Power system optimization scheduling method and device, storage medium and computer equipment

Abstract

According to the power system optimization scheduling method and device, the storage medium and the computer equipment provided by the invention, when power system optimization scheduling is carried out, the initial collaborative optimization model is firstly constructed based on the provincial power grid operation constraint and the local power grid operation constraint; then constructing a feasible region of the adjustable capability of the geographic power grid based on a vertex search method so as to eliminate internal variables of the geographic power grid, and constructing a cost analytic function of the adjustable capability of the geographic power grid by adopting a polynomial chaos expansion method so as to accurately represent the cost of the adjustable capability of the geographic power grid; and then, the feasible region and the cost analytic function are adopted to reconstruct the power grid collaborative optimization model, and a target collaborative optimization model is generated, so that when the target collaborative optimization model is utilized to carry out optimal scheduling on the power system, information exchange only needs to be carried out on a geographic power grid and a provincial power grid scheduling mechanism once, and therefore, the data privacy of the geographic power grid is effectively protected; and meanwhile, the adjustable capability of the flexible load of the ground-level power grid can be effectively utilized, and the consumption of high-proportion new energy can be assisted.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular, to a power system optimal scheduling method, device, storage medium, and computer device. Background Art

[0002] To address the increasingly prominent issues of energy security, environmental pollution, climate change, etc., building a power grid with a high proportion of new energy access is an inevitable way to achieve China's "dual carbon" goal. However, the strong uncertainty of new energy threatens the safe and economic operation of the power grid. High-proportion new energy access necessarily requires the power grid to have sufficient flexible regulation capabilities. In this context, power system operators have begun to pay more attention to the flexibility provided by flexible loads.

[0003] Flexible loads, that is, those loads that can be adjusted according to grid demand or electricity price signals, have great potential for adjustable resources and are of great significance for balancing the instability of new energy generation. However, in practical applications, fully mobilizing flexible loads to participate in the coordinated scheduling of the power grid still faces challenges. For example, the large number, diverse entities, wide distribution of distributed adjustable resources, and the lack of interaction and coordination between independent scheduling and provincial power grids result in poor optimization and efficient utilization of resources. Summary of the Invention

[0004] The purpose of the present application aims to at least solve one of the above technical defects, especially the technical defect that in the prior art, the large number, diverse entities, wide distribution of distributed adjustable resources, and the lack of interaction and coordination between independent scheduling and provincial power grids result in poor optimization and efficient utilization of resources.

[0005] The present application provides a power system optimal scheduling method, and the method includes:

[0006] Construct an initial coordinated optimization model based on the operating constraints of the provincial power grid and the operating constraints of the prefecture-level power grid;

[0007] Construct a feasible region of the adjustable capacity of the prefecture-level power grid based on the vertex search method, and construct a cost analytical function of the adjustable capacity of the prefecture-level power grid using the polynomial chaos expansion method;

[0008] Reconstruct the grid coordinated optimization model using the feasible region and the cost analytical function to obtain a target coordinated optimization model, and use the target coordinated optimization model to optimize the scheduling of the power system.

[0009] Optionally, the constructing an initial coordinated optimization model based on the operating constraints of the provincial power grid and the operating constraints of the prefecture-level power grid includes:

[0010] Construct a provincial power grid function based on the operating constraints of the provincial power grid, and construct a prefecture-level power grid function based on the operating constraints of the prefecture-level power grid;

[0011] Generate an objective function by combining the provincial power grid function and the prefecture-level power grid function, and construct an initial collaborative optimization model based on the objective function.

[0012] Optionally, the objective function of the initial collaborative optimization model includes:

[0013]

[0014] In the formula, represents the objective function of the initial collaborative optimization model; represents the provincial power grid function, where , and respectively represent the quadratic, linear, and constant power generation cost coefficients of gas turbine i in the provincial power grid, and respectively represent the active power output and reactive power output of gas turbine i in the provincial power grid; represents the prefecture-level power grid function, where , and respectively represent the quadratic, linear, and constant power generation cost coefficients of micro gas turbine i in prefecture-level power grid m, , respectively represent the active power output and reactive power output of gas turbine i in prefecture-level power grid m.

[0015] Optionally, constructing the feasible region of the adjustable capacity of the prefecture-level power grid based on the vertex search method includes:

[0016] Search for the initial vertices on the feasible region plane using a non-linear programming problem, and generate an initial polyhedron based on the initial vertices;

[0017] Move each boundary of the initial polyhedron in the normal direction to obtain boundary vertices based on the movement result, and generate a target polyhedron based on the boundary vertices;

[0018] Use the target polyhedron as the new initial polyhedron for iterative operations until the volume difference between two adjacent polyhedrons is less than a preset threshold, and then stop the iteration;

[0019] Mark the target polyhedron generated in the last iteration as the feasible region of the adjustable capacity of the prefecture-level power grid.

[0020] Optionally, the method of constructing the cost analytical function of the adjustable capacity of the prefecture-level power grid using the polynomial chaos expansion method includes:

[0021] Determine the optimal orthogonal polynomial basis function based on the parameter distribution type of the prefecture-level power grid, and solve the expansion term coefficients of the optimal orthogonal polynomial basis function;

[0022] Generate a cost analysis function for the adjustable capacity of the regional power grid based on the expansion term coefficients and the optimal orthogonal polynomial basis functions.

[0023] Optionally, solving for the expansion term coefficients of the optimal orthogonal polynomial basis functions includes:

[0024] Use Latin hypercube sampling to sample a data set from the feasible region of the boundary transmission power.

[0025] Input the data set into the optimal power flow model of the regional power grid to obtain the response result output by the optimal power flow model of the regional power grid.

[0026] Use the least squares regression method to fit the data set and the response data to determine the expansion term coefficients of the optimal orthogonal polynomial basis functions according to the fitting result.

