DC micro-grid adjusting method and system based on multi-energy complementary analysis, and medium

Abstract

The invention discloses a DC micro-grid adjusting method and system based on multi-energy complementary analysis, and a medium, and relates to the technical field of DC micro-grids, and the method comprises the steps: obtaining the multi-energy type distribution of a DC micro-grid, carrying out the supply feature analysis, and building a multi-energy supply feature library; collecting supply energy influence features, and performing state updating on the multi-energy supply feature library to obtain multi-energy supply state time sequence features; establishing a multi-gradient load analysis window to perform multi-gradient analysis on the DC micro-grid load to obtain load demand characteristics; and aligning the load demand characteristics with the multi-energy supply state time sequence characteristics according to the time sequence relationship, carrying out supply complementation strategy matching according to the alignment relationship, obtaining a supply complementation strategy which meets the load demand and has the maximum supply stability, and carrying out direct current micro-grid energy supply adjustment. According to the invention, the technical problem that the dynamic load demand is difficult to meet due to the weak capability of coordinating and adjusting multiple energy sources in the prior art is solved, and the technical effect of improving the stability and matching precision of micro-grid energy supply is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of direct current microgrids, and in particular to a direct current microgrid regulation method, system and medium based on multi-energy complementary analysis. Background Art

[0002] As a comprehensive energy supply platform integrating multiple distributed energy sources, including wind, photovoltaic, biomass, and energy storage systems, DC microgrids must manage their operation and regulation, taking into account both the output characteristics of each energy source and the dynamic demands of the load. In practice, significant differences exist in the response speed, output stability, and cost-effectiveness of different energy sources, making efficient coordinated scheduling difficult for variable loads. Summary of the Invention

[0003] The present application provides a DC microgrid regulation method, system and medium based on multi-energy complementary analysis, which is used to solve the technical problems in the existing technology that the coordinated regulation capability of multiple energy sources is weak and it is difficult to meet dynamic load requirements.

[0004] In view of the above problems, the present application provides a DC microgrid regulation method, system and medium based on multi-energy complementary analysis.

[0005] In a first aspect of the present application, a DC microgrid regulation method based on multi-energy complementary analysis is provided, the method comprising:

[0006] The multi-energy type distribution of the DC microgrid is obtained, the supply characteristics of each energy are analyzed, and a multi-energy supply characteristic library is established; the supply energy impact characteristics are collected in real time, and the state of the multi-energy supply characteristic library is updated using the supply energy impact characteristics as influencing variables to obtain the multi-energy supply state timing characteristics; a multi-gradient load analysis window is established, and a multi-gradient analysis is performed on the DC microgrid load to obtain the load demand characteristics; the load demand characteristics are aligned with the multi-energy supply state timing characteristics according to the timing relationship, and the supply complementary strategy is matched according to the alignment relationship to obtain a supply complementary strategy that meets the load demand and maximizes supply stability, and the DC microgrid energy supply is adjusted.

[0007] A second aspect of the present application provides a DC microgrid regulation system based on multi-energy complementary analysis, the system comprising:

[0008] A multi-energy supply feature library establishment module is used to obtain the multi-energy type distribution of the DC microgrid, analyze the supply characteristics of each energy source, and establish a multi-energy supply feature library; a state update module is used to collect the supply energy impact characteristics in real time, use the supply energy impact characteristics as the influencing variable to update the state of the multi-energy supply feature library, and obtain the multi-energy supply state timing characteristics; a multi-gradient analysis module is used to establish a multi-gradient load analysis window, perform multi-gradient analysis on the DC microgrid load, and obtain the load demand characteristics; an energy supply regulation module is used to align the load demand characteristics with the multi-energy supply state timing characteristics according to the timing relationship, match the supply complementary strategy according to the alignment relationship, obtain a supply complementary strategy that meets the load demand and has the greatest supply stability, and regulate the DC microgrid energy supply.

[0009] The third aspect of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the DC microgrid regulation method based on multi-energy complementary analysis provided in the present application.

