A micro-grid real-time energy management method and system
By combining short-term and long-term data analysis of microgrid power supply change trends and environmental factors, the problems of insufficient accuracy and low robustness in microgrid energy management are solved, and high-precision and stable power supply adjustment is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ORDOS INST OF APPLIED TECH
- Filing Date
- 2026-04-15
- Publication Date
- 2026-07-10
Smart Images
Figure CN122371167A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of power control technology, specifically, it is a method and system for real-time energy management of microgrids. Background Technology
[0002] Microgrids play a vital role in industrial production within industrial parks. However, considering the extremely high demands of users within these parks for stable power supply and rapid power adjustment, appropriate methods are needed to achieve real-time energy management of microgrids. Current methods for microgrid energy adjustment primarily involve acquiring energy data from a short historical period and making retrospective predictions. This method relies solely on short-term power parameters for adjustment, and its problems are twofold: firstly, it's difficult to verify the power demand within the microgrid based on longer-term historical energy data, resulting in insufficient data for energy demand analysis and significantly reduced accuracy; secondly, relying on only one type of data makes it susceptible to external environmental interference, leading to insufficient system robustness and potentially excessive errors in microgrid energy management.
[0003] Therefore, how to improve the accuracy of energy analysis in real-time energy management of microgrids and enhance the robustness of the energy management system is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention
[0004] To address the issues of insufficient energy analysis accuracy and low robustness of energy management systems in existing microgrid real-time energy management technologies, this application discloses a microgrid real-time energy management method and system, specifically:
[0005] The beneficial effects of this application include:
[0006] 1. It enables advance prediction of microgrid power supply information. In the technical solution of this application, in order to realize real-time management of microgrid power supply parameters, an advance prediction method for microgrid power supply parameters is adopted. Then, the parameter adjustments that the microgrid needs to make when the corresponding time point of power supply is reached are obtained. Therefore, under this situation, the adjustment speed and accuracy of microgrid power supply can be significantly improved.
[0007] 2. Improved accuracy of microgrid power supply information analysis. The technical solution of this application verifies the power supply status of the microgrid. In the specific processing, it analyzes two types of microgrid power supply information obtained based on short-term and long-term data, and selects and determines the microgrid power supply data accordingly. Based on the processing results, it determines the adjustment method for the microgrid power supply data to finalize the microgrid power supply scheme, thereby improving analysis accuracy.
[0008] 3. Improved robustness of microgrid power supply information processing methods. In the technical solution of this application, the analysis of microgrid power supply data and the real-time processing stage of microgrid energy are affected by various factors, which can lead to a decrease in the accuracy of the obtained data. In order to achieve good processing results, this application adopts a common analysis mode based on a combination of long-term and short-term analysis and the factors affecting accuracy, thereby processing these elements together and significantly improving the robustness of the entire system. Attached Figure Description
[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the embodiments of this application or the prior art will be briefly introduced below. Obviously, the following description is only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The drawings are used to provide a further understanding of this disclosure and constitute a part of the specification. They are used together with the following detailed description to explain this disclosure, but do not constitute a limitation of this disclosure. In the drawings:
[0010] Figure 1 A flowchart of a microgrid real-time energy management method provided in this application embodiment;
[0011] Figure 2 The coordinate system for energy demand analysis at the next power supply time node in a microgrid real-time energy management method provided in this application embodiment;
[0012] Figure 3 A coordinate system for historical energy data of a microgrid, provided in an embodiment of this application, for a real-time energy management method for microgrids;
[0013] Figure 4 This is a schematic diagram of a real-time energy management system for a microgrid, provided as an embodiment of this application. Detailed Implementation
[0014] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Furthermore, in the embodiments of this application, "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.
[0015] In real-time energy management of microgrids, it is necessary to analyze the energy demand at various time points based on the coverage area of the microgrid and the configuration of the electronic systems within the entire area. To achieve real-time performance, it is necessary to determine the constituent elements of this information. In other words, the optimal method is to determine the energy supply parameters of the microgrid at the next energy supply moment and then implement specific adjustments to the system upon reaching the corresponding time point. However, in existing technologies, the prediction of the energy supply parameters for the next moment in a microgrid is usually based on long-term forecasts or short-term forecasts. This method is difficult to determine energy supply parameters over a large scale and adjust energy supply data within a short period of time. This leads to insufficient accuracy in the overall system information analysis and makes it difficult to guarantee the accuracy of data analysis.
