Adjustable resource aggregation scheduling method and system for power distribution network

Abstract

The invention discloses an adjustable resource aggregation scheduling method of a power distribution network. Comprising the following steps: obtaining adjustable resources of various demand sides, and classifying the adjustable resources based on a preset classification rule; extracting resource features of each category of adjustable resources, and establishing an adjustable resource model according to the resource features; adopting a clustering algorithm to aggregate all types of adjustable resource models to obtain a resource aggregate; constructing a multi-objective optimization model for the resource aggregate, and determining an objective function and a constraint condition of the multi-objective optimization model; and solving the multi-objective optimization model by adopting a Pareto solving method to obtain an optimal scheduling scheme. The aggregation efficiency and the resource utilization rate of distributed resources can be improved, the safe and stable operation level of a power grid is improved, and the dispatching pressure of the power grid is relieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for aggregate scheduling of adjustable resources in a distribution network. Background Art

[0002] For a long time, the power grid has mainly met the requirements of peak load regulation and frequency regulation by dispatching the power supply side. With the access of a large number of distributed resources such as renewable energy generation, energy storage, and controllable loads to the power grid, giving full play to their regulation potential has become the key to building a new power system. The multi-social resource aggregate aggregates multiple types of distributed resources through advanced communication, regulation, and metering technologies, thereby achieving stable and reliable overall output. Therefore, how to achieve the optimal dispatch of the aggregate is the key to the effective operation of the power grid.

[0003] Scholars at home and abroad have conducted in-depth research on the participation of distributed energy sources such as distributed power sources, energy storage systems, and controllable loads in power grid operations, and have made practical progress. However, there are still shortcomings in terms of aggregation form and model construction. Dispersed resources have the problems of scattered location, small size, difficult communication, and large scale, which leads to low aggregation efficiency of distributed resources and difficulty in accurate scheduling and rapid response of power systems. Summary of the invention

[0004] The embodiment of the present invention provides an adjustable resource aggregation scheduling method for a distribution network, which can improve the aggregation efficiency and resource utilization of distributed resources and enhance the safe and stable operation level of the power grid.

[0005] In a first aspect, an embodiment of the present invention provides a method for aggregate scheduling of adjustable resources in a distribution network, including:

[0006] Acquire various types of adjustable resources on the demand side, and classify the adjustable resources based on preset classification rules;

[0007] Extracting resource characteristics of each category of adjustable resources, and establishing an adjustable resource model according to the resource characteristics;

[0008] A clustering algorithm is used to aggregate the adjustable resource models of all categories to obtain a resource aggregate; a multi-objective optimization model is constructed for the resource aggregate, and an objective function and constraint conditions of the multi-objective optimization model are determined;

[0009] The Pareto solution method is used to solve the multi-objective optimization model to obtain an optimized scheduling solution.

[0010] Furthermore, the adjustable resources include resource operation data of distributed photovoltaics, distributed wind power, distributed energy storage, controllable commercial building loads, controllable park loads and electric vehicle battery swap stations.

[0011] Furthermore, extracting resource features of each category of adjustable resources and establishing an adjustable resource model according to the resource features includes:

[0012] Analyze the charge and discharge time, charge and discharge capacity, and charge and discharge efficiency of each type of adjustable resource to extract resource characteristics;

[0013] An adjustable resource model for each category of adjustable resources is established according to the resource characteristics.

[0014] Furthermore, after classifying the adjustable resources, the method further includes:

[0015] Each category of adjustable resources is divided according to the control capability, resulting in adjustable resources at the whole network layer, adjustable resources at the administrative division layer, adjustable resources at the grid operation division layer, adjustable resources at the distribution station area layer, and adjustable resources at the distribution feeder layer.

[0016] Furthermore, the resource characteristics include the start time of power consumption, the end time of power consumption, the maximum power consumption, the maximum power demand, the minimum power demand, the actual power demand and the sampling period.

[0017] Furthermore, the objective function includes a safety objective, an economic objective and a green objective. Then, the multi-objective optimization model is solved by using the Pareto solution method to obtain an optimized scheduling solution, including:

[0018] The multi-objective optimization model is solved by using the Pareto solution method to obtain optimization weights; wherein the optimization weights include safety target weights, economic target weights, and green target weights;

[0019] An optimized scheduling scheme is determined according to the optimization weights.