[0010] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0011] The present application obtains the multi-energy type distribution of the DC microgrid, analyzes the supply characteristics of each energy source, and establishes a multi-energy supply characteristic library; collects the supply energy impact characteristics in real time, uses the supply energy impact characteristics as the influencing variables to update the state of the multi-energy supply characteristic library, and obtains the multi-energy supply state time series characteristics; establishes a multi-gradient load analysis window, performs multi-gradient analysis on the DC microgrid load, and obtains the load demand characteristics; aligns the load demand characteristics with the multi-energy supply state time series characteristics according to the time series relationship, matches the supply complementary strategy according to the alignment relationship, obtains the supply complementary strategy that meets the load demand and has the greatest supply stability, and performs DC microgrid energy supply regulation. The present invention solves the technical problem in the prior art that the coordinated regulation capability of multiple energy sources is weak and it is difficult to meet dynamic load demand. By aligning and matching the multi-energy supply state modeling with the demand characteristics, the technical effect of improving the stability and matching accuracy of the microgrid energy supply is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] 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.

[0013] Figure 1 A flow chart of a DC microgrid regulation method based on multi-energy complementary analysis provided in an embodiment of the present application;

[0014] Figure 2 Schematic diagram of the structure of a DC microgrid regulation system based on multi-energy complementary analysis provided in an embodiment of the present application.

[0015] Explanation of the accompanying symbols: multi-energy supply feature library establishment module 11, state update module 12, multi-gradient analysis module 13, energy supply regulation module 14. DETAILED DESCRIPTION

[0016] This application provides a DC microgrid regulation method, system and medium based on multi-energy complementary analysis, aiming to solve the technical problems in the existing technology that the coordinated regulation capabilities of multiple energy sources are weak and difficult to meet dynamic load demands. By aligning and matching multi-energy supply state modeling with demand characteristics, the technical effect of improving the stability and matching accuracy of microgrid energy supply is achieved.

[0017] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0018] It should be noted that any variations of the terms "include" and "have" 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 clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products or devices.

[0019] Example 1, as Figure 1 As shown, the present application provides a DC microgrid regulation method based on multi-energy complementary analysis, the method comprising:

[0020] Step S100: Obtain the multi-energy type distribution of the DC microgrid, analyze the supply characteristics of each energy source, and establish a multi-energy supply characteristic library.

[0021] Furthermore, in the method provided in the application embodiment, the multiple energy types include: wind energy, solar energy, biomass energy, and energy storage system.

[0022] In an embodiment of the present application, first, the structural topology and access device information of the DC microgrid are analyzed to obtain the distribution of various energy types connected to the system. Specifically, the monitoring system and energy management system of the microgrid are combined to identify the access nodes and corresponding equipment types of various energy sources, and the type classification and distribution range of energy such as wind power generation, photovoltaic power generation, biomass power generation and energy storage systems are obtained through equipment identification, communication protocol or energy management interface, so as to form a complete multi-energy type distribution map. Subsequently, for each type of energy that has been identified, characteristic parameter analysis including maximum output power, response speed, output volatility and unit electricity cost is carried out to extract a set of key indicators representing its power supply performance; further analysis is performed on external factors that affect the energy supply capacity, such as meteorological conditions, load interference or equipment status, and a response relationship between these influencing factors and each characteristic indicator is established. Finally, a multi-energy supply feature library is constructed based on the corresponding mapping of energy type, performance characteristics and influencing relationship.

[0023] Furthermore, in the method provided in the embodiment of the application, supply characteristics of each energy source are analyzed to establish a multi-energy supply characteristics library, which also includes:

[0024] The multi-energy supply characteristics are analyzed according to the maximum output power, response speed, output volatility index, and unit electricity cost to obtain the multi-energy characteristics; the energy supply influencing factors are analyzed to establish the influence relationship between each energy supply influencing factor and the multi-energy characteristics; according to the mapping relationship between the multi-energy characteristics, the influence relationship and the multi-energy type, the multi-energy supply feature library is constructed.

[0025] In the embodiment of the present application, first of all, for the various energy sources such as wind energy, solar energy, biomass energy and energy storage system connected to the DC microgrid, based on historical operation data and real-time monitoring results, core supply characteristics such as maximum output power, response speed, output volatility and unit electricity cost are extracted. Specifically, the maximum output power is obtained by counting the maximum instantaneous power value of each energy source during the stable operation cycle; the response speed is calculated by recording the time difference between the issuance of the control instruction and the actual power reaching the target value; the output volatility uses the standard deviation of the power change in the sliding time window to evaluate the instability of the power output; the unit electricity cost is converted by dividing the total operating cost (including energy consumption, fuel, labor, etc.) by the total power generation in the corresponding period. After the above processing, each type of energy is converted into a set of quantitative performance characteristics to obtain multi-energy characteristics.