[0016] To address the problems existing in the prior art, this application discloses a real-time energy management method for microgrids, such as... Figure 1 The diagram shows a flowchart of a real-time energy management method for a microgrid provided in an embodiment of this application. Specifically:
[0017] S110. Obtain and record the energy supply data of the microgrid to update the microgrid energy supply information and obtain the historical energy data of the microgrid.
[0018] S120. Based on the historical energy data obtained during the current energy supply cycle of the microgrid, analyze the energy supply at the next energy supply time node during the current energy supply cycle of the microgrid to obtain the expected energy supply data.
[0019] S130. Obtain the environment of the microgrid and obtain the power supply fluctuation data of the microgrid based on the environmental information.
[0020] S140. Obtain the trend of energy supply data changes of the microgrid in each energy supply cycle to obtain the energy demand of the microgrid in the current energy supply cycle.
[0021] S150. Utilize energy demand to verify expected energy supply data and energy supply fluctuation data in order to obtain a benchmark for energy supply data analysis.
[0022] S160. Determine the microgrid energy adjustment mode for the next energy supply time node based on the energy supply data analysis benchmark, and execute it when the next energy supply time node is reached.
[0023] The beneficial effects of the above steps are that they enable accurate analysis of microgrid power supply data, advance analysis of microgrid data, and corresponding adjustments upon reaching the appropriate time points. This allows for the adjustment and management of microgrid data and improves the robustness of the entire microgrid real-time energy management system.
[0024] The following will provide a detailed explanation of all the steps above:
[0025] As described in step S110, the purpose of this step is to extract and record microgrid data in real time, thereby obtaining all information generated during the operation of the microgrid, and this information is used as historical data in subsequent processing. Specifically:
[0026] S111. Based on the power sensors within the microgrid, acquire power supply data from the microgrid to all power supply branches.
[0027] The purpose of this step is to make full use of various sensors within the microgrid to acquire power parameters of each subsystem within the microgrid and to utilize these parameters in the acquisition of microgrid data.
[0028] This involves using all the power sensors installed in the microgrid to monitor power data in real time.
[0029] Among them, power sensors include all devices that can record the energy consumption of each power supply branch in the microgrid, such as electricity meters, voltmeters, and ammeters.
[0030] Within a microgrid, there are likely to be multiple power supply branches. That is, if there are multiple electricity users in the entire park, then each electricity user belongs to a corresponding power supply branch.
[0031] Each power supply branch needs to be monitored and its values recorded independently, and the obtained data results need to be matched with the power supply branch.
[0032] S112. Send the power supply data of all power supply branches to the database and record it in the database. The power supply data of the microgrid recorded in the database is the historical energy data of the microgrid.
[0033] The purpose of this step is to define the historical energy data in the microgrid data, thereby enabling the processing of historical data.
[0034] Each piece of data obtained needs to be directly added to the system's database, thereby recording the database and the obtained data results.
[0035] For each new energy supply data obtained, the data is directly input into the database, and the data becomes the historical energy data of a microgrid.
[0036] The beneficial effect of step S110 is that all power data in the microgrid can be recorded and analyzed, and then the generated data can be recorded. The analysis results of the recorded data can be recorded as information, and the obtained power data can be directly used as historical energy data of the microgrid.
[0037] As described in step S120, the purpose of this step is that the obtained power data is actually linearly correlated with time, meaning the data is time-continuous. In microgrid data analysis, power parameters for the next power supply time can be predicted based on this time-related data. Therefore, in this case, power supply data can be predicted based on this short-term monitored data. Specifically:
[0038] S121. Based on the historical energy data obtained during the current energy supply cycle of the microgrid, obtain the energy supply data change trend of all energy supply branches.
[0039] The purpose of this step is to determine the trend of energy supply data changes for each energy supply branch by analyzing the historical energy data of the microgrid.
[0040] For microgrids, the so-called power supply cycle is usually one day, but it can also be other cycles, such as half a day or a week, etc. This application does not limit it.
[0041] In this process, it is necessary to set energy data collection time nodes according to a specific time step within each energy supply cycle, so as to determine the energy supply time at that time node.
[0042] In this process, it is necessary to record the energy supply data obtained in each energy supply cycle of the microgrid. In subsequent information processing, it is necessary to obtain the energy supply data corresponding to the data collection time points that have been exceeded in that cycle.
