Wind-solar-storage integrated power grid energy distribution scheduling method

Abstract

The application provides a wind-solar-storage integrated power grid energy distribution scheduling method, relates to the technical field of power grid scheduling, and comprises the following steps: collecting wind-solar-storage equipment distribution information and constructing a regional wind-solar-storage topology graph; collecting a historical wind-solar-storage operation data set; performing storage energy module regulation burden distribution analysis on the historical wind-solar-storage operation data set to determine storage energy regulation burden distribution; performing wind power generation abandoned wind rate distribution analysis and photovoltaic power generation abandoned light rate distribution analysis on the historical wind-solar-storage operation data set to generate abandoned wind rate distribution and abandoned light rate distribution; and performing wind-solar-storage balanced scheduling optimization in combination with the storage energy regulation burden distribution, the abandoned wind rate distribution and the abandoned light rate distribution to generate a power grid energy distribution decision. Through the application, the technical problem of unbalanced distribution of storage energy resources among different regions in the prior art can be solved, the technical target of wind-solar-storage coordinated optimal scheduling is achieved, and the technical effect of improving the operation balance of the storage energy system is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid dispatching, in particular to a wind-solar-storage integrated power grid energy distribution dispatching method. BACKGROUND

[0002] In a wind-solar-storage multi-energy complementary system, power grid energy distribution dispatching usually relies on independent management mechanisms of local energy nodes or regional dispatching strategies based on decentralized control structures. This approach can achieve local energy balance and real-time control to some extent, but lacks system-level coordination capabilities across regions. In practical applications, wind power plants and photovoltaic power stations are usually self-adaptively adjusted according to the load demand of their respective access points, while the energy storage system is configured in a specific region to undertake the energy balance task of the region. However, due to differences in new energy fluctuation amplitude and load characteristics in different regions, some energy storage modules are frequently adjusted, the load is too heavy, the operating efficiency is reduced, and even overcharging and over-discharging risks occur, while other energy storage modules are in a low-load or idle state and cannot participate in the adjustment process, resulting in uneven allocation of overall energy storage resources.

[0003] In summary, the existing technology has the technical problem that due to the lack of system-level collaborative dispatching mechanisms and global awareness capabilities, the allocation of energy storage resources between different regions is imbalanced, some energy storage modules have heavy adjustment loads while other modules are idle, further affecting the overall operating efficiency of the power grid and the new energy consumption capacity of the wind-solar-storage multi-energy complementary system. SUMMARY

[0004] The purpose of the present application is to provide a wind-solar-storage integrated power grid energy distribution dispatching method to solve the technical problem in the prior art that due to the lack of system-level collaborative dispatching mechanisms and global awareness capabilities, the allocation of energy storage resources between different regions is imbalanced, some energy storage modules have heavy adjustment loads while other modules are idle, further affecting the overall operating efficiency of the power grid and the new energy consumption capacity of the wind-solar-storage multi-energy complementary system.

[0005] In view of the above problems, the present application provides a wind-solar-storage integrated power grid energy distribution dispatching method, comprising: collecting wind-solar-storage equipment distribution information in a preset power grid region, and constructing a regional wind-solar-storage topology graph; collecting a historical wind-solar-storage operation data set based on the regional wind-solar-storage topology graph; performing adjustment burden distribution analysis of energy storage modules based on the historical wind-solar-storage operation data set, marking on the regional wind-solar-storage topology graph, and determining the energy storage adjustment burden distribution; performing wind curtailment rate distribution analysis and photovoltaic curtailment rate distribution analysis based on the historical wind-solar-storage operation data set, marking on the regional wind-solar-storage topology graph, and generating wind curtailment rate distribution and photovoltaic curtailment rate distribution; performing wind-solar-storage balance dispatching optimization in combination with the energy storage adjustment burden distribution, the wind curtailment rate distribution, and the photovoltaic curtailment rate distribution, and generating a power grid energy distribution decision.

[0006] Preferably, the wind-solar-storage integrated power grid energy distribution scheduling method further comprises: each node in the regional wind-solar-storage topology map represents an energy unit, and each edge represents a power grid connection relationship.

[0007] Preferably, the wind-solar-storage integrated power grid energy distribution scheduling method further comprises: based on the historical wind-solar-storage operation data set, the frequency of the adjustment behavior of each energy storage module in the scheduling period is statistically analyzed and the mean value is calculated to obtain the adjustment frequency of each energy storage module; the ratio of the adjustment frequency of each energy storage module to the sum of the adjustment frequencies of each module is calculated, and is marked in the regional wind-solar-storage topology map to generate the energy storage adjustment burden distribution.

[0008] Preferably, the wind-solar-storage integrated power grid energy distribution scheduling method further comprises: the frequency of the adjustment behavior in the scheduling period is obtained by counting the charge-discharge switching frequency of each energy storage module.

[0009] Preferably, the wind-solar-storage integrated power grid energy distribution scheduling method further comprises: based on the historical wind-solar-storage operation data set, passive curtailment rates of wind and passive curtailment rates of light are identified to generate historical passive curtailment rate data sequences of wind and historical passive curtailment rate data sequences of light; based on the historical passive curtailment rate data sequences of wind and the historical passive curtailment rate data sequences of light, the mean value of the curtailment rate of wind and the curtailment rate of light in the scheduling period is calculated to generate the mean value data of the curtailment rate of wind of each wind power generation module and the mean value data of the curtailment rate of light of each photovoltaic power generation module; the mean value data of the curtailment rate of wind of each wind power generation module and the mean value data of the curtailment rate of light of each photovoltaic power generation module are marked in the regional wind-solar-storage topology map to generate the curtailment rate distribution of wind and the curtailment rate distribution of light.

