Wind power system optimization operation method based on cloud edge cooperation and related device

Abstract

The invention discloses a wind power electric power system optimization operation method based on cloud edge collaboration and a related device, and belongs to the technical field of electric power system operation scheduling. The method comprises the following steps of: establishing an upper cloud regulation and control model by taking the minimum total operation cost in a day-ahead scheduling operation period as an objective function and taking power balance constraint, transmission capacity constraint, thermal power generating unit output constraint and energy storage constraint as constraint conditions; establishing a lower-layer side node model by taking the lowest wind curtailment penalty cost as an objective function and taking the output constraint of the wind turbine generator as a constraint condition; and solving the upper-layer cloud regulation and control model and the lower-layer edge node model to obtain a cloud-edge collaborative double-layer distributed optimization operation scheme. According to the scheduling method provided by the invention, the uncertainty of wind power generation can be effectively handled, the coping cost of the system is reduced, and the economical efficiency is improved; according to the invention, collaborative optimization of wind power output of edge nodes and charging and discharging of energy storage equipment can be realized, economical efficiency of operation of an interconnection system is realized, and privacy of data transmission can be ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation and dispatching, and relates to a wind power system optimization operation method based on cloud-edge collaboration and related devices. Background Art

[0002] In order to cope with the uncertainty of wind power output, academia and industry have explored various optimization methods. Among them, robust optimization methods have attracted much attention because they can effectively deal with uncertainty problems. Robust optimization constructs an optimization model containing uncertain parameters and designs corresponding solution algorithms so that the optimization scheme can maintain good performance within a certain range of uncertain parameters. In the description of wind power output, the robust optimization method can consider the various possibilities of wind speed changes, thereby formulating a more robust power generation plan. In addition to the robust optimization method, the participation of energy storage technology is also an important means to smooth wind power output and improve the stability of the power system. The energy storage system can store electricity when wind power output is excessive and release electricity when wind power output is insufficient, thereby achieving effective regulation of wind power output. This regulation effect not only helps to balance the supply and demand relationship of the power system, but also plays a role in peak load reduction and valley filling for the load of the power grid, that is, reducing the load demand during peak hours and filling the power supply shortage during valley hours.

[0003] However, in the existing research on optimal dispatching of wind power and energy storage, most of them still adopt centralized dispatching. Although centralized dispatching can achieve global optimization, it has extremely high requirements for communication speed and privacy due to the large scale of dispatching. In large-scale power systems, wind farms and energy storage devices are often distributed in different geographical locations, and the process of data collection, transmission and processing is complex and time-consuming. In addition, data exchange between different regions also involves privacy issues. How to ensure the secure transmission of data and prevent leakage is also an urgent problem to be solved. Traditional centralized dispatching methods seem to be powerless in the face of these problems. On the one hand, the large scale of data leads to long transmission time and low processing efficiency; on the other hand, there is no privacy between regions, which makes data exchange have security risks. Therefore, it is imperative to find a more efficient, secure and flexible dispatching method. Summary of the invention

[0004] The purpose of the present invention is to provide a wind power system optimization operation method and related devices based on cloud-edge collaboration, so as to solve the technical problems that the scheduling methods in the prior art are difficult to cope with the uncertainty of wind power generation and the privacy of data transmission is difficult to ensure.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for optimizing operation of a wind power system based on cloud-edge collaboration, comprising the following steps:

[0007] Taking the minimum total operating cost within the day-ahead dispatching operation cycle as the objective function, and taking the power balance constraint, transmission capacity constraint, thermal power unit output constraint and energy storage constraint as the constraint conditions, an upper-layer cloud control model is established.

[0008] Taking the minimum penalty cost of wind abandonment as the objective function and the output constraint of wind turbines as the constraint condition, the lower layer edge node model is established;

[0009] The upper-layer cloud control model and the lower-layer edge node model are solved to obtain a two-layer distributed optimization operation solution for cloud-edge collaboration.