[0043] This requires acquiring the power supply data for each power supply branch, setting this data into the data axis, and then making power supply predictions based on the data axis.
[0044] S122. Based on the trend of energy supply data changes, obtain the expected energy supply data at the next energy supply time node.
[0045] The purpose of this step is to determine, based on the results obtained, the expected energy supply data for the next energy supply time point after analyzing the trend of energy supply data changes.
[0046] Specifically, in the energy demand analysis for the next energy supply time node, this information should be used to conduct an energy demand analysis for the next time node in order to obtain usable data.
[0047] The required expected energy supply data can be obtained based on a coordinate system as shown in step S121, such as... Figure 2 The diagram shows the coordinate system for energy demand analysis at the next energy supply time node in a microgrid real-time energy management method provided in this application embodiment. Specifically, in the stage of determining the energy demand at the next energy supply time node, energy supply prediction is performed based on the coordinate equations of the energy supply data corresponding to the two previous time nodes. The equations are as follows:
[0048] ,
[0049] Where Q3 represents the expected energy supply data at time node t3 (i.e., the next energy supply time node); Q2 represents the energy supply data at the previous time node before the next energy supply time node; Q1 represents the energy supply data at the two time nodes before the next energy supply time node; and t3 represents the time of the next energy supply time node.
[0050] Once the data equation is determined, the next time node can be obtained. That is, once the parameter t is determined, the expected energy supply data can be determined directly.
[0051] In some embodiments, after acquiring historical data, a holistic equation simulation can be performed on the acquired historical data, and the expected energy supply data corresponding to the next energy supply node can be determined based on the simulation results.
[0052] The beneficial effect of step S120 is that it actually realizes the determination of the microgrid power supply data for each short-term record, and this data may be directly applied to the subsequent real-time energy management process of the microgrid, thereby enabling real-time management of the microgrid power supply data.
[0053] As described in step S130, the purpose of this step is to analyze the fluctuations and changes in the microgrid power supply data that may be caused by changes in environmental factors, thereby fully considering the energy data under the environmental influences on the microgrid system during operation. Specifically:
[0054] S131. Obtain the environment of the microgrid and quantify the environment to obtain environmental data.
[0055] The purpose of this step is to determine the current environment of the microgrid in the analysis of energy fluctuations, and to determine the environmental data based on the analysis results.
[0056] In this process, all environmental data that can cause changes in microgrid power supply data are identified and quantified to obtain the corresponding environmental data.
[0057] In some embodiments, the data can also be processed according to the state of the correlation between the environmental data used in the microgrid and the microgrid power supply data, so as to quantify the type of environmental information and the environmental information according to the correlation scheme.
[0058] S132. Obtain the output energy fluctuations of all power supply branches within the microgrid under the influence of environmental data, in order to obtain the energy fluctuation data of the microgrid.
[0059] The purpose of this step is to determine the output energy fluctuation parameters of all power supply branches within the microgrid based on how environmental data is processed during the specific information processing.
[0060] After acquiring all environmental data, the energy proportion and energy source within the energy supply facilities of different energy supply branches are analyzed to determine the energy fluctuation situation.
[0061] For all the energy sources that need to be obtained, it is necessary to ensure that all energy supply data and environmental relationships are jointly determined. At this time, based on the environmental analysis results that can be obtained, the energy fluctuation data that each energy supply branch can generate during operation can be analyzed.
[0062] The beneficial effect of step S130 is that by analyzing the environmental information of the area where the microgrid is located, the environmental impact that all power supply branches in the microgrid area can be affected can be determined, and the energy fluctuation data of the microgrid can be accurately determined based on the quantitative environmental information.
[0063] As described in step S140, the purpose of this step is that after short-term power supply data prediction for the microgrid, the data is prone to large deviations, especially when the production status in the industrial park reaches an inflection point, where this deviation becomes more pronounced. Therefore, to address the large errors or even data erroneousness in the power supply data, it is necessary to use long-term data to determine the power supply data, thereby enabling subsequent verification of the microgrid power supply data based on this data. Specifically:
[0064] S141. Based on the energy supply cycle time length set in the energy relationship system, obtain the historical energy data of the microgrid within each energy supply cycle.
[0065] The purpose of this step is to analyze the long-term microgrid power supply data. The entire database contains a large amount of data, which is basically stored according to the time length set according to the power supply cycle. Therefore, by analyzing the information of each time length, we can analyze the changes in power supply data in each cycle over a longer period of time.