[0027] Optionally, the objective function of the target collaborative optimization model includes:

[0028]

[0029] In the formula, represents the objective function of the initial collaborative optimization model; represents the provincial power grid function, where , and respectively represent the quadratic, linear, and constant power generation cost coefficients of gas turbine i in the provincial power grid; represents the cost analysis function of all adjustable capacities of the regional power grid; and respectively represent the active boundary transmission power and the reactive boundary transmission power of the feasible region m of the regional power grid.

[0030] This application also provides an optimized power system dispatching device, including:

[0031] A model construction module for constructing an initial collaborative optimization model based on the operating constraints of the provincial power grid and the operating constraints of the regional power grid;

[0032] A function construction module for constructing the feasible region of the adjustable capacity of the regional power grid based on the vertex search method and constructing the cost analysis function of the adjustable capacity of the regional power grid by using the polynomial chaos expansion method;

[0033] A model reconstruction module for reconstructing the grid collaborative optimization model by using the feasible region and the cost analysis function to obtain a target collaborative optimization model, and using the target collaborative optimization model to optimize the dispatching of the power system.

[0034] The present application also provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors are caused to execute the steps of the power system optimal scheduling method as described in any one of the above embodiments.

[0035] The present application also provides a computer device, including: one or more processors, and a memory;

[0036] The memory stores computer-readable instructions. When the computer-readable instructions are executed by the one or more processors, the steps of the power system optimal scheduling method as described in any one of the above embodiments are executed.

[0037] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0038] For the power system optimal scheduling method, device, storage medium and computer device provided by the present application, when optimizing the scheduling of the power system, an initial collaborative optimization model can be constructed based on the provincial power grid operation constraints and the prefecture-level power grid operation constraints, so that the power generation plan with the highest cost-benefit under the operation constraints of the entire power system can be sought through this model. Then, a feasible region of the adjustable capacity of the prefecture-level power grid can be constructed based on the vertex search method to eliminate the internal variables of the prefecture-level power grid, and a cost analytical function of the adjustable capacity of the prefecture-level power grid can be constructed by using the polynomial chaos expansion method to accurately represent the cost of the adjustable capacity of the prefecture-level power grid. Next, the present application can reconstruct the power grid collaborative optimization model by using the feasible region and the cost analytical function to obtain a target collaborative optimization model, so that the model can comprehensively capture the operation characteristics and economic characteristics of the prefecture-level power system through the cost analytical function and the feasible region. Therefore, when using the target collaborative optimization model to optimize the scheduling of the power system, only one information exchange is required between the prefecture-level power grid and the provincial power grid dispatching agency, which can effectively protect the data privacy of the prefecture-level power grid, and at the same time can effectively utilize the adjustable capacity of the flexible load of the prefecture-level power grid to help the accommodation of a high proportion of new energy. Description of the Drawings

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0040] Figure 1 It is a schematic flowchart of a power system optimal scheduling method provided by an embodiment of the present application;

[0041] Figure 2A flowchart showing the process of constructing a feasible region provided by an embodiment of the present application;

[0042] Figure 3 A flowchart showing the process of solving the expansion term coefficients of an optimal orthogonal polynomial basis function provided by an embodiment of the present application;

[0043] Figure 4 A structural diagram of an IEEE 30-node provincial power grid system provided by an embodiment of the present application;

[0044] Figure 5 A structural diagram of an IEEE 33-node prefectural power grid system provided by an embodiment of the present application;

[0045] Figure 6 A structural diagram of a power system optimal dispatch device provided by an embodiment of the present application;

[0046] Figure 7 An internal structural diagram of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0047] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0048] Flexible loads, that is, those loads that can be adjusted according to grid demand or electricity price signals, have great potential for adjustable resources and are of great significance for balancing the instability of new energy power generation. However, in practical applications, fully mobilizing flexible loads to participate in the coordinated dispatch of the power grid still faces challenges. For example, the large number of distributed adjustable resources, diverse entities, wide distribution, and lack of interaction and coordination between independent dispatch and the provincial power grid result in poor optimization and efficient utilization of resources.

[0049] Based on this, the present application proposes the following technical solutions. For details, see the following text:

[0050] In one embodiment, as Figure 1 shown, Figure 1 A flowchart showing the process of a power system optimal dispatch method provided by an embodiment of the present application; the present application provides a power system optimal dispatch method, specifically including the following:

[0051] S110: Construct an initial collaborative optimization model based on the operation constraints of the provincial power grid and the operation constraints of the prefectural power grid.

[0052] In this step, when optimizing the dispatching of the power system, an initial collaborative optimization model can be constructed based on the operating constraints of the provincial power grid and the prefecture-level power grid, so as to find the power generation plan with the highest cost-effectiveness under the operating constraints of the entire power system through this model.

[0053] Among them, as a large-scale power transmission and distribution network covering the entire provincial administrative region, the provincial power grid is uniformly dispatched and managed by the national or regional power grid company. Its operating constraints can include aspects such as power flow balance, generator output limit, line transmission capacity, voltage stability, etc., to ensure the safe, stable and efficient operation of the entire power grid. And the prefecture-level power grid, as a power transmission and distribution network covering the prefecture-level administrative region, is usually overall dispatched by the provincial power grid and has independent dispatching capabilities within a certain range. Its operating constraints can include internal power flow constraints, boundary power exchange limits, flexible load dispatching, voltage constraints and local line carrying capacity, etc., aiming to ensure the stability and flexibility of power supply within the region.

[0054] It can be understood that in the optimization dispatching of this application, an initial collaborative optimization model can be used to organically combine the operating constraints of the provincial power grid and the prefecture-level power grid, enabling the two to work together under the constraint conditions, thus avoiding resource waste or system instability caused by uncoordinated dispatching. Specifically speaking, the operating constraints of the provincial power grid mainly focus on large-scale power dispatching, while the operating constraints of the prefecture-level power grid mainly focus on local power balance and the management of flexible loads. Through the initial collaborative optimization model constructed by the two, the coordination between the prefecture-level power grid and the provincial power grid can be achieved through boundary power exchange, and the adjustable capacity of the prefecture-level power grid is equivalently projected into the optimization model of the provincial power grid. Therefore, this model can not only optimize the economic benefits of the entire power grid but also ensure the independence and data privacy of the prefecture-level power grid.