[0020] In a second aspect, an embodiment of the present invention provides an adjustable resource aggregation scheduling system for a distribution network, including:

[0021] Acquire various types of adjustable resources on the demand side, and classify the adjustable resources based on preset classification rules;

[0022] Extracting resource characteristics of each category of adjustable resources, and establishing an adjustable resource model according to the resource characteristics;

[0023] A clustering algorithm is used to aggregate the adjustable resource models of all categories to obtain a resource aggregate;

[0024] Constructing a multi-objective optimization model for the resource aggregate, and determining the objective function and constraint conditions of the multi-objective optimization model;

[0025] The Pareto solution method is used to solve the multi-objective optimization model to obtain an optimized scheduling solution.

[0026] In a third aspect, an embodiment of the present invention provides an electronic device, including:

[0027] Memory for storing computer programs;

[0028] A processor, configured to execute the computer program;

[0029] Wherein, when the processor executes the computer program, it implements the adjustable resource aggregation scheduling method of the distribution network as described in any one of the first aspects above.

[0030] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the adjustable resource aggregation scheduling method for the distribution network described in any one of the first aspects above is implemented.

[0031] In a fifth aspect, an embodiment of the present invention provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the adjustable resource aggregation scheduling method for a distribution network as described in any one of the first aspects above.

[0032] Compared with the prior art, the embodiment of the present invention provides an adjustable resource aggregation scheduling method for a distribution network, which has the following beneficial effects: by obtaining various types of adjustable resources on the demand side, and classifying the adjustable resources based on preset classification rules; extracting resource characteristics of each category of adjustable resources, and establishing an adjustable resource model based on the resource characteristics; using a clustering algorithm to aggregate the adjustable resource models of all categories to obtain a resource aggregate; constructing a multi-objective optimization model for the resource aggregate, and determining the objective function and constraints of the multi-objective optimization model; using a Pareto solution method to solve the multi-objective optimization model to obtain an optimized scheduling plan. The present invention can improve the aggregation efficiency and resource utilization of distributed resources, improve the safe and stable operation level of the power grid, and reduce the pressure of power grid scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical features of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 It is a flow chart of an embodiment of a method for aggregate scheduling of adjustable resources in a distribution network provided by the present invention;

[0035] Figure 2It is a structural schematic diagram of an embodiment of an adjustable resource aggregation scheduling system for a distribution network provided by the present invention;

[0036] Figure 3 It is a structural schematic diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. 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.

[0038] In a first aspect, an embodiment of the present invention provides a method for aggregate scheduling of adjustable resources in a distribution network, see Figure 1 , which is a flow chart of an embodiment of an adjustable resource aggregation scheduling method for a distribution network provided by the present invention.

[0039] like Figure 1 As shown, the method comprises the following steps:

[0040] S1: Acquire various types of adjustable resources on the demand side, and classify the adjustable resources based on preset classification rules;

[0041] S2: extracting resource features of each category of adjustable resources, and establishing an adjustable resource model according to the resource features;

[0042] S3: using a clustering algorithm to aggregate the adjustable resource models of all categories to obtain a resource aggregate;

[0043] S4: constructing a multi-objective optimization model for the resource aggregate, and determining the objective function and constraint conditions of the multi-objective optimization model;

[0044] S5: Using the Pareto solution method to solve the multi-objective optimization model, and obtain an optimized scheduling solution.

[0045] In the specific implementation, firstly, obtain various types of adjustable resources with dispatchable potential on the demand side, investigate the automation and information level of various access resource sites, make classification planning for source, load and storage resources with different regulation capabilities and different site conditions, and classify the adjustable resources based on preset classification rules. The resources can be classified according to resource type, grid association, resource regulation characteristics or grid regulation. Extract resource characteristics based on the category of adjustable resources, and establish an adjustable resource model for each category of adjustable resources according to the resource characteristics. Use a clustering algorithm to aggregate all categories of adjustable resource models, and construct a multi-objective optimization model for the aggregated resource aggregate, including objective functions and constraints. Use the Pareto solution method to solve the multi-objective optimization model to obtain an optimized scheduling plan.