[0010] Furthermore, in the step of establishing the upper cloud control model with the minimum total operating cost within the day-ahead scheduling operation cycle as the objective function and the power balance constraint, transmission capacity constraint, thermal power unit output constraint and energy storage constraint as the constraint conditions, the expression of the objective function is:

[0011] min F=min(F1+F2)

[0012]

[0013]

[0014] In the formula, F is the total operating cost during the operation cycle; F1 is the power generation cost of the power system; T is the dispatching cycle, and N is the number of thermal power units; is the output of thermal power unit i at time t; a i is the quadratic term of the coal consumption coefficient; b i is the first-order term of the coal consumption coefficient; c i is the constant term of the coal consumption coefficient; P w is the actual output of wind power; c w is the cost coefficient of unit wind power output; P s is the charging or discharging power of energy storage; c s is the cost coefficient of energy storage unit power; F2 is the cloud-edge collaborative computing cost; C1 is the computing cost coefficient; λ i is the number of calculation cycles; K j is the data processing capacity; W j The time required to transmit one bit of data; f j Allocate a quantity for computing resources.

[0015] Furthermore, the expression of the power balance constraint is:

[0016] ∑P i +∑P w +∑|P s|=∑P L1

[0017] Where P w is the actual output of wind power; P s is the charging or discharging power of energy storage; P i Provide power for thermal power units;

[0018] The expression of the transmission capacity constraint is:

[0019]

[0020] Where, L n is the power flow transmitted on the line; is the upper limit of the transmission flow;

[0021] The expression of the output constraint of the thermal power unit is:

[0022]

[0023] Where P i max is the upper limit of the output of thermal power units; P i min It is the lower limit of thermal power unit output;

[0024] The expression of the energy storage constraint is:

[0025]

[0026]

[0027] S min ≤S t ≤S max

[0028] In the formula, S t is the state of charge of the energy storage battery at time t; η ch , η dis are the charging and discharging efficiency of the battery at time t; S max , S min are the maximum and minimum values ​​of the battery energy storage charge state; E max Indicates the maximum amount of energy stored in the battery; They are all variables ranging from 0 to 1, representing the charging and discharging status of the battery energy storage at time t. 1 represents charging, and 0 represents discharging.

[0029] Furthermore, the expression of the upper cloud control model is:

[0030]

[0031] In the formula, x represents the output power of the conventional unit; y is an uncertain variable, representing the output power of the wind turbine unit; Indicates the allowed output range of wind power; represents the wind power prediction interval, and A, B, and D are constant coefficient matrices.

[0032] Furthermore, in the step of establishing the lower layer edge node model with the lowest wind abandonment penalty cost as the objective function and the wind turbine output constraint as the constraint condition, the expression of the objective function is:

[0033]

[0034] Where, F3 is the penalty cost for wind curtailment; C2 and C3 are penalty coefficients respectively; P w The upper limit of wind power output; It is the lower limit of wind power output.

[0035] Furthermore, the expression of the wind turbine output constraint is:

[0036]

[0037] Where P w The upper limit of wind power output; It is the lower limit of wind power output.

[0038] Furthermore, the step of solving the upper-layer cloud control model and the lower-layer edge node model to obtain a cloud-edge collaborative two-layer distributed optimization operation solution specifically includes:

[0039] Based on the duality theory, the double-layer model of the upper cloud control model is transformed into a single-layer model for solution;

[0040] The lower layer edge node model is transformed into a linear model using the binary expansion method;

[0041] Perform multiple iterations until the model convergence criteria are met and output a two-layer distributed optimization operation plan for cloud-edge collaboration. Otherwise,

[0042]

[0043] Wherein, k1, k2 and k3 are all preset positive real numbers;

[0044] Enter the next iteration;

[0045] The model convergence criterion is:

[0046]

[0047] In the formula, P w The upper limit of wind power output; It is the lower limit of wind power output.

[0048] In a second aspect, the invention provides a wind power system optimization operation system based on cloud-edge collaboration, comprising:

[0049] The upper cloud control module is used to establish an upper cloud control model with the minimum total operating cost within the day-ahead scheduling operation cycle as the objective function and the power balance constraint, transmission capacity constraint, thermal power unit output constraint and energy storage constraint as the constraint conditions;

[0050] The lower edge node control module is used to establish the lower edge node model with the minimum wind abandonment penalty cost as the objective function and the wind turbine output constraint as the constraint condition;

[0051] The optimal solution solving module is used to solve the upper-layer cloud control model and the lower-layer edge node model to obtain a two-layer distributed optimization operation solution for cloud-edge collaboration.