[0055] Specifically, for the operating constraints of the provincial power grid, the line power flow calculation formula can be expressed as follows:

[0056]

[0057]

[0058] In the formula, and respectively represent the active power output and reactive power output of the gas turbine i in the provincial power grid; represents the set of lines in the provincial power grid; and respectively represent the active power and reactive power of the provincial power grid line ij; and respectively represent the conductance and susceptance of the provincial power grid line ij; and respectively represent the voltage amplitude and phase angle at the provincial power grid node i, ; sin and cos represent the sine function and cosine function respectively.

[0059] The active and reactive power balance equation can be expressed as follows:

[0060]

[0061]

[0062] In the formula, represents the set of nodes of the provincial power grid; m is the set of the number of prefecture-level power grids; represents the set of nodes of the gas turbines in the provincial power grid; represents the set of load nodes of the provincial power grid; and respectively represent the active power and reactive power of the load in the provincial power grid; and respectively represent the active boundary transmission power and reactive boundary transmission power between the provincial power grid and the prefecture-level power grid.

[0063] The grid node voltage constraint can be expressed as follows:

[0064]

[0065] In the formula, represents the grid node voltage i; and respectively represent the minimum and maximum values of the node voltage i.

[0066] The generator output constraint can be expressed as follows:

[0067]

[0068] In the formula, represents the active output of generator i; and respectively represent the minimum and maximum values of the active output of generator i; represents the reactive output of generator i; and respectively represent the minimum and maximum values of the reactive output of generator i.

[0069] The transmission power capacity constraint of the provincial power grid line can be expressed as follows:

[0070]

[0071] In the formula, represents the upper limit of the transmission power capacity of line ij.

[0072] For the operation constraints of the prefecture-level power grid, the power flow calculation formula for the m lines of the prefecture-level power grid can be expressed as follows:

[0073]

[0074]

[0075] In the formula, represents the line set of the prefecture-level power grid; and respectively represent the active power and reactive power of the ij line of the prefecture-level power grid; and respectively represent the conductance and susceptance of the ij line of the m line of the prefecture-level power grid; and respectively represent the voltage amplitude and phase angle at the i node of the prefecture-level power grid, ; sin and cos respectively represent the sine function and cosine function.

[0076] The active and reactive power balance equation can be expressed as follows:

[0077]

[0078]

[0079] In the formula, represents the node set of the prefecture-level power grid; represents the node set of the micro gas turbines in the prefecture-level power grid; represents the load node set of the prefecture-level power grid; m represents the number set of the prefecture-level power grids; and respectively represent the active power and reactive power of the m load of the prefecture-level power grid; and are respectively the active boundary transmission power and reactive boundary transmission power between the m of the prefecture-level power grid and the provincial power grid.

[0080] The node voltage constraint of the m of the prefecture-level power grid can be expressed as follows:

[0081]

[0082] In the formula, represents the node voltage i; and respectively represent the minimum and maximum values of the node voltage i.

[0083] The output constraint of the micro gas turbines in the m of the prefecture-level power grid can be expressed as follows:

[0084]

[0085] Wherein, represents the active power output of the micro gas turbine i; and represent the minimum and maximum values of the active power output of the micro gas turbine i respectively; represents the reactive power output of the micro gas turbine i; and represent the minimum and maximum values of the reactive power output of the micro gas turbine i respectively.

[0086] The line transmission power capacity constraint of the prefecture-level power grid m can be expressed as follows:

[0087]

[0088] Wherein, represents the upper limit of the transmission power capacity of the line ij.

[0089] The flexible load operation constraint of the prefecture-level power grid can be expressed as follows:

[0090]

[0091] Wherein, represents the set of flexible loads of the prefecture-level power grid; represents the offset power of the flexible load of the prefecture-level power grid m; represents the offset power of the flexible load of the prefecture-level power grid m; and represent the lower and upper limits of the offset power of the flexible load of the prefecture-level power grid m respectively; and represent the actual scheduled active and reactive power outputs of the flexible load of the prefecture-level power grid m respectively; and represent the initial planned active and reactive power outputs of the flexible load of the prefecture-level power grid m respectively.

[0092] S120: Construct the feasible region of the adjustable capacity of the prefecture-level power grid based on the vertex search method, and construct the cost analytical function of the adjustable capacity of the prefecture-level power grid by using the polynomial chaos expansion method.

[0093] In this embodiment, after the initial collaborative optimization model is constructed through step S110, the computer device can also construct the feasible region of the adjustable capacity of the prefecture-level power grid based on the vertex search method to eliminate the internal variables of the prefecture-level power grid, and construct the cost analytical function of the adjustable capacity of the prefecture-level power grid by using the polynomial chaos expansion method to accurately represent the cost of the adjustable capacity of the prefecture-level power grid.

[0094] It can be understood that the provincial power grid and the prefecture-level power grid belong to different dispatching centers. The topological structure, power source configuration, and load data in the network are all private information, and the two can only interact through boundary transmission variables. Therefore, this application can use the vertex search theory method to eliminate the internal variables of the prefecture-level power grid, equivalently represent the prefecture-level power grid model through boundary variables, and construct a feasible region for the adjustable capacity of the prefecture-level power grid considering flexible loads. This feasible region refers to the set of all power grid operating states that satisfy physical, economic, and operating constraints, and in this application, it is mainly composed of the variable combinations of the active transmission power and reactive transmission power of the boundary nodes. In mathematics, the definition of the feasible region is expressed as:

[0095]

[0096] In the formula, represents the feasible region; and respectively represent the active transmission power and reactive transmission power of the boundary nodes of the prefecture-level power grid; represents the solution problem of the operating constraints of the prefecture-level power grid.

[0097] In addition, in order to accurately represent the cost function of the adjustable capacity of the prefecture-level power grid considering flexible loads, this application can also construct a cost analytical function considering the coupling relationship between active power and reactive power within the feasible region based on polynomial chaos expansion to accurately represent the cost of the adjustable capacity of the prefecture-level power grid.

