A self-adaptive dispatching method and system for distribution network

Abstract

The present invention discloses an adaptive scheduling method and system for a distribution network, and relates to the technical field of electric power scheduling. The method includes: extracting the first time zone in a predetermined power grid cycle; extracting the first output value under the first time zone, and comparing it with the first absorption value. If the first output value is greater than the first absorption value, the first time zone is added to the energy storage scheduling time zone set, and if it is less than, it is added to the energy release scheduling time zone set; obtaining the dynamic time zone, traversing the energy storage scheduling time zone set and the energy release scheduling time zone set; calling the predetermined adaptive scheduling mechanism to analyze the traversal results, generate dynamic scheduling decisions, and perform adaptive scheduling processing. The present invention solves the technical problem in the prior art that the intermittent and volatile nature of renewable energy affects the flexibility and reliability of power grid energy scheduling, and achieves the technical effect of improving the flexibility and operational stability of power grid energy scheduling by scheduling the storage and release of renewable energy through time zones.

Description

Technical Field

[0001] The present invention relates to the technical field of power dispatching, and in particular to an adaptive dispatching method and system for a distribution network. Background Art

[0002] With the rapid development of renewable energy, such as the access of distributed energy such as wind energy and solar energy, the distribution network needs a more flexible and intelligent dispatching method to achieve the coordinated operation of multiple energy sources. The existing energy dispatching technology of the distribution network mainly uses smart meters, sensors and other equipment to collect data information such as voltage, current, power, etc. of the distribution network in real time, and uses big data and artificial intelligence technology to conduct simulation analysis and formulate dispatching plans in combination with the actual situation of the distribution network.

[0003] However, renewable energy sources such as solar and wind power have significant intermittency and volatility, and their output size and timing are difficult to accurately predict, which affects the flexibility and reliability of grid energy dispatch. Summary of the invention

[0004] The present application provides an adaptive dispatching method and system for a distribution network, which is used to solve the technical problem in the prior art that the flexibility and reliability of power grid energy dispatching are affected by the intermittent and fluctuating nature of renewable energy.

[0005] In a first aspect of the present application, a method for adaptively scheduling a distribution network is provided, the method comprising: obtaining a target output time series database, the target output time series database being a database constructed based on historical output records of target renewable energy; reading a predetermined power grid cycle, and extracting a first time zone in the predetermined power grid cycle; extracting a first output value in the first time zone in the target output time series database, and comparing the first output value with a first absorption value to obtain a first comparison result, wherein the first absorption value is an absorption value traversed and matched in the target absorption time series database, and the target absorption time series database is Refers to a database constructed based on the historical consumption records of the target renewable energy; according to the first comparison result, if the first output value is greater than the first consumption value, the first time zone is added to the energy storage scheduling time zone set; if the first output value is less than the first consumption value, the first time zone is added to the energy release scheduling time zone set; obtain the dynamic time zone, and traverse the dynamic time zone in the energy storage scheduling time zone set and the energy release scheduling time zone set to obtain the traversal result; call the predetermined adaptive scheduling mechanism to analyze the traversal result and generate a dynamic scheduling decision, wherein the dynamic scheduling decision is used to perform adaptive scheduling processing on the target renewable energy.

[0006] According to a second aspect of the present application, there is provided an adaptive dispatching system for a distribution network, the system comprising: a target output timing database construction module, the target output timing database construction module is used to obtain a target output timing database, the target output timing database refers to a database constructed based on historical output records of target renewable energy; a first time zone extraction module, the first time zone extraction module is used to read a predetermined power grid cycle and extract a first time zone in the predetermined power grid cycle; a production and consumption comparison module, the production and consumption comparison module is used to extract a first output value in the first time zone in the target output timing database, and compare the first output value with a first consumption value to obtain a first comparison result, wherein the first consumption value refers to the consumption value traversed and matched in the target consumption timing database for the first time zone, the target An absorption time series database refers to a database constructed based on the historical absorption records of the target renewable energy; a time zone classification module, the time zone classification module is used to add the first time zone to the energy storage scheduling time zone set according to the first comparison result, if the first output value is greater than the first absorption value, if the first output value is less than the first absorption value, add the first time zone to the energy release scheduling time zone set; a time zone traversal module, the time zone traversal module is used to obtain a dynamic time zone, and traverse the dynamic time zone in the energy storage scheduling time zone set and the energy release scheduling time zone set to obtain a traversal result; an adaptive scheduling module, the adaptive scheduling module is used to call a predetermined adaptive scheduling mechanism to analyze the traversal result and generate a dynamic scheduling decision, wherein the dynamic scheduling decision is used to perform adaptive scheduling processing on the target renewable energy.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The present application provides an adaptive scheduling method and system for a distribution network, which relates to the technical field of power scheduling. The method extracts the output value in each time zone in a predetermined power grid cycle and compares it with the consumption value. According to the comparison result, each time zone is classified into a storage scheduling time zone set or a release scheduling time zone set to obtain a dynamic time zone. The energy storage scheduling time zone set and the release scheduling time zone set are traversed, and the traversal results are analyzed according to a predetermined adaptive scheduling mechanism to generate a dynamic scheduling decision for adaptive scheduling processing. The method solves the technical problem in the prior art that the intermittent and volatile nature of renewable energy affects the flexibility and reliability of power grid energy scheduling, and realizes the storage and release scheduling of renewable energy by time zone, thereby improving the flexibility of power grid energy scheduling and the technical effect of operating stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a flow chart of an adaptive scheduling method for a distribution network provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of a flow chart of adjusting a dynamic dispatching decision in an adaptive dispatching method for a distribution network provided in an embodiment of the present application;