[0066] Among them, the microgrid power supply data corresponding to the power supply cycle length obtained from the database is retrieved.
[0067] For each energy supply cycle, it is necessary to acquire all microgrid energy supply data for that cycle and perform result analysis based on that data.
[0068] S142. Analyze the historical energy data of the microgrid in each power supply cycle to obtain the cycle energy data.
[0069] The purpose of this step is that, over a relatively long period of time, the data changes within each cycle are actually relatively fixed, or exhibit a pattern of initial sharp fluctuations followed by stabilization. In this case, the analysis results of the microgrid's historical energy data can be applied to the process of determining the microgrid's energy consumption. Therefore, the determination of the cycle energy data can be used for subsequent verification of energy supply parameters.
[0070] For all the obtained historical energy data of the microgrid, it is necessary to determine the energy data of all the power supply branches.
[0071] This requires selecting multiple cycles for each energy branch. Typically, the obtained cycle energy data should cover one year, and patterns should be found from it.
[0072] Specifically, the obtained periodic energy data needs to be recorded in the same coordinate system according to the coverage of the period. For example... Figure 3 As shown, this is a coordinate system of historical energy data of a microgrid for a real-time energy management method for a microgrid provided in an embodiment of this application. In the current energy supply cycle, the previous one is the energy supply cycle n. The two endpoints connected by the dashed line are the same energy supply data acquisition time nodes in two energy supply cycles. The deviation between these two identical energy supply data acquisition time nodes is calculated.
[0073] In some embodiments, the obtained periodic energy data can also be recorded and analyzed based on the power type in the microgrid system.
[0074] S143. Perform consistency checks on the microgrid energy supply data for the current energy supply cycle to determine the microgrid energy demand during the current energy supply cycle.
[0075] The purpose of this step is that the microgrid power supply information obtained within the current power supply cycle is clearly not complete for the entire cycle. Once recorded, the data for that cycle is stored in the database and cannot be altered. Furthermore, predictions based on current short-term data analysis may have reliability issues; therefore, processing based on long-term analysis results is necessary. Specifically:
[0076] S1431. Obtain the rate of change of energy supply data between the energy supply data collection time points that have passed in the current energy supply cycle and the same energy supply data collection time points in the previous energy supply cycle, so as to obtain the rate of change of energy supply at the corresponding time points.
[0077] The purpose of this step is to identify the data collection points that have already occurred within the current energy supply cycle, and to determine which data points have the highest similarity to the energy supply data from the previous energy supply cycle. Therefore, in order to more accurately verify the similarity, it is necessary to compare the data with the adjacent previous energy supply cycle for data analysis.
[0078] Among them, such as Figure 3 As shown, the energy supply data corresponding to the same time point in the current cycle and the previous cycle is obtained.
[0079] Specifically, the rate of change of energy supply data is calculated for the energy supply data corresponding to two identical time points, and the calculation equation is as follows:
[0080] ,
[0081] Where r represents the rate of change of power data, Q n+1 This represents the latest energy supply data collected within the current energy supply cycle; Q n This indicates the energy supply data collected at the same time point in the previous energy supply cycle, corresponding to the latest time point in the current energy supply cycle.
[0082] The calculated result is the rate of change in energy supply at the current data acquisition time.
[0083] S1432. Compare the energy supply change rate at the corresponding time point with the preset change rate. If the corresponding energy supply change rate is not higher than the preset change rate, the energy supply data collection time point passes the consistency test; otherwise, it fails.
[0084] The purpose of this step is to verify and analyze whether the obtained rate of change is consistent with the deviation rate of the calculation results obtained in the short term. At this point, it is obviously necessary to set a benchmark value.
[0085] Specifically, for the obtained preset rate of change, the average rate of change of energy supply data at the same energy supply data collection time point within all historical energy supply cycles is used as the rate of change.
[0086] Among them, directly using the average as the preset rate of change can easily eliminate seasonal changes or production cycle changes in the energy supply data of the microgrid. To solve this problem, it is also necessary to obtain the rate of change between the energy supply data change rate and all subsequent time points after the time point when a sudden change is found in the historical energy supply data period. When it is found that the rate of change of the nodes after the time point is not higher than the preset rate of change, it is considered that the energy supply data of the microgrid has changed significantly since the time point. In the new multiple periods, data comparison and application should be carried out according to this new energy supply data.