[0010] Preferably, the wind-solar-storage integrated power grid energy distribution scheduling method further comprises: the passive curtailment rate of wind and the passive curtailment rate of light are the deviation degree of deviating from the theoretical power generation capacity due to the artificial reduction of power generation output of each wind power generation module and each photovoltaic power generation module after the external scheduling platform issues a limit generation instruction.

[0011] Preferably, the wind-solar-storage integrated power grid energy distribution scheduling method further comprises: extracting data with generation instruction identifiers from the historical wind-solar-storage operation data set, identifying the limit generation adjustment parameters of each wind power generation module and the limit generation adjustment parameters of each photovoltaic power generation module; extracting the operation parameter difference and the wind power generation output difference of each wind power generation module under the same meteorological environmental conditions from the historical wind-solar-storage operation data set, and training the limit generation adjustment predictor of each wind power generation module; extracting the operation parameter difference and the photovoltaic power generation output difference of each photovoltaic power generation module under the same meteorological environmental conditions from the historical wind-solar-storage operation data set, and training the limit generation adjustment predictor of each photovoltaic power generation module; calling the limit generation adjustment predictor of each wind power generation module to analyze the limit generation adjustment parameters of each wind power generation module, and completing passive wind curtailment rate identification; calling the limit generation adjustment predictor of each photovoltaic power generation module to analyze the limit generation adjustment parameters of each photovoltaic power generation module, and completing passive light curtailment rate identification.

[0012] Preferably, the wind-solar-storage integrated power grid energy distribution scheduling method further comprises: based on the storage adjustment burden distribution, identifying high-frequency storage modules higher than a preset burden index and low-frequency storage modules lower than a preset burden index for storage scheduling optimization to generate a first balance optimization result; based on the wind curtailment rate distribution and the light curtailment rate distribution, performing traditional energy scheduling of each wind power generation module and each photovoltaic power generation module connected to the power grid to minimize the wind curtailment rate and the light curtailment rate to generate a second balance optimization result; and generating the power grid energy distribution decision based on the first balance optimization result and the second balance optimization result.

[0013] Preferably, the wind-solar-storage integrated power grid energy distribution scheduling method further comprises: determining a set of storage energy units in a high-regulation-demand storage module connected to the high-frequency storage module and a set of storage energy units in a low-regulation-demand storage module connected to the low-frequency storage module; collecting the regulation demand of the set of storage energy units in the high-regulation-demand storage module and the set of storage energy units in the low-regulation-demand storage module, performing regulation demand equalization processing through exchange of elements in the set to generate an equalization processing result; and performing scheduling relationship adjustment of each storage module and the set of storage energy units in the storage module based on the equalization processing result to generate the first balance optimization result.

[0014] Preferably, the wind-solar-storage integrated power grid energy distribution scheduling method further comprises: the power grid connected through the grid-connected device is a power system accessed by each wind power generation module and each photovoltaic power generation module.

[0015] The technical solutions provided in the application have at least the following technical effects or advantages: by achieving the technical target of coordinated optimization scheduling of wind, light and storage based on the distribution of storage regulation burden and the distribution of abandoned wind and light, the technical effects of improving the operation balance of the storage system, reducing the loss of abandoned wind and light, and achieving the global optimal distribution of energy at the power grid level are achieved.

[0016] The above description is only a summary of the technical solutions of the application. In order to more clearly understand the technical means of the application, the specific embodiments of the application can be implemented according to the content of the specification. In order to make the above and other purposes, characteristics and advantages of the application more obvious and easy to understand, the following specific embodiments of the application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the application, nor is it intended to limit the scope of the application. Other features of the application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only exemplary, and other drawings can be obtained by those skilled in the art without creating labor on the basis of the provided drawings.

[0018] Figure 1 The flowchart of the wind, light and storage integrated power grid energy distribution scheduling method of the application.

[0019] Figure 2 The flowchart of determining the storage regulation burden distribution in the wind, light and storage integrated power grid energy distribution scheduling method of the application. DETAILED DESCRIPTION

[0020] The application provides a wind, light and storage integrated power grid energy distribution scheduling method, which solves the technical problem in the prior art that due to the lack of system-level collaborative scheduling mechanism and global awareness, the distribution of storage resources between different regions is unbalanced, some storage modules are overloaded and other modules are idle, which further affects the overall operation efficiency of the power grid and the new energy consumption capacity of the wind, light and storage multi-energy complementary system. The technical target of coordinated optimization scheduling of wind, light and storage based on the distribution of storage regulation burden and the distribution of abandoned wind and light is achieved, and the technical effects of improving the operation balance of the storage system, reducing the loss of abandoned wind and light, and achieving the global optimal distribution of energy at the power grid level are achieved.

[0021] Below, the technical solutions in the present application will be described clearly and completely with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. It should be understood that the present application is not limited by the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application. In addition, it should be noted that, for the convenience of description, only the parts related to the present application are shown in the drawings, not all.