[0052] In a third aspect, the invention provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0053] In a fourth aspect, the invention provides a computer-readable storage medium storing a computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0054] Furthermore, the objective function expression of the model in the upper cloud control module is:

[0055] min F=min(F1+F2)

[0056]

[0057]

[0058] The expression of the power balance constraint is:

[0059] ∑P i +∑P w +∑|P s |=ΣP L1

[0060] The expression of the transmission capacity constraint is:

[0061]

[0062] The expression of the output constraint of the thermal power unit is:

[0063]

[0064] The expression of the energy storage constraint is:

[0065]

[0066]

[0067] S min ≤S t ≤S max

[0068] Where: L n is the power flow transmitted on the line; is the upper limit of the transmission flow; u i is the start and stop status of the thermal power unit; P i max and P i min are the upper and lower limits of the thermal power unit output respectively; S t is the state of charge of the energy storage battery at time t; η ch , η dis are the charging and discharging efficiency of the battery at time t; S max , S min are the maximum and minimum values ​​of the battery energy storage charge state; E max Indicates the maximum amount of energy stored in the battery; They are all 0-1 variables, representing the charging and discharging status of the battery energy storage at time t. 1 means charging, and 0 means discharging.

[0069] Furthermore, the expression of the model objective function in the lower edge node control module is:

[0070]

[0071] The expression of the wind turbine output constraint is:

[0072]

[0073] Furthermore, the optimal solution solving module transforms the double-layer model of the upper cloud control model into a single-layer model for solving based on the duality theory;

[0074] The lower layer edge node model is transformed into a linear model using the binary expansion method;

[0075] Perform multiple iterations until the model convergence criteria are met and output a two-layer distributed optimization operation plan for cloud-edge collaboration. Otherwise,

[0076]

[0077] Wherein, k1, k2 and k3 are all preset positive real numbers;

[0078] Enter the next iteration;

[0079] The model convergence criterion is:

[0080]

[0081] Where P w The upper limit of wind power output; It is the lower limit of wind power output.

[0082] Compared with the prior art, the present invention has the following beneficial effects:

[0083] The present invention discloses a method and related devices for optimizing the operation of a wind power system based on cloud-edge collaboration. By establishing an upper-layer cloud control model with the minimum total operating cost within the day-ahead scheduling operation cycle as the objective function, it is possible to comprehensively consider the optimal configuration of various resources in the power system, thereby minimizing the global cost. This helps power companies reduce operating costs and improve economic benefits while ensuring the quality of power supply. The lower-layer edge node model takes the lowest wind abandonment penalty cost as the objective function and considers the constraints on the output of wind turbines. The output of wind power can be predicted and controlled more accurately, reducing the phenomenon of wind abandonment caused by insufficient or excessive wind power. The present invention obtains a two-layer distributed optimization operation scheme for cloud-edge collaboration by solving the upper-layer cloud control model and the lower-layer edge node model. This layered optimization method not only takes into account the global optimization goals, but also takes into account the local actual needs, and realizes the coordinated operation of various parts of the power system. The scheduling method proposed in the present invention can effectively deal with the uncertainty of wind power generation, reduce the cost of system response, and improve economic efficiency; it can achieve the coordinated optimization of wind power output of edge nodes and charging and discharging of energy storage equipment, realize the economy of interconnected system operation, effectively reduce the scale of solving optimization scheduling problems, and ensure the privacy of data transmission. Cloud-edge collaborative optimization can independently send the optimization results of adjacent areas to the cloud master station. Because it is parallel computing, it can also effectively reduce the solution time. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0085] Figure 1 is a flow chart of the method of the present invention;

[0086] Figure 2is a schematic diagram of the system of the present invention;

[0087] Figure 3 A schematic diagram of a two-layer distributed optimization based on cloud-edge collaboration according to an embodiment of the present invention;

[0088] Figure 4 It is a schematic diagram of the computer device structure of the present invention. DETAILED DESCRIPTION

[0089] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0090] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0091] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0092] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0093] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0094] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0095] The present invention is further described in detail below in conjunction with the accompanying drawings:

[0096] See also Figure 1 The embodiment of the present invention discloses a method for optimizing the operation of a wind power system based on cloud-edge collaboration, and establishes a two-layer distributed optimization model of a wind power system based on cloud-edge collaboration. The scheduling goal of the "cloud" node control layer is to minimize the operating cost of the power system from the perspective of the overall economy and safety of the power system operation. The "edge" node layer starts from the economy and safety of the node area, and mainly considers smoothing the change in the power of the node through the charging and discharging of energy storage equipment, with the goal of minimizing the wind abandonment rate, and is constrained by the output of new energy power stations and the charging and discharging of energy storage equipment. The cloud side can be considered as the dispatch of a provincial power grid, such as Beijing's dispatch; the edge side can be considered as the dispatch of energy storage in a certain district of Beijing. The city dispatch and the dispatch coordinate with each other to achieve the ultimate goal of optimizing economic operation.