[0098] S130: Reconstruct the power grid collaborative optimization model using the feasible region and the cost analytical function to obtain the target collaborative optimization model, and use the target collaborative optimization model to optimize the dispatching of the power system.

[0099] In this step, after constructing the feasible region and the cost analytical function of the adjustable capacity of the prefecture-level power grid through step S120, the computer device can reconstruct the power grid collaborative optimization model using the feasible region and the cost analytical function to obtain the target collaborative optimization model, enabling the model to comprehensively capture the operating characteristics and economic characteristics of the prefecture-level power system through the cost analytical function and the feasible region. Therefore, when using this target collaborative optimization model to optimize the dispatching of the power system, only one information exchange is required between the prefecture-level power grid and the provincial power grid dispatching agency, which can effectively protect the data privacy of the prefecture-level power grid and at the same time effectively utilize the adjustable capacity of the flexible loads in the prefecture-level power grid to assist in the accommodation of a high proportion of new energy.

[0100] It can be understood that the obtained target collaborative optimization model after reconstruction can accurately describe the operating cost of the prefecture-level power grid by using the cost analysis function. At the same time, combined with the construction method of the feasible region, the flexibility boundary of the prefecture-level power grid is mapped to the provincial power grid dispatching system, enabling it to comprehensively capture the operating characteristics and economic characteristics of the prefecture-level power grid. Therefore, when the provincial power grid conducts optimal dispatching, it can accurately grasp the adjustable capacity of the prefecture-level power grid without directly obtaining the detailed operating data of the prefecture-level power grid, thereby ensuring the accuracy of the optimal calculation while effectively protecting the data information security of the prefecture-level power grid. In the actual optimal dispatching process, the introduction of this target collaborative optimization model enables the information exchange between the prefecture-level power grid and the provincial power grid dispatching agency to be carried out only once, completing the optimization of the overall power grid dispatching plan, and avoiding the computational complexity and communication burden brought by repeated iterative calculations and multiple data transmissions in the traditional method.

[0101] In addition, through the target collaborative optimization model, the adjustable capacity of flexible loads in the prefecture-level power grid is fully utilized, enabling it to participate in dispatching optimization more flexibly, enhancing the overall regulation capacity of the power grid, and improving the operating efficiency and stability of the system. Especially in the context of high-proportion new energy access, this model can improve the consumption capacity of new energy, alleviate the power grid dispatching challenges brought by the volatility of new energy, and thus achieve a more economical, efficient and sustainable power grid operation.

[0102] In the above embodiment, when optimizing the dispatching of the power system, an initial collaborative optimization model can be constructed based on the operating constraints of the provincial power grid and the operating constraints of the prefecture-level power grid, so as to seek the power generation plan with the highest cost-benefit under the operating constraints of the entire power system through this model. Then, a feasible region of the adjustable capacity of the prefecture-level power grid can be constructed based on the vertex search method to eliminate the internal variables of the prefecture-level power grid, and the polynomial chaos expansion method can be used to construct the cost analysis function of the adjustable capacity of the prefecture-level power grid to accurately represent the cost of the adjustable capacity of the prefecture-level power grid. Next, this application can reconstruct the power grid collaborative optimization model by using the feasible region and the cost analysis function to obtain the target collaborative optimization model, enabling this model to comprehensively capture the operating characteristics and economic characteristics of the prefecture-level power system through the cost analysis function and the feasible region. Therefore, when using this target collaborative optimization model to optimize the dispatching of the power system, only one information exchange is required between the prefecture-level power grid and the provincial power grid dispatching agency, which can effectively protect the data privacy of the prefecture-level power grid and at the same time effectively utilize the adjustable capacity of the flexible loads in the prefecture-level power grid to assist in the consumption of high-proportion new energy.

[0103] In one embodiment, the process of constructing the initial collaborative optimization model based on the operating constraints of the provincial power grid and the operating constraints of the prefecture-level power grid in step S110 may include:

[0104] S111: Construct a provincial power grid function based on the operating constraints of the provincial power grid, and construct a prefecture-level power grid function based on the operating constraints of the prefecture-level power grid.

[0105] S112: Generate an objective function according to the combination of the provincial power grid function and the prefecture-level power grid function, and construct an initial collaborative optimization model based on the objective function.

[0106] In this embodiment, after obtaining the operating constraints of the provincial power grid and the prefecture-level power grid, the computer device can optimize and solve to obtain the provincial power grid function in combination with the operating constraints of the provincial power grid, and optimize and solve to obtain the prefecture-level power grid function in combination with the operating constraints of the prefecture-level power grid. Then, generate an objective function according to the combination of the provincial power grid function and the prefecture-level power grid function, and construct an initial collaborative optimization model based on the objective function.

[0107] It can be understood that the provincial power grid function can accurately represent the operating characteristics, resource allocation methods, and dispatching objectives of the provincial power grid, thus providing a basis for optimization calculations; while the prefecture-level power grid function can completely depict the flexibility, economy, and adjustable capacity of the prefecture-level power grid. Therefore, the computer device can reasonably combine the provincial power grid function and the prefecture-level power grid function to establish a global objective function, and then generate the corresponding initial collaborative optimization model. In this way, the model can not only comprehensively describe the operating state of the entire power grid system, but also achieve the optimal dispatching of power resources on the premise of meeting the operating constraints of each level of the power grid to match the operating requirements of different levels of the power grid.

[0108] In one embodiment, the objective function of the initial collaborative optimization model in step S112 may include:

[0109]

[0110] In the formula, represents the objective function of the initial collaborative optimization model; represents the provincial power grid function, where , and respectively represent the quadratic, linear, and constant power generation cost coefficients of the gas turbine i in the provincial power grid, and respectively represent the active power output and reactive power output of the gas turbine i in the provincial power grid; represents the prefecture-level power grid function, where , and respectively represent the quadratic, linear, and constant power generation cost coefficients of the micro gas turbine i in the prefecture-level power grid m, , respectively represent the active power output and reactive power output of the gas turbine i in the prefecture-level power grid m.

