Wind-solar-storage complementary power distribution network comprehensive optimization method based on various regulation and control devices

Abstract

The invention, which relates to the technical field of the power grid, discloses a comprehensive optimization method for a wind-solar-storage complementary power distribution network based on various regulation and control devices, comprising the following steps: converting solar energy by using a photovoltaic power generation system; based on the load time sequence prediction model, establishing a wind-solar-storage complementary power distribution network dispatching optimization mathematical model; and verifying the dispatching optimization mathematical model of the wind-solar-storage complementary power distribution network by using the node system of the regulation and control equipment. According to the method, the complexity of actual power grid operation, including factors of different types of loads, market price fluctuation, environmental policies and the like, is considered, and a comprehensive optimization scheme is provided for efficient integration of renewable energy sources and reliable operation of the power grid.

Description

Technical Field

[0001] The present invention relates to the technical field of power grids, and in particular to a comprehensive optimization method for a wind-solar-storage complementary power distribution network based on multiple control devices. Background Art

[0002] In the context of global energy transformation, vigorously developing and making full use of renewable energy has become the key to building a new power system and a new distribution network. In this process, the penetration rate of new energy equipment such as wind, solar and energy storage in the distribution network continues to increase. How to maximize the adjustable potential of various resources and achieve high-quality power supply and economic operation of the distribution network is particularly important. At present, although the wind, solar and energy storage synergistic interaction technology has been studied and applied to a certain extent in the power system, most of the research focuses on the optimal scheduling of a single energy source or a simple combination of multiple energy sources. These methods still have obvious limitations in dealing with complex distribution network conditions and comprehensively optimizing a wide variety of new energy equipment with a large number of types. In addition, with the development of the power market and the advancement of power grid technology, the requirements for distribution network optimization models are also increasing, and a more comprehensive and systematic approach is needed to cope with the complexity of the power grid environment. Summary of the invention

[0003] In view of the problems existing in the existing comprehensive optimization method of wind-solar-storage complementary distribution network based on multiple control devices, the present invention is proposed.

[0004] Therefore, in response to the existing technology which focuses on the optimal scheduling of a single energy source or a simple combination of multiple energy sources, the present invention adopts a comprehensive optimization method of a wind, solar and storage complementary distribution network based on multiple control devices to solve the problem.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In the first aspect, an embodiment of the present invention provides a comprehensive optimization method for a wind-solar-storage complementary distribution network based on multiple control devices, which includes converting wind energy through a wind power generation model, calculating the power generated by a wind turbine, introducing a solar power generation equipment model to capture solar energy, and converting solar energy using a photovoltaic power generation system;

[0007] Build a load time series prediction model based on wind and photovoltaic power generation systems, and establish a distribution network dispatch optimization mathematical model for wind, solar and energy storage complementarity based on the load time series prediction model;

[0008] The node system of the control equipment is used to verify the mathematical model of distribution network dispatch optimization.

[0009] As a preferred solution of the comprehensive optimization method of the wind-solar-storage complementary distribution network based on multiple control devices described in the present invention, the wind energy conversion through the wind power generation model includes the blades of the wind turbine rotating under the action of the wind, and the mechanical energy is transmitted to the wind generator through the shaft, and the power generated by the wind generator is calculated. The specific calculation formula is:

[0010]

[0011] in, Indicates the power value of the wind turbine, A i represents the coverage area of ​​the fan blades, Indicates wind speed.

[0012] As a preferred solution of the comprehensive optimization method of the wind-solar-storage complementary distribution network based on multiple control devices described in the present invention, wherein: the conversion of solar energy includes collecting solar photovoltaic panels, capturing sunlight and converting it into direct current, connecting the photovoltaic panels to a direct current combiner box, and feeding the direct current in the combiner box into a DC-AC inverter, and calculating the output power of the photovoltaic power generation system. The specific calculation formula is:

[0013]

[0014] in, Indicates the effective power of the equipment, η PV Indicates the conversion efficiency, A i represents the sunlight exposure area, S i,t Indicates the radiation intensity.