[0026] After obtaining multi-energy characteristics, supply influencing factors closely related to changes in energy characteristics are collected, including environmental factors (such as wind speed, light intensity, and temperature) and equipment status factors (such as fuel calorific value and battery state of charge). This data is obtained through deployed environmental sensors and the equipment's built-in monitoring interfaces. A multivariate linear regression method is then used to establish a functional relationship between each energy source's multi-energy characteristics and the corresponding influencing factors. A regression model is constructed using a characteristic value of each energy source as the dependent variable and the corresponding influencing factors as independent variables. The model is then fitted using existing historical data. Regression equations are used to describe the changing trends of different influencing factors in affecting energy characteristics. For example, in a wind power generation system, a regression model is constructed using wind speed and wind direction as independent variables and output volatility as the dependent variable to determine the impact of wind speed changes on output volatility at different stages. In a photovoltaic system, a model is constructed using irradiance and temperature as independent variables and response speed as the dependent variable to describe the impact of changing environmental conditions on the dynamic response capability of photovoltaic systems. Through this process, the relationship between the external and internal influencing factors of each energy source and its supply capacity characteristics is clearly expressed in the form of a model, and the influence relationship between each energy supply influencing factor and multi-energy characteristics is established.

[0027] Finally, by classifying the influence relationship between multi-energy characteristics and supply influencing factors, identifying and distinguishing static characteristics from dynamic characteristics, constructing energy static characteristic tables and supply influencing factor tables respectively, and establishing an influence mapping table based on the response relationship between characteristics and factors, and then combining it with time series data to form a time series feature table. Finally, the above-mentioned various structured data are integrated, and a multi-energy supply feature library is constructed based on the mapping relationship between multi-energy characteristics, influence relationships and multi-energy types.

[0028] Furthermore, in the method provided in the embodiment of the application, establishing the multi-energy supply feature library further includes:

[0029] According to the multi-energy characteristics and influence relationships, static characteristics and dynamic characteristics are identified; based on the static characteristics, an energy static characteristic table is established; based on the dynamic characteristics, the target influencing variables for real-time monitoring are extracted, a supply influence factor table is established, the influence relationship of the energy supply influence factors on the supply characteristics is extracted, and an influence mapping table is established; according to the supply influence factor table and the influence mapping table, the time series characteristic relationship is sorted out, and a time series characteristic table is established to record the output status of various energy sources over time; the energy static characteristic table, the supply influence factor table, the influence mapping table and the time series characteristic table are integrated to construct the multi-energy supply characteristic library.

[0030] Furthermore, in the method provided in the application embodiment, the static characteristics include: maximum output power, response speed, unit electricity cost, and basic supply characteristics that do not change with time.

[0031] In the embodiment of the present application, the multi-energy features are first classified according to the extracted multi-energy features and their corresponding influence relationships. The attribute classification method is used to determine whether each feature changes over time. Parameters that remain stable during the operating cycle, such as maximum output power, response speed, and unit electricity cost, are classified as static features. Parameters that are greatly affected by changes in the operating environment, such as output volatility, are classified as dynamic features, thereby completing the preliminary structural division of the feature dimension. Subsequently, the field archiving modeling method is used to establish an energy static feature table, uniformly using the energy number as the primary key, recording the static performance indicators of each type of energy item by item, and forming a standardized basic feature data table.

[0032] For dynamic characteristics, real-time data collection is employed. Through on-site equipment such as anemometers, light intensity sensors, temperature sensors, and battery management systems, operational data closely related to energy output, such as wind speed, irradiance, temperature, and battery state of charge, is continuously acquired. This data, serving as target influencing variables, is structured and organized by energy number and chronological order to create a supply influencing factor table, which represents the impact of the external environment and state on energy at a specific moment. Next, combined with the energy characteristic model established earlier using multiple linear regression, the mapping relationship between each influencing factor and the dynamic characteristics is extracted to form an impact mapping table. This table records the functional expression between the influencing factors and the corresponding characteristics.

[0033] Subsequently, based on the time index in the supply impact factor table and the impact mapping table, the statistical method of the sliding time window is used to sort out and summarize the changing process of dynamic characteristics in different time periods, and establish a time series feature table. This table uses energy number and timestamp as the joint index to record the output change trend of various energy sources in continuous operation and reflect their dynamic operation characteristics.

[0034] Finally, using primary key consistency and field alignment strategies, and using energy number and time information as the connection basis, we unified and integrated the energy static feature table, supply impact factor table, impact mapping table, and time series feature table. Through field cascading and index linkage, we constructed a structured multi-energy supply feature library.