[0111] In this embodiment, the objective function of the initial collaborative optimization model consists of two parts: the operating cost of the provincial power grid and the operating cost of the prefecture-level power grid. It is mainly used to minimize the operating cost of the entire power system while ensuring the operating safety and economy of the provincial power grid and the prefecture-level power grid. Therefore, when optimizing and solving this objective function, the power generation cost of the provincial power grid, the operating characteristics of the prefecture-level power grid, and the boundary power transmission between the two can be considered simultaneously, so that the overall operating economy and safety of the power grid reach the optimal state.

[0112] In one embodiment, as Figure 2 shown, Figure 2 is a schematic flow chart of a feasible region construction process provided by an embodiment of the present application; Figure 2 In, the process of constructing the feasible region of the adjustable capacity of the prefecture-level power grid based on the vertex search method in step S120 may include:

[0113] S121: Use a nonlinear programming problem to search for the initial vertices on the feasible region plane and generate an initial polyhedron according to the initial vertices.

[0114] S122: Move each boundary of the initial polyhedron in the normal direction to obtain boundary vertices according to the movement result, and generate a target polyhedron according to the boundary vertices.

[0115] S123: Use the target polyhedron as the new initial polyhedron for iterative operations until the volume difference between two adjacent polyhedra is less than a preset threshold, and then stop the iteration.

[0116] S124: Mark the target polyhedron generated in the last iteration as the feasible region of the adjustable capacity of the prefecture-level power grid.

[0117] In this embodiment, when the computer device constructs the feasible region of the adjustable capacity of the prefecture-level power grid, it can first use a nonlinear programming problem to search for the initial vertices on the feasible region plane and generate an initial polyhedron according to the initial vertices. Then, move each boundary of the initial polyhedron in the normal direction to obtain boundary vertices according to the movement result, and generate a target polyhedron according to the boundary vertices. Next, the target polyhedron can be used as the new initial polyhedron for iterative operations until the volume difference between two adjacent polyhedra is less than a preset threshold, and then stop the iteration. Finally, mark the target polyhedron generated in the last iteration as the feasible region of the adjustable capacity of the prefecture-level power grid.

[0118] Among them, the nonlinear programming problem (Nonlinear Programming, NLP) refers to an optimization problem in which at least one of the objective function or the constraint conditions is nonlinear.

[0119] It can be understood that the feasible region of the adjustable capacity of the prefecture-level power grid can be approximately characterized as a polyhedron. By solving a set of programming problems, its boundary vertices can be found, and connecting the boundary vertices can characterize its feasible region. The basic idea of the vertex search theory is to search for new vertices by translating each plane of the initial polyhedron along its normal direction. Then, a polyhedron with higher accuracy is constructed from the newly obtained vertices and the previous vertices, and it is repeatedly expanded until the preset accuracy is met.

[0120] Specifically, the construction process of the feasible region of the adjustable capacity of the prefecture-level power grid is as follows:

[0121] 1) Step 1: Construct the initial polyhedron

[0122] A nonlinear programming problem is used to search for the initial vertices on the plane of the feasible region. The nonlinear programming problem is expressed as follows:

[0123]

[0124]

[0125] In the formula, first let = 1 and respectively, and then solve the problem of the operation constraints of the prefecture-level power grid to obtain 4 groups of optimal solutions . The initial polyhedron consists of a set of vertex sets . Through two vertices, the j-th boundary of the polyhedron can be characterized as , where and can be calculated through two vertices. Therefore, the initial polyhedron can be expressed as , , .

[0126] 2) Step 2: Iteratively update the polyhedron

[0127] For the k-th iteration, each boundary of the polyhedron needs to be moved along the normal direction to find more boundary vertices of the polyhedron. For the j-th boundary of the polyhedron , the following optimization problem is used to find the new boundary nodes:

[0128]

[0129] In the formula, is the j-th row of the matrix , which can be calculated through two vertices of the polyhedron . Assume that is the optimal solution of the optimization problem, and it is the new vertex obtained by translating the j-th boundary outward. Together with the previously searched boundary points, a new set of boundary points is established The new polyhedron is composed of an updated set of points . Through each iteration, more boundary vertices can be searched, making the constructed polyhedron more accurate.

[0130] 3) Step 3: Iterative convergence criterion

[0131] The difference between the new polyhedron and the previous one can be reflected by the change in volume. When the volume difference between two adjacent polyhedra is less than the preset threshold, the iterative calculation can be terminated; otherwise, continue to execute Step 2 to find new vertices. Therefore, the volume change difference of the feasible region is used as the termination condition of the algorithm.

[0132]

[0133] In the formula, represents the threshold for terminating the iterative calculation, represents the volume of the polyhedron at the k-th iteration.

[0134] For example, when is taken as 0.05, after 21 iterations of iterative solution, a boundary point set composed of 12 vertices is obtained, which are respectively ={(-7.8, 1.9), (0, 11), (1.3, -9.6), (11, 0), (77, -7.8), (7.8, 7.8), (-7.6, 7.9), (-7.5, 7.9), (-3.5, -8.5), (10.2, -4.2), (4.2, 10.2)}. The active power unit of the above point set is MW, and the reactive power unit is MVar.

[0135] In one embodiment, the process of constructing the cost analytical function of the adjustable capacity of the prefecture-level power grid by using the polynomial chaos expansion method in step S120 may include:

[0136] S125: Determine the optimal orthogonal polynomial basis function based on the parameter distribution type of the prefecture-level power grid, and solve the expansion term coefficients of the optimal orthogonal polynomial basis function;

[0137] S126: Generate the cost analytical function of the adjustable capacity of the prefecture-level power grid according to the expansion term coefficients and the optimal orthogonal polynomial basis function.