[0022] Embodiment one, please refer to the attached Figure 1 The present application provides a wind-solar-storage integrated power grid energy distribution scheduling method, specifically comprising the following steps:

[0023] S1: Collecting the distribution information of wind-solar-storage equipment in a preset power grid area, and constructing a regional wind-solar-storage topology graph.

[0024] Specifically, the preset power grid area refers to a designated spatial range, such as a certain city power distribution grid, a township micro-grid area or a power generation area rich in wind and solar resources, and the wind-solar-storage equipment refers to wind power and photovoltaic power generation facilities that can generate renewable power and electrochemical energy storage, battery systems or other energy storage equipment for energy regulation. Obtain the specific location, type, capacity, access node, electrical connection of wind power generation device, photovoltaic power generation device and energy storage system in a specific power grid range, form a graphical data structure according to the actual connection relationship of wind-solar-storage equipment in the power grid, and construct a regional wind-solar-storage topology graph.

[0025] S2: Collecting historical wind-solar-storage operation data set based on the regional wind-solar-storage topology graph.

[0026] Specifically, based on the regional wind-solar-storage topology graph, the historical wind-solar-storage operation data set is collected, and the information related to each energy unit and its connection relationship in the regional wind-solar-storage topology graph is extracted, including the historical output power of wind turbine generator, the irradiation response of photovoltaic array, the charge-discharge state of battery pack, the voltage fluctuation of each node of power grid, the current flow direction and operation control instruction, etc., also containing scheduling response, limit record, external meteorological environment and other information.

[0027] S3: Performing storage module regulation burden distribution analysis with the historical wind-solar-storage operation data set, marking on the regional wind-solar-storage topology graph, and determining the storage regulation burden distribution.

[0028] Specifically, the energy storage module refers to a unit with the functions of storing and releasing electric energy, such as a battery pack, a super capacitor or other energy storage medium, and the regulation burden indicates the frequency and intensity of participating in power balance of the power grid. The regulation burden distribution analysis of the energy storage module is performed on the historical wind-solar-storage operation data set, the energy regulation tasks undertaken by each energy storage module in different time periods are counted and calculated, so as to determine the load degree in the entire wind-solar-storage equipment. The regulation burden results of each energy storage module obtained by analysis are marked on the regional wind-solar-storage topology diagram, and are attached to the corresponding graphic nodes in the form of numerical labels or color levels, so that the differences in regulation task distribution between different energy storage modules are intuitively displayed in the regional wind-solar-storage topology diagram, the distribution of energy storage regulation burden is determined, the energy storage modules in high-frequency charging and discharging state and the energy storage modules in low-use state can be clearly identified, and the subsequent scheduling strategy can be optimized.

[0029] S4: Perform wind curtailment rate distribution analysis and photovoltaic curtailment rate distribution analysis on the historical wind-solar-storage operation data set, mark on the regional wind-solar-storage topology diagram, and generate wind curtailment rate distribution and photovoltaic curtailment rate distribution.

[0030] Specifically, the wind curtailment rate distribution analysis and the photovoltaic curtailment rate distribution analysis are performed on the historical wind-solar-storage operation data set, the proportion of each wind power module and photovoltaic module that fails to realize the total available power under certain conditions due to limited instruction and other factors is identified and calculated, and the proportion data is classified and analyzed to form a distribution result describing the severity of power curtailment in different locations. The wind curtailment rate is the proportion of the electric energy that the wind power module fails to generate under the available wind energy conditions to the theoretical available power, and the photovoltaic curtailment rate is the proportion of the electric energy that the photovoltaic module fails to output under the available solar energy conditions. Subsequently, the regional wind-solar-storage topology diagram is marked, and the analysis results of the power curtailment rate are attached to the nodes of the corresponding wind power module and photovoltaic module, so that the wind curtailment rate and photovoltaic curtailment rate of each node in the entire graphic structure are presented.

[0031] S5: Perform wind-solar-storage balancing scheduling optimization in combination with the energy storage regulation burden distribution, the wind curtailment rate distribution and the photovoltaic curtailment rate distribution, and generate power grid energy distribution decision.

[0032] Specifically, the wind-solar-storage balancing scheduling optimization is performed in combination with the storage regulation burden distribution, the abandoned wind rate distribution and the abandoned light rate distribution. The regulation frequency of the storage system in different regions and the abandoned electricity proportion of the wind power and photovoltaic system in different positions are simultaneously taken into account in the scheduling analysis. A global optimal energy allocation scheme is found through a unified optimization model. The storage regulation burden distribution reflects the frequency of the adjustment tasks undertaken by each storage module in a certain period. If some modules perform charging and discharging too frequently, the service life of the modules will be shortened and the system efficiency will be reduced. The abandoned wind rate distribution refers to the difference in the proportion of unused wind energy in the space due to the power generation limitation of each module in the wind power system. The abandoned light rate distribution reflects the location characteristics of the proportion of wasted solar energy in the system due to the power generation limitation of each photovoltaic module. Through scheduling optimization, the regulation pressure of the high-burden storage module is reasonably transferred to the low-burden module, and the power generation capacity of the area with high abandoned wind rate and abandoned light rate is preferentially consumed, so as to maximize the new energy utilization rate and the storage use efficiency of the overall system. According to the comprehensive balancing result, the power generation capacity, the charging and discharging capacity and the power transmission direction of each node in each period are determined, so as to ensure the balance between power supply and demand, the optimal power generation efficiency and the reasonable equipment load, and an energy distribution decision of the power grid is generated.