[0035] Step S200: collecting supply energy impact characteristics in real time, using the supply energy impact characteristics as influencing variables to update the state of the multi-energy supply feature library, and obtaining multi-energy supply state time series characteristics.

[0036] In the embodiment of the present application, first, environmental sensors and equipment monitoring devices are deployed in the microgrid system to collect real-time parameters such as wind speed, irradiance, temperature, biomass fuel status, battery charge level, etc. that can reflect the external conditions of energy and the operation status of equipment, which are collectively referred to as supply energy impact characteristics.

[0037] Next, the supply energy impact characteristics collected in real time are input into the multi-energy supply feature library as influencing variables. Dynamic feature updates are performed based on the supply impact factor table and the impact mapping table, and the results are written into the time series feature table. Finally, a multi-energy supply status time series feature is formed that reflects the changing trends of the output capacity of various energy sources.

[0038] Furthermore, in the method provided in the embodiment of the application, the state of the multi-energy supply feature library is updated by using the supply energy impact feature as an influencing variable to obtain the multi-energy supply state time series feature, and the method further includes:

[0039] The supply energy impact characteristics collected in real time are substituted into the multi-energy supply feature library, the data change characteristics in the supply impact factor table are updated, and the dynamic values ​​are updated according to the impact mapping table; the collected data and dynamic values ​​of the supply impact factor table of each energy are recorded in a rolling manner, and the supply status and trend time series prediction is performed through the time series feature table to obtain the supply status time series characteristics of each energy type.

[0040] In this embodiment of the present application, to dynamically update the status of various energy source characteristics within an established multi-energy supply feature library, the real-time collected supply energy impact characteristics are first used as influencing variables, located by energy number and timestamp, and entered into the corresponding supply impact factor table to update the current operating environment data. This update step is accomplished through field replacement and appending, ensuring that the environmental and device status information for each energy type recorded in the supply impact factor table remains up-to-date at different time points.

[0041] Then, based on the pre-established characteristic function relationship in the influence mapping table, the updated influence variables are substituted into the function to calculate the dynamic characteristic values ​​under the current operating conditions, such as the current values ​​of parameters such as output volatility or response speed, to complete the dynamic value update of the characteristics.

[0042] After dynamic value calculations are complete, a time series recording method is used to structure the current influencing variables and updated dynamic characteristic values ​​into a time series characteristic table. This table uses the energy number and timestamp as the joint primary key, and records are added in a rolling manner, forming a continuous trajectory of energy status changes over time. Through sliding analysis and trend extraction of data in this table, real-time tracking of energy supply capacity trends and periodic forecasting are achieved.

[0043] Finally, based on updating the supply impact factor table, calculating dynamic characteristics, and maintaining the time series characteristic table, the status update of various energy sources in the multi-energy supply feature library is completed, and the multi-energy supply status time series characteristics that can be used for subsequent decision analysis are obtained.

[0044] Step S300: establishing a multi-gradient load analysis window, performing multi-gradient analysis on the DC microgrid load, and obtaining load demand characteristics.

[0045] In an embodiment of the present application, in the process of establishing a multi-gradient load parsing window, a multi-time scale sliding division method is first adopted. Based on the power time series data of the load, multiple time granularities including minute level, hour level and day level are set, and the power data is divided into different time periods by sliding windows, which respectively constitute a set of time windows at different granularities. Subsequently, combined with the type information of the load in the microgrid, a load type mapping method is adopted to map different categories of loads (such as basic loads, adjustable loads, and interruptible loads) to time windows of corresponding granularities according to their response characteristics and operation strategies, forming a binding relationship between the load type and the time window, thereby structurally constituting a multi-dimensional multi-gradient load parsing window. This window structure allows the behavioral characteristics of various types of loads to be independently evaluated at different time scales, realizing unified modeling from rapid response to long-term trends.

[0046] After the multi-gradient load analysis window is established, power demand analysis, fluctuation tolerance assessment and adjustment priority determination are performed on different load types according to the granularity of each window to extract and form load demand characteristics.

[0047] Furthermore, in the method provided in the embodiment of the application, obtaining the load demand characteristics further includes:

[0048] According to the analysis granularity of the multi-gradient load analysis window, the power demand, fluctuation tolerance, and regulation priority of different load types are analyzed to obtain the load demand characteristics of each type of load at each window granularity level.