[0138] In this embodiment, when the computer device constructs the cost analytical function of the adjustable capacity of the prefecture-level power grid, it can first determine the optimal orthogonal polynomial basis function based on the distribution type of the parameters, and solve the expansion term coefficients of the optimal orthogonal polynomial basis function, and then generate the cost analytical function of the adjustable capacity of the prefecture-level power grid according to the expansion term coefficients and the optimal orthogonal polynomial basis function.

[0139] Specifically, when selecting the optimal orthogonal polynomial basis function based on the distribution type of parameters, for a set of independent variables , a function f(x) corresponding to the system output response Y can be constructed. In the optimal power flow problem of the prefecture-level power grid, the input variable x is selected as the boundary transmission power and , and the output response Y is the minimum operating cost of the system. Based on the polynomial chaos expansion theory, the complex functional relationship between the minimum operating cost Y and the boundary transmission power x can be analyzed into a representation formula, which is specifically expressed as follows:

[0140]

[0141] In the formula, represents the coefficient of the expansion term; represents the multivariate polynomial basis function; represents the number of expansion terms; r and n respectively represent the highest order of the multivariate polynomial basis function and the number of input variables.

[0142] The multivariate polynomial basis function can be represented by the tensor product of the univariate polynomial basis:

[0143]

[0144] In the formula, represents the -th order polynomial basis function of the variable . The selection of the univariate input variable polynomial basis is based on its probability distribution, and this corresponding relationship is also called the Askey orthogonal polynomial system. The boundary transmission power of the prefecture-level power grid follows a uniform distribution, and the Legendre orthogonal polynomial is selected as the basis function.

[0145] Furthermore, after selecting the optimal orthogonal polynomial basis function according to the probability distribution type of the input random variables, the computer device can continue to solve the coefficients of its expansion terms, and then complete the construction of the cost analysis function according to the solved coefficients of the expansion terms.

[0146] In one embodiment, as Figure 3 shown, Figure 3 is a schematic flowchart of the process for solving the coefficients of the expansion terms of an optimal orthogonal polynomial basis function provided by an embodiment of the present application; Figure 3 In

[0147] S1251: Sampling is performed using Latin hypercube sampling to obtain a data set from the feasible region of the boundary transmission power.

[0148] S1252: The data set is input into the optimal power flow model of the prefecture-level power grid to obtain the response result output by the optimal power flow model of the prefecture-level power grid.

[0149] S1253: The least squares regression method is used to fit the dataset and the response data to determine the expansion term coefficients of the optimal orthogonal polynomial basis function according to the fitting result.

[0150] In this embodiment, when solving the expansion term coefficients, the computer device can use Latin hypercube sampling to sample a dataset from the feasible region of the boundary transmission power, then input the dataset into the optimal power flow model of the prefecture-level power grid to obtain the response result output by the optimal power flow model of the prefecture-level power grid. Then, the computer device can use the least squares regression method to fit the dataset and the response data to determine the expansion term coefficients of the optimal orthogonal polynomial basis function according to the fitting result.

[0151] Among them, Latin Hypercube Sampling (LHS) is a random sampling method in statistics for efficient uniform sampling in a multi-dimensional parameter space. Its core idea is to divide the value range of each variable into intervals with equal probabilities, and then randomly select a sample point within each interval while ensuring that the sample points in all dimensions are evenly distributed. Through Latin hypercube sampling, this application can effectively reduce the redundancy of sampling, improve the sampling efficiency, and ensure that the entire input space can still be covered with a relatively small sample size.

[0152] Specifically, this application can use Latin hypercube sampling within the feasible region of the boundary transmission power to generate a set of datasets with a quantity of N. These datasets are introduced into the optimal power flow model of the prefecture-level power grid for calculation to obtain the corresponding responses. Specifically, for the input and output of a set of datasets , where , . Based on the least squares regression method, the expansion term coefficients are calculated by minimizing the sum of squared residuals .

[0153]

[0154] Among them, ; , , .

[0155] Taking the derivative of the above formula, the coefficients of the polynomial chaos expansion terms can be solved from the minimum operating cost of the prefecture-level power grid:

[0156]

[0157] After calculating the coefficients of the expansion terms, the analytical cost function of the prefecture-level power grid can be obtained. The analytical cost of the prefecture-level power grid can effectively estimate the minimum operating cost of the prefecture-level power grid only from the boundary transmission power, which is specifically expressed as follows:

[0158]

[0159] The coefficients of the expansion terms calculated by the least squares method are respectively = 225.11, = -96.87, = -1.18, = 3.77, = 1.13, = -0.02. Therefore, the analytical function of the cost of the prefecture-level power grid considering flexible loads is:

[0160]

[0161] In one embodiment, the objective function of the target collaborative optimization model in step S130 may include:

[0162]

[0163] In the formula, represents the objective function of the initial collaborative optimization model; represents the provincial power grid function, where , and respectively represent the quadratic, linear, and constant power generation cost coefficients of the gas turbine i in the provincial power grid; represents the cost analytical function of the adjustable capabilities of all prefecture-level power grids; and respectively represent the active boundary transmission power and the reactive boundary transmission power of the feasible region m of the prefecture-level power grid.

[0164] Furthermore, the operation constraints of the target collaborative optimization model specifically include the following:

[0165]

[0166]

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173] Among them, represents the feasible region of the prefecture-level power grid m, and respectively represent the coefficients of the feasible region, and respectively represent the boundary transmission power of the prefecture-level power grid m.

[0174] To better explain the power system optimal scheduling method of this application, the following will use Figure 4 and Figure 5 to further illustrate. Schematically, as Figure 4 and Figure 5 shown, Figure 4 is a schematic structural diagram of an IEEE 30-node provincial power grid system provided by an embodiment of this application, Figure 5 is a schematic structural diagram of an IEEE 33-node prefecture-level power grid system provided by an embodiment of this application.

