Balance control scheduling method and system based on distributed new energy

Abstract

The invention discloses a balance control scheduling method and system based on distributed new energy, and the method comprises the steps: obtaining power grid parameters, and constructing a time-space characteristic model of multi-type adjustable resources and a distributed scheduling frame based on the obtained parameters; based on the space-time characteristic model and the distributed scheduling framework, establishing a distributed new energy participation power grid adjustable and controllable resource group internal cooperation strategy; establishing a distributed new energy participation power grid dispatching collaborative interaction and balance optimization control strategy according to the collaborative strategy; and realizing active / reactive coupling balance collaborative optimization of the high-permeability distributed new energy access power grid based on a collaborative strategy and an optimization control strategy. According to the invention, the utilization rate of new energy can be improved, cooperative work of multiple types of adjustable and controllable resources is realized, the flexibility and stability of a power grid are improved, dynamic adjustment of a power system is realized, and optimal matching of power supply and demand at different time and space scales is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid control, and in particular to a balanced control scheduling method and system based on distributed renewable energy. Background Art

[0002] In recent years, the penetration rate of new energy power generation represented by wind power and photovoltaic power has continued to increase. New energy has not only injected more clean electricity into the power system, but its continuous increase in penetration is gradually changing the structure and operation mode of the traditional power system. Active distribution networks integrate a variety of distributed power sources, energy storage devices and flexible demand-side response mechanisms, which not only improves the flexibility and controllability of the power system, but also better adapts to the intermittent and uncertain characteristics of new energy power generation. For example, by accurately predicting weather changes and power demand, active distribution networks can adjust power generation plans in advance to ensure supply and demand balance; using advanced energy storage technology, it can store electricity when there is a surplus of electricity and release it when there is a shortage of electricity, thereby smoothing the power supply and demand curve; with the help of smart metering systems and advanced data analysis tools, it is possible to achieve refined management of user electricity consumption behavior, encourage users to use electricity during non-peak hours, and reduce the pressure on the power grid.

[0003] However, at present, there are many types of controllable resources, small capacity, different response characteristics, and uncertainty. How to coordinate the distributed new energy and controllable loads in the distribution network, and achieve active and reactive optimization of the distribution network through the synergy and complementarity between the two, and improve the consumption of new energy, is the difficulty of studying the distribution network under the high penetration rate of distributed controllable resources to achieve active power flow and reactive voltage coordinated optimization strategy; the distribution network still has problems such as low intelligence level, low communication reliability, and slow speed, and it is impossible to conduct real-time observation of all-node measurement data, which leads to the difficulty of cluster division in the absence of power flow physical model. Therefore, it is very necessary to design a balanced control and scheduling method based on distributed new energy. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a balanced control scheduling method and system based on distributed new energy to solve the current problems that the types of controllable resources are many, the capacity is small, the response characteristics are different, and there is uncertainty. It is impossible to achieve accurate coordination and complementarity between new energy and controllable loads, and it is impossible to perform real-time observation of all-node measurement data, which leads to the difficulty of cluster division in the absence of a physical model of the power flow.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a balanced control scheduling method based on distributed renewable energy, comprising: acquiring grid parameters, and constructing a spatiotemporal characteristic model of multiple types of adjustable resources and a distributed scheduling framework based on the acquired parameters;

[0008] Based on the spatiotemporal characteristic model and the distributed dispatching framework, a coordinated strategy for distributed renewable energy to participate in the internal resource group of the power grid controllable resource group is established;

[0009] Establish a coordinated interaction and balanced optimization control strategy for distributed new energy to participate in power grid dispatching based on the collaborative strategy;

[0010] Based on the collaborative strategy and the optimization control strategy, active / reactive coupling balance collaborative optimization of high-penetration distributed new energy access to the power grid is achieved.

[0011] As a preferred solution of the balanced control scheduling method based on distributed renewable energy described in the present invention, the construction of the spatiotemporal characteristic model of multiple types of adjustable resources includes:

[0012] Construct an out-group characteristic model of a controllable resource cluster, including:

[0013] Capture the dynamic characteristics of adjustable resources and the aggregation characteristics of clusters;

[0014] Mathematical models are established for the mechanical module, electrical module and control module of the adjustable resources respectively;

[0015] Based on the mathematical model combined with the homology equivalent method and the aggregation method, an equivalent clustering model taking into account the spatiotemporal characteristics of high-proportion controllable resources is constructed to obtain the influence trend and sensitivity relationship of the internal parameters of each module on the stimulus response;

[0016] Based on the influence trend and sensitivity relationship, the parameters that play a leading role are selected, and the identifiability of each parameter and the identification of equivalent model parameters are obtained to construct an out-of-group characteristic model of a controllable resource cluster.

