High-reliability optimization and reconstruction method for flexible interconnected power distribution network

Abstract

The invention discloses a high-reliability optimization reconstruction method for a flexible interconnected power distribution network, and the method comprises the steps: building a power grid optimization scheduling model, setting optimization constraint conditions, and carrying out the calculation of an extension scheduling model through a multi-objective function; a voltage load curve is measured through the model for softening the power distribution load prediction of the power grid, and a predicted scheduling strategy model with the optimal economic performance is obtained; after variables are introduced, an active power distribution network optimization operation model is converted into a measurement and calculation softening model; measuring and calculating according to a predicted model result to obtain an optimal effect; power generation data of renewable energy sources is established by a softened power grid in a scheduling period, so that load and power consumption data used by a user are practiced; a column constraint generation algorithm is utilized to obtain an optimal solution, so that an actual scheduling strategy scheme of the flexible power grid is adjusted; establishing a real power supply relation matrix of the power distribution network through a result; obtaining and implementing an optimal rule scheme of the softened power grid after actual measurement again; according to the method, the energy consumption and profit of the power grid can be adjusted to the optimal effect.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid optimization, and in particular to a high-reliability optimization and reconstruction method for a flexible interconnected distribution network. Background Art

[0002] With the changes in energy consumption structure and the diversification of energy supply methods, traditional distribution networks can no longer meet the needs of modern society. Flexible interconnection of distribution networks refers to the use of information technology to conduct intelligent monitoring, data analysis and integrated control of each link of the distribution network, realize flexible interconnection of distribution networks, and improve the reliability, safety, stability and economy of distribution networks. The realization of flexible interconnection requires data, networking and intelligence of each link of the distribution network based on technical means such as big data, cloud computing and the Internet of Things. Flexible interconnection of distribution networks has been widely used in various fields, such as urban power distribution, industrial power distribution, new energy power distribution, etc. In the future, flexible interconnection of distribution networks will continue to be widely used and promoted, and become an important support for promoting energy transformation and smart city construction.

[0003] Among them, power system failure refers to a state in which the equipment cannot work according to the expected indicators, that is, the equipment does not achieve the function it should achieve. The main failures are as follows: generator set failure, transmission line failure, substation failure, bus failure, etc. The development of the power grid and the progress of society have put forward higher requirements for the operation of the power grid, and it is particularly important to strengthen the diagnosis and treatment of power grid failures. However, as the scale of the power system becomes larger and the structure becomes more complex, failures will be inevitable. In the entire power production process, the most common, most dangerous and most serious failure is the short circuit failure. The causes of short circuit failures include aging or mechanical damage of insulation materials of electrical equipment, overvoltage caused by lightning strikes, tower collapse or wire breakage caused by natural disasters, short circuit caused by birds and animals jumping wires, and misoperation by operators, which have brought some new problems to the safe operation of the power system.

[0004] At the same time, with the development of computer technology, communication technology, network technology, etc., the use of more advanced intelligent technology to improve the performance of fault diagnosis systems has important research value and practical significance. When the distribution network is in operation, it is necessary to inspect and repair the faults of the power grid, optimize the power grid, and repair the defects in the actual use of the power grid in a timely manner. In view of this, the applicant has developed a high-reliability optimization and reconstruction method for flexible interconnected distribution networks. Summary of the invention

[0005] The purpose of the present invention is to provide a high-reliability optimization reconstruction method for a flexible interconnected distribution network in response to the above-mentioned problems during the specific operation of the existing distribution network. After the optimal rules are established and implemented through the dispatching model, the grid dispatching plan is reasonably planned, and the energy consumption and profit of the grid are adjusted to the optimal effect.

[0006] The present invention is achieved through the following technical solutions:

[0007] The present invention provides a high-reliability optimization and reconstruction method for a flexible interconnected distribution network, comprising the following steps:

[0008] S1. Establish a power grid optimization dispatching model, set optimization constraints, and expand the dispatching model through multi-objective function calculation;

[0009] S2, measuring the voltage load curve by using the model for forecasting the power distribution load of the softened power grid, and obtaining the dispatching strategy model with the best predicted economic performance;

[0010] S3, after the variables are introduced, the active distribution network optimization operation model is transformed into a measurement and softening model;

[0011] S4. Calculate through the predicted model results to obtain the best effect;

[0012] S5. Establish the power generation data of renewable energy with the flexible grid within the dispatching period, so as to implement the load and power consumption data used by users;

[0013] S6. derive an optimal solution by using a column constraint generation algorithm, thereby adjusting the actual dispatching strategy of the flexible power grid;

[0014] S7. Establishing the actual power supply relationship matrix of the distribution network through the results;

[0015] S8. After further actual measurement, the optimal planning scheme for the flexible power grid is obtained and implemented.