[0033] Further, the application further includes: each node in the regional wind-solar-storage topology diagram represents an energy unit, and each edge represents a power grid connection relationship.

[0034] Specifically, each node in the regional wind-solar-storage topology diagram represents an energy unit, and each drawn point represents an entity capable of generating or regulating electric energy, including a wind turbine generator, a photovoltaic array, a storage module, etc. The energy unit not only has the output or absorption capacity of electric energy, but also has the scheduling value of interacting energy with other units. As a basic component of the topology diagram, the node is used for abstract modeling, and the relationship and action between various devices are uniformly processed subsequently.

[0035] Each edge represents a power grid connection relationship, representing the physical line or electrical connection existing in the power system, including the power flow path established by the power transmission line, the distribution line or the electrical switch device, which determines how electric energy is transmitted from one unit to another unit, and also affects key factors such as power distribution, line load and scheduling control.

[0036] Further, as shown in Figure 2 The application further includes: based on the historical wind-solar-storage operation data set, the frequency of the adjustment behavior of each storage module in the scheduling period is statistically analyzed and the mean value is calculated to obtain the regulation frequency of each storage module. The ratio of the regulation frequency of each storage module to the sum of the regulation frequencies of all modules is calculated and marked in the regional wind-solar-storage topology diagram to generate the storage regulation burden distribution.

[0037] Specifically, the dispatching cycle refers to a basic control time period of power grid operation, such as 15 minutes, 1 hour or 1 day, and the adjustment behavior is an active energy adjustment action of the wind-solar-storage device in the dispatching cycle, including switching from a charging state to a discharging state, or switching from a discharging state to a standby or charging state, etc. Based on a historical wind-solar-storage operation data set, the frequency of the adjustment behavior of each energy storage module in the dispatching cycle is statistically analyzed and the mean value is calculated. The number of charging and discharging switching behaviors of each energy storage module in a complete dispatching cycle is counted, and the average frequency in the dispatching cycle is further calculated, reflecting the activity level of the energy storage module participating in grid regulation, that is, the adjustment frequency of each energy storage module. The higher the adjustment frequency value is, the more dynamic response tasks the energy storage module undertakes.

[0038] The adjustment frequency of each energy storage module is divided by the sum of the adjustment frequencies of all energy storage modules to calculate the ratio of the adjustment frequency of each energy storage module to the sum of the adjustment frequencies of all modules, obtaining the relative adjustment burden proportion of the module, representing the proportion of dynamic adjustment tasks undertaken by each energy storage module in the entire wind-solar-storage system. Then, the regional wind-solar-storage topology is marked to generate the energy storage adjustment burden distribution. Table 1 is a record of the energy storage adjustment frequency and burden distribution in the last wind-solar-storage operation cycle.

[0039] Table 1: Record of energy storage adjustment frequency and burden distribution in the last wind-solar-storage operation cycle

[0040]

[0041] Further, the present application further comprises: the frequency of the adjustment behavior in the dispatching cycle is obtained by counting the charging and discharging switching frequency of each energy storage module.

[0042] Specifically, the frequency of the adjustment behavior in the dispatching cycle is obtained by counting the charging and discharging switching frequency of each energy storage module. In a set dispatching time period, each energy storage module is monitored, the number of times the energy storage module switches from a charging state to a discharging state or from a discharging state to a charging state is recorded, and this is used as the number of adjustment behaviors of the energy storage module in the dispatching cycle. The charging and discharging switching frequency reflects the activity level of the energy storage module. The higher the frequency is, the more the energy storage module participates in the energy conversion process, and the heavier the load is; on the contrary, if the frequency is low, it indicates that the energy storage module runs smoothly or is utilized to a low degree in the dispatching cycle.

[0043] Further, the application also includes: identifying passive curtailment rates and passive curtailment rates based on the historical wind light storage operation data set, generating historical passive curtailment rate data sequence and historical passive curtailment rate data sequence; calculating the average of the curtailment rate and the curtailment rate within the period based on the historical passive curtailment rate data sequence and the historical passive curtailment rate data sequence within the scheduling period, generating the average curtailment rate data of each wind power generation module and the average curtailment rate data of each photovoltaic power generation module; marking the average curtailment rate data of each wind power generation module and the average curtailment rate data of each photovoltaic power generation module to the regional wind light storage topology map for marking, generating the curtailment rate distribution and the curtailment rate distribution.

[0044] Specifically, based on the historical wind light storage operation data set, the passive curtailment rate and the passive curtailment rate are identified, the historical operation records of wind power generation, photovoltaic power generation and energy storage system are utilized, the limit generation instruction information and meteorological condition data issued by the scheduling platform are combined, and the proportion of each power generation module that fails to generate all theoretical power due to non-equipment failure during the limit generation period is judged. The passive curtailment rate refers to the proportion of the power loss of the wind power generation module due to the failure to fully generate power due to scheduling or system limitations in the presence of available wind speed, and the passive curtailment rate refers to the proportion of power generation loss of the photovoltaic power generation module due to external intervention such as limit generation in the presence of sufficient solar radiation. The limit generation adjustment predictor is relied on to model the theoretical power generation during the limit generation period, and then compared with the actual power generation, so as to obtain accurate curtailment ratio. Each passive curtailment event identified is organized into continuous numerical data records in chronological order, and historical passive curtailment rate data sequence and historical passive curtailment rate data sequence are constructed, reflecting the curtailment proportion encountered by the wind power generation module and the photovoltaic power generation module at each time in history, which can be used for subsequent trend analysis, average calculation and strategy optimization.