[0012] Figure 3 A schematic diagram of the structure of an adaptive dispatching system for a distribution network provided in an embodiment of the present application.

[0013] Explanation of the accompanying drawings: target output time series database construction module 11, first time zone extraction module 12, production and consumption comparison module 13, time zone classification module 14, time zone traversal module 15, adaptive scheduling module 16. DETAILED DESCRIPTION

[0014] The present application provides an adaptive dispatching method and system for a distribution network, which is used to solve the technical problem in the prior art that the flexibility and reliability of power grid energy dispatching are affected by the intermittent and fluctuating nature of renewable energy.

[0015] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Embodiment 1

[0017] like Figure 1 As shown, the present application provides an adaptive scheduling method for a distribution network, the method comprising:

[0018] P10: Obtain a target output time series database, where the target output time series database refers to a database constructed based on historical output records of target renewable energy sources.

[0019] Specifically, a target output time series database is constructed based on the historical output records of the target renewable energy. The target renewable energy may be solar energy, wind energy, etc. For example, the historical output records of the target renewable energy are obtained, and the historical output records contain a large number of data points, each of which corresponds to the energy output within a specific time (such as hours, minutes or even seconds). Sensors and monitoring equipment installed on renewable energy power generation facilities can be used to collect energy output data in real time or regularly, and data cleaning is performed to ensure the accuracy and completeness of the data. The cleaned data is stored in the database to form a target output time series database, which provides a data basis for subsequent time zone analysis, scheduling strategy formulation, and real-time scheduling decisions.

[0020] P20: Reading a predetermined power grid cycle, and extracting a first time zone in the predetermined power grid cycle.

[0021] Optionally, a predetermined power grid cycle is read, that is, a time range set for scheduling analysis and decision-making, such as one day, one week or one month. The setting of this cycle is usually based on the actual needs of power grid operation and the convenience of scheduling management. Based on the predetermined power grid cycle, the first time zone within the cycle is randomly extracted. The time zone here is a relative time period division, representing different time periods within the power grid cycle. Each time zone has its specific energy supply and demand characteristics and scheduling requirements. For example, if the predetermined power grid cycle is one day, the time zone can be a time interval of one hour or half an hour.

[0022] Exemplarily, the system should have a built-in powerful time management module that can handle complex time data, including the setting of time periods, the division of time zones, and the conversion between time zones. And it can accurately extract the data of the corresponding time zone from the target output time series database and the target consumption time series database according to the preset time period and time zone division. At the same time, although the predetermined grid cycle and time zone division are set in advance, the system should have a certain degree of dynamic adaptability to cope with changes that may occur in actual operation. For example, the system should be able to flexibly adjust the length of the time zone or add additional time zones to meet the scheduling needs in special circumstances.

[0023] By extracting the first time zone, the time range of the analysis and the specific analysis object (i.e., the time zone) can be clearly defined, providing a clear time frame and analysis unit for subsequent time zone output and consumption comparison, scheduling strategy formulation, and real-time scheduling decision-making.

[0024] P30: Extract the first output value in the first time zone in the target output time series database, and compare the first output value with the first absorption value to obtain a first comparison result, wherein the first absorption value refers to the absorption value traversed and matched in the first time zone in the target absorption time series database, and the target absorption time series database refers to a database constructed based on the historical absorption records of the target renewable energy.

[0025] It should be understood that the first output value corresponding to the first time zone is accurately extracted from the target output time series database. The first output value represents the actual output of the target renewable energy in the time zone, and is an important basis for scheduling decisions. At the same time, in order to evaluate the supply and demand balance of energy in the time zone, it is necessary to obtain the corresponding first absorption value. The first absorption value can be obtained by traversing and matching in the target absorption time series database by taking the first time zone as the query condition. Among them, the target absorption time series database is a database constructed based on historical absorption records, which records the actual absorption capacity of the power grid for renewable energy in different time periods. By searching for data points matching the first time zone in the target absorption time series database, the corresponding first absorption value can be obtained.

[0026] Furthermore, the first output value is compared with the first consumption value to obtain a first comparison result, that is, a comparison result of energy output and consumption in the first time zone, which can serve as an important reference for subsequent scheduling strategy formulation and real-time scheduling decision-making.

[0027] P40: According to the first comparison result, if the first output value is greater than the first consumption value, the first time zone is added to the energy storage scheduling time zone set; if the first output value is less than the first consumption value, the first time zone is added to the energy release scheduling time zone set.