[0016] In a further technical solution of the present application, in step S1, the comprehensive power supply cost and voltage deviation of the flexible interconnected distribution network are minimized as optimization objectives, and a power grid optimization scheduling model is established based on classical model predictive control.

[0017] In a further technical solution of the present application, in step S6, the topological structure of the distribution network is analyzed, and data of relevant nodes corresponding to the optimization details of the distribution network are detected.

[0018] In a further technical solution of the present application, the data in step S6 is the analysis content of power loss, node loss and energy consumption and wear ratio.

[0019] In a further technical solution of the present application, the scheduling model determines the difference in matching degree between the historical running status information and each candidate action, and selects the running status information that meets the matching requirements within a preset range.

[0020] In a further technical solution of the present application, before implementing the power grid measurement, a resource scheduling model is constructed, and the simplified resource scheduling model and reference vector obtained by offline calculation are stored in an offline knowledge base.

[0021] In a further technical solution of the present application, based on an evolutionary multi-objective optimization algorithm and multiple variable parameter subsets, the resource scheduling model is optimized offline to determine the Pareto optimal solution set for each task optimization sub-cycle, as well as the sensitivity and adaptability of each resource optimization target model in multiple resource optimization target models to the decision variables.

[0022] In a further technical solution of the present application, the scheduling model is established in an APP software of a computer or mobile phone terminal device.

[0023] The present invention also provides a storage medium, which includes a stored program, wherein the program executes the above-mentioned high-reliability optimization reconstruction method for the flexible interconnected distribution network.

[0024] The present invention also provides a processor, which is used to run a program, wherein the program executes the above-mentioned high-reliability optimization and reconstruction method of the flexible interconnected distribution network when running.

[0025] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0026] The present invention establishes a power grid optimization dispatching model, sets optimization constraints, and expands the dispatching model through multi-objective function calculation; then measures the voltage load curve through the model of flexible power grid distribution load prediction to obtain the dispatching strategy model with the best predicted economic performance; after introducing variables, the active distribution network optimization operation model is converted into a measurement and softening model; then the predicted model results are used for calculation to obtain the optimal effect; the power generation data of renewable energy is established with the flexible power grid within the dispatching cycle, so as to practice the load and power consumption data used by users; the column constraint generation algorithm is used to obtain the optimization solution, so as to adjust the actual dispatching strategy plan of the flexible power grid; the actual power supply relationship matrix of the distribution network is established through the results; the optimal planning plan of the flexible power grid is obtained after another actual measurement and implemented; in this method, the optimal rules are established and implemented through the dispatching model, the power grid dispatching plan is reasonably planned, and the energy consumption and profit of the power grid are adjusted to the optimal effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings:

[0028] Figure 1 This is a flow chart of the high-reliability optimization and reconstruction method of the flexible interconnected distribution network in the present invention. DETAILED DESCRIPTION

[0029] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0031] In the description of the embodiments of the present application, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features.

[0032] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0033] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists, A and B exist at the same time, and B exists. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0034] In the embodiments of the present application, the same reference numerals represent the same components, and for the sake of brevity, detailed descriptions of the same components are omitted in different embodiments. It should be understood that the thickness, length, width and other dimensions of various components in the embodiments of the present application shown in the drawings, as well as the overall thickness, length, width and other dimensions of the integrated device are only exemplary descriptions and should not constitute any limitation to the present application.

[0035] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces), unless otherwise clearly and specifically defined.

[0036] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the embodiments of the present application.

[0037] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like 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 an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0038] like Figure 1 The method flow chart of the present application is shown. A high-reliability optimization and reconstruction method for a flexible interconnected distribution network provided in an embodiment of the present application includes the following steps:

[0039] S1. Establish a power grid optimization dispatching model, set optimization constraints, and expand the dispatching model through multi-objective function calculation;

[0040] S2, measuring the voltage load curve by using the model for forecasting the power distribution load of the softened power grid, and obtaining the dispatching strategy model with the best predicted economic performance;

[0041] S3, after the variables are introduced, the active distribution network optimization operation model is transformed into a measurement and softening model;

[0042] S4. Calculate through the predicted model results to obtain the best effect;

[0043] S5. Establish the power generation data of renewable energy with the flexible grid within the dispatching period, so as to implement the load and power consumption data used by users;

[0044] S6. derive an optimal solution by using a column constraint generation algorithm, thereby adjusting the actual dispatching strategy of the flexible power grid;

[0045] S7. Establishing the actual power supply relationship matrix of the distribution network through the results;

[0046] S8. After further actual measurement, the optimal planning scheme for the flexible power grid is obtained and implemented.