[0097] The specific steps include:

[0098] S2.1, upper cloud control model

[0099] S2.1.1, Objective Function

[0100] The goal is to minimize the total operating cost within the day-ahead dispatching operation cycle, which consists of two parts: the power generation cost of the power system and the cloud-edge collaborative computing cost:

[0101] min F=min(F1+F2) (1)

[0102] (1) Power generation cost of the power system:

[0103]

[0104] (2) Cloud-edge collaborative computing costs

[0105]

[0106] Where: F is the total operating cost during the operation cycle; F1 is the power generation cost of the power system; T is the dispatch cycle, and N is the number of thermal power units; is the output of thermal power unit i at time t; a i 、b i 、c i are the quadratic term, linear term and constant term of the coal consumption coefficient; P w is the actual output of wind power; c w is the cost coefficient of unit wind power output; P s is the charging or discharging power of energy storage; c s is the cost coefficient of energy storage unit power; F2 is the cloud-edge collaborative computing cost; C1 is the computing cost coefficient; λ i is the number of calculation cycles; K j is the data processing capacity; W j The time required to transmit one bit of data; f j Allocate a quantity for computing resources.

[0107] S2.1.2, Constraints

[0108] The expression of power balance constraint is:

[0109] ∑P i +∑P w +∑|P s |=∑P L1 (4)

[0110] The expression of transmission capacity constraint is:

[0111]

[0112] The expression of thermal power unit output constraint is:

[0113] u i P i min ≤P i ≤u i P i max (6)

[0114] The expression of energy storage constraint is:

[0115]

[0116] S min ≤S t ≤S max (9)

[0117] Where: L n is the power flow transmitted on the line; is the upper limit of the transmission flow; u i is the start and stop status of the thermal power unit; P i max and P imin are the upper and lower limits of the thermal power unit output respectively; S t is the state of charge of the energy storage battery at time t; η ch , η dis are the charging and discharging efficiency of the battery at time t; S max , S min are the maximum and minimum values ​​of the battery energy storage charge state; E max Indicates the maximum amount of energy stored in the battery; They are all 0-1 variables, representing the charging and discharging status of the battery energy storage at time t. 1 means charging, and 0 means discharging.

[0118] S2.1.3, Description of wind power output uncertainty

[0119] The robust optimization operation of the power system with wind power uncertainty can be described by the following model:

[0120]

[0121] In the formula, x represents the output power of the conventional unit; y is an uncertain variable, representing the output power of the wind turbine unit; Indicates the permitted output range of wind power; [P w d ,P w u ] represents the wind power prediction interval, and A, B, and D are constant coefficient matrices.

[0122] Based on the duality theory, the two-layer model can be transformed into a single-layer model:

[0123]

[0124] From equations (10-11), we can see that equation (1-9) can be transformed into a form without wind power output. The added constraints are the same as equation (11), so that the output range of thermal power unit output and wind power unit output can be solved. Then, based on the power balance constraint, the actual wind power output can be obtained.

[0125] S2.2, Lower layer edge node model

[0126] S2.2.1, Objective Function

[0127] The goal of the lower model is to minimize the penalty for wind abandonment, and the objective function is to minimize the penalty cost for wind abandonment:

[0128]

[0129] Where: F3 is the penalty cost for wind curtailment; C2 and C3 are penalty coefficients respectively; P w , are the upper and lower limits of wind power output respectively;

[0130] S2.2.2, Constraints

[0131] Wind turbine output constraints:

[0132]

[0133] S2.2.3, Binary expansion method

[0134] Formula (12) is a bilinear term, which makes the constructed model a non-convex nonlinear model. In this section, the bilinear model is transformed into a linear model using the binary expansion method. The basic principle of the binary expansion method is to approximate continuous decision variables through a set of discrete values. For example, for a continuous decision variable α i (α in ,n=0,l,...,N1,where is a non-negative integer. Assuming that the feasible value of the decision variable αi is in the interval αid, αiu, the specific discrete approximate expression is:

[0135]

[0136] here

[0137] in,

[0138] Where: U and L are the upper and lower limits of the variable; x ki ,y ki is a binary variable; j, ΔP u , ΔP d , P u,U , P d,U is the intermediate variable, N j is the number of wind turbines.