[0045] Within the scheduling period, the average of the curtailment rate and the curtailment rate within the period is calculated based on the historical passive curtailment rate data sequence and the historical passive curtailment rate data sequence, i.e. the historical curtailment rate data sequence is averaged within the period according to the time dimension of grid scheduling, such as every hour or every day, to obtain the average curtailment level of each wind power generation module or photovoltaic power generation module within a unit time, as a quantitative index of the influence degree of the wind power generation module or photovoltaic power generation module under the action of scheduling strategy, for reflecting its utilization efficiency and power generation limitation risk. The calculated average values are respectively summarized and the identities of the corresponding modules are clearly indicated to generate the average curtailment rate data of each wind power generation module and the average curtailment rate data of each photovoltaic power generation module, facilitating visualization and subsequent system scheduling evaluation.

[0046] The average data of the wind power module and the average data of the light power module are marked on each wind power node or light power node in the regional wind light storage topology diagram to form attribute labels in the graph structure. The color, symbol or text can be used to intuitively reflect the difference of the power abandonment level of each module in the topology structure. After marking on the regional wind light storage topology diagram, the power abandonment level of all wind power nodes and light power nodes is summarized to form a set of distribution information, and the wind power abandonment rate distribution and the light power abandonment rate distribution are generated, which can be used for further scheduling optimization, power generation adjustment or resource configuration analysis.

[0047] Further, the present application also includes: passive wind power abandonment rate and passive light power abandonment rate are the deviation degree of deviating from the theoretical power generation capacity due to the artificial reduction of power generation output of each wind power module and each light power module after the external scheduling platform issues the limited generation instruction.

[0048] Specifically, the passive wind power abandonment rate and the passive light power abandonment rate refer to the deviation between the wind power module or the light power module and the theoretical power generation capacity under the natural resource conditions at that time, due to the limited generation instruction issued by the external scheduling platform for the safety or stability of the power system during the operation of the power grid, which leads to the artificial reduction of the power output that should be generated, and the deviation ratio is the passive power abandonment rate. The external scheduling platform is the operation control center of the power grid, which is responsible for determining whether to implement the power limiting operation on part of the new energy power generation equipment according to the power balance, power transmission capacity, load demand and other factors of the whole network. The limited generation instruction is a control command issued by the scheduling platform, which requires the power generation end to adjust the operation state and reduce the energy output.

[0049] In the wind power module, the variable pitch angle of the blade or the operating point of the generator set is adjusted in response to the limited generation instruction, so that it no longer tracks the maximum power point operation, but actively reduces the wind energy utilization rate. The variable pitch angle adjustment refers to changing the angle between the fan blade and the wind direction to reduce the wind energy capture amount, so as to control the output power. At the same time, the power output of the generator can also be directly limited by the controller to let the wind turbine set still operate in a low power state under suitable wind speed.

[0050] In the light power module, the limited generation is realized by adjusting the operating mode of the inverter, which will enter the limited power operation state, that is, artificially deviating from the maximum power point tracking, that is, deviating from the MPPT control, so that even if the solar radiation is sufficient and the component temperature is suitable, it will not output the theoretical maximum power. The inverter is the core device of the light power module for converting direct current into alternating current, and its operating mode directly determines the output capacity of the light power module, so the control of the inverter is the main way of light power limiting.

[0051] Further, the application also includes: extracting data with a limit instruction identifier from the historical wind-solar-storage operation data set, identifying limit generation adjustment parameters of each wind power generation module and limit generation adjustment parameters of each photovoltaic power generation module; extracting operation parameter differences and wind power generation output differences of each wind power generation module under the same meteorological environmental conditions from the historical wind-solar-storage operation data set, and training the limit generation adjustment predictor of each wind power generation module; extracting operation parameter differences and photovoltaic power generation output differences of each photovoltaic power generation module under the same meteorological environmental conditions from the historical wind-solar-storage operation data set, and training the limit generation adjustment predictor of each photovoltaic power generation module; calling the limit generation adjustment predictor of each wind power generation module to analyze the limit generation adjustment parameters of each wind power generation module, and completing passive wind curtailment rate identification; and calling the limit generation adjustment predictor of each photovoltaic power generation module to analyze the limit generation adjustment parameters of each photovoltaic power generation module, and completing passive light curtailment rate identification.

[0052] Specifically, the limit instruction identifier refers to a command of a dispatching center to require part of power generation equipment to reduce power output in certain time periods in order to control system load or grid stability, indicating that the equipment operation in the corresponding time period is completed in a limit generation control state. The data with the limit instruction identifier is extracted from the historical wind-solar-storage operation data set, and the data generated during the limit generation command issued by the dispatching platform is screened out, so that the control behavior characteristics of wind power or photovoltaic equipment during the limit generation can be further identified.

[0053] The limit generation adjustment parameters of each wind power generation module and the limit generation adjustment parameters of each photovoltaic power generation module are identified, and key variables are extracted from the data with the limit instruction identifier to describe the response mode of wind power and photovoltaic during the limit generation, such as the change of wind turbine pitch angle, the speed control instruction, the current reduction amplitude, or the load shedding rate of photovoltaic inverters, voltage control actions, etc.