[0015] As a preferred solution of the comprehensive optimization method of the wind-solar-storage complementary distribution network based on multiple control devices described in the present invention, the photovoltaic power generation system includes using a radial network structure to simulate the distribution network layout and establish the objective function of the distribution network. The specific calculation formula is:

[0016]

[0017] Among them, P loss Represents the power loss of the power grid, G ij represents the connection conductivity between nodes i and j, θ ij Represents the voltage phase angle difference, V i and V j represents the voltage corresponding to nodes i and j, and N represents the number of nodes;

[0018] The power balance constraints in the network are calculated based on the established objective function. The specific calculation formula is:

[0019]

[0020] Among them, δ ij Indicates the phase angle between nodes, B ij Indicates the admittance value, P Gi and Q Gi Represent the active power and reactive power of the generator node, P Li and Q Li Represents the active power and reactive power of the load node, Q Ci Represents the reactive power compensation capacity of the node, Q i represents the reactive power flowing from node i, P i Indicates active power.

[0021] As a preferred solution of the comprehensive optimization method of the wind-solar-storage complementary distribution network based on multiple control devices described in the present invention, the simulation of the distribution network layout includes analyzing the economic losses when the distribution network equipment fails, and the analysis includes establishing the optimization objective function in the power balance constraint condition. The specific calculation formula established is:

[0022]

[0023] Among them, f D1 represents the operating cost function, ΔP represents the difference in active power loss, Δu Ti Indicates OLTC action, Δu Ci represents capacitor switching, Δu Bi Column vector representing the change in energy storage device, C T Indicates OLTC,C S represents a synchronous phase-shifting camera, C B represents the unit adjustment cost of the energy storage device, n T Indicates load voltage regulator switch, n Q represents the generator, n B Indicates the adjustment of the energy storage device;

[0024] When the various control devices are in operation, the variable constraints in the operation process are dynamically planned to determine the inequality constraints. The specific formula of the inequality constraints is:

[0025]

[0026] Among them, C Ct Indicates the number of capacitor banks, Q gt represents the reactive power output of the generator node, B t Represents the output of the energy storage device. The upper and lower lines represent the upper and lower limits respectively. V Gt represents the reactive power in the generator, and h represents the constraint function.

[0027] As a preferred solution of the comprehensive optimization method of the wind-solar-storage complementary distribution network based on multiple control devices described in the present invention, wherein: the construction of the load time series prediction model includes adding a transformer SVC to the optimized objective function, and reconstructing the optimized objective function. The calculation formula of the second optimized objective function is:

[0028]

[0029] Among them, C SVC represents the adjustment cost of SVC, Δu i,SVC A column vector representing the operation, nSVC Indicates the number of SVC device adjustments.

[0030] As a preferred solution of the comprehensive optimization method of the wind-solar-storage complementary distribution network based on multiple control devices described in the present invention, wherein: the verification of the distribution network dispatch optimization mathematical model includes using a second-order cone programming relaxation algorithm to convert the nonlinear programming problem in the power system into a second-order cone programming problem, and using a CPLEX solver to solve the second-order cone programming problem. The specific calculation formula of the second-order cone programming relaxation algorithm is:

[0031]

[0032] Where t represents the last number of the sequence and I represents the identity matrix.

[0033] In a second aspect, an embodiment of the present invention provides a comprehensive optimization system for a wind-solar-storage complementary power distribution network based on multiple control devices, which includes: a calculation module, which converts wind energy through a wind power generation model and calculates the power generated by a wind turbine;

[0034] A construction module, which constructs a load time series forecasting model based on wind power and photovoltaic power generation systems;

[0035] The optimization module verifies the mathematical model of distribution network dispatch optimization by using the node system of the control equipment.

[0036] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned method for comprehensive optimization of a wind-solar-storage complementary distribution network based on multiple control devices.