[0028] Specifically, the first comparison result is analyzed, that is, the relative size of the first output value and the first absorption value is compared, that is, the actual output of the target renewable energy in the first time zone and the actual absorption capacity of the power grid for the energy. If the first output value is greater than the first absorption value, it means that the output of energy in the time zone exceeds the absorption capacity of the power grid, and there is an energy surplus. In order to effectively utilize the excess energy, the time zone is added to the energy storage scheduling time zone set. The energy storage scheduling time zone set is used to record the time zones that need to be energy storage scheduled and perform corresponding energy storage so that the energy can be released and utilized as needed in subsequent time periods.

[0029] On the contrary, if the first output value is less than the first absorption value, it means that the absorption capacity of the power grid in the time zone exceeds the output of energy, and there is an energy shortage. In order to make up for this shortage, the time zone is added to the energy release scheduling time zone set. The energy release scheduling time zone set is used to record the time zones in which the previously stored energy needs to be released to meet the needs of the power grid.

[0030] P50: Acquire a dynamic time zone, and traverse the dynamic time zone in the energy storage scheduling time zone set and the energy release scheduling time zone set to obtain a traversal result.

[0031] Optionally, a dynamic time zone is obtained, that is, the time zone period at the current moment, and the dynamic time zone is traversed and matched in the energy storage scheduling time zone set and the energy release scheduling time zone set, that is, the dynamic time zone is used as a query condition, and the energy storage scheduling time zone set and the energy release scheduling time zone set established in the previous step are traversed to determine whether the energy supply and demand of the power grid tends to be in excess or shortage under the current dynamic time zone. Specifically, if the dynamic time zone appears in the energy storage scheduling time zone set, then according to historical data and real-time conditions, the energy output in the current time zone is likely to exceed the immediate absorption capacity of the power grid. Therefore, the system will make a decision on energy storage scheduling, that is, consider storing excess energy so that it can be released when needed later. On the contrary, if the dynamic time zone appears in the energy release scheduling time zone set, then it is determined that the absorption capacity of the power grid in the current time zone may be insufficient, and the previously stored energy needs to be released to meet the demand.

[0032] Furthermore, step P50 of the embodiment of the present application also includes:

[0033] P51a: Acquire the dynamic energy information of the dynamic time zone, wherein the dynamic energy information includes a dynamic output value and a dynamic consumption value; P52a: Compare the dynamic output value and the dynamic consumption value to obtain a dynamic comparison result; P53a: Verify the traversal result based on the dynamic comparison result.

[0034] In a possible embodiment of the present application, the dynamic energy information of the dynamic time zone is obtained, that is, the energy output and consumption obtained by actual monitoring means in the current dynamic time zone (that is, the time segment corresponding to the real time moment). The dynamic energy information includes a dynamic output value and a dynamic consumption value, which respectively represent the actual output of the target renewable energy in the time zone and the actual consumption of the energy by the power grid. Furthermore, the dynamic output value and the dynamic consumption value are compared to obtain a dynamic comparison result, that is, the supply and demand balance of energy in the current time zone is evaluated by comparing the relative sizes of the two values. If the dynamic output value is greater than the dynamic consumption value, it means that there is an energy surplus in the current time zone; otherwise, it means that there is an energy shortage.

[0035] Furthermore, since historical data may not fully reflect the current real-time situation, the dynamic comparison results obtained through real-time monitoring are used to verify the traversal results obtained by traversing the energy storage scheduling time zone set and the energy release scheduling time zone set to ensure the accuracy and timeliness of the scheduling decision. If the dynamic comparison result is consistent with the traversal result, the traversal result can be retained; if not, the traversal result needs to be corrected according to the dynamic comparison result to ensure the accuracy of subsequent scheduling decisions.

[0036] P60: Calling a predetermined adaptive scheduling mechanism to analyze the traversal result and generate a dynamic scheduling decision, wherein the dynamic scheduling decision is used to perform adaptive scheduling processing on the target renewable energy.

[0037] Specifically, a predetermined adaptive scheduling mechanism is retrieved. The predetermined adaptive scheduling mechanism is pre-designed and contains a series of scheduling rules and strategies for specific scenarios and needs. It can achieve efficient utilization of energy and stable operation of the power grid based on the basic principles of power grid operation, the characteristics of renewable energy, and statistical analysis of historical data. The traversal results are used as input and input into the predetermined adaptive scheduling mechanism for analysis. During the analysis process, the predetermined adaptive scheduling mechanism can use a variety of technical means and algorithm support, such as prediction models, optimization algorithms, decision trees, etc., to comprehensively consider the real-time supply and demand of the power grid, the output forecast of renewable energy, the state of the energy storage system and other related factors, and obtain the best scheduling plan by combining machine learning and optimization algorithms to ensure the accuracy and effectiveness of the scheduling decision. After analysis and processing by the predetermined adaptive scheduling mechanism, a dynamic scheduling decision is generated, and the target renewable energy is adaptively scheduled to ensure the supply and demand balance and stable operation of the power grid.