[0017] As a preferred solution of the balanced control scheduling method based on distributed new energy described in the present invention, wherein: the construction of the spatiotemporal characteristic model of multiple types of adjustable resources also includes:

[0018] Based on the group external characteristic model, the reactive voltage and active power flow are coordinated optimized under different penetration rates and operating conditions, including:

[0019] Considering the randomness of distributed power output and load volatility in the distribution network, typical scenarios of adjustable resources participating in grid operation are generated to set constraints;

[0020] By combining the constraint adjustment with the out-group characteristic model, the active and reactive joint optimization of a high-penetration new energy power grid is achieved.

[0021] As a preferred solution of the balanced control scheduling method based on distributed renewable energy described in the present invention, a distributed scheduling framework suitable for access of massive group units is constructed according to the optimization results, specifically including:

[0022] Based on multi-source data fusion, feature extraction and distribution network dynamic state estimation, a distributed energy coordinated optimization scheduling model is constructed;

[0023] Based on the problems existing in the distributed energy coordinated optimization scheduling model, the operating status perception method of high-penetration distributed new energy and the data-driven partitioning and key node identification method are combined to construct a distributed scheduling framework suitable for the access of massive group units.

[0024] As a preferred solution of the balanced control and dispatching method based on distributed renewable energy described in the present invention, the internal coordination strategy of the distributed renewable energy participating in the controllable resource group of the power grid is established, including:

[0025] Based on the spatiotemporal power consumption characteristics of adjustable resources, a fast power allocation strategy within the subgroup is constructed;

[0026] Constructing power forecasting strategy for power grid sections based on data-driven approach;

[0027] Obtain the specific manifestations of new energy uncertainty and its negative impact on distribution network voltage control, and construct a coordinated operation control strategy for peak shaving, voltage regulation, and loss reduction for distributed new energy participating in grid voltage balance control under different time and space scenarios.

[0028] As a preferred solution of the balanced control dispatching method based on distributed renewable energy described in the present invention, a coordinated interaction and balanced optimization control strategy for distributed renewable energy participating in power grid dispatching is established according to the coordinated strategy, including:

[0029] Obtain regional resource endowment and distribution of existing renewable energy units, divide controllable resources in renewable energy balance areas based on hierarchical and graded ideas, build renewable energy network energy management strategies, integrate stable control systems and renewable energy network energy management hierarchical structures, establish renewable energy rapid power regulation system architecture, and build rapid power control strategies in different scenarios;

[0030] Obtain user-side parameters, build a user-side flexible load response characteristic and regulation capability model, obtain regional source-load characteristics of distributed renewable energy access to the power grid, and build a fast optimization strategy for coordinated and balanced control of multi-region optimal controllable resource control with the goal of improving the balance between the power grid and user-side flexible load distribution in each region;

[0031] As a preferred solution of the balanced control scheduling method based on distributed renewable energy described in the present invention, the coordinated optimization of active / reactive coupling balance of high-penetration distributed renewable energy access to the power grid is realized based on the coordinated strategy and the optimized control strategy, including:

[0032] Based on the power self-balancing terminal, the massive multi-dimensional heterogeneous power data of distributed renewable energy access to the distribution network is collected, the characteristics of data in different time and space dimensions are obtained, and a high-penetration distributed renewable energy grid-connected data modeling strategy is constructed.

[0033] The data error correction model automatically checks the measured data deviation, obtains the distribution of active / reactive power fluctuation rate in the qualified data samples, sets a reasonable power fluctuation threshold, selects stable qualified areas, and builds a distributed new energy grid-connected balance control optimization calculation model;

[0034] The high-penetration distributed renewable energy grid-connected data modeling strategy is combined with the distributed renewable energy grid-connected balance control optimization calculation model to obtain a data calculation model for achieving grid active / reactive coupling balance coordinated optimization.

[0035] In a second aspect, the present invention provides a balanced control and dispatching system based on distributed renewable energy, comprising: a first construction module for acquiring grid parameters, and constructing a spatiotemporal characteristic model of multiple types of adjustable resources and a distributed dispatching framework based on the acquired parameters;

[0036] The second building module is used to establish an internal coordination strategy for the distributed new energy participating in the controllable resource group of the power grid based on the spatiotemporal characteristic model and the distributed scheduling framework;

[0037] The third building module is used to establish a coordinated interaction and balanced optimization control strategy for distributed new energy participating in power grid dispatching according to the coordination strategy;

[0038] The optimization module is used to achieve active / reactive coupling balance collaborative optimization of high-penetration distributed new energy access to the power grid based on the collaborative strategy and the optimization control strategy.