[0037] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned method for comprehensive optimization of a wind-solar-storage complementary distribution network based on multiple control devices is implemented.

[0038] The beneficial effects of the present invention are as follows: the present invention constructs a multi-energy complementary distribution network model containing regulating devices such as wind energy, solar energy and energy storage equipment, and optimizes the power generation and charging power of various types of equipment on the basis of comprehensive consideration of factors such as grid stability, economy and environmental impact. The present invention will also consider the complexity of actual grid operation, including different types of loads, market price fluctuations and environmental policies, and provide a comprehensive optimization solution for the efficient integration of renewable energy and the reliable operation of the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0040] Figure 1 An optimization flow chart of a comprehensive optimization method for a wind-solar-storage complementary distribution network based on multiple control devices provided by an embodiment of the present invention.

[0041] Figure 2 A schematic diagram of a wind power generation equipment model for a comprehensive optimization method of a wind-solar-storage complementary distribution network based on multiple control devices provided by an embodiment of the present invention.

[0042] Figure 3 A schematic diagram of a bus distribution node system for a comprehensive optimization method for a wind-solar-storage complementary distribution network based on multiple control devices provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0046] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0047] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0048] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, 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 also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication 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.

[0049] Example 1

[0050] Reference Figure 1 to Figure 3 , which is the first embodiment of the present invention, and provides a comprehensive optimization method for a wind-solar-storage complementary distribution network based on multiple control devices, including:

[0051] S1: Convert wind energy through the wind power generation model, calculate the power generated by the wind turbine, introduce the solar power generation equipment model to capture solar energy, and use the photovoltaic power generation system to convert solar energy.

[0052] Among them, the conversion of wind energy through the wind power generation model includes the rotation of the wind turbine blades under the action of wind, the transmission of mechanical energy to the wind turbine through the shaft, the electromagnetic induction of relative motion between the rotor and stator of the wind turbine, and the calculation of the power of the wind turbine. The specific calculation formula is:

[0053]

[0054] in, Indicates the power value of the wind turbine, A i represents the coverage area of ​​the fan blades, Indicates wind speed.

[0055] Further, when the wind turbine receives the kinetic energy of the wind, its blades begin to rotate. Assuming the wind speed is 10 meters per second, the blade coverage area of ​​the wind turbine is 10 square meters, the air density is 1.225 kilograms per cubic meter, and the conversion efficiency coefficient is assumed to be 0.35, in this case, the wind turbine can convert the wind energy under these conditions into about 10687.5 kilowatts of electrical energy. The comparison of wind energy conversion parameters is shown in Table 1 below:

[0056] Table 1 Comparison of wind energy conversion parameters

[0057]

[0058] This table shows how wind turbines convert wind energy into electrical energy under different parameter conditions. When the wind speed is 10 meters per second, the blade coverage area is 10 square meters, the air density is 1.225 kilograms per cubic meter, and the conversion efficiency coefficient is 0.35, the wind turbine can generate about 10,687.5 kilowatts of electricity.

[0059] S1.1: The conversion of solar energy includes the collection of photovoltaic panels of solar energy, capturing sunlight and converting it into direct current, connecting the photovoltaic panels to a direct current combiner box, feeding the direct current in the combiner box into a DC-AC inverter, and calculating the output power of the photovoltaic power generation system. The specific calculation formula is:

[0060]

[0061] in, Indicates the effective power of the device during this time period, η PV Indicates the conversion efficiency, A i represents the sunlight exposure area, S i,t Indicates the radiation intensity.

[0062] Further, the process of converting solar energy into electrical energy involves the working principle of the photovoltaic power generation system. Photovoltaic panels are installed in appropriate sunlight exposure areas, such as a sunlight intensity of 1,000 watts per square meter. When sunlight shines on the photovoltaic panels, the photovoltaic panels convert solar energy into direct current. The current is connected to a direct current combiner box, which gathers the current and transmits it to a DC-AC inverter. The inverter converts direct current into alternating current and provides it to the power grid or for use in an independent power system. At this time, the system can effectively convert solar energy into usable electrical energy based on sunlight conditions and equipment efficiency.