[0038] Further, such as Figure 2 As shown, the embodiment of the present application further includes step P70, and step P70 further includes:

[0039] P71: Obtain the dynamic factor conditions of the dynamic time zone, wherein the dynamic factor conditions include dynamic environmental information and dynamic meteorological information; P72: Read the predetermined factor condition benchmark, wherein the predetermined factor condition benchmark includes an environmental factor benchmark and a meteorological factor benchmark; P73: Combine the environmental factor benchmark and the meteorological factor benchmark in sequence to perform weighted calculations on the normalized dynamic environmental information and the dynamic meteorological information to obtain a dynamic environmental index and a dynamic meteorological index, respectively; P74: Read the predetermined factor weights, and weight the dynamic environmental index and the dynamic meteorological index based on the predetermined factor weights to obtain a dynamic comprehensive index; P75: When the dynamic comprehensive index exceeds the predetermined comprehensive index limit, adjust the dynamic scheduling decision.

[0040] Optionally, in order to ensure the accuracy and adaptability of scheduling decisions, it is necessary to more comprehensively consider various factors and conditions within the dynamic time zone, and make necessary adjustments to the dynamic scheduling decisions by comprehensively analyzing the dynamic environmental information and dynamic meteorological information to adapt to special weather or other atypical situations. Specifically, first, the dynamic factor conditions of the dynamic time zone are obtained through real-time monitoring equipment or a meteorological forecast system. These conditions include but are not limited to dynamic environmental information and dynamic meteorological information. The dynamic environmental information may involve changes in environmental factors such as temperature, humidity, and pressure, while the dynamic meteorological information covers meteorological elements such as wind speed, wind direction, and precipitation.

[0041] Further, a predetermined factor condition benchmark is read, i.e., a series of benchmark values ​​for comparison. These benchmark values ​​include an environmental factor benchmark and a meteorological factor benchmark, which represent normal or standard environmental and meteorological conditions, respectively. By comparing the dynamic factor conditions obtained in real time with the predetermined factor condition benchmark, the abnormality of the current environmental and meteorological conditions can be evaluated.

[0042] Furthermore, the dynamic environmental information and the dynamic meteorological information are normalized to eliminate the differences in dimensions and magnitudes between different indicators, so that each indicator can be compared and calculated on a unified scale. Then, the normalized dynamic environmental information and the dynamic meteorological information are weighted and calculated in combination with the environmental factor benchmark and the meteorological factor benchmark. Exemplarily, the deviations of the dynamic actual values ​​and benchmarks of each environmental indicator and meteorological indicator are compared, and the dynamic environmental index and the dynamic meteorological index are calculated respectively. The larger the deviation, the larger the corresponding dynamic environmental index and dynamic meteorological index.

[0043] Furthermore, the predetermined factor weights, i.e., the weight distribution values ​​of environmental factors and meteorological factors, are read to reflect the degree of influence of different factors in the scheduling decision. The dynamic environmental index and the dynamic meteorological index are weighted and summed based on the predetermined factor weights to obtain a dynamic comprehensive index. The dynamic comprehensive index comprehensively reflects the overall status of the environment and meteorological conditions in the current dynamic time zone.

[0044] Finally, it is determined whether the dynamic comprehensive index exceeds the predetermined comprehensive index limit, which is the comprehensive threshold of environmental and meteorological conditions and can be set according to empirical data. If the limit is exceeded, it means that the current environmental and meteorological conditions are significantly different from normal conditions, which may have a significant impact on the operation of the power grid and the dispatch of renewable energy. Therefore, adjustments are made to previous dynamic dispatch decisions, including personalized dispatch plans with manual intervention, to cope with special weather or other atypical situations, to ensure the safe, stable and efficient operation of the power grid.

[0045] Furthermore, step P75 of the embodiment of the present application also includes:

[0046] P75-1: Obtain the dynamic output value and dynamic absorption value corresponding to the dynamic time zone according to the traversal result; P75-2: Adjust the dynamic output value with the dynamic comprehensive index as the coefficient to obtain the effective dynamic output value; P75-3: Compare the effective dynamic output value with the dynamic absorption value and perform scheduling adjustments in combination with the predetermined adaptive scheduling mechanism.

[0047] Specifically, when the dynamic comprehensive index exceeds the predetermined comprehensive index limit, the specific process of adjusting the dynamic scheduling decision may be, first, according to the traversal result, extracting the dynamic output value and dynamic absorption value corresponding to the current dynamic time zone. These two values ​​represent the actual output capacity of the target renewable energy in the time zone and the actual absorption capacity of the power grid for the energy, respectively.

[0048] Furthermore, the dynamic output value is adjusted using the dynamic comprehensive index as a coefficient. The dynamic comprehensive index is an indicator that comprehensively reflects the impact of the current environment and meteorological conditions on the operation of the power grid. By using it as a coefficient, the impact of external factors on the output of renewable energy can be quantified, and the output value can be adjusted accordingly. Exemplarily, if the dynamic comprehensive index is 0-1, the smaller the dynamic comprehensive index is, the greater the adverse impact of the current environment and meteorological conditions on the operation of the power grid, and the smaller the corresponding dynamic output value is. Conversely, the smaller the impact is, the greater the dynamic output value is. Through adjustment, an effective dynamic output value is obtained. This value more accurately reflects the actual energy output of the target renewable energy under the current environmental and meteorological conditions.