[0139] Multiply the left and right sides of equation (16) by C2 and C3 respectively, and let z ki =C2x ki ,w ki =C3y ki ,but:

[0140]

[0141] In the formula, C4 and C5 are also intermediate variables, so the output range of the wind turbine output can also be solved

[0142] S2.3, Model Convergence Criteria

[0143] when:

[0144] The model iteration ends, otherwise: Enter the next iteration.

[0145] Among them: k1, k2, k3 are positive real numbers.

[0146] The scheduling method proposed in the embodiment of the present invention can effectively deal with the uncertainty of wind power generation, reduce the cost of system response, and improve economic efficiency. It can achieve the coordinated optimization of wind power output of edge nodes and charging and discharging of energy storage equipment, realize the economy of interconnected system operation, effectively reduce the scale of solving optimization scheduling problems, and ensure the privacy of data transmission. Cloud-edge collaborative optimization can independently send the optimization results of adjacent areas to the cloud master station. Because it is parallel computing, it can also effectively reduce the solution time.

[0147] See also Figure 2 The embodiment of the present invention discloses a wind power system optimization operation system based on cloud-edge collaboration, including an upper cloud control module, a lower edge node control module and an optimal solution solving module.

[0148] It should be noted that the upper cloud control module is used to establish an upper cloud control model with the minimum total operating cost within the day-ahead scheduling operation cycle as the objective function and the power balance constraint, transmission capacity constraint, thermal power unit output constraint and energy storage constraint as the constraint conditions;

[0149] The expression of the objective function is:

[0150] minF=min(F1+F2)

[0151]

[0152]

[0153] Where: F is the total operating cost during the operation cycle; F1 is the power generation cost of the power system; T is the dispatch cycle, and N is the number of thermal power units; is the output of thermal power unit i at time t; a i 、b i 、c i are the quadratic term, linear term and constant term of the coal consumption coefficient; P w is the actual output of wind power; c w is the cost coefficient of unit wind power output; P s is the charging or discharging power of energy storage; c s is the cost coefficient of energy storage unit power; F2 is the cloud-edge collaborative computing cost; C1 is the computing cost coefficient; λ i is the number of calculation cycles; K j is the data processing capacity; W jThe time required to transmit one bit of data; f j Allocate a quantity for computing resources.

[0154] The expression of the power balance constraint is:

[0155] ∑P i +∑P w +∑|P s |=∑P L1

[0156] The expression of the transmission capacity constraint is:

[0157]

[0158] The expression of the output constraint of the thermal power unit is:

[0159]

[0160] The expression of the energy storage constraint is:

[0161]

[0162]

[0163] S min ≤S t ≤S max

[0164] Where: L n is the power flow transmitted on the line; is the upper limit of the transmission flow; u i is the start and stop status of the thermal power unit; P i max and P i min are the upper and lower limits of the thermal power unit output respectively; S t is the state of charge of the energy storage battery at time t; η ch , η dis are the charging and discharging efficiency of the battery at time t; S max , S min are the maximum and minimum values ​​of the battery energy storage charge state; E max Indicates the maximum amount of energy stored in the battery; They are all 0-1 variables, representing the charging and discharging status of the battery energy storage at time t. 1 means charging, and 0 means discharging.

[0165] The lower edge node control module is used to establish the lower edge node model with the lowest wind abandonment penalty cost as the objective function and the wind turbine output constraint as the constraint condition;

[0166] The expression of the objective function is:

[0167]

[0168] The expression of the wind turbine output constraint is:

[0169]

[0170] The optimal solution solution module is used to solve the upper cloud control model and the lower edge node model to obtain a two-layer distributed optimization operation solution for cloud-edge collaboration. Specifically, it includes:

[0171] Based on the duality theory, the double-layer model of the upper cloud control model is transformed into a single-layer model for solution; Equation (1-9) can be transformed into a form without wind power output, and the added constraints are the same as those in Equation (11), so that the output range of the thermal power unit output and the wind power unit output can be solved. Then, based on the power balance constraint, the actual wind power output can be obtained.