[0063] S2: Construct a load time series prediction model based on wind and photovoltaic power generation systems, and establish a wind-solar-storage complementary distribution network dispatch optimization mathematical model based on the load time series prediction model.

[0064] Among them, the photovoltaic power generation system includes using a radial network structure to simulate the layout of the distribution network and establish the objective function of the distribution network. The specific calculation formula is:

[0065]

[0066] Among them, P loss Represents the power loss of the power grid, G ij represents the connection conductivity between nodes i and j, θ ij Represents the voltage phase angle difference, V i and V j represents the voltage corresponding to nodes i and j, and N represents the number of nodes;

[0067] Based on the established objective function, the power balance constraints in the network are calculated. The specific calculation formula is:

[0068]

[0069] Among them, δ ij Indicates the phase angle between nodes, B ij Indicates the admittance value, P Gi and Q Gi Represent the active power and reactive power of the generator node, P Li and Q Li Represents the active power and reactive power of the load node, Q Ci Represents the reactive power compensation capacity of the node, Q i represents the reactive power flowing from node i, P i Indicates active power.

[0070] S2.1: The simulation of the distribution network layout includes analyzing the economic losses when the distribution network equipment fails, and then establishing the optimization objective function in the power balance constraint condition. The specific calculation formula is:

[0071]

[0072] Among them, f D1 represents the operating cost function, ΔP represents the difference in active power loss, Δu Ti Indicates OLTC action, Δu Ci represents capacitor switching, Δu Bi Column vector representing the change in energy storage device, C T Indicates OLTC,C S represents a synchronous phase-shifting camera, CB represents the unit adjustment cost of the energy storage device, n T Indicates load voltage regulator switch, n Q represents the generator, n B Indicates the adjustment of the energy storage device;

[0073] During operation, dynamic programming determines the state variable and control variable constraints as inequality constraints. The specific formula of the inequality constraint is:

[0074]

[0075] Among them, C Ct Indicates the number of capacitor banks, Q gt represents the reactive power output of the generator node, B t Represents the output of the energy storage device. The upper and lower lines represent the upper and lower limits respectively. V Gt represents the reactive power in the generator, and h represents the constraint function.

[0076] S2.2: Constructing the load time series prediction model includes adding the transformer SVC to the optimized objective function and reconstructing the optimized objective function. The specific calculation formula is:

[0077]

[0078] Among them, C SVC represents the adjustment cost of SVC, Δu i,SVC Column vector representing the operation, n SVC Indicates the number of SVC device adjustments.

[0079] S3: Use the node system of the control equipment to verify the mathematical model of distribution network dispatch optimization.

[0080] Among them, the verification of the mathematical model for dispatch optimization of wind-solar-storage complementary distribution network includes using the second-order cone programming relaxation algorithm to transform the nonlinear programming problem in the power system into a second-order cone programming problem, using the CPLEX solver to solve the second-order cone programming problem, and using the cone optimization method when optimizing the complementary storage of wind and solar energy. The specific calculation formula of the second-order cone programming is:

[0081]

[0082] Where t represents the last number of the sequence and I represents the identity matrix.

[0083] This embodiment also provides a computer device, which is suitable for the multi-source power grid information fusion method based on the Internet of Things, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the multi-source power grid information fusion method based on the Internet of Things proposed in the above embodiment.

[0084] This embodiment also provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method in any optional implementation of the above embodiment is executed. Wherein, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0085] In summary: The present invention constructs a multi-energy complementary distribution network model containing regulating devices such as wind energy, solar energy and energy storage equipment, and optimizes the power generation and charging power of various types of equipment on the basis of comprehensive consideration of factors such as grid stability, economy and environmental impact. The present invention will also consider the complexity of actual grid operation, including different types of loads, market price fluctuations and environmental policies, and provide a comprehensive optimization solution for the efficient integration of renewable energy and the reliable operation of the grid.