[0175] Specifically, based on Figure 4 and Figure 5 the provincial power grid system and the prefecture-level power grid system are used for experiments. The results of the collaborative scheduling (centralized optimization) of the original provincial power grid and the prefecture-level power grid are used as reference values, and the accuracy of the feasible region of the prefecture-level power grid considering flexible loads and the cost analysis function of this application method are compared. The comparison results are shown in the following table:

[0176]

[0177] It can be seen from the experimental results that this application can equivalently project the operating characteristics and economic characteristics of the prefecture-level power grid considering flexible loads to the system boundary nodes, construct the feasible region and cost function of the adjustable capacity of the prefecture-level power grid to participate in the optimal operation of the provincial power grid system, and can effectively utilize the adjustable capacity of the flexible loads of the prefecture-level power grid on the premise of effectively protecting the data privacy of the prefecture-level power grid.

[0178] Next, the power system optimal scheduling device provided by the embodiment of this application will be described. The power system optimal scheduling device described below can be correspondingly referred to the power system optimal scheduling method described above.

[0179] In one embodiment, as Figure 6 shown, Figure 6 is a schematic structural diagram of a power system optimal scheduling device provided by an embodiment of this application; this application also provides a power system optimal scheduling device, including a model construction module 210, a function construction module 220, and a model reconstruction module 230, which specifically include the following:

[0180] The model construction module 210 is configured to construct an initial collaborative optimization model based on the operating constraints of the provincial power grid and the operating constraints of the prefecture-level power grid.

[0181] The function construction module 220 is configured to construct a feasible region of the adjustable capacity of the prefecture-level power grid based on the vertex search method, and construct a cost analytical function of the adjustable capacity of the prefecture-level power grid by using the polynomial chaos expansion method.

[0182] The model reconstruction module 230 is configured to reconstruct the power grid collaborative optimization model by using the feasible region and the cost analytical function to obtain a target collaborative optimization model, and use the target collaborative optimization model to optimize the dispatching of the power system.

[0183] In the above embodiments, when optimizing the dispatching of the power system, an initial collaborative optimization model can be constructed first based on the operating constraints of the provincial power grid and the operating constraints of the prefecture-level power grid, so that the power generation plan with the highest cost-benefit under the operating constraints of the entire power system can be sought through this model. Then, a feasible region of the adjustable capacity of the prefecture-level power grid can be constructed based on the vertex search method to eliminate the internal variables of the prefecture-level power grid, and a cost analytical function of the adjustable capacity of the prefecture-level power grid can be constructed by using the polynomial chaos expansion method to accurately represent the cost of the adjustable capacity of the prefecture-level power grid. Next, the present application can reconstruct the power grid collaborative optimization model by using the feasible region and the cost analytical function to obtain a target collaborative optimization model, so that the model can comprehensively capture the operating characteristics and economic characteristics of the prefecture-level power system through the cost analytical function and the feasible region. Therefore, when using the target collaborative optimization model to optimize the dispatching of the power system, only one information exchange is required between the prefecture-level power grid and the provincial power grid dispatching agency, which can effectively protect the data privacy of the prefecture-level power grid, and at the same time can effectively utilize the adjustable capacity of the flexible load of the prefecture-level power grid to assist in the accommodation of a high proportion of new energy.

[0184] In one embodiment, the model construction module 210 may include:

[0185] The function construction sub-model is configured to construct a provincial power grid function based on the operating constraints of the provincial power grid, and construct a prefecture-level power grid function based on the operating constraints of the prefecture-level power grid.

[0186] The model generation sub-model is configured to generate an objective function according to the combination of the provincial power grid function and the prefecture-level power grid function, and construct an initial collaborative optimization model according to the objective function.

[0187] In one embodiment, the model generation sub-model may include:

[0188]

[0189] In the formula, represents the objective function of the initial collaborative optimization model; Represents the provincial power grid function, where , and respectively represent the quadratic, linear, and constant power generation cost coefficients of the gas turbine i in the provincial power grid, and respectively represent the active power output and reactive power output of the gas turbine i in the provincial power grid; Represents the prefecture-level power grid function, where , and respectively represent the quadratic, linear, and constant power generation cost coefficients of the micro gas turbine i in the prefecture-level power grid m, , respectively represent the active power output and reactive power output of the gas turbine i in the prefecture-level power grid m.

[0190] In one embodiment, the function construction module 220 may include:

[0191] A polyhedron generation sub-model, which is used to search for the initial vertices on the feasible region plane by using a non-linear programming problem and generate an initial polyhedron according to the initial vertices.

[0192] A polyhedron update sub-model, which is used to move each boundary of the initial polyhedron in the normal direction to obtain boundary vertices according to the movement result and generate a target polyhedron according to the boundary vertices.

[0193] A polyhedron iteration sub-model, which is used to perform iterative operations with the target polyhedron as the new initial polyhedron until the volume difference between two adjacent polyhedrons is less than a preset threshold and then stop the iteration.

[0194] A feasible region marking sub-model, which is used to mark the target polyhedron generated by the last iteration as the feasible region of the adjustable capacity of the prefecture-level power grid.

[0195] In one embodiment, the function construction module 220 may further include:

[0196] A coefficient solving sub-model, which is used to determine the optimal orthogonal polynomial basis function based on the parameter distribution type of the prefecture-level power grid and solve the expansion term coefficients of the optimal orthogonal polynomial basis function;

[0197] A function generation sub-model, which is used to generate a cost analysis function of the adjustable capacity of the prefecture-level power grid according to the expansion term coefficients and the optimal orthogonal polynomial basis function.

[0198] In one embodiment, the coefficient solving sub-model may include:

[0199] A data sampling unit, which is used to sample a data set from the feasible region located at the boundary transmission power by using Latin hypercube sampling.

[0200] A model output unit, configured to input a data set into an optimal power flow model of a prefecture-level power grid, and obtain a response result output by the optimal power flow model of the prefecture-level power grid.

[0201] A data fitting unit, configured to fit the data set and the response data by using a least squares regression method, so as to determine expansion term coefficients of an optimal orthogonal polynomial basis function according to the fitting result.