[0054] The operation parameter differences and wind power generation output differences of each wind power generation module under the same meteorological environmental conditions are extracted from the historical wind-solar-storage operation data set, the differences between the actual output and the expected output of the wind-solar-storage equipment in the time period with basically consistent meteorological conditions such as wind speed and wind direction are analyzed, and the difference data is used as an input feature to train a machine learning model to train the limit generation adjustment predictor of each wind power generation module, so that the limit generation adjustment predictor can predict the wind power output deviation according to the future limit generation instruction. The limit generation adjustment predictor is an algorithm model for inferring how much power the equipment may output under the influence of the limit generation, which is constructed by using a regression neural network, a decision tree or an ensemble learning method.

[0055] In the historical weather data set, the operation parameter difference and the photovoltaic power generation output difference of each photovoltaic power generation module under the same meteorological environment condition are extracted, and the limited generation adjustment predictor of each photovoltaic power generation module is trained. On the basis of ensuring that the sunlight irradiance, ambient temperature and the like are consistent, the deviation between the actual output and the theoretical output of the photovoltaic power generation module is extracted, and the limited generation adjustment predictor of each photovoltaic power generation module is established.

[0056] The limited generation adjustment predictor of each wind power generation module is called to analyze the limited generation adjustment parameter of each wind power generation module, evaluate the gap between the output power and the generated power, calculate the power loss ratio caused by the artificial limited generation, that is, the passive wind curtailment rate, and complete the passive wind curtailment rate identification. The higher the passive wind curtailment rate is, the more the wind resources are not effectively utilized, and the scheduling strategy needs to be optimized.

[0057] The limited generation adjustment predictor of each photovoltaic power generation module is called to analyze the limited generation adjustment parameter of each photovoltaic power generation module, calculate the power reduction ratio caused by the limited generation instruction, and thus obtain the passive light curtailment rate, complete the passive light curtailment rate identification, and the passive light curtailment rate reflects the influence of the scheduling behavior on the utilization efficiency of the photovoltaic.

[0058] Further, the application also includes: identifying high-frequency energy storage modules higher than a preset burden index and low-frequency energy storage modules lower than the preset burden index based on the energy storage adjustment burden distribution to perform energy storage scheduling optimization, generate a first balanced optimization result; based on the wind curtailment rate distribution and the light curtailment rate distribution, performing traditional energy scheduling of each wind power generation module and each photovoltaic power generation module connected to the grid, to generate a second balanced optimization result; and generating the grid energy distribution decision based on the first balanced optimization result and the second balanced optimization result.

[0059] Specifically, high-frequency energy storage modules higher than a preset burden index and low-frequency energy storage modules lower than the preset burden index are identified based on the energy storage adjustment burden distribution to perform energy storage scheduling optimization. By analyzing the frequency of the energy storage system in the historical operation to undertake the adjustment task, the energy storage units with the adjustment frequency exceeding the system set threshold are found as the high-frequency energy storage modules, and the low-frequency energy storage modules with the adjustment frequency significantly lower than the threshold. The energy storage adjustment burden distribution refers to the distribution of all energy storage units undertaking the charging and discharging adjustment task in a unit time, reflecting the concentration or dispersion degree of the adjustment load. The preset burden index is a reference standard artificially set, for example, the daily adjustment frequency of each energy storage module should not exceed 10 times. After identifying the high-frequency and low-frequency modules, the scheduling relationship between the energy storage module and the new energy or load can be adjusted to realize the pressure reduction of the high-burden unit and the efficiency increase of the low-burden unit, thereby generating a first balanced optimization result with more balanced load.

[0060] Based on the abandoned wind rate distribution and the abandoned light rate distribution, the traditional energy scheduling of the grid-connected access of each wind power generation module and each photovoltaic power generation module to the power grid is performed to minimize the abandoned wind rate and the abandoned light rate, the spatial distribution information of the abandoned wind rate and the abandoned light rate on the wind-solar-storage topology diagram is utilized to re-plan the access path or scheduling order of the wind power generation module and the photovoltaic power generation module to the power grid, so as to reduce the power loss caused by the limited generation of each power generation module. The abandoned wind rate distribution refers to the differentiated performance of the abandoned wind ratio of each wind power generation module in the historical or predicted period in space, and the abandoned light rate distribution refers to the distribution of the abandoned light level of each photovoltaic module. The grid-connected access refers to the process that the wind power and photovoltaic power generation modules transmit the generated power to the public power grid through transformers, inverters and other devices, and the traditional energy scheduling refers to the dynamic adjustment of the priority or power upper and lower limit of the power supply access by the scheduling platform under the given power system structure. By optimizing the access point and the scheduling strategy, the total abandoned power is reduced, and finally the second balanced optimization result reducing the abandoned wind and light loss is generated.

[0061] Based on the first balanced optimization result and the second balanced optimization result, a power grid energy distribution decision is generated, which considers the balance of the regulation load between the energy storage modules and the consumability of the wind power and photovoltaic power generation, and jointly constructs a power grid energy distribution scheme facing the global optimal. The power grid energy distribution decision refers to giving the energy input-output ratio, power upper and lower limit, scheduling priority and other instructions of different energy units in each time period based on the actual operating state, device capacity and power generation potential and other factors, to guide the coordinated operation of the entire wind-solar-storage system. This decision scheme finally guides the scheduling platform to adopt a more reasonable distribution, lower loss and more flexible operation mode in actual operation.