[0049] In an embodiment of the present application, based on the constructed multi-gradient load analysis window, a hierarchical feature analysis is performed on different types of loads in the DC microgrid according to each analysis granularity (such as seconds, minutes, and hours), and the power demand characteristics, fluctuation tolerance characteristics, and regulation priority characteristics are extracted in turn, and finally the load demand characteristics of each type of load at each window granularity level are obtained.

[0050] Specifically, within each window corresponding to the resolution granularity, a power statistical analysis method is first applied to process the power time series data within the window. The average power, maximum power, minimum power, and power variation within the period are extracted to construct the power demand characteristics of the load at that time scale. For example, for an energy storage system in an hourly window, the average power and power fluctuation range during the discharge period are extracted to quantify its power consumption behavior during steady-state operation. Secondly, the coefficient of variation assessment method is applied within each time window to calculate the volatility of the power data. Specifically, the coefficient of variation is calculated by calculating the ratio of the standard deviation of the power series to the mean. Combined with the load's tolerance for parameters such as voltage and current, a tolerance matching method is used to determine whether the degree of variation is within the allowable range, thereby determining its fluctuation tolerance characteristics. For example, for office equipment, a low power coefficient of variation within a minute-level window indicates that the equipment can adapt to a wide range of power supply adjustments.

[0051] Next, based on the load's dispatchable attributes, a dispatch level calibration method is used. This method combines controllability, the importance of the task, the load's historical response history, and policy configuration to assign priorities within the multi-level control strategy, thereby generating a regulation priority signature. For example, critical equipment (communications equipment, lighting systems) is assigned a first-level priority, while interruptible equipment such as electric heaters are assigned a lower priority, allowing for prioritization of critical loads during load shedding or regulation.

[0052] Finally, the power demand characteristics, fluctuation tolerance characteristics and regulation priority characteristics under the time window are structured and combined to generate a complete load demand feature vector, which is then stored and archived according to the window granularity, load number and time index.

[0053] Step S400: Align the load demand characteristics with the multi-energy supply state timing characteristics according to the timing relationship, match the supply complementary strategy according to the alignment relationship, obtain the supply complementary strategy that meets the load demand and maximizes supply stability, and adjust the DC microgrid energy supply.

[0054] In an embodiment of the present application, a time index alignment method is adopted, with a unified timestamp as a reference, to correspond one-to-one between the load demand characteristics under each time window and the multi-energy supply status timing characteristics of the corresponding time period. Through field mapping and structured indexing, accurate alignment of supply and demand data in the time dimension is achieved.

[0055] Based on the temporal alignment between load demand characteristics and multi-energy supply characteristics, the system first identifies matching and missing matches. It then activates a complementary strategy search for missing matches. With the multiple objectives of covering load demand, minimizing output volatility, minimizing costs, and maximizing response speed, it conducts a search for combinations of multi-energy supply characteristics. When the search process meets the preset optimization criteria, it outputs the optimal complementary supply strategy.

[0056] Finally, the selected supply complementarity strategy is applied to the operation control of the DC microgrid. By issuing energy regulation instructions to each energy interface, dynamic power allocation and scheduling of wind energy, photovoltaic energy, biomass energy and energy storage systems are realized, and a periodic DC microgrid energy supply regulation based on load characteristics is completed, realizing closed-loop coordination of supply and demand and multi-source optimized operation.

[0057] Furthermore, in the method provided in the embodiment of the application, matching supply complementary strategies according to the alignment relationship to obtain a supply complementary strategy that meets the load demand and maximizes supply stability also includes:

[0058] According to the alignment relationship, the load demand characteristics are matched with the multi-energy supply characteristics to obtain a matching correspondence relationship and a matching missing relationship. The matching correspondence relationship is a relationship in which the supply characteristics directly match the load demand characteristics, and the matching missing relationship is a relationship in which the supply characteristics do not directly match the load demand characteristics. The complementary strategy search instruction is activated for the matching missing relationship, with covering load demand, lowest output volatility, lowest cost, and fast response speed as multiple search evaluation targets, and a multi-energy supply feature combination relationship search is performed. When the set target value is reached through iterative search, the supply complementary strategy is output.

[0059] In an embodiment of the present application, in order to achieve accurate dynamic adaptation of the load side and the energy side, based on the aforementioned time alignment results, a matching relationship identification method is used to perform feature comparison for the load demand characteristics and multi-energy supply characteristics in each time window, and the matching correspondence relationship and matching missing relationship are output. Then, by activating the complementary strategy search instruction, a multi-objective combination optimization method and an iterative candidate strategy generation method are used to finally generate the optimal supply complementary strategy.