[0039] In a third aspect, the present invention provides a computing device, comprising:

[0040] Memory and processor;

[0041] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the balanced control scheduling method based on distributed new energy are implemented.

[0042] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the balanced control and scheduling method based on distributed new energy.

[0043] Compared with the prior art, the present invention has the following beneficial effects: the present invention constructs spatiotemporal characteristic modeling and distributed scheduling framework of multiple types of adjustable resources, combines the internal coordination strategy of distributed new energy participating in the adjustable resource group of the power grid, and the coordinated interaction and balanced optimization control strategy of distributed new energy participating in the power grid scheduling, and finally forms an active / reactive coupling balanced collaborative optimization control system for high-penetration distributed new energy access to the power grid, which can improve the utilization rate of new energy, the coordinated work of multiple types of adjustable resources, especially the interaction of distributed new energy with traditional power sources, energy storage equipment, demand-side response, etc., can quickly respond to changes in power grid demand, improve the flexibility and stability of the power grid, and reduce the imbalance of the power grid caused by fluctuations in new energy; at the same time, through collaborative interaction and balanced optimization control strategies, dynamic adjustment of the power system can be achieved to ensure the optimal matching of power supply and demand at different time and space scales, and avoid power waste or shortage. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0045] Figure 1 This is a schematic diagram of the overall process of a balanced control scheduling method based on distributed renewable energy according to an embodiment of the present invention;

[0046] Figure 2 A schematic diagram of the process of constructing spatiotemporal characteristic modeling and distributed scheduling framework for multiple types of adjustable resources in a balanced control scheduling method based on distributed renewable energy according to an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of a process flow of establishing an internal coordination strategy for distributed renewable energy participating in a group of controllable resources of a power grid in a balanced control and dispatching method based on distributed renewable energy according to an embodiment of the present invention;

[0048] Figure 4 A schematic diagram of a flow chart of establishing a coordinated interaction and balanced optimization control strategy for distributed renewable energy participating in power grid dispatching in a balanced control dispatching method based on distributed renewable energy according to an embodiment of the present invention;

[0049] Figure 5A schematic diagram of a process for constructing an active / reactive coupling balanced collaborative optimization control system for accessing a power grid with high penetration rate distributed renewable energy in a balanced control and dispatching method based on distributed renewable energy according to an embodiment of the present invention;

[0050] Figure 6 The present invention is a schematic diagram of a process for realizing coordinated optimization of active and reactive power based on differentiated spatiotemporal scenarios of distributed renewable energy in a balanced control and scheduling method based on distributed renewable energy according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] 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.

[0052] Example 1

[0053] Reference Figure 1-Figure 6 , which is an embodiment of the present invention, provides a balanced control scheduling method based on distributed new energy, including:

[0054] S100: Obtain power grid parameters, and build a spatiotemporal characteristic model and a distributed scheduling framework for multiple types of adjustable resources based on the obtained parameters;

[0055] S200: Establishing the internal coordination strategy of distributed renewable energy participating in the controllable resource group of the power grid based on the spatiotemporal characteristic model and distributed dispatch framework;

[0056] S300: Establish a coordinated interaction and balance optimization control strategy for distributed renewable energy participating in power grid dispatching based on the coordinated strategy;

[0057] S400: Based on collaborative strategy and optimization control strategy, it realizes active / reactive coupling balance and collaborative optimization of high-penetration distributed renewable energy access to the power grid.

[0058] It should be noted that the spatiotemporal characteristic modeling and distributed scheduling framework of multi-type adjustable resources are constructed to provide basic power flow algorithm support for subsequent steps S200-S400; based on the spatiotemporal characteristic modeling and distributed scheduling framework of multi-type adjustable resources, a coordinated strategy for distributed new energy participating in the internal controllable resource group of the power grid is established to provide a theoretical basis for steps S300-S400.

[0059] It should also be noted that if Figure 2As shown, step S100 constructs the spatiotemporal characteristic modeling and distributed scheduling framework of multiple types of adjustable resources. In order to solve the challenges brought by the power changes of high-penetration distributed new energy units and massive flexible load access to the operation and scheduling of the power system, based on the cluster clustering idea, a group out-of-group characteristic model considering the spatiotemporal characteristics of adjustable resources is built, and distributed new energy is used as a dispatchable resource. An optimal configuration strategy for the distributed new energy capacity of the distribution network considering the local consumption of new energy is proposed, and a collaborative optimization technology of reactive voltage and active power flow under different penetration rates and operating conditions is proposed to improve the grid-connected flexibility and complementarity of distributed controllable resources. Based on the operating status perception technology of high-penetration distributed new energy, a distributed energy spatiotemporal scale optimization scheduling framework is formulated that considers reducing the scheduling cost, improving energy utilization efficiency, and suppressing power fluctuations, so as to realize large-scale and efficient grid-connected operation of adjustable resources.