[0086] Example 2

[0087] Reference Figure 1 to Figure 3 , which is the second embodiment of the present invention, provides a comprehensive optimization method for a wind-solar-storage complementary distribution network based on multiple control devices. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through simulation experiments.

[0088] The proposed wind solar energy storage dynamic programming model and algorithm were verified using the IEEE-33 node system. The system integrates wind power, photovoltaic and energy storage equipment to form a complex nonlinear problem, which was solved using the particle swarm algorithm. The experiment was conducted on a laptop equipped with an Intel Core i7-4600M processor, and the PSO iteration was limited to 100 to ensure responsiveness. Table 1 shows the catalog number equipment and parameters, and the comparison of processor restrictions on PSO iterations is shown in Table 2 below:

[0089] Table 2 Comparison of processor restrictions on PSO iterations

[0090] system Processor model PSO Iteration Limit IEEE-33 Node System Intel Core i7-4600M 100 IEEE-33 Node System AMD Ryzen 7 5800X 150 IEEE-33 Node System Intel Core i9-11900K 200 IEEE-33 Node System AMD EPYC 7742 250 IEEE-33 Node System Intel Xeon Platinum 8280 300

[0091] This table shows a more comprehensive comparison of the limitations of PSO iterations under different processor models, which is used to verify the experiment of wind energy solar energy storage dynamic programming model and algorithm. The comparison between the present invention and the prior art is shown in Table 3 below:

[0092] Table 3 Comparison between the present invention and the prior art

[0093] Comparison Items The present invention Prior art Data integration capabilities Integrated wind power, photovoltaic and energy storage equipment Most integrate only some energy types System complexity Complex nonlinear problems Simpler linear or partially nonlinear problems Algorithm selection Particle Swarm Optimization Single optimization algorithm Processor performance requirements Suitable for all processor models Specific processor requirements Iterative optimization speed Quick response to needs Slow response time Energy efficiency Improving the utilization of solar and wind energy Low energy efficiency

[0094] This table shows the advantages of the present invention over the prior art in multiple aspects, including data integration capability, system complexity, algorithm selection, processor performance requirements, iterative optimization speed, and energy efficiency.

[0095] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. Comprehensive optimization method of wind-solar-storage complementary distribution network based on multiple control devices, Features: include, The wind power generation model is used to convert wind energy, calculate the power generated by the wind turbine, introduce the solar power generation equipment model to capture solar energy, and use the photovoltaic power generation system to convert solar energy; Build a load time series prediction model based on wind and photovoltaic power generation systems, and establish a distribution network dispatch optimization mathematical model for wind, solar and energy storage complementarity based on the load time series prediction model; The node system of the control equipment is used to verify the mathematical model of distribution network dispatch optimization.

2. The comprehensive optimization method for wind-solar-storage complementary distribution network based on multiple control devices as claimed in claim 1, Features: The wind power generation model converts wind energy by rotating the blades of the wind turbine under the action of wind, and the mechanical energy is transmitted to the wind turbine through the shaft. The power generated by the wind turbine is calculated. The specific calculation formula is: in, Indicates the power value of the wind turbine, A i represents the coverage area of ​​the fan blades, Indicates wind speed.

3. The comprehensive optimization method for wind-solar-storage complementary distribution network based on multiple control devices as claimed in claim 2, Features: The conversion of solar energy includes collecting solar photovoltaic panels, capturing sunlight and converting it into direct current, connecting the photovoltaic panels to a direct current combiner box, feeding the direct current in the combiner box into a DC-AC inverter, and calculating the output power of the photovoltaic power generation system. The specific calculation formula is: in, Indicates the effective power of the equipment, η PV Indicates the conversion efficiency, A i represents the sunlight exposure area, S i,t Indicates the radiation intensity.