[0172] The binary expansion method is used to transform the lower layer edge node model into a linear model; Formula (12) is a bilinear term, which makes the constructed model a non-convex nonlinear model. In this section, the bilinear model is transformed into a linear model using the binary expansion method. The basic principle of the binary expansion method is to approximate continuous decision variables through a set of discrete values. For example, for a continuous decision variable α i (α in ,n=0,l,...,N1), where (K1 is a non-negative integer).

[0173] Perform multiple iterations until the model convergence criteria are met and output a two-layer distributed optimization operation plan for cloud-edge collaboration. Otherwise,

[0174]

[0175] Wherein, k1, k2 and k3 are all preset positive real numbers;

[0176] Enter the next iteration;

[0177] The model convergence criterion is:

[0178]

[0179] In the formula, P w The upper limit of wind power output; It is the lower limit of wind power output.

[0180] This embodiment establishes an upper cloud control model with the minimum total operating cost within the day-ahead scheduling operation cycle as the objective function, which can comprehensively consider the optimal configuration of various resources in the power system, thereby minimizing the global cost. This helps power companies reduce operating costs and improve economic benefits while ensuring the quality of power supply. The lower edge node model takes the lowest wind abandonment penalty cost as the objective function and considers the constraints of wind turbine output. The output of wind power can be predicted and controlled more accurately, reducing the abandonment of wind power caused by insufficient or excessive wind power. The present invention obtains a cloud-edge collaborative double-layer distributed optimization operation scheme by solving the upper cloud control model and the lower edge node model. This hierarchical optimization method takes into account both the global optimization goals and the local actual needs, and realizes the coordinated operation of various parts of the power system. The scheduling method proposed in the present invention can effectively cope with the uncertainty of wind power generation, reduce the cost of system response, and improve economic efficiency; it can realize the coordinated optimization of wind power output of edge nodes and charging and discharging of energy storage equipment, realize the economy of interconnected system operation, effectively reduce the scale of solving the optimization scheduling problem, and ensure the privacy of data transmission. Cloud-edge collaborative optimization can send the optimization results of adjacent areas to the cloud master station independently. Because it is parallel computing, it can also effectively reduce the solution time.

[0181] The present invention Figure 3 The power system shown in the figure is analyzed by using yalmip programming in MATLAB 2016 and calling GUROBI optimization software for solution. A regional power system with a load of about 700MW in a province in North China is selected for simulation analysis, and the wind power output of the system is calculated based on the typical wind power scene in autumn in the province. As a toolbox of MATLAB, yalmip provides a powerful modeling and solving interface, which is particularly suitable for dealing with complex optimization problems. In order to efficiently solve the two-layer distributed optimization model, we called the industry-leading GUROBI optimization software. With its powerful solving ability and high efficiency, GUROBI performs well in dealing with large-scale linear and nonlinear optimization problems. Through the analysis of the simulation results, we found that the proposed method can not only effectively reduce the total operating cost of the power system, but also significantly reduce the amount of abandoned wind and improve the utilization rate of wind energy. At the same time, under the premise of ensuring the safe and stable operation of the power system, the method realizes the distributed optimization of cloud-edge collaboration, provides new ideas and solutions for the optimized operation of wind power systems, and verifies the effectiveness and practicality of the wind power system optimization operation method based on cloud-edge collaboration.

[0182] In one embodiment of the present invention, see Figure 4, a computer device is provided, the computer device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a wind power system optimization operation method based on cloud-edge collaboration.

[0183] The present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here may include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for optimizing the operation of a wind power system based on cloud-edge collaboration in the above-mentioned embodiment.

[0184] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0185] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0186] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0187] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the operation of a wind power system based on cloud-edge collaboration, characterized in that: The following steps are involved: Taking the minimum total operating cost within the day-ahead dispatching operation cycle as the objective function, and taking the power balance constraint, transmission capacity constraint, thermal power unit output constraint and energy storage constraint as the constraint conditions, an upper-layer cloud control model is established. Taking the minimum penalty cost of wind abandonment as the objective function and the output constraint of wind turbines as the constraint condition, the lower layer edge node model is established; The upper-layer cloud control model and the lower-layer edge node model are solved to obtain a two-layer distributed optimization operation solution for cloud-edge collaboration.