[0062] Further, the application also includes: determining a set of energy storage energy units in a high-regulation-demand energy storage module connected to the high-frequency energy storage module and a set of energy storage energy units in a low-regulation-demand energy storage module connected to the low-frequency energy storage module; collecting the regulation demand of the set of energy storage energy units in the high-regulation-demand energy storage module and the set of energy storage energy units in the low-regulation-demand energy storage module, performing regulation demand equalization processing through the exchange of elements in the set, and generating an equalization processing result; performing scheduling relationship adjustment of each energy storage module and the energy storage energy units in the energy storage module based on the equalization processing result, and generating the first balanced optimization result.

[0063] Specifically, the energy storage units in the energy storage module to which each energy storage module belongs are classified according to the adjustment frequency of each energy storage module, the energy storage modules frequently performing charging and discharging adjustment in a unit time are identified respectively, and it is determined that the energy storage units in the energy storage module connected by the energy storage module belong to a high adjustment demand type, the energy storage modules with low adjustment frequency and the energy storage units in the energy storage module connected by the energy storage modules belong to a low adjustment demand type, the energy storage unit set in the high adjustment demand energy storage module connected by the high frequency energy storage module and the energy storage unit set in the low adjustment demand energy storage module connected by the low frequency energy storage module are determined. The energy storage module is a system unit for absorbing and releasing electric energy to balance the supply and demand fluctuations, and the energy storage unit in the energy storage module usually refers to a wind farm, a photovoltaic power station or a load cluster, etc., which directly affects the power supply and demand of the power grid. The adjustment demand refers to the degree to which the energy storage unit in the energy storage module needs to perform power balance adjustment more frequently due to large fluctuations or fast load changes.

[0064] The adjustment behavior data of the energy storage units in the energy storage module in the historical operation, such as frequently fluctuating wind speed curve, load change rate or instability of output power, are obtained, which are used to measure the adjustment load intensity, and the adjustment demand of the energy storage unit set in the high adjustment demand energy storage module and the energy storage unit set in the low adjustment demand energy storage module are collected. The adjustment demand equalization processing is performed by exchanging elements in the set, part of the energy storage units in the high adjustment demand energy storage module are transferred from the high frequency energy storage module to the low frequency energy storage module, and the energy storage units in the low adjustment demand energy storage module are moved to the high frequency energy storage module, so that the energy storage module with heavy load can obtain the energy storage units in the energy storage module with light adjustment load, thereby realizing balanced allocation of the overall adjustment task. Element exchange is a matching optimization operation, which adjusts the connection relationship between the energy storage and the energy storage units in the energy storage module, and reduces the overload risk of a single module.

[0065] The scheduling relationship adjustment of each energy storage module and the energy storage units in the energy storage module is performed based on the equalization processing result, the scheduling structure between the energy storage system and the energy storage units in each energy storage module is updated, that is, the power fluctuation of the energy storage module in response to the energy storage units in the energy storage module is changed, an optimized energy storage scheduling scheme is formed, a first equalization optimization result is generated, which helps to prolong the service life of the energy storage equipment and improve the system adjustment efficiency.

[0066] Further, the application also includes: the grid-connected power grid is a power system to which each wind power generation module and each photovoltaic power generation module is connected through a grid-connected device.

[0067] Specifically, grid connection refers to the process of converting the power generation source from an independent running state to a connected running state with the power system, so that the power generated by wind or photovoltaic can be directly used by the power grid. The power grid connected by the grid connection device of each wind power generation module and each photovoltaic generation module is the process of delivering the power generated by each wind power generation module and each photovoltaic generation module to the public power network through a specific physical interface. The power system is a network composed of power generation, power transmission, power distribution and power consumption, which is responsible for reasonably scheduling and transmitting power of different sources to the terminal load. The power generated by the wind power generation module and the photovoltaic generation module cannot be directly connected to the power grid, and needs to be converted in voltage, frequency synchronized and inverted by the grid connection device. The grid connection device includes transformer, inverter, circuit breaker and protection device, etc., which converts the direct current or unstable alternating current from the new energy module into power meeting the grid standard.

[0068] In summary, the wind-solar-storage integrated power grid energy distribution and dispatching method provided by the present application has the following technical effects: by achieving the technical target of wind-solar-storage coordinated optimization dispatching based on storage regulation burden distribution and abandoned wind and light rate distribution, the technical effects of improving the balance of storage system operation, reducing abandoned wind and light loss and achieving global optimal distribution of energy at the power grid level are achieved.

[0069] The above description of disclosed embodiments enables one of ordinary skill in the art to make or use the application. Various modifications to these embodiments will be apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0070] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application also intends to include these modifications and variations.