[0046] To summarize, the present invention is based on the operating data of typical resources with dispatchable potential, such as distributed photovoltaic, distributed wind power, distributed energy storage, controllable commercial building load, controllable park load and electric vehicle charging / swapping station, and adopts a clustering algorithm to aggregate and classify the data, establish an adjustable resource model suitable for market operation and system operation, consider safety, economy and green as the goals, establish a multi-objective optimization model, determine the weights of multiple objectives, and can increase the grid's photovoltaic and other renewable energy power generation and consumption capacity, improve the aggregation efficiency and resource utilization of distributed resources, enhance the grid's regulation capability and safe and stable operation level, and reduce the grid's dispatching pressure.

[0047] In an optional implementation, the adjustable resources include resource operation data of distributed photovoltaics, distributed wind power, distributed energy storage, controllable commercial building loads, controllable park loads and electric vehicle battery swap stations.

[0048] Specifically, through a special data acquisition system, the operating data of typical adjustable resources with dispatchable potential are collected. The adjustable resources include distributed photovoltaics, distributed wind power, distributed energy storage, controllable commercial building loads, controllable park loads, electric vehicle charging / swapping stations, load aggregators, virtual power plants, etc. Based on big data technology, the decentralized adjustable resources are integrated into an aggregate with large adjustable quantity and simple regulation, and the aggregate is dynamically connected to the power system, which is conducive to the efficient use of resources.

[0049] In an optional implementation, extracting resource features of each category of adjustable resources and establishing an adjustable resource model according to the resource features includes:

[0050] Analyze the charge and discharge time, charge and discharge capacity, and charge and discharge efficiency of each type of adjustable resource to extract resource characteristics;

[0051] An adjustable resource model for each category of adjustable resources is established according to the resource characteristics.

[0052] Specifically, by analyzing the charging and discharging time, charging and discharging amount, and charging and discharging efficiency of each category of adjustable resources, resource characteristics are extracted. The resource characteristics include the start time of power consumption, the end time of power consumption, the maximum power consumption, the maximum power demand, the minimum power demand, the actual power demand, and the sampling period. The boundary conditions of the adjustable resource model are calculated according to the resource characteristics, so as to establish the adjustable resource model of each category of adjustable resources.

[0053] The boundary conditions of the adjustable resource model include the following:

[0054]

[0055] Among them, t a Indicates the time when electricity starts to be used, t d Indicates the end time of power consumption, p max Indicates the maximum power consumption, e max Indicates the maximum power demand, e min Indicates the minimum electricity demand, e need represents the actual electricity demand, and Δt represents the sampling period.

[0056] In an optional implementation manner, after classifying the adjustable resources, the method further includes:

[0057] Each category of adjustable resources is divided according to the control capability, resulting in adjustable resources at the whole network layer, adjustable resources at the administrative division layer, adjustable resources at the grid operation division layer, adjustable resources at the distribution station area layer, and adjustable resources at the distribution feeder layer.

[0058] Specifically, based on the classified adjustable resources, they are divided into five levels according to the control capabilities, namely the entire network, administrative divisions, grid operation divisions, distribution substations, and distribution feeders, to meet the needs of participating in precise control of the power grid.

[0059] In an optional implementation, the resource characteristics include a start time of power usage, an end time of power usage, a maximum power usage, a maximum power demand, a minimum power demand, an actual power demand, and a sampling period.

[0060] In an optional implementation, the objective function includes a safety objective, an economic objective, and a green objective. Then, the multi-objective optimization model is solved by using the Pareto solution method to obtain an optimized scheduling solution, including:

[0061] The multi-objective optimization model is solved by using the Pareto solution method to obtain optimization weights; wherein the optimization weights include safety target weights, economic target weights, and green target weights;

[0062] An optimized scheduling solution is obtained according to the optimization weights.

[0063] Specifically, we first need to clarify the characteristics and constraints of each decentralized adjustable resource in the multi-objective optimization model. For distributed photovoltaics, it is affected by the weather, the power generation is unstable, and there is a maximum output power limit. Distributed wind power is also affected by the weather, the power generation is unstable, and there is a maximum output power limit. Distributed energy storage has charging and discharging power limits and energy storage limits, etc. Controllable commercial building loads and controllable park loads have constraints such as load demand prediction and adjustable range. Electric vehicle battery swap stations have charging demand, charging power and other limitations. Based on these characteristics and constraints, a series of equality and inequality constraints can be established.