2. The method for optimizing operation of a wind power system based on cloud-edge collaboration according to claim 1, characterized in that: In the step of establishing the upper cloud control model with the minimum total operating cost within the day-ahead scheduling operation cycle as the objective function and the power balance constraint, transmission capacity constraint, thermal power unit output constraint and energy storage constraint as the constraint conditions, the objective function is expressed as: minF=min(F1+F2) In the formula, F is the total operating cost during the operation cycle; F1 is the power generation cost of the power system; T is the dispatching cycle, and N is the number of thermal power units; is the output of thermal power unit i at time t; a i is the quadratic term of the coal consumption coefficient; b i is the first-order term of the coal consumption coefficient; c i is the constant term of the coal consumption coefficient; P w is the actual output of wind power; c w is the cost coefficient of unit wind power output; P s is the charging or discharging power of energy storage; c s is the cost coefficient of energy storage unit power; F2 is the cloud-edge collaborative computing cost; C1 is the computing cost coefficient; λ i is the number of calculation cycles; K j is the data processing capacity; W j The time required to transmit one bit of data; f j Allocate a quantity for computing resources.

3. The method for optimizing operation of a wind power system based on cloud-edge collaboration according to claim 1, characterized in that: The expression of the power balance constraint is: ∑P i +∑P w +∑|P s |=∑P L1 Where P w is the actual output of wind power; P s is the charging or discharging power of energy storage; P i Provide power for thermal power units; The expression of the transmission capacity constraint is: Where, L n is the power flow transmitted on the line; is the upper limit of the transmission flow; The expression of the output constraint of the thermal power unit is: u i Q i min ≤P i ≤u i Q i max Where P i max The upper limit of the output of thermal power units; P i min It is the lower limit of thermal power unit output; The expression of the energy storage constraint is: S min ≤S t ≤S max In the formula, S t is the state of charge of the energy storage battery at time t; η ch , η dis are the charging and discharging efficiency of the battery at time t; S max , S min are the maximum and minimum values ​​of the battery energy storage charge state; E max Indicates the maximum amount of energy stored in the battery; They are all variables ranging from 0 to 1, representing the charging and discharging status of the battery energy storage at time t. 1 represents charging, and 0 represents discharging.

4. The method for optimizing operation of a wind power system based on cloud-edge collaboration according to claim 1, characterized in that: The expression of the upper cloud control model is: In the formula, x represents the output power of the conventional unit; y is an uncertain variable, representing the output power of the wind turbine unit; Indicates the permitted output range of wind power; [P w d ,P w u ] represents the wind power prediction interval, and A, B, and D are constant coefficient matrices.

5. The method for optimizing operation of a wind power system based on cloud-edge collaboration according to claim 1, characterized in that: In the step of establishing the lower layer edge node model with the lowest wind abandonment penalty cost as the objective function and the wind turbine output constraint as the constraint condition, the expression of the objective function is: Where F3 is the penalty cost for wind curtailment; C2 and C3 are penalty coefficients respectively; P w The upper limit of wind power output; It is the lower limit of wind power output.

6. The method for optimizing operation of a wind power system based on cloud-edge collaboration according to claim 1, characterized in that: The expression of the wind turbine output constraint is: Where P w The upper limit of wind power output; It is the lower limit of wind power output.

7. The method for optimizing operation of a wind power system based on cloud-edge collaboration according to claim 1, characterized in that: The step of solving the upper-layer cloud control model and the lower-layer edge node model to obtain a cloud-edge collaborative two-layer distributed optimization operation solution specifically includes: Based on the duality theory, the double-layer model of the upper cloud control model is transformed into a single-layer model for solution; The lower layer edge node model is transformed into a linear model using the binary expansion method; Perform multiple iterations until the model convergence criteria are met and output a two-layer distributed optimization operation plan for cloud-edge collaboration. Otherwise, Wherein, k1, k2 and k3 are all preset positive real numbers; Enter the next iteration; The model convergence criterion is: Where P w The upper limit of wind power output; It is the lower limit of wind power output.

8. A wind power system optimization operation system based on cloud-edge collaboration, characterized in that: include: The upper cloud control module is used to establish an upper cloud control model with the minimum total operating cost within the day-ahead scheduling operation cycle as the objective function and the power balance constraint, transmission capacity constraint, thermal power unit output constraint and energy storage constraint as the constraint conditions; The lower edge node control module is used to establish the lower edge node model with the minimum wind abandonment penalty cost as the objective function and the wind turbine output constraint as the constraint condition; The optimal solution solving module is used to solve the upper-layer cloud control model and the lower-layer edge node model to obtain a two-layer distributed optimization operation solution for cloud-edge collaboration.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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