[0060] Specifically, a matching relationship identification method is first used to compare features to determine whether the matching relationship conditions are met: the energy source power output covers the load power demand; the energy source output volatility does not exceed the load fluctuation tolerance threshold; and the energy source response speed does not exceed the load regulation priority requirement. Combinations that meet these three conditions are marked as matching relationships, and energy supply control can be directly executed. If any of these conditions are not met, the relationship is marked as missing, indicating that the current energy source cannot independently provide load coverage.

[0061] For all loads marked as missing, the complementary strategy search command is activated within the current time window, entering the multi-energy supply feature combination search process. A combination enumeration method is used to construct multiple energy output solutions from wind, solar, biomass, and energy storage systems. The search evaluation objectives are set as multiple targets: "covering load demand, minimizing output volatility, minimizing cost, and responsiveness." Each combination solution undergoes a compatibility analysis. During the adaptation process, the combined total power output capacity is statistically analyzed to determine whether it meets the load power demand threshold. The combined output volatility of the combined energy sources (such as the maximum fluctuation amplitude or the combined standard deviation) is calculated to determine whether it is below the load's fluctuation tolerance. The energy source with the shortest response time is selected as a representative to determine whether it meets the load's regulation priority. Based on the compatibility verification, the combination cost is estimated. Using a combination cost estimation method, the unit output cost of each energy source in the combination is multiplied and summed with its corresponding output power to obtain the total cost. The unit equivalent output cost is then calculated using the total power as the denominator. This value is compared with the set cost controllability threshold to determine whether the combination is economically acceptable. If the total cost is lower than or equal to the set upper limit, it means that the combination meets the requirements in terms of economic feasibility and can be marked as a feasible combination strategy.

[0062] Through an iterative candidate strategy generation method, new energy combination schemes are generated in successive rounds, and the aforementioned adaptability analysis process is repeated. The supply and demand adaptation effect is continuously monitored during the search process. When a combination scheme reaches the set target values ​​for coverage, volatility, cost, and response speed, or meets the termination conditions, the optimal supply complementation strategy for the current window is output. This strategy is applied to actual microgrid operation, dynamically adjusting the output of various energy sources to achieve precise adaptation to load-side power supply requirements, completing the multi-energy complementary energy supply regulation based on time series feature alignment.

[0063] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0064] The present application obtains the multi-energy type distribution of the DC microgrid, analyzes the supply characteristics of each energy source, and establishes a multi-energy supply characteristic library; collects the supply energy impact characteristics in real time, uses the supply energy impact characteristics as the influencing variables to update the state of the multi-energy supply characteristic library, and obtains the multi-energy supply state time series characteristics; establishes a multi-gradient load analysis window, performs multi-gradient analysis on the DC microgrid load, and obtains the load demand characteristics; aligns the load demand characteristics with the multi-energy supply state time series characteristics according to the time series relationship, matches the supply complementary strategy according to the alignment relationship, obtains the supply complementary strategy that meets the load demand and has the greatest supply stability, and performs DC microgrid energy supply regulation. The present invention solves the technical problem in the prior art that the coordinated regulation capability of multiple energy sources is weak and it is difficult to meet dynamic load demand. By aligning and matching the multi-energy supply state modeling with the demand characteristics, the technical effect of improving the stability and matching accuracy of the microgrid energy supply is achieved.

[0065] Embodiment 2 is based on the same inventive concept as the DC microgrid regulation method based on multi-energy complementary analysis in the above embodiment. Figure 2 As shown, the present application provides a DC microgrid regulation system based on multi-energy complementary analysis. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0066] A multi-energy supply feature library establishment module 11 is used to obtain the multi-energy type distribution of the DC microgrid, analyze the supply characteristics of each energy source, and establish a multi-energy supply feature library; a state update module 12 is used to collect the supply energy impact characteristics in real time, use the supply energy impact characteristics as the influencing variables to update the state of the multi-energy supply feature library, and obtain the multi-energy supply state timing characteristics; a multi-gradient analysis module 13 is used to establish a multi-gradient load analysis window, perform multi-gradient analysis on the DC microgrid load, and obtain the load demand characteristics; an energy supply adjustment module 14 is used to align the load demand characteristics with the multi-energy supply state timing characteristics according to the timing relationship, match the supply complementary strategy according to the alignment relationship, obtain a supply complementary strategy that meets the load demand and has the greatest supply stability, and adjust the DC microgrid energy supply.

[0067] Furthermore, the system is also used to implement the following functions:

[0068] The multi-energy types include: wind energy, solar energy, biomass energy, and energy storage systems.