[0049] Furthermore, the effective dynamic output value is compared with the dynamic absorption value, and the final dispatch adjustment is made in combination with the predetermined adaptive dispatch mechanism. If the effective dynamic output value is greater than the dynamic absorption value, it means that the current power grid cannot fully absorb the output of renewable energy. At this time, the energy storage strategy can be adjusted to store excess energy for subsequent use; conversely, if the effective dynamic output value is less than the dynamic absorption value, electricity can be supplemented from other energy sources to meet the needs of the power grid. Through this flexible dispatch adjustment mechanism, the system can respond to changes in environmental and meteorological conditions in real time to ensure the safe, stable and efficient operation of the power grid.

[0050] Furthermore, step P75-3 of the embodiment of the present application also includes:

[0051] P75-31: Obtain the dynamic energy deviation value of the dynamic time zone according to the dynamic output value and the dynamic absorption value, and calculate the dynamic energy scheduling rate; P75-32: When the dynamic time zone is in the energy storage scheduling time zone set, the energy output of the target renewable energy is dynamically distributed stored at the dynamic energy scheduling rate; when the dynamic time zone is in the energy release scheduling time zone set, the energy output of the target renewable energy is dynamically distributed released at the dynamic energy scheduling rate; P75-33: Use the dynamic distributed storage or the dynamic distributed release as the dynamic scheduling decision.

[0052] It should be understood that the specific process of comparing the effective dynamic output value with the dynamic absorption value for scheduling adjustment may be, first, according to the difference between the dynamic output value and the dynamic absorption value, calculating and obtaining the dynamic energy deviation value of the dynamic time zone, that is, the energy value that needs to be stored or released at present. Further, the dynamic energy deviation value is divided by the time length of the dynamic time zone (such as one hour) to obtain the dynamic energy scheduling rate, which provides guidance for subsequent energy storage or release operations and ensures the smooth and efficient energy scheduling.

[0053] Furthermore, different energy scheduling strategies are adopted according to the category to which the current dynamic time zone belongs (energy storage scheduling time zone set or energy release scheduling time zone set). If the current time zone belongs to the energy storage scheduling time zone set, the energy output of the target renewable energy will be dynamically distributed stored based on the calculated dynamic energy scheduling rate. This means that the energy will be stored in multiple energy storage devices to optimize the overall efficiency and life of the energy storage system. On the contrary, if the current time zone belongs to the energy release scheduling time zone set, the energy output of the target renewable energy will be dynamically distributed released at the same rate to meet the immediate needs of the power grid.

[0054] Furthermore, the dynamic distributed storage or the dynamic distributed release is output as the final dynamic scheduling decision, which not only includes the specific operation of energy storage or release, but also clarifies the rate of operation, providing clear guidance for the real-time scheduling of the power grid. In this way, the system can flexibly respond to fluctuations in power grid supply and demand, ensuring the stable operation of the power grid and the efficient use of renewable energy.

[0055] Furthermore, step P75-32 of the embodiment of the present application also includes:

[0056] P75-321: Obtain a distributed energy storage device, wherein the distributed energy storage device includes a plurality of energy storage devices with capacity and location identifiers; P75-322: Introduce an energy storage device priority evaluation function to evaluate and analyze the first energy storage device to obtain a first priority index, wherein the first energy storage device refers to any one of the plurality of energy storage devices with capacity and location identifiers, and the first energy storage device corresponds to a first capacity and a first position; P75-323: Arrange the plurality of energy storage devices with capacity and location identifiers in descending order based on the first priority index to obtain a descending list of energy storage devices; P75-324: Use the dynamic energy scheduling rate as the energy storage rate constraint and the descending list of energy storage devices as the energy storage priority order to form the dynamic distributed storage. Among them, the expression of the energy storage device priority evaluation function is as follows:

[0057] ;

[0058] in, Refers to the distributed energy storage device The priority index of the energy storage device, The location of the energy storage device is , the location of the target renewable energy source is ， refers to the The capacity of the energy storage device, and They refer to distance weight and capacity weight respectively.

[0059] Optionally, the specific process of dynamic distributed storage may be to first obtain distributed energy storage devices, which are composed of multiple energy storage devices with capacity and location identifiers, and are dispersedly deployed at different locations of the power grid, providing a material basis for distributed storage of energy. Each energy storage device has unique capacity and location information. Furthermore, an energy storage device priority evaluation function is introduced to evaluate and analyze the first energy storage device, and the expression of the energy storage device priority evaluation function is as follows:

[0060] ;in, Refers to the distributed energy storage device The priority index of the energy storage device, The location of the energy storage device is , the location of the target renewable energy source is ， refers to the The capacity of the energy storage device, and They refer to distance weight and capacity weight respectively.