[0064] The objective function of the multi-objective optimization model mainly considers three main objectives, including safety objectives, economic objectives and green objectives. The safety objective ensures that the system can operate stably under various operating conditions without unsafe situations such as overload and undervoltage. The economic objective minimizes operating costs, including energy purchase costs, equipment maintenance costs, etc. The green objective maximizes the utilization of renewable energy and reduces carbon emissions. Mathematical expressions can be used to represent these objectives.

[0065] For multi-objective optimization problems, there is usually no solution that can optimize all objectives at the same time, so the Pareto solution method is used to solve the multi-objective optimization model. Without sacrificing other objectives, the solution set of a certain objective cannot be optimized. In the solution process, different multi-objective selections can be tried and their impact on the results can be observed. The optimization weights of the multiple objectives, namely the safety objective weight, the economic objective weight and the green objective weight, are determined according to actual needs and preferences, and the optimized scheduling plan is obtained according to the optimization weights.

[0066] In a second aspect, an embodiment of the present invention provides an adjustable resource aggregation scheduling system for a distribution network, see Figure 2 As shown, it is a structural diagram of an adjustable resource aggregation scheduling system for a distribution network provided by an embodiment of the present invention.

[0067] like Figure 2 As shown, the system comprises:

[0068] The resource acquisition module 21 is used to acquire various types of adjustable resources on the demand side and classify the adjustable resources based on preset classification rules;

[0069] A feature extraction module 22, used to extract resource features of each category of adjustable resources, and establish an adjustable resource model according to the resource features;

[0070] A resource aggregation module 23, configured to aggregate all types of adjustable resource models using a clustering algorithm to obtain a resource aggregate;

[0071] A model building module 24, used to build a multi-objective optimization model for the resource aggregate and determine the objective function and constraint conditions of the multi-objective optimization model;

[0072] The model solving module 25 is used to solve the multi-objective optimization model using the Pareto solving method to obtain an optimized scheduling solution.

[0073] In an optional implementation, the adjustable resources include resource operation data of distributed photovoltaics, distributed wind power, distributed energy storage, controllable commercial building loads, controllable park loads and electric vehicle battery swap stations.

[0074] In an optional implementation, extracting resource features of each category of adjustable resources and establishing an adjustable resource model according to the resource features includes:

[0075] Analyze the charge and discharge time, charge and discharge capacity, and charge and discharge efficiency of each type of adjustable resource to extract resource characteristics;

[0076] An adjustable resource model for each category of adjustable resources is established according to the resource characteristics.

[0077] In an optional implementation manner, after classifying the adjustable resources, the method further includes:

[0078] Each category of adjustable resources is divided according to the control capability, resulting in adjustable resources at the whole network layer, adjustable resources at the administrative division layer, adjustable resources at the grid operation division layer, adjustable resources at the distribution station area layer, and adjustable resources at the distribution feeder layer.

[0079] In an optional implementation, the resource characteristics include a start time of power usage, an end time of power usage, a maximum power usage, a maximum power demand, a minimum power demand, an actual power demand, and a sampling period.

[0080] In an optional implementation, the objective function includes a safety objective, an economic objective, and a green objective. Then, the multi-objective optimization model is solved by using the Pareto solution method to obtain an optimized scheduling solution, including:

[0081] The multi-objective optimization model is solved by using the Pareto solution method to obtain optimization weights; wherein the optimization weights include safety target weights, economic target weights, and green target weights;

[0082] An optimized scheduling solution is obtained according to the optimization weights.

[0083] In a third aspect, an embodiment of the present invention provides an electronic device, see Figure 3FIG. 1 is a schematic diagram of the structure of an electronic device provided by the present invention.

[0084] like Figure 3 As shown, the device includes:

[0085] A memory 31, used for storing computer programs;

[0086] A processor 32, configured to execute the computer program;

[0087] Wherein, when the processor 32 executes the computer program, the adjustable resource aggregation scheduling method of the distribution network as described in any of the above embodiments is implemented.

[0088] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 31 and executed by the processor 32 to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0089] The processor 32 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 gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0090] The memory 31 can be used to store the computer program and / or module, and the processor 32 realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory 31, and calling the data stored in the memory 31. The memory 31 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 31 can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0091] It should be noted that the above electronic device includes, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 3 The structural diagram is merely an example of the electronic device described above and does not constitute a limitation on the electronic device, and may include more components than shown in the figure, or a combination of certain components, or different components.