[0069] Furthermore, the system is also used to implement the following functions:

[0070] The multi-energy supply characteristics are analyzed according to the maximum output power, response speed, output volatility index, and unit electricity cost to obtain the multi-energy characteristics; the energy supply influencing factors are analyzed to establish the influence relationship between each energy supply influencing factor and the multi-energy characteristics; according to the mapping relationship between the multi-energy characteristics, the influence relationship and the multi-energy type, the multi-energy supply feature library is constructed.

[0071] Furthermore, the system is also used to implement the following functions:

[0072] According to the multi-energy characteristics and influence relationships, static characteristics and dynamic characteristics are identified; based on the static characteristics, an energy static characteristic table is established; based on the dynamic characteristics, the target influencing variables for real-time monitoring are extracted, a supply influence factor table is established, the influence relationship of the energy supply influence factors on the supply characteristics is extracted, and an influence mapping table is established; according to the supply influence factor table and the influence mapping table, the time series characteristic relationship is sorted out, and a time series characteristic table is established to record the output status of various energy sources over time; the energy static characteristic table, the supply influence factor table, the influence mapping table and the time series characteristic table are integrated to construct the multi-energy supply characteristic library.

[0073] Furthermore, the system is also used to implement the following functions:

[0074] The static characteristics include: maximum output power, response speed, unit electricity cost, and basic supply characteristics that do not change over time.

[0075] Furthermore, the system is also used to implement the following functions:

[0076] The supply energy impact characteristics collected in real time are substituted into the multi-energy supply feature library, the data change characteristics in the supply impact factor table are updated, and the dynamic values ​​are updated according to the impact mapping table; the collected data and dynamic values ​​of the supply impact factor table of each energy are recorded in a rolling manner, and the supply status and trend time series prediction is performed through the time series feature table to obtain the supply status time series characteristics of each energy type.

[0077] Furthermore, the system is also used to implement the following functions:

[0078] According to the analysis granularity of the multi-gradient load analysis window, the power demand, fluctuation tolerance, and regulation priority of different load types are analyzed to obtain the load demand characteristics of each type of load at each window granularity level.

[0079] Furthermore, the system is also used to implement the following functions:

[0080] According to the alignment relationship, the load demand characteristics are matched with the multi-energy supply characteristics to obtain a matching correspondence relationship and a matching missing relationship. The matching correspondence relationship is a relationship in which the supply characteristics directly match the load demand characteristics, and the matching missing relationship is a relationship in which the supply characteristics do not directly match the load demand characteristics. The complementary strategy search instruction is activated for the matching missing relationship, with covering load demand, lowest output volatility, lowest cost, and fast response speed as multiple search evaluation targets, and a multi-energy supply feature combination relationship search is performed. When the set target value is reached through iterative search, the supply complementary strategy is output.

[0081] In the third embodiment, based on the same inventive concept as the DC microgrid regulation method based on multi-energy complementary analysis in the aforementioned embodiment, the present application further provides a computer-readable storage medium, on which a computer program is stored, and the computer program implements the steps of any one of the methods described in the aforementioned embodiment one when executed.

[0082] It should be noted that the order in which the embodiments of the present application are presented is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. Furthermore, the foregoing descriptions of specific embodiments of this specification are provided. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential sequence shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0083] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0084] This specification and drawings are merely illustrative of the present application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, this application is intended to include such modifications and variations as fall within the scope of this application and its equivalents.

Claims

1. A DC microgrid regulation method based on multi-energy complementary analysis is characterized by: include: Obtain the multi-energy type distribution of the DC microgrid, analyze the supply characteristics of each energy source, and establish a multi-energy supply characteristic library; collecting supply energy impact characteristics in real time, using the supply energy impact characteristics as influencing variables to update the state of the multi-energy supply feature library, and obtaining multi-energy supply state time series characteristics; Establish a multi-gradient load analysis window to perform multi-gradient analysis on the DC microgrid load and obtain load demand characteristics; The load demand characteristics are aligned with the multi-energy supply state timing characteristics according to the timing relationship, and the supply complementary strategy is matched according to the alignment relationship to obtain a supply complementary strategy that meets the load demand and has the greatest supply stability, and to adjust the DC microgrid energy supply.

2. The DC microgrid regulation method based on multi-energy complementary analysis according to claim 1 is characterized in that: The multi-energy types include: wind energy, solar energy, biomass energy, and energy storage systems.