[0060] In the embodiment of the present application, the spatiotemporal characteristic model of multiple types of adjustable resources is constructed in step S100, including the following steps A1:

[0061] A1: Constructing a model of out-group characteristics of regulatable resource clusters;

[0062] In A1, the following steps A1.1-A1.4 are specifically included:

[0063] A1.1: Capture the dynamic characteristics of adjustable resources and the aggregation characteristics of clusters;

[0064] Specifically, taking into account the regulation characteristics and response randomness of resources such as distributed power sources and controllable loads, the out-group characteristic model of the controllable resource cluster is studied. Based on the different types of structures and regulation characteristics of distributed power sources and controllable loads, the dynamic characteristics of the controllable resources and the aggregation characteristics of the cluster are analyzed and obtained.

[0065] A1.2: Establish mathematical models for the mechanical module, electrical module and control module of the controllable resources respectively;

[0066] A1.3: Based on the mathematical model combined with the homology equivalent method and the aggregation method, an equivalent clustering model is constructed that takes into account the spatiotemporal characteristics of high-proportion controllable resources to obtain the influence trend and sensitivity relationship of the internal parameters of each module on the stimulus response;

[0067] A1.4: Select the dominant parameters based on the impact trend and sensitivity relationship, obtain the identifiability of each parameter and identify the equivalent model parameters to construct an out-group characteristic model of the controllable resource cluster.

[0068] It should be noted that the dynamic characteristics of controllable resources and the aggregation characteristics of clusters are analyzed and obtained based on the different types of structures and regulation characteristics of distributed power sources and controllable loads.

[0069] In the embodiment of the present application, the spatiotemporal characteristic model of multiple types of adjustable resources is constructed in step S100, including the following steps A2:

[0070] A2: Coordinated optimization of reactive voltage and active power flow under different penetration rates and operating conditions based on the group out-of-group characteristic model;

[0071] In A2, the following steps are specifically included: A2.1-A2.2:

[0072] A2.1: Considering the randomness of the output of distributed generation and the volatility of load in the distribution network, generate typical scenarios of adjustable resources participating in the operation of the power grid, which are used to set constraints;

[0073] Specifically, the parameters that affect the randomness of distributed power output and load volatility in the distribution network mainly include: active / reactive regulation characteristics of distributed new energy units, dynamic active / reactive regulation requirements, and active / reactive sensitivity.

[0074] In one implementation, the response characteristics of the above-mentioned multiple controllable resource distribution networks can be combined to establish an external characteristic model that accurately describes the characteristics of the adjustable resource cluster, taking into account multiple constraints such as distributed power source uncertainty, controllable load response characteristics, and system power supply reliability constraints. Based on second-order cone relaxation and linearization technology, a joint optimization method for active and reactive power of a new energy high penetration power grid based on second-order cone programming is implemented. Specifically, Figure 6 shown.

[0075] It should be noted that the generation of typical scenarios for the participation of controllable resources in power grid operation aims to reduce distribution network losses, improve system voltage stability, and improve economic benefits, taking into account multiple constraints such as the uncertainty of distributed power sources, controllable load response characteristics, and system power supply reliability constraints.

[0076] A2.2: Combine constraint regulation with the out-group characteristic model to achieve joint optimization of active and reactive power in high-penetration renewable energy power grids.

[0077] In the embodiment of the present application, the spatiotemporal characteristic model of multiple types of adjustable resources is constructed in step S100, including the following steps A3:

[0078] A3: Based on the optimization results, a distributed scheduling framework suitable for access to massive group units is constructed;

[0079] In A3, the following steps are specifically included: A3.1-A3.2:

[0080] A3.1: Construct a coordinated optimization dispatch model for distributed energy based on multi-source data fusion, feature extraction and dynamic state estimation of distribution network;

[0081] A3.2: Based on the existing problems in the coordinated optimization scheduling model of distributed energy, the operating status perception method of high-penetration distributed new energy and the data-driven partitioning and key node identification method are combined to construct a distributed scheduling framework suitable for the access of massive group units.