[0087] If a sudden change is found in the energy supply data within one or more cycles, that data collection cycle will not be included in the calculation process of the preset rate of change.
[0088] If the rate of change of the microgrid power supply data for the current power supply cycle is found to be no higher than the preset rate of change, that is, the rate of change is very low, then the consistency test is considered to have passed.
[0089] S1433. When the number of energy supply data collection time points that have passed the consistency test is not less than the percentage of the number of data collection time points that have already passed, the energy supply data in the previous energy supply cycle shall be used as the energy demand of the microgrid in the current energy supply cycle.
[0090] The purpose of this step is to determine the rationality of the current energy supply data collection time point during the consistency detection process, which often involves multiple energy supply data collection time points. If it is found that the data points that have already passed are all without problems, then the proportion can be used to determine whether the previous energy supply cycle can be directly applied to the analysis of the current microgrid energy demand.
[0091] Specifically, this involves obtaining the ratio of the energy supply data collection time points that have passed consistency checks to the total number of data collection time points that have passed in the current energy supply data.
[0092] The preset percentage can vary depending on the total number of data collection time nodes. For example, when the number of data points is no more than 10, the preset percentage is 90%; when the number of data points is 11 to 20, the preset percentage is 85%; and when the number of data points is 21 to the upper limit of the number of energy supply data collection points within a cycle, the preset percentage is 80%. The calculation is based on this. Of course, the preset percentage value can be determined according to the actual situation.
[0093] If the number of energy supply data collection time points that have passed the consistency test is not less than the preset percentage of the number of data collection time points that have already passed, then the correspondence between the energy supply data and data collection in the previous energy supply cycle can be directly used and applied to the current energy supply data collection time within the territory.
[0094] In some cases, if the ratio of the number of data collection time points that have already been collected to the total number of energy supply data collection time points is not less than 90%, and the proportion of those that pass the consistency test is not less than the preset proportion, then the energy supply data corresponding to the next energy supply data collection time point in the current energy supply cycle will no longer be collected. Instead, the correspondence between the time points and energy supply data in the previous energy supply cycle will be used to replace the current energy supply data collection cycle.
[0095] S1434. When the number of energy supply data collection time points that have passed the consistency test is lower than the proportion of the number of data collection time points that have already passed, search for the same periodic energy data as the energy supply data corresponding to the data collection time points that have already passed, in order to obtain the energy demand of the microgrid.
[0096] The purpose of this step is to determine the data analysis status within the current energy data collection cycle for those energy data collection cycles that failed the consistency test.
[0097] Specifically, if the number of energy supply data collection time points that pass the consistency test is found to be lower than the percentage of the number of data collection time points that have already passed, then the usable data will be selected from the historical data period based on the energy supply data corresponding to the data collection time points that have already passed.
[0098] For the energy supply data that has already been obtained, the obtained energy supply data will be compared with the energy supply data at the same time point within the energy supply cycle, and the data that are completely identical or whose rate of change of energy supply data completely conforms to the standard energy supply cycle will be found.
[0099] The analysis method and step S1433 are the same as for whether the selected power supply cycle can be used to replace the current power supply data acquisition cycle, and will not be repeated here.
[0100] For microgrids, the energy they generate is equivalent to the energy demand within the microgrid, which is the amount of energy the microgrid needs to provide over a period of time.
[0101] The beneficial effect of step S140 is that by judging the consistency of the energy supply data and comparing it with all the data in the historical energy supply cycle, the historical data that has been obtained can be used to replace the current data collection cycle. At this time, the operating burden of the entire system can be significantly reduced, and the results of short-term data analysis and long-term data analysis can be organically combined, thereby improving the accuracy of data analysis.
[0102] As described in step S150, the purpose of this step is to ensure that the microgrid's expected energy supply data and environmental information can meet the energy demand requirements. Energy demand is a core indicator, and the energy supply performance of the microgrid is verified based on this data. Specifically:
[0103] S151. Sum the expected energy supply data and the energy supply fluctuation data to obtain the total energy supply at the next energy supply time node.
[0104] The purpose of this step is to calculate the predicted output of the microgrid in order to obtain the total energy supply.
[0105] The expected energy supply data and the energy supply fluctuation data caused by the environment are summed to obtain the total energy supply at the next energy supply time point.
[0106] S152. Within the energy demand quantity obtained, obtain the energy demand for the next energy supply time node.