4. The comprehensive optimization method for wind-solar-storage complementary distribution network based on multiple control devices as claimed in claim 3, Features: The photovoltaic power generation system includes using a radial network structure to simulate the layout of the distribution network and establish the objective function of the distribution network. The specific calculation formula is: Among them, P loss Represents the power loss of the power grid, G ij represents the connection conductivity between nodes i and j, θ ij Represents the voltage phase angle difference, V i and V j represents the voltage corresponding to nodes i and j, and N represents the number of nodes; The power balance constraints in the network are calculated based on the established objective function. The specific calculation formula is: Among them, δ ij Indicates the phase angle between nodes, B ij Indicates the admittance value, P Gi and Q Gi Represent the active power and reactive power of the generator node, P Li and Q Li Represents the active power and reactive power of the load node, Q Ci Represents the reactive power compensation capacity of the node, Q i represents the reactive power flowing from node i, P i Indicates active power.

5. The comprehensive optimization method for wind-solar-storage complementary distribution network based on multiple control devices as claimed in claim 4, Features: The simulation of the distribution network layout includes analyzing the economic losses when the distribution network equipment fails. The analysis includes establishing an optimization objective function in the power balance constraint condition. The specific calculation formula established is: Among them, f D1 represents the operating cost function, ΔP represents the difference in active power loss, Δu Ti Indicates OLTC action, Δu Ci represents capacitor switching, Δu Bi Column vector representing the change in energy storage device, C T Indicates OLTC,C S represents a synchronous phase-shifting camera, C B represents the unit adjustment cost of the energy storage device, n T Indicates load voltage regulator switch, n Q represents the generator, n B Indicates the adjustment of the energy storage device; When the various control devices are in operation, the variable constraints in the operation process are dynamically planned to determine the inequality constraints. The specific formula of the inequality constraints is: Among them, C Ct Indicates the number of capacitor banks, Q gt represents the reactive power output of the generator node, B t Represents the output of the energy storage device. The upper and lower lines represent the upper and lower limits respectively. V Gt represents the reactive power in the generator, and h represents the constraint function.

6. The comprehensive optimization method for wind-solar-storage complementary distribution network based on multiple control devices as claimed in claim 5, Features: The construction of the load time series prediction model includes adding the transformer SVC to the optimized objective function and reconstructing the optimized objective function. The calculation formula of the reconstructed optimized objective function is: Among them, C SVC represents the adjustment cost of SVC, Δu i,SVC A column vector representing the operation, nSVC Indicates the number of SVC device adjustments.

7. The comprehensive optimization method for wind-solar-storage complementary distribution network based on multiple control devices as claimed in claim 6, Features: The verification of the distribution network dispatch optimization mathematical model includes converting the nonlinear programming problem in the power system into a second-order cone programming problem using a second-order cone programming relaxation algorithm, and solving the second-order cone programming problem using a CPLEX solver. The specific calculation formula of the second-order cone programming relaxation algorithm is: Where t represents the last number of the sequence and I represents the identity matrix.

8. A comprehensive optimization system for a wind-solar-storage complementary distribution network based on multiple control devices, based on a comprehensive optimization method for a wind-solar-storage complementary distribution network based on multiple control devices as described in any one of claims 1 to 7, Features: include, A calculation module, which converts wind energy through a wind power generation model and calculates the power generated by the wind turbine; A construction module, which constructs a load time series forecasting model based on wind power and photovoltaic power generation systems; The optimization module verifies the mathematical model of distribution network dispatch optimization by using the node system of the control equipment.

9. A computer device comprising a memory and a processor, the memory storing a computer program, Features: When the processor executes the computer program, the steps of the comprehensive optimization method of the wind-solar-storage complementary distribution network based on multiple control devices as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, Features: When the computer program is executed by a processor, the steps of the comprehensive optimization method of a wind-solar-storage complementary distribution network based on multiple control devices as described in any one of claims 1 to 7 are implemented.

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