[0047] According to some embodiments of the present application, in step S1, the comprehensive power supply cost and voltage deviation of the flexible interconnected distribution network are minimized as optimization objectives, and a power grid optimization scheduling model is established based on classical model predictive control.

[0048] It should be noted that the scheduling model is an abstraction and description of practical problems using mathematical language and methods, while simulation is the use of computers to simulate research objects. For systems described by mathematical formulas or rules, computers simulate them numerically to achieve visualization.

[0049] According to some embodiments of the present application, in step S6, the topological structure of the distribution network is analyzed, and the data of the relevant nodes corresponding to the optimization details of the distribution network are detected. The commonly used topological structures in the distribution network are mainly the following: radial topology, ring topology, mesh topology, ring network topology and multi-terminal network topology. In actual applications, the topological structure of the distribution network will be selected and designed according to various factors such as actual needs, geographical environment, economic conditions, etc., to achieve the best power supply effect.

[0050] According to some embodiments of the present application, the data in step S6 is analysis content of power loss, node loss, and energy consumption and wear ratio.

[0051] Among them, power loss refers to the energy lost during the transmission and distribution of electricity. Power loss mainly includes line loss and transformer loss. Line loss refers to the energy loss generated by transmission lines and transformers when transmitting electricity, while transformer loss includes no-load loss, load loss, etc.

[0052] Node loss usually refers to the power loss of each node (such as substation, distribution room, etc.) in the power network. Node loss includes transformer loss, line loss and management loss. Transformer loss is mainly composed of no-load loss and load loss, while line loss is related to factors such as transmission voltage and line resistance.

[0053] The energy consumption and wear ratio usually refers to the percentage of power loss to power supply, that is, the line loss rate. The line loss rate can be calculated by counting the power supply and power sales. The specific formula is: line loss rate = (power supply - power sales) / power supply × 100%. In practical applications, the line loss rate can help evaluate the operating efficiency of the power system and take corresponding measures to reduce line losses.

[0054] The column constraint generation algorithm used in step S6 is an efficient algorithm for solving linear programming problems, also known as the "column search algorithm" or "column generation algorithm". This algorithm is mainly used to solve large-scale and complex problems in linear programming problems. Its basic idea is to gradually build the constraints of the linear programming problem by dynamically adding variables (columns), so as to achieve the purpose of optimizing the problem. The advantages of the column constraint generation algorithm are: 1. It can efficiently solve large-scale and complex linear programming problems; 2. It can dynamically add variables according to actual conditions, and there is no need to build complete constraints at the beginning, thereby saving computing time and space; 3. It can be used in combination with other solution algorithms to further improve the solution efficiency.

[0055] According to some embodiments of the present application, the scheduling model determines the difference in matching degree between the historical running status information and each candidate action, and selects the running status information that meets the matching degree within a preset range.

[0056] According to some embodiments of the present application, before implementing power grid measurement, a resource scheduling model is constructed, and the simplified resource scheduling model and reference vector obtained by offline calculation are stored in an offline knowledge base. Since the calculation link adopts an offline mode, more computing resources and sufficient running time can be used to obtain a more reasonable simplified resource scheduling model and reference vector.

[0057] According to some embodiments of the present application, an offline multi-objective optimization is performed on a resource scheduling model based on an evolutionary multi-objective optimization algorithm and multiple variable parameter subsets to determine the Pareto optimal solution set for each task optimization sub-cycle, as well as the sensitivity and adaptability of each resource optimization target model in multiple resource optimization target models to the decision variables. Among them, the active and reactive power of the converter of the flexible interconnection device can be used as the decision variable.

[0058] Among them, the evolutionary multi-objective optimization algorithm is a method that uses evolutionary computing methods to solve multi-objective optimization problems. Multi-objective optimization problems involve two or more conflicting objective functions, which usually cannot be optimal at the same time. Therefore, it is necessary to find a set of "Pareto optimal solutions", in which no solution is better than other solutions in all objectives. That is, in a multi-objective optimization problem, a solution may be better for a certain objective, but may be worse for other objectives. Therefore, there is a set of compromise solutions, called the Pareto optimal solution set or non-dominated solution set.