[0061] By using the energy storage device priority evaluation function, each energy storage device in the distributed energy storage device, for example, the first energy storage device, is evaluated and analyzed to obtain a first priority index, wherein the first energy storage device refers to any one of the multiple energy storage devices with capacity and position identifiers, and the first energy storage device corresponds to a first capacity and a first position. The first priority index comprehensively considers factors such as the location and capacity of the energy storage devices and their role in the power grid, and can reflect the energy storage priority of the first energy storage device.

[0062] Furthermore, based on the first priority index obtained by the above evaluation, multiple energy storage devices in the distributed energy storage device are arranged in descending order to form a descending list of energy storage devices. This list is sorted from high to low according to the priority index of the energy storage device, providing a clear priority order for subsequent energy storage operations. Furthermore, a dynamic distributed storage strategy is formed with the dynamic energy scheduling rate as the energy storage rate constraint and the descending list of energy storage devices as the energy storage priority order. In this strategy, the system selects energy storage devices for energy storage operations in sequence according to the order in the descending list of energy storage devices and the dynamic energy scheduling rate. In this way, it is ensured that under limited energy storage resources, energy storage devices that are closer, have larger remaining capacity or better meet the needs of the power grid are used for energy storage first, thereby improving the overall efficiency and response speed of the energy storage system.

[0063] Furthermore, step P75-32 of the embodiment of the present application also includes:

[0064] P75-325: Arrange the multiple energy storage devices with capacity and location identifiers in ascending order based on the first priority index to obtain an ascending list of energy storage devices; P75-326: Use the dynamic energy scheduling rate as the energy release rate constraint and the descending list of energy storage devices as the energy release priority order to form the dynamic distributed release.

[0065] In a possible embodiment of the present application, the specific process of performing dynamic distributed release may be, firstly, based on the first priority index, the multiple energy storage devices with capacity and location identifications are arranged in ascending order to obtain an ascending list of energy storage devices, and the ascending order means that the system will first release the energy in energy storage devices with relatively low storage priorities, such as giving priority to releasing temporary energy storage devices that are not suitable for long-term energy storage, or energy in energy storage devices with less remaining storage space. Furthermore, the dynamic energy scheduling rate is used as the energy release rate constraint to control the release speed, and the descending list of energy storage devices is used as the energy release priority order to select a suitable energy storage device for energy release operation, thereby forming a dynamic distributed release strategy. Ensure that energy can be released quickly and effectively when needed.

[0066] In summary, the embodiments of the present application have at least the following technical effects:

[0067] The present application extracts the output value in each time zone in a predetermined power grid cycle, compares it with the consumption value, and classifies each time zone into a storage scheduling time zone set or a release scheduling time zone set according to the comparison result, obtains the dynamic time zone, traverses the storage scheduling time zone set and the release scheduling time zone set, and analyzes the traversal results according to the predetermined adaptive scheduling mechanism to generate a dynamic scheduling decision for adaptive scheduling processing.

[0068] The technical effect of storing and releasing renewable energy through time zones and improving the flexibility of power grid energy scheduling and operational stability has been achieved. Embodiment 2

[0069] Based on the same inventive concept as the adaptive dispatching method for a distribution network in the aforementioned embodiment, Figure 3 As shown, the present application provides an adaptive dispatching system for a distribution network, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. The system includes:

[0070] The target output time series database construction module 11 is used to obtain a target output time series database. The target output time series database refers to a database constructed based on historical output records of target renewable energy.

[0071] The first time zone extraction module 12 is used to read a predetermined power grid cycle and extract a first time zone in the predetermined power grid cycle.

[0072] The production and consumption comparison module 13 is used to extract the first output value in the first time zone in the target output time series database, and compare the first output value with the first consumption value to obtain a first comparison result, wherein the first consumption value refers to the consumption value traversed and matched in the first time zone in the target consumption time series database, and the target consumption time series database refers to a database constructed based on the historical consumption records of the target renewable energy.

[0073] The time zone classification module 14 is used to add the first time zone to the energy storage scheduling time zone set if the first output value is greater than the first consumption value based on the first comparison result, and add the first time zone to the energy release scheduling time zone set if the first output value is less than the first consumption value.

[0074] The time zone traversal module 15 is used to obtain a dynamic time zone, and traverse the dynamic time zone in the energy storage scheduling time zone set and the energy release scheduling time zone set to obtain a traversal result.

[0075] The adaptive scheduling module 16 is used to call a predetermined adaptive scheduling mechanism to analyze the traversal result and generate a dynamic scheduling decision, wherein the dynamic scheduling decision is used to perform adaptive scheduling processing on the target renewable energy.

[0076] Furthermore, the time zone traversal module 15 is also used to perform the following steps:

[0077] The dynamic energy information of the dynamic time zone is obtained, wherein the dynamic energy information includes a dynamic output value and a dynamic consumption value; the dynamic output value and the dynamic consumption value are compared to obtain a dynamic comparison result; and the traversal result is verified based on the dynamic comparison result.