[0082] Specifically, the problems existing in the coordinated optimization scheduling model of distributed energy include the uncertainty of distributed new energy and the problem that the state quantity in the system changes with the fluctuation of new energy.

[0083] In the embodiment of the present application, the step S200 establishes a distributed new energy participating in the internal coordination strategy of the grid controllable resource group, including the following steps B1-B3:

[0084] B1: Based on the spatiotemporal power consumption characteristics of adjustable resources, a fast power allocation strategy is constructed within the subgroup;

[0085] Specifically, the spatiotemporal electricity consumption characteristics of controllable resources mainly include: dynamic characteristics of distributed renewable energy, controllable load response characteristics, grid topology and access location;

[0086] It should be noted that based on the above-mentioned power consumption characteristics, a subgroup division strategy within the group is constructed, and based on the subgroup division strategy within the group, a power rapid allocation strategy within the subgroup is constructed.

[0087] B2: Constructing power forecasting strategy for power grid sections based on data-driven approach;

[0088] Specifically, we study the grid section load forecasting technology based on data-driven methods, and combine it with the prediction of new energy power generation to achieve a comprehensive prediction of grid connection point power.

[0089] B3: Obtain the specific manifestations of uncertainty in renewable energy and its negative impact on distribution network voltage control, and construct a coordinated operation control strategy for peak load regulation, voltage regulation, and loss reduction in which distributed renewable energy participates in grid voltage balance control in different time and space scenarios.

[0090] It should be noted that if Figure 3As shown, in step S200, based on the spatiotemporal characteristic modeling of multiple types of adjustable resources and the distributed scheduling framework, a coordinated strategy for the internal group of adjustable resources participating in the power grid is established. Based on the spatiotemporal power consumption characteristics of adjustable resources, a method for grouping adjustable resource groups based on artificial intelligence algorithms is proposed to achieve rapid power distribution and local balance within the subgroups. A comprehensive prediction method for grid connection point power considering renewable energy generation and load power is proposed to reduce the impact of distributed renewable energy uncertainty on the scheduling and optimization decision-making of distribution networks. A voltage regulation priority evaluation index considering the dynamic characteristics of adjustable resources is established, and a peak regulation, voltage regulation, and loss reduction coordinated operation control strategy for distributed renewable energy participating in grid voltage control under different spatiotemporal scenarios is proposed to improve the economic performance and reliability of high-penetration distributed renewable energy distribution networks under multiple scenarios.

[0091] In the embodiment of the present application, in step S300, a distributed new energy participating in the coordinated interaction and balanced optimization control strategy of power grid dispatch is established according to the coordinated strategy, including the following steps C1-C2:

[0092] C1: Obtain regional resource endowment and distribution of existing renewable energy units, divide controllable resources in renewable energy balance areas based on hierarchical and graded ideas, build renewable energy network energy management strategies, integrate stable control systems and renewable energy network energy management hierarchical structures, establish renewable energy rapid power regulation system architecture, and build rapid power control strategies under different scenarios;

[0093] Specifically, the distribution of new energy units mainly comprehensively analyzes factors such as geographical location, access method, and operation strategy, and explores the networking mode of distributed new energy networks.

[0094] C2: Obtain user-side parameters, build a user-side flexible load response characteristic and regulation capability model, obtain the regional source-load characteristics of distributed renewable energy access to the power grid, and build a rapid optimization strategy for coordinated and balanced control of multi-region optimal controllable resource control with the goal of improving the balance between the power grid and user-side flexible load distribution in each region.

[0095] In another embodiment, the source-load characteristics of the distributed renewable energy access grid area are obtained, and the causes of the flexible load imbalance characteristics can also be combined to consider the grid safety operation constraints, inequality constraints, and equality constraints to construct a rapid optimization strategy for coordinated and balanced control of multi-region optimal controllable resources.

[0096] In addition, it is necessary to develop a distributed new energy network power self-balancing terminal that integrates communication adaptation, collection, analysis, control and other functions, and to develop a distributed new energy network reactive voltage control complete set of management devices: In response to the needs of distributed new energy applications, develop a distributed new energy network power self-balancing terminal that integrates communication adaptation, collection, analysis, control and other functions, to upload edge data to the edge cluster system, and realize the interactive coordination of voltage regulation target values ​​between smart terminals through Lora. The smart terminals in adjacent substations aggregate data to the adjacent feeder terminals through Lora communication, and send it to the application platform server through the wireless private network via the encryption module; based on the needs of reactive voltage management in medium and low voltage distribution networks, develop a distributed reactive voltage control complete set of management devices.