[0059] According to some embodiments of the present application, the scheduling model is established in APP software of a computer or mobile phone terminal device.

[0060] According to some embodiments of the present application, based on the scheduling model, multiple sub-scheduling models with the same network results are generated, and the historical operating status information is input into each sub-scheduling model to obtain the first matching degree between the historical operating status information output by each scheduling model and each candidate action, and based on the matching degrees corresponding to the multiple sub-scheduling models, the initial scheduling model is corrected to generate an initial scheduling model. Based on the initial scheduling model, the operation of generating multiple sub-scheduling models can be returned until the matching degree output by the initial scheduling model meets the convergence condition, and the power grid system scheduling model is obtained.

[0061] A storage medium is provided in an embodiment of the present application, and the storage medium includes a stored program, wherein the program executes the high-reliability optimization and reconstruction method of the flexible interconnected distribution network described above.

[0062] A processor is provided in an embodiment of the present application, and the processor is used to run a program, wherein the program executes the above-mentioned high-reliability optimization and reconstruction method for a flexible interconnected distribution network when running.

[0063] Those skilled in the art should understand that the present application 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 codes.

[0064] 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.

[0065] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0066] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0067] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0068] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A high-reliability optimization and reconstruction method for a flexible interconnected distribution network, characterized in that: The following steps are involved: S1. Establish a power grid optimization dispatching model, set optimization constraints, and expand the dispatching model through multi-objective function calculation; S2, measuring the voltage load curve by using the model for forecasting the power distribution load of the softened power grid, and obtaining the dispatching strategy model with the best predicted economic performance; S3, after the variables are introduced, the active distribution network optimization operation model is transformed into a measurement and softening model; S4. Calculate through the predicted model results to obtain the best effect; S5. Establish the power generation data of renewable energy with the flexible grid within the dispatching period, so as to implement the load and power consumption data used by users; S6. derive an optimal solution by using a column constraint generation algorithm, thereby adjusting the actual dispatching strategy of the flexible power grid; S7. Establishing the actual power supply relationship matrix of the distribution network through the results; S8. After further actual measurement, the optimal planning scheme for the flexible power grid is obtained and implemented.

2. The high-reliability optimization and reconstruction method for a flexible interconnected distribution network according to claim 1 is characterized in that: In step S1, the comprehensive power supply cost and voltage deviation of the flexible interconnected distribution network are minimized as optimization objectives, and a power grid optimization scheduling model is established based on classical model predictive control.

3. The high-reliability optimization and reconstruction method for a flexible interconnected distribution network according to claim 1 is characterized in that: In step S6, the topological structure of the distribution network is analyzed, and the data of the relevant nodes are detected for the corresponding optimization details of the distribution network.

4. The high-reliability optimization and reconstruction method for a flexible interconnected distribution network according to claim 3 is characterized in that: The data in step S6 is the analysis content of power loss, node loss and energy consumption wear ratio.

5. The high-reliability optimization and reconstruction method for a flexible interconnected distribution network according to claim 1 is characterized in that: The scheduling model determines the difference in matching degree between the historical running state information and each candidate action, and the running state information that meets the matching requirements is selected within a preset range.

6. The high-reliability optimization and reconstruction method for a flexible interconnected distribution network according to claim 1 is characterized in that: Before implementing power grid measurement, a resource scheduling model is constructed, and the simplified resource scheduling model and reference vector obtained by offline calculation are stored in the offline knowledge base.

7. The high-reliability optimization and reconstruction method for a flexible interconnected distribution network according to claim 6 is characterized in that: Based on the evolutionary multi-objective optimization algorithm and multiple variable parameter subsets, the resource scheduling model is optimized offline to determine the Pareto optimal solution set of each task optimization sub-cycle and the sensitivity and adaptability of each resource optimization target model to the decision variables in multiple resource optimization target models.

8. The high-reliability optimization and reconstruction method for a flexible interconnected distribution network according to claim 1 is characterized in that: The scheduling model is established in APP software of computer and mobile phone terminal devices.

9. A storage medium, characterized in that: The storage medium includes a stored program, wherein the program executes the high-reliability optimization and reconstruction method for a flexible interconnected distribution network according to any one of claims 1 to 8.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the high-reliability optimization and reconstruction method for a flexible interconnected distribution network according to any one of claims 1 to 8.

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