[0078] Furthermore, the system also includes:

[0079] A dynamic factor acquisition module, the dynamic factor acquisition module is used to acquire the dynamic factor conditions of the dynamic time zone, the dynamic factor conditions include dynamic environmental information and dynamic meteorological information; a predetermined factor condition benchmark acquisition module, the predetermined factor condition benchmark acquisition module is used to read the predetermined factor condition benchmark, the predetermined factor condition benchmark includes the environmental factor benchmark and the meteorological factor benchmark; a dynamic index calculation module, the dynamic index calculation module is used to perform weighted calculation on the normalized dynamic environmental information and the dynamic meteorological information in combination with the environmental factor benchmark and the meteorological factor benchmark in sequence, to obtain a dynamic environmental index and a dynamic meteorological index respectively; a comprehensive index calculation module, the comprehensive index calculation module is used to read the predetermined factor weight, and weight the dynamic environmental index and the dynamic meteorological index based on the predetermined factor weight to obtain a dynamic comprehensive index; a dynamic scheduling decision adjustment module, the weighting is performed to obtain a dynamic comprehensive index; the dynamic scheduling decision adjustment module is used to adjust the dynamic scheduling decision when the dynamic comprehensive index exceeds the predetermined comprehensive index limit.

[0080] Furthermore, the dynamic scheduling decision adjustment module is also used to perform the following steps:

[0081] The dynamic output value and the dynamic absorption value corresponding to the dynamic time zone are obtained according to the traversal result; the dynamic output value is adjusted with the dynamic comprehensive index as a coefficient to obtain an effective dynamic output value; the effective dynamic output value is compared with the dynamic absorption value and scheduling adjustment is performed in combination with the predetermined adaptive scheduling mechanism.

[0082] Furthermore, the dynamic scheduling decision adjustment module is also used to perform the following steps:

[0083] The dynamic energy deviation value of the dynamic time zone is obtained according to the dynamic output value and the dynamic absorption value, and the dynamic energy scheduling rate is calculated; when the dynamic time zone is in the energy storage scheduling time zone set, the energy output of the target renewable energy is dynamically distributed stored at the dynamic energy scheduling rate; when the dynamic time zone is in the energy release scheduling time zone set, the energy output of the target renewable energy is dynamically distributed released at the dynamic energy scheduling rate; the dynamic distributed storage or the dynamic distributed release is used as the dynamic scheduling decision.

[0084] Furthermore, the dynamic scheduling decision adjustment module is also used to perform the following steps:

[0085] Obtain a distributed energy storage device, the distributed energy storage device includes multiple energy storage devices with capacity and location identifications; introduce an energy storage device priority evaluation function to evaluate and analyze the first energy storage device to obtain a first priority index, the first energy storage device refers to any one of the multiple energy storage devices with capacity and location identifications, and the first energy storage device corresponds to a first capacity and a first position; based on the first priority index, arrange the multiple energy storage devices with capacity and location identifications in descending order to obtain a descending list of energy storage devices; use the dynamic energy scheduling rate as the energy storage rate constraint and the descending list of energy storage devices as the energy storage priority order to form the dynamic distributed storage. The expression of the energy storage device priority evaluation function is as follows:

[0086] ;in, Refers to the distributed energy storage device The priority index of the energy storage device, The location of the energy storage device is , the location of the target renewable energy source is ， refers to the The capacity of the energy storage device, and They refer to distance weight and capacity weight respectively.

[0087] Furthermore, the dynamic scheduling decision adjustment module is also used to perform the following steps:

[0088] The plurality of energy storage devices with capacity and location identifiers are arranged in ascending order based on the first priority index to obtain an ascending list of energy storage devices; the dynamic energy scheduling rate is used as an energy release rate constraint, and the descending list of energy storage devices is used as an energy release priority order to form the dynamic distributed release.

[0089] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0090] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0091] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the 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 intends to include these modifications and variations.