3. The DC microgrid regulation method based on multi-energy complementary analysis according to claim 1 is characterized in that: Analyze the supply characteristics of each energy source and establish a multi-energy supply characteristics database, including: Analyze the multi-energy supply characteristics according to the maximum output power, response speed, output volatility index, and unit electricity cost to obtain multi-energy characteristics; Analyze various energy supply influencing factors and establish the influence relationship between each energy supply influencing factor and the multi-energy characteristics; The multi-energy supply feature library is constructed according to the mapping relationship between the multi-energy features, the influence relationship and the multi-energy types.

4. The DC microgrid regulation method based on multi-energy complementary analysis according to claim 3 is characterized in that: Establishing the multi-energy supply feature library includes: Identify static features and dynamic features based on the multifunctional features and influence relationships; Based on the static characteristics, establishing an energy static characteristics table; Based on the dynamic characteristics, extract the target influencing variables for real-time monitoring, establish a supply influencing factor table, extract the impact relationship between the energy supply influencing factors and the supply characteristics, and establish an impact mapping table; Sorting out time series feature relationships based on the supply impact factor table and the impact mapping table to establish a time series feature table for recording the output status of various energy sources over time; The energy static characteristic table, supply impact factor table, impact mapping table and time series characteristic table are integrated to construct the multi-energy supply characteristic library.

5. The DC microgrid regulation method based on multi-energy complementary analysis according to claim 4 is characterized in that: The static characteristics include: maximum output power, response speed, unit electricity cost, and basic supply characteristics that do not change over time.

6. The DC microgrid regulation method based on multi-energy complementary analysis according to claim 4 is characterized in that: The state of the multi-energy supply feature library is updated by using the supply energy impact feature as an influencing variable to obtain a multi-energy supply state time series feature, including: Substituting the real-time collected supply energy impact characteristics into the multi-energy supply feature library, updating the data change characteristics in the supply impact factor table, and dynamically updating the values ​​according to the impact mapping table; The collected data and dynamic values ​​of the supply impact factor table of each energy source are recorded in a rolling manner, and the supply status and trend time series prediction is performed through the time series feature table to obtain the supply status time series characteristics of each energy type.

7. The DC microgrid regulation method based on multi-energy complementary analysis according to claim 1 is characterized in that: Obtain load demand characteristics, including: According to the analysis granularity of the multi-gradient load analysis window, the power demand, fluctuation tolerance, and regulation priority of different load types are analyzed to obtain the load demand characteristics of each type of load at each window granularity level.

8. The DC microgrid regulation method based on multi-energy complementary analysis according to claim 7 is characterized in that: Match supply complementation strategies based on the alignment relationship to obtain a supply complementation strategy that meets load demand and maximizes supply stability, including: According to the alignment relationship, the load demand characteristics are matched with the multi-energy supply characteristics to obtain a matching correspondence relationship and a matching missing relationship, wherein the matching correspondence relationship is a relationship in which the supply characteristics directly match the load demand characteristics, and the matching missing relationship is a relationship in which the supply characteristics do not directly match the load demand characteristics; The complementary strategy search instruction is activated for the matching missing relationship, and a search for a combination relationship of multiple energy supply features is performed with multiple search evaluation targets including covering load demand, lowest output volatility, minimum cost, and fast response speed. When the set target value is reached through iterative search, the supply complementary strategy is output.

9. A DC microgrid regulation system based on multi-energy complementary analysis is characterized by: The system is used to execute the DC microgrid regulation method based on multi-energy complementary analysis according to any one of claims 1 to 8, and the system includes: The multi-energy supply feature library establishment module is used to obtain the multi-energy type distribution of the DC microgrid, analyze the supply characteristics of each energy source, and establish a multi-energy supply feature library; a state update module, configured to collect supply energy impact characteristics in real time, use the supply energy impact characteristics as influencing variables to perform state updates on the multi-energy supply feature library, and obtain multi-energy supply state time series characteristics; Multi-gradient analysis module, used to establish a multi-gradient load analysis window, perform multi-gradient analysis on the DC microgrid load, and obtain load demand characteristics; The energy supply regulation module is used to align the load demand characteristics with the multi-energy supply state timing characteristics according to the timing relationship, match the supply complementary strategy according to the alignment relationship, obtain the supply complementary strategy that meets the load demand and maximizes the supply stability, and regulate the DC microgrid energy supply.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the DC microgrid regulation method based on multi-energy complementary analysis as described in any one of claims 1 to 8 is implemented.

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