[0097] It should be noted that if Figure 4 As shown, in step S300, based on the spatiotemporal characteristics modeling of multi-type adjustable resources and the distributed dispatching framework and the internal coordination strategy of the distributed new energy participating in the adjustable resource group of the power grid, the coordinated interaction and balanced optimization control strategy of the distributed new energy participating in the power grid dispatching is established, with the goal of improving the supporting capacity of the new energy units to the power grid and improving the complementary and balanced regulation capacity of the new power system, and the equipment performance improvement and "source-load interaction" balance mode optimization method are proposed to solve the problems of large peak-shaving pressure, imbalance of power supply and demand, and serious "abandonment" in the system under the access of a large number of distributed new energy. The energy management strategy and rapid power regulation technology of the new energy network are proposed to achieve regional power supply and demand matching and dynamic and static performance improvement; based on this, a multifunctional distributed new energy network power self-balancing terminal and a complete set of reactive voltage regulation and control devices are further developed to provide a hardware foundation for the implementation of the balanced control of the new energy distribution network; based on this, the unbalanced characteristics of the flexible load on the user side of the power grid area where the distributed new energy is connected are further analyzed in detail, and a rapid optimization algorithm for the overall coordination of the optimal controllable resource control in multiple regions is proposed to achieve rapid group control and group coordination of new energy configuration equipment in multiple scenarios. Stable and reliable operation.

[0098] In the embodiment of the present application, step S400 realizes active / reactive coupling balance and coordinated optimization of high-penetration distributed new energy access to the power grid based on the coordinated strategy and the optimized control strategy, including the following steps D1-D3:

[0099] D1: Based on the power self-balancing terminal, massive multi-dimensional heterogeneous power data of distributed renewable energy access to the distribution network is collected, the characteristics of data in different time and space dimensions are obtained, and a high-penetration distributed renewable energy grid-connected data modeling strategy is constructed.

[0100] D2: A data error correction model that automatically checks the measured data deviation obtains the distribution of active / reactive power fluctuation rates in qualified data samples, sets a reasonable power fluctuation threshold, selects stable qualified areas, and builds a distributed new energy grid-connected balance control optimization calculation model;

[0101] D3: Combine the high penetration distributed renewable energy grid-connected data modeling strategy with the distributed renewable energy grid-connected balance control optimization calculation model to obtain a data calculation model for achieving grid active / reactive coupling balance coordinated optimization.

[0102] It should be noted that if Figure 5 As shown, in step S400, based on the internal coordination strategy of the distributed new energy participating in the controllable resource group of the power grid and the coordinated interaction and balance optimization control strategy of the distributed new energy participating in the power grid dispatching, a high-penetration distributed new energy access power grid active / reactive coupling balanced coordinated optimization control system is constructed, a high-penetration distributed new energy grid-connected data modeling method is proposed, and a distributed new energy grid-connected balance control optimization calculation model is established; a distributed new energy access intelligent distribution network system combining "cloud-edge-end" integration and edge autonomy is proposed, and distributed new energy access power grid dispatching coordinated optimization management software is developed to give full play to the control potential of massive distributed resources and promote the participation of massive controllable resources in power grid interaction; suitable pilot projects are selected to carry out demonstration and application of distributed new energy participating in the power grid active / reactive balance coordinated optimization balance control technology, so as to provide a theoretical and practical basis for building a source-grid-load coordinated balance control operation and dispatching system.

[0103] Based on the above modeling and analysis scheme, it is also necessary to develop a coordinated optimization management platform for distributed new energy access to the power grid. Specifically, we can build an intelligent new power grid system that combines "cloud-edge-end" integration with edge autonomy, develop coordinated optimization management software for distributed new energy access to the power grid, and realize the joint operation monitoring function of multi-region and multi-level voltage regulation and loss reduction on the power grid and user side: study the software and hardware architecture of the Internet of Things system for distribution network dispatch optimization under distributed photovoltaic access combined with "cloud-edge-end" integration and edge autonomy, and realize localized recording and machine learning analysis of equipment actions; develop a software module for coordinated optimization management of distributed new energy access to the power grid, embed it into the edge cluster system as a micro-application, build a regional distributed new energy panoramic control demonstration micro-application, open up distributed photovoltaic data access management, and use standardized protocols to upload new distributed photovoltaics to the platform software via smart terminals. The existing distributed photovoltaics are accessed to the power grid through protocol coordination and uploaded to the platform software via smart terminals.

[0104] Finally, based on the distributed renewable energy grid-connected balance control optimization calculation model and the distributed renewable energy access grid dispatch collaborative optimization management platform, the distributed renewable energy participating in the grid active / reactive balance collaborative optimization control application is carried out.