Claims

1. A method for energy distribution and dispatching of a wind-solar-storage integrated power grid, characterized in that, The method comprises the following steps: Collecting the distribution information of wind, light and storage devices in a preset power grid area, and constructing a regional wind, light and storage topology graph; Collecting historical wind, light and storage operation data sets based on the regional wind, light and storage topology graph; Performing adjustment burden distribution analysis of the energy storage module based on the historical wind, light and storage operation data sets, marking on the regional wind, light and storage topology graph, and determining the energy storage adjustment burden distribution; Performing wind power curtailment rate distribution analysis and photovoltaic power curtailment rate distribution analysis based on the historical wind, light and storage operation data sets, marking on the regional wind, light and storage topology graph, and generating wind power curtailment rate distribution and photovoltaic power curtailment rate distribution; Combining the energy storage adjustment burden distribution, the wind power curtailment rate distribution and the photovoltaic power curtailment rate distribution to perform wind, light and storage balancing scheduling optimization, and generating a power grid energy distribution decision, comprising: Based on the energy storage adjustment burden distribution, identifying high-frequency energy storage modules higher than a preset burden index and low-frequency energy storage modules lower than a preset burden index to perform energy storage scheduling optimization, and generating a first balancing optimization result; Based on the wind power curtailment rate distribution and the photovoltaic power curtailment rate distribution, performing traditional energy scheduling of each wind power module and each photovoltaic power module connected to the grid to minimize wind power curtailment rate and photovoltaic power curtailment rate, and generating a second balancing optimization result; Generating the power grid energy distribution decision based on the first balancing optimization result and the second balancing optimization result; The method for generating the first balancing optimization result based on the energy storage adjustment burden distribution by identifying high-frequency energy storage modules higher than a preset burden index and low-frequency energy storage modules lower than a preset burden index to perform energy storage scheduling optimization, comprising: Determining a set of energy storage units in high-regulation-demand energy storage modules connected to the high-frequency energy storage modules and a set of energy storage units in low-regulation-demand energy storage modules connected to the low-frequency energy storage modules; Collecting the regulation demand of the set of energy storage units in the high-regulation-demand energy storage modules and the set of energy storage units in the low-regulation-demand energy storage modules, performing regulation demand equalization processing by exchanging elements in the sets, and generating an equalization processing result; Performing scheduling relationship adjustment of each energy storage module and the energy storage units in the energy storage module based on the equalization processing result, and generating the first balancing optimization result; The grid-connected power grid is a power system accessed by each wind power module and each photovoltaic power module through a grid connection device. 2.The wind-solar-storage integrated power grid energy allocation and scheduling method of claim 1, wherein, Each node in the regional wind, light and storage topology graph represents an energy unit, and each edge represents a power grid connection relationship. 3.The wind-solar-storage integrated power grid energy allocation and scheduling method of claim 1, wherein, The method for performing adjustment burden distribution analysis of the energy storage module based on the historical wind, light and storage operation data sets, marking on the regional wind, light and storage topology graph, and determining the energy storage adjustment burden distribution, comprising: Based on the historical wind, light and storage operation data sets, statistically analyzing the regulation behavior occurrence frequency of each energy storage module in a scheduling period and performing mean value calculation to obtain the regulation frequency of each energy storage module; Calculating the ratio of the regulation frequency of each energy storage module to the sum of the regulation frequencies of each module, and marking on the regional wind, light and storage topology graph to generate the energy storage adjustment burden distribution. 4.The wind-solar-storage integrated power grid energy allocation and scheduling method of claim 3, wherein, The regulation behavior occurrence frequency in the scheduling period is obtained by counting the charge-discharge switching frequency of each energy storage module. 5.The wind-solar-storage integrated power grid energy allocation and scheduling method of claim 1, wherein, Performing wind curtailment rate distribution analysis and light curtailment rate distribution analysis of photovoltaic power generation based on the historical wind-light-storage operation data set, marking on the regional wind-light-storage topology map, generating wind curtailment rate distribution and light curtailment rate distribution, including: Based on the historical wind-light-storage operation data set, identifying passive wind curtailment rate and passive light curtailment rate, generating historical passive wind curtailment rate data sequence and historical passive light curtailment rate data sequence; Based on the historical passive wind curtailment rate data sequence and the historical passive light curtailment rate data sequence, calculating the mean value of wind curtailment rate and light curtailment rate within the scheduling period, generating wind curtailment rate mean value data of each wind power module and light curtailment rate mean value data of each photovoltaic power module; Marking the wind curtailment rate mean value data of each wind power module and the light curtailment rate mean value data of each photovoltaic power module on the regional wind-light-storage topology map, generating the wind curtailment rate distribution and the light curtailment rate distribution. 6.The wind-solar-storage integrated power grid energy allocation and scheduling method of claim 5, wherein, Passive wind curtailment rate and passive light curtailment rate are the deviation from the theoretical power generation capacity caused by the deviation of each wind power module and each photovoltaic power module from the theoretical power generation capacity due to the issuance of power generation limit instruction by the external scheduling platform.

7. The wind-solar-storage integrated power grid energy allocation scheduling method of claim 6, wherein, Based on the historical wind-light-storage operation data set, identifying passive wind curtailment rate and passive light curtailment rate, including: Extracting data with power generation limit instruction identification from the historical wind-light-storage operation data set, identifying the power generation limit adjustment parameters of each wind power module and the power generation limit adjustment parameters of each photovoltaic power module; Extracting the operation parameter difference and wind power output difference of each wind power module under the same meteorological environmental conditions from the historical wind-light-storage operation data set, training the power generation limit adjustment predictor of each wind power module; Extracting the operation parameter difference and photovoltaic power output difference of each photovoltaic power module under the same meteorological environmental conditions from the historical wind-light-storage operation data set, training the power generation limit adjustment predictor of each photovoltaic power module; Calling the power generation limit adjustment predictor of each wind power module to analyze the power generation limit adjustment parameters of each wind power module, completing passive wind curtailment rate identification; calling the power generation limit adjustment predictor of each photovoltaic power module to analyze the power generation limit adjustment parameters of each photovoltaic power module, completing passive light curtailment rate identification.

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