Claims

1. An adaptive dispatching method for a distribution network, characterized in that: include: Acquire a target output time series database, where the target output time series database refers to a database constructed based on historical output records of target renewable energy; Reading a predetermined power grid cycle, and extracting a first time zone in the predetermined power grid cycle; Extracting a first output value in the first time zone from the target output time series database, and comparing the first output value with a first consumption value to obtain a first comparison result, wherein the first consumption value refers to a consumption value traversed and matched in the first time zone in the target consumption time series database, and the target consumption time series database refers to a database constructed based on historical consumption records of the target renewable energy; According to the first comparison result, if the first output value is greater than the first consumption value, the first time zone is added to the energy storage scheduling time zone set; if the first output value is less than the first consumption value, the first time zone is added to the energy release scheduling time zone set; Acquire a dynamic time zone, and traverse the dynamic time zone in the energy storage scheduling time zone set and the energy release scheduling time zone set to obtain a traversal result; Retrieving a predetermined adaptive scheduling mechanism to analyze the traversal result and generate a dynamic scheduling decision, wherein the dynamic scheduling decision is used to perform adaptive scheduling processing on the target renewable energy; Acquire dynamic energy information of the dynamic time zone, wherein the dynamic energy information includes a dynamic output value and a dynamic consumption value; Comparing the dynamic output value with the dynamic consumption value to obtain a dynamic comparison result; Verifying the traversal result based on the dynamic comparison result; Acquire dynamic factor conditions of the dynamic time zone, wherein the dynamic factor conditions include dynamic environmental information and dynamic meteorological information; Reading a predetermined factor condition benchmark, wherein the predetermined factor condition benchmark includes an environmental factor benchmark and a meteorological factor benchmark; Combining the environmental factor benchmark and the meteorological factor benchmark in sequence, weighted calculation is performed on the normalized dynamic environmental information and the dynamic meteorological information to obtain a dynamic environmental index and a dynamic meteorological index, respectively; Reading a predetermined factor weight, and weighting the dynamic environment index and the dynamic meteorological index based on the predetermined factor weight to obtain a dynamic comprehensive index; When the dynamic comprehensive index exceeds a predetermined comprehensive index limit, adjusting the dynamic scheduling decision; According to the traversal result, the dynamic output value and the dynamic consumption value corresponding to the dynamic time zone are obtained; The dynamic output value is adjusted by taking the dynamic comprehensive index as a coefficient to obtain an effective dynamic output value; Comparing the effective dynamic output value with the dynamic consumption value and performing scheduling adjustment in combination with the predetermined adaptive scheduling mechanism; Obtaining a dynamic energy deviation value of the dynamic time zone according to the dynamic output value and the dynamic consumption value, and calculating a dynamic energy scheduling rate; When the dynamic time zone is in the energy storage scheduling time zone set, the energy output of the target renewable energy is dynamically distributed stored at the dynamic energy scheduling rate; when the dynamic time zone is in the energy release scheduling time zone set, the energy output of the target renewable energy is dynamically distributed released at the dynamic energy scheduling rate; The dynamic distributed storage or the dynamic distributed release is used as the dynamic scheduling decision.

2. The adaptive dispatching method of a distribution network according to claim 1, characterized in that: include: Acquire a distributed energy storage device, wherein the distributed energy storage device includes a plurality of energy storage devices with capacity and location identifiers; An energy storage device priority evaluation function is introduced to evaluate and analyze a first energy storage device to obtain a first priority index, wherein the first energy storage device refers to any one of the multiple energy storage devices with capacity and position identifiers, and the first energy storage device corresponds to a first capacity and a first position; Arrange the plurality of energy storage devices with capacity and location identifiers in descending order based on the first priority index to obtain a descending list of energy storage devices; The dynamic distributed storage is formed by taking the dynamic energy scheduling rate as the energy storage rate constraint and taking the descending list of the energy storage devices as the energy storage priority order.

3. The adaptive dispatching method of a distribution network according to claim 2, characterized in that: include: Arrange the plurality of energy storage devices with capacity and location identifiers in ascending order based on the first priority index to obtain an ascending list of energy storage devices; The dynamic distributed release is formed by taking the dynamic energy scheduling rate as the energy release rate constraint and taking the descending list of the energy storage devices as the energy release priority order.

4. The adaptive dispatching method of a distribution network according to claim 2, characterized in that: The expression of the energy storage priority evaluation function is as follows: ; in, Refers to the distributed energy storage device The priority index of the energy storage device, The location of the energy storage device is , the location of the target renewable energy source is ， refers to the The capacity of the energy storage device, and They refer to distance weight and capacity weight respectively.

5. An adaptive dispatching system for a distribution network, characterized in that: The system is used to execute the method according to any one of claims 1 to 4, and the system comprises: A target output time series database construction module, wherein the target output time series database construction module is used to obtain a target output time series database, wherein the target output time series database refers to a database constructed based on historical output records of target renewable energy; A first time zone extraction module, the first time zone extraction module is used to read a predetermined power grid cycle and extract a first time zone in the predetermined power grid cycle; A production and consumption comparison module, the production and consumption comparison module is used to extract the first output value in the first time zone in the target output time series database, and compare the first output value with the first consumption value to obtain a first comparison result, wherein the first consumption value refers to the consumption value traversed and matched in the first time zone in the target consumption time series database, and the target consumption time series database refers to a database constructed based on the historical consumption records of the target renewable energy; A time zone classification module, wherein the time zone classification module is used to add the first time zone to the energy storage scheduling time zone set if the first output value is greater than the first consumption value according to the first comparison result, and add the first time zone to the energy release scheduling time zone set if the first output value is less than the first consumption value; A time zone traversal module, the time zone traversal module is used to obtain a dynamic time zone, and traverse the dynamic time zone in the energy storage scheduling time zone set and the energy release scheduling time zone set to obtain a traversal result; An adaptive scheduling module is used to call a predetermined adaptive scheduling mechanism to analyze the traversal result and generate a dynamic scheduling decision, wherein the dynamic scheduling decision is used to perform adaptive scheduling processing on the target renewable energy.

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