[0105] Exemplarily, the above scheme is used to study the typical characteristics of high-penetration distributed photovoltaic access to the distribution network, select demonstration application areas, and propose an implementation plan for the application of distributed new energy participating in the coordinated optimization and control of active / reactive balance in the power grid: select an industrial park with typical distributed new energy power consumption characteristics or a typical distributed new energy agricultural trade and wholesale power consumption area with one or several 10kV lines and several subordinate substations as demonstration application areas. The area has large-scale distributed photovoltaic grid-connected access, has more energy storage device access and more distribution network voltage adjustable resources (the adjustable resources are mainly the distributed new energy network reactive voltage control and management equipment developed in this project. In addition, according to the regulation needs, some of the existing equipment on site that meets the transformation conditions is connected to the distributed new energy network power self-balancing terminal to realize the voltage regulation control function); based on the intelligent new distribution network system combining cloud-edge-end integration and edge autonomy, study the implementation plan for the application of distributed new energy participating in the coordinated optimization and balance control of active / reactive balance in the power grid; develop and deploy various functional modules in the distributed new energy access to the power grid dispatching coordinated optimization management micro-application to realize the functional test of high-penetration distributed new energy participating in the coordinated optimization and balance control technology of active / reactive balance in the power grid.

[0106] The above is a schematic scheme of a balanced control scheduling method based on distributed renewable energy in this embodiment. It should be noted that the technical scheme of the system for balanced control scheduling based on distributed renewable energy and the technical scheme of the balanced control scheduling method based on distributed renewable energy belong to the same concept. For details not described in detail in the technical scheme of the balanced control scheduling system based on distributed renewable energy in this embodiment, please refer to the description of the technical scheme of the balanced control scheduling method based on distributed renewable energy.

[0107] This embodiment also provides a system for a balanced control scheduling method based on distributed new energy, including:

[0108] The first construction module is used to obtain power grid parameters and build a spatiotemporal characteristic model and a distributed scheduling framework of multiple types of adjustable resources based on the obtained parameters;

[0109] The second building block is used to establish an internal coordination strategy for distributed new energy resources participating in the grid controllable resource group based on the spatiotemporal characteristic model and the distributed scheduling framework;

[0110] The third building block is used to establish a coordinated interaction and balance optimization control strategy for distributed new energy participating in power grid dispatching based on the coordinated strategy;

[0111] The optimization module is used to achieve active / reactive coupling balance and coordinated optimization of high-penetration distributed renewable energy access to the power grid based on collaborative strategies and optimized control strategies.

[0112] This embodiment also provides a computing device, which is suitable for the situation of balanced control scheduling based on distributed new energy, 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 balanced control scheduling method based on distributed new energy as proposed in the above embodiment.

[0113] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the balance control scheduling method based on distributed new energy proposed in the above embodiment is implemented.

[0114] The storage medium proposed in this embodiment and the method for realizing balanced control scheduling based on distributed new energy proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0115] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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. A balanced control scheduling method based on distributed new energy, characterized in that: include: Obtain grid parameters, and build spatiotemporal characteristic models and distributed scheduling frameworks for multiple types of adjustable resources based on the obtained parameters; Based on the spatiotemporal characteristic model and the distributed dispatching framework, a strategy for internal coordination of distributed renewable energy resources participating in the controllable resource group of the power grid is established; Establish a coordinated interaction and balanced optimization control strategy for distributed new energy to participate in power grid dispatching based on the collaborative strategy; Based on the collaborative strategy and the optimization control strategy, active / reactive coupling balance collaborative optimization of high-penetration distributed new energy access to the power grid is achieved.

2. The method for balancing control and dispatching based on distributed renewable energy as claimed in claim 1, characterized in that: The construction of the spatiotemporal characteristic model of multiple types of adjustable resources includes: Construct an out-group characteristic model of a controllable resource cluster, including: Capture the dynamic characteristics of adjustable resources and the aggregation characteristics of clusters; Mathematical models are established for the mechanical module, electrical module and control module of the adjustable resources respectively; Based on the mathematical model combined with the homology equivalent method and the aggregation method, an equivalent clustering model taking into account the spatiotemporal characteristics of high-proportion controllable resources is constructed to obtain the influence trend and sensitivity relationship of the internal parameters of each module on the stimulus response; Based on the influence trend and sensitivity relationship, the parameters that play a leading role are selected, and the identifiability of each parameter and the identification of equivalent model parameters are obtained to construct an out-of-group characteristic model of a controllable resource cluster.