[0092] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed, the adjustable resource aggregation scheduling method for the distribution network described in any of the above embodiments is implemented.

[0093] It should be understood that the present invention implements all or part of the processes in the above-mentioned adjustable resource aggregation scheduling method for distribution network, and can also be completed by instructing related hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned adjustable resource aggregation scheduling method for distribution network can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0094] In a fifth aspect, an embodiment of the present invention further provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the adjustable resource aggregation scheduling method for the distribution network described in any of the above embodiments.

[0095] The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. It should be pointed out that for those skilled in the art, several equivalent obvious variations and / or equivalent substitutions can be made without departing from the technical principles of the present invention. These obvious variations and / or equivalent substitutions should also be regarded as the protection scope of the present invention.

Claims

1. A method for aggregate scheduling of adjustable resources in a distribution network, characterized in that: include: Acquire various types of adjustable resources on the demand side, and classify the adjustable resources based on preset classification rules; Extracting resource characteristics of each category of adjustable resources, and establishing an adjustable resource model according to the resource characteristics; A clustering algorithm is used to aggregate the adjustable resource models of all categories to obtain a resource aggregate; Constructing a multi-objective optimization model for the resource aggregate, and determining the objective function and constraint conditions of the multi-objective optimization model; The Pareto solution method is used to solve the multi-objective optimization model to obtain an optimized scheduling solution.

2. The adjustable resource aggregation scheduling method of the distribution network according to claim 1, characterized in that: The adjustable resources include resource operation data of distributed photovoltaic, distributed wind power, distributed energy storage, controllable commercial building loads, controllable park loads and electric vehicle battery swap stations.

3. The adjustable resource aggregation scheduling method of the distribution network according to claim 1, characterized in that: The step of extracting resource features of each category of adjustable resources and establishing an adjustable resource model according to the resource features includes: Analyze the charge and discharge time, charge and discharge capacity, and charge and discharge efficiency of each type of adjustable resource to extract resource characteristics; An adjustable resource model for each category of adjustable resources is established according to the resource characteristics.

4. The adjustable resource aggregation scheduling method of the distribution network according to claim 1, characterized in that: After classifying the adjustable resources, the method further includes: Each category of adjustable resources is divided according to the control capability, resulting in adjustable resources at the whole network layer, adjustable resources at the administrative division layer, adjustable resources at the grid operation division layer, adjustable resources at the distribution station area layer, and adjustable resources at the distribution feeder layer.

5. The adjustable resource aggregation scheduling method of the distribution network according to claim 1, characterized in that: The resource characteristics include the start time of power consumption, the end time of power consumption, the maximum power consumption, the maximum power demand, the minimum power demand, the actual power demand and the sampling period.

6. The adjustable resource aggregation scheduling method of the distribution network according to claim 1, characterized in that: The objective function includes a safety objective, an economic objective and a green objective. Then, the multi-objective optimization model is solved by using the Pareto solution method to obtain an optimized scheduling solution, including: The multi-objective optimization model is solved by using the Pareto solution method to obtain optimization weights; wherein the optimization weights include safety target weights, economic target weights, and green target weights; An optimized scheduling scheme is determined according to the optimization weights.

7. An adjustable resource aggregation dispatching system for a distribution network, characterized in that: include: A resource acquisition module, used to acquire various types of adjustable resources on the demand side and classify the adjustable resources based on preset classification rules; A feature extraction module, used to extract resource features of each category of adjustable resources, and establish an adjustable resource model according to the resource features; A resource aggregation module, used to aggregate all types of adjustable resource models using a clustering algorithm to obtain a resource aggregate; A model building module, used to build a multi-objective optimization model for the resource aggregate and determine the objective function and constraint conditions of the multi-objective optimization model; The model solving module is used to solve the multi-objective optimization model using the Pareto solving method to obtain an optimized scheduling solution.

8. An electronic device, characterized in that: include: Memory for storing computer programs; A processor, configured to execute the computer program; Wherein, when the processor executes the computer program, the adjustable resource aggregation scheduling method for the distribution network as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed, the method for aggregate scheduling of adjustable resources in a distribution network according to any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that It comprises computer instructions, which, when executed by a processor, implement the adjustable resource aggregation scheduling method for a distribution network as described in any one of claims 1 to 6.

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