[0202] In one embodiment, the model reconstruction module 230 may include:

[0203]

[0204] In the formula, represents the objective function of the initial collaborative optimization model; represents a provincial power grid function, where , and respectively represent the quadratic, linear, and constant power generation cost coefficients of the gas turbine i in the provincial power grid; represents a cost analysis function of the adjustable capabilities of all prefecture-level power grids; and respectively represent the active boundary transmission power and the reactive boundary transmission power of the feasible region m of the prefecture-level power grid.

[0205] In one embodiment, the present application further provides a storage medium, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors are caused to execute the steps of the power system optimal scheduling method according to any one of the above embodiments.

[0206] In one embodiment, the present application further provides a computer device, in which computer-readable instructions are stored. When the computer-readable instructions are executed by one or more processors, the one or more processors are caused to execute the steps of the power system optimal scheduling method according to any one of the above embodiments.

[0207] Schematically, as Figure 7 shown, Figure 7 is an internal structural diagram of a computer device provided by an embodiment of the present application. The computer device 300 may be provided as a server. Referring to Figure 7 , the computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by a memory 301 for storing instructions executable by the processing component 302, such as application programs. The application programs stored in the memory 301 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 302 is configured to execute instructions to perform the power system optimal scheduling method of any of the above embodiments.

[0208] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server TM, Mac OS XTM, Unix TM, Linux TM, Free BSDTM, or the like.

[0209] Those skilled in the art can understand that Figure 7 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component layout.

[0210] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0211] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0212] The above description of the disclosed embodiments enables those skilled in the art to implement or use this application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing and dispatching a power system, characterized in that: The method comprises: Construct an initial collaborative optimization model based on provincial power grid operation constraints and prefecture-level power grid operation constraints; A feasible domain of adjustable capacity of a prefectural power grid is constructed based on a vertex search method, and a cost analytical function of the adjustable capacity of the prefectural power grid is constructed using a polynomial chaos expansion method; The feasible domain and the cost analysis function are used to reconstruct the power grid collaborative optimization model to obtain a target collaborative optimization model, and the target collaborative optimization model is used to optimize the scheduling of the power system.

2. The power system optimization dispatching method according to claim 1, characterized in that: The initial collaborative optimization model is constructed based on the provincial power grid operation constraints and the prefectural power grid operation constraints, including: Constructing a provincial power grid function based on provincial power grid operation constraints, and constructing a prefectural power grid function based on prefectural power grid operation constraints; An objective function is generated based on the combination of the provincial power grid function and the prefectural power grid function, and an initial collaborative optimization model is constructed based on the objective function.

3. The power system optimization dispatching method according to claim 2, characterized in that: The objective function of the initial collaborative optimization model includes: In the formula, represents the objective function of the initial collaborative optimization model; represents the provincial power grid function, where , and denote the quadratic, linear and constant generation cost coefficients of gas turbine i in the provincial power grid, respectively, and They represent the active output and reactive output of gas turbine i in the provincial power grid respectively; represents the local power grid function, where , and denote the quadratic, linear and constant power generation cost coefficients of micro gas turbine i in local grid m, respectively. , They represent the active output and reactive output of gas turbine i in the local power grid m respectively.

4. The power system optimization dispatching method according to claim 1, characterized in that: The feasible domain of adjustable capacity of local power grid is constructed based on vertex search method, including: Using a nonlinear programming problem to search for initial vertices on a feasible domain plane, and generating an initial polyhedron according to the initial vertices; Move each boundary of the initial polyhedron along the normal direction to obtain boundary vertices according to the movement result, and generate a target polyhedron according to the boundary vertices; The target polyhedron is used as a new initial polyhedron for iterative operation until the volume difference between two adjacent polyhedrons is less than a preset threshold and the iteration is stopped; The target polyhedron generated in the last iteration is marked as the feasible domain of the adjustable capacity of the local power grid.

5. The power system optimization dispatching method according to claim 1, characterized in that: The polynomial chaos expansion method is used to construct the cost analysis function of the adjustable capacity of the local power grid, including: Determining an optimal orthogonal polynomial basis function based on the parameter distribution type of the local power grid, and solving the expansion term coefficients of the optimal orthogonal polynomial basis function; A cost analysis function of the adjustable capacity of the local power grid is generated according to the expansion term coefficients and the optimal orthogonal polynomial basis function.

6. The power system optimization dispatching method according to claim 5, characterized in that: The step of solving the expansion term coefficients of the optimal orthogonal polynomial basis function comprises: Latin hypercube sampling is used to obtain the data set from the feasible area located at the boundary transmission power; Inputting the data set into an optimal power flow model of a prefecture-level power grid to obtain a response result output by the optimal power flow model of the prefecture-level power grid; The data set and the response data are fitted using a least squares regression method to determine expansion term coefficients of the optimal orthogonal polynomial basis function according to the fitting result.

7. The power system optimization dispatching method according to claim 1, characterized in that: The objective function of the target collaborative optimization model includes: In the formula, represents the objective function of the initial collaborative optimization model; represents the provincial power grid function, where , and denote the quadratic, linear and constant generation cost coefficients of gas turbine i in the provincial power grid, respectively; A cost-analytic function representing the adjustable capacity of all local power grids; and They respectively represent the active boundary transmission power and reactive boundary transmission power of the feasible domain m of the local power grid.

8. A power system optimization dispatching device, characterized in that: include: A model building module, used to build an initial collaborative optimization model based on provincial power grid operation constraints and prefecture-level power grid operation constraints; A function construction module, used to construct a feasible domain of adjustable capacity of a prefecture-level power grid based on a vertex search method, and to construct a cost analysis function of the adjustable capacity of the prefecture-level power grid using a polynomial chaos expansion method; The model reconstruction module is used to reconstruct the power grid collaborative optimization model using the feasible domain and the cost analysis function to obtain a target collaborative optimization model, and use the target collaborative optimization model to optimize the power system.

9. A storage medium, characterized in that: The storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the power system optimization dispatching method as described in any one of claims 1 to 7.

10. A computer device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the power system optimization dispatching method as described in any one of claims 1 to 7 are performed.

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