3. The method for balancing control and dispatching based on distributed renewable energy as claimed in claim 2, characterized in that: The construction of the spatiotemporal characteristic model of multiple types of adjustable resources also includes: Based on the group external characteristic model, the reactive voltage and active power flow are coordinated optimized under different penetration rates and operating conditions, including: Considering the randomness of distributed power output and load volatility in the distribution network, typical scenarios of adjustable resources participating in grid operation are generated for setting constraints; By combining the constraint adjustment with the out-group characteristic model, the active and reactive joint optimization of a high-penetration new energy power grid is achieved.

4. The method for balancing control and dispatching based on distributed renewable energy as claimed in claim 3, characterized in that: Based on the optimization results, a distributed scheduling framework suitable for massive group unit access is constructed, including: Based on multi-source data fusion, feature extraction and distribution network dynamic state estimation, a distributed energy coordinated optimization scheduling model is constructed; Based on the problems existing in the distributed energy coordinated optimization scheduling model, the operating status perception method of high-penetration distributed new energy and the data-driven partitioning and key node identification method are combined to construct a distributed scheduling framework suitable for the access of massive group units.

5. The method for balancing control and dispatching based on distributed renewable energy as claimed in claim 4, characterized in that: Establish a coordinated strategy for distributed renewable energy to participate in the controllable resource group of the power grid, including: Based on the spatiotemporal power consumption characteristics of adjustable resources, a fast power allocation strategy within the subgroup is constructed; Constructing power forecasting strategy for power grid sections based on data-driven approach; Obtain the specific manifestations of new energy uncertainty and its negative impact on distribution network voltage control, and construct a coordinated operation control strategy for peak shaving, voltage regulation, and loss reduction for distributed new energy participating in grid voltage balance control under different time and space scenarios.

6. The method for balancing control and dispatching based on distributed renewable energy as claimed in claim 5, characterized in that: Based on the collaborative strategy, a distributed new energy participating in the grid dispatching collaborative interaction and balance optimization control strategy is established, including: Obtain regional resource endowment and distribution of existing renewable energy units, divide controllable resources in renewable energy balance areas based on hierarchical and graded ideas, build renewable energy network energy management strategies, integrate stable control systems and renewable energy network energy management hierarchical structures, establish renewable energy rapid power regulation system architecture, and build rapid power control strategies in different scenarios; Obtain user-side parameters, build a user-side flexible load response characteristic and regulation capability model, obtain regional source-load characteristics of distributed renewable energy access to the power grid, and build a rapid optimization strategy for coordinated and balanced control of multi-regional optimal controllable resource control with the goal of improving the balance of flexible load distribution between the power grid and the user side in each region.

7. The method for balancing control and dispatching based on distributed renewable energy as claimed in claim 6, characterized in that: Based on the collaborative strategy and the optimization control strategy, active / reactive coupling balance collaborative optimization of high-penetration distributed new energy access to the power grid is achieved, including: Based on the power self-balancing terminal, the massive multi-dimensional heterogeneous power data of distributed renewable energy access to the distribution network is collected, the characteristics of data in different time and space dimensions are obtained, and a high-penetration distributed renewable energy grid-connected data modeling strategy is constructed. The data error correction model automatically checks the measured data deviation, obtains the distribution of active / reactive power fluctuation rate in the qualified data samples, sets a reasonable power fluctuation threshold, selects stable qualified areas, and builds a distributed new energy grid-connected balance control optimization calculation model; The high-penetration distributed renewable energy grid-connected data modeling strategy is combined with the distributed renewable energy grid-connected balance control optimization calculation model to obtain a data calculation model for achieving grid active / reactive coupling balance coordinated optimization.

8. A system applied to the distributed renewable energy balance control scheduling method according to any one of claims 1 to 7, characterized in that: include: The first construction module is used to obtain power grid parameters and build a spatiotemporal characteristic model and a distributed scheduling framework of multiple types of adjustable resources based on the obtained parameters; The second building module is used to establish an internal coordination strategy for the distributed new energy participating in the controllable resource group of the power grid based on the spatiotemporal characteristic model and the distributed scheduling framework; The third building module is used to establish a coordinated interaction and balanced optimization control strategy for distributed new energy participating in power grid dispatching according to the coordination strategy; The optimization module is used to achieve active / reactive coupling balance collaborative optimization of high-penetration distributed new energy access to the power grid based on the collaborative strategy and the optimization control strategy.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the balancing control and scheduling method based on distributed new energy are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the balanced control and scheduling method based on distributed new energy as described in any one of claims 1 to 7.

Images