based on a fully embedded algorithm of the present invention, it includes: a pre-collection input module, a resource equivalence module, a fully embedded algorithm construction module, a linear programming model establishment and solution module, and a result generation output module;

[0102] The front-end acquisition and input module is used to acquire parameters of the line model and resource model, including: the regulation characteristics and operating output range of the diverse flexible resources, the power grid topology under study, line parameters, node voltage limits, the access location of the diverse flexible resources in the power grid topology, and other resource regulation costs and operating limit parameters used to construct the optimization problem;

[0103] The resource equivalence module is used to utilize the resource operation and control mode to convert resources into PQ and PV nodes in power flow calculation.

[0104] The fully embedded algorithm building module is used to build a solution model for power system power flow calculation using fully embedded algorithms;

[0105] The linear programming model building and solution module is used to set the objective function and constraints of the optimized scheduling model, linearize the power flow equations represented by the fully embedded algorithm, generate a linear programming model, and solve the linear model.

[0106] The results generation module converts the results of the linear programming model into control instructions for multiple flexible resources, thereby enabling the output of control instructions for multiple flexible resources.

[0107] This invention also provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the multi-variable flexible resource scheduling method based on the fully embedded algorithm as described above.

[0108] This invention also provides a computer-readable storage medium storing a computer program; when the computer program is executed by a processor, it implements the steps of the multi-variable flexible resource scheduling method based on the fully embedded algorithm described above.

[0109] Computer-readable storage media can be tangible devices that hold and store instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0110] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0111] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute 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 a remote computer, the remote computer may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A multi-dimensional flexible resource scheduling method based on a fully pure embedding algorithm, characterized in that: include, Obtain the types of diverse flexible resources and the parameters of the power grid to which each type of flexible resource belongs; Based on the fully embedded algorithm, a power flow equation containing multiple flexible resources is constructed; wherein, the power flow equation uses the coefficient components of each order term of each variable series in the fully embedded algorithm as power flow constraints. The optimization objective is determined based on the characteristic quantities of the optimal operating state that the power dispatching system needs to achieve, which requires the participation of multiple flexible resources. The constraints are determined based on the limiting conditions of multiple flexible operating states and ensuring the safety of system operation. A linear programming model is constructed based on the power flow equation. Solve the linear programming model to obtain the coefficients of each order of the series of variables. Based on the coefficients of each order of the series of variables, generate the running instruction values ​​of the multi-variable flexible resources.

2. The method as described in claim 1, characterized in that: Based on the fully embedded algorithm, a power flow equation incorporating diverse and flexible resources is constructed. include, The variables x(s) in the power flow equation containing multiple flexible resources are expressed as the power series expansion of the coefficient x[n] multiplied by the embedding factor α to the power of n, where x[n] and α are complex numbers; Expand the equations in the fully embedded format using series expansion with respect to the embedding factor α. By comparing the coefficients of each series, recursively calculate the required variable series expression. Here, when the embedding factor α is 0, it is defined as the initial value for recursion. When the embedding factor α is 1, it is defined as the target value of the variable series.

3. The method as described in claim 1, characterized in that: Based on the coefficients of each term in the series of variables, the operating instruction values ​​for the diverse and flexible resources are generated, including: By adding the coefficients of each order term, the required approximate value of the variable is obtained, thus yielding the operating instruction value for various types of diverse and flexible resources.

4. A multi-element flexible resource scheduling system based on a fully embedded algorithm, characterized in that: include, The front-end data acquisition module is configured to acquire the types of diverse flexible resources and the parameters of the power grid to which each type of flexible resource belongs; The fully embedded algorithm construction module is configured to construct a power flow equation containing multiple flexible resources based on the fully embedded algorithm; wherein, the power flow equation uses the coefficient components of each order term of each variable series in the fully embedded algorithm as power flow constraints. The linear programming model building and solution module is configured to determine the optimization objective based on the characteristic quantities of the optimal operating state that a power dispatching system requiring the participation of multiple flexible resources needs to achieve, determine the constraints based on the limiting conditions of multiple flexible operating states and ensuring system operation safety, and construct a linear programming model based on the power flow equations; and ， The result generation module is configured to solve the linear programming model, obtain the coefficients of each order of the series of variables, and generate the running instruction values ​​of the multi-variable flexible resources based on the coefficients of each order of the series of variables.

5. The system as described in claim 4, characterized in that: Based on the fully embedded algorithm, a power flow equation incorporating diverse and flexible resources is constructed. include, The variables x(s) in the power flow equation containing multiple flexible resources are expressed as the power series expansion of the coefficient x[n] multiplied by the embedding factor α to the power of n, where x[n] and α are complex numbers; Expand the equations in the fully embedded format using series expansion with respect to the embedding factor α. By comparing the coefficients of each series, recursively calculate the required variable series expression. Here, when the embedding factor α is 0, it is defined as the initial value for recursion. When the embedding factor α is 1, it is defined as the target value of the variable series.

6. The system as described in claim 4, characterized in that: Based on the coefficients of each term in the series of variables, the operating instruction values ​​for the diverse and flexible resources are generated, including: By adding the coefficients of each order term, the required approximate value of the variable is obtained, thus yielding the operating instruction value for various types of diverse and flexible resources.

7. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the multi-variable flexible resource scheduling method based on the fully embedded algorithm as described in any one of claims 1 to 3.

8. A computer-readable storage medium storing a computer program; characterized in that: When the computer program is executed by the processor, it implements the steps of the multi-variable flexible resource scheduling method based on the fully embedded algorithm as described in any one of claims 1 to 3.

Images