[0107] The purpose of this step is to analyze the energy demand for the next energy supply time point.
[0108] In this process, based on the processing results obtained in step S140, the energy demand corresponding to the next energy supply time node is obtained, thus obtaining the energy supply demand for the next energy supply time node.
[0109] S153. Compare the total energy supply and energy demand corresponding to the next energy supply time node, and obtain standard data for energy management based on the comparison results to obtain the energy supply data analysis benchmark.
[0110] The purpose of this step is to compare total energy supply and energy demand data. In this case, it is necessary to select specific usable data from these two sets of data. Specifically:
[0111] S1531. Obtain the total energy supply and energy demand matching degree corresponding to the next energy supply time node.
[0112] The purpose of this step is to calculate the degree of matching between the two parameters.
[0113] The matching degree can be determined by the deviation ratio of these two types of data, or calculated according to the equation mentioned in step S140, which will not be elaborated here.
[0114] S1532. When the matching degree is not lower than the preset matching degree, the energy supply data analysis benchmark is the energy supply demand of the next energy supply time node.
[0115] The purpose of this step is to compare the calculated matching score with the preset matching score to obtain the specific matching situation of the two types of data.
[0116] The so-called preset matching degree can be set based on the work experience of technical personnel, or based on the average deviation of these two types of data in historical energy supply cycles, or determined based on the periodic changes in energy supply data.
[0117] Specifically, when the matching degree is found to be no less than the preset matching degree, that is, when there is a high matching degree between the energy demand and the energy supply data, it is considered that the two types of data can be used interchangeably, and the energy demand is directly used as the benchmark for energy supply data analysis.
[0118] S1533. When the matching degree is lower than the preset matching degree, the energy supply data analysis benchmark is the larger value of the total energy supply and the energy supply demand corresponding to the next energy supply time node.
[0119] The purpose of this step is to select a benchmark for energy supply data analysis when a mismatch is found between total energy supply and energy demand.
[0120] Specifically, when the matching degree is found to be lower than the preset matching degree, that is, when the matching degree between the two types of data is too low, the larger value of the total energy supply and the energy demand is selected as the benchmark for energy supply data analysis.
[0121] The beneficial effect of step S150 is that by determining the energy supply data analysis benchmark, the energy supply scheme of the distribution network in the next time period can be specifically determined, so that the subsequent energy supply data standard can be determined based on the analysis results.
[0122] As described in step S160, the purpose of this step is to determine, based on the obtained power supply setup results, the adjustment methods that the microgrid needs to make in the coming period, and to make practical determinations based on these methods. Specifically:
[0123] S161. Based on the energy supply data analysis benchmark, obtain the theoretical power supply of all energy supply branches in the microgrid at the next energy supply time node.
[0124] The purpose of this step is to determine the theoretical power supply for each power supply analysis in the next period of time, or at the next power supply time node, after obtaining the power supply data analysis baseline.
[0125] In all the above steps, the analysis of energy supply data is based on the energy supply branches. In other words, it is necessary to obtain the theoretical power supply of each energy supply branch at the next energy supply time node.
[0126] S162. Based on the theoretical power supply of all power supply branches, determine the operating actions of the controllers corresponding to all power supply branches.
[0127] The purpose of this step is to determine the adjustment scheme for the power supply branch after obtaining the theoretical power supply capacity of the power supply branch, based on the automatic control device in the microgrid.
[0128] Once the power supply branch is determined, the control actions of the controller within that power supply branch need to be determined.
[0129] Furthermore, the individual branches within each power supply branch can also be defined, thereby setting the controller's operating actions.
[0130] S163. When the next power supply time point is reached, execute the controller's operation action to adjust the microgrid energy.
[0131] The purpose of this step is to determine the operating status of the controller based on the available power supply branch control results, thereby adjusting the energy state of the microgrid.
[0132] Once the operating actions of the controller are determined, it is necessary to control all the corresponding controllers accordingly.
[0133] In particular, when adjusting the operating status within a microgrid, it is also necessary to ensure that when the next power supply time arrives, the corresponding controllers must complete the corresponding actions to adjust the power supply status and parameters within the microgrid.
[0134] The beneficial effect of step S160 is that by determining the operating status of the controller in the microgrid in advance, a response can be made immediately when the corresponding time node is reached, thereby improving the adjustment efficiency and realizing real-time energy management of the microgrid.
[0135] This application also discloses a real-time energy management system for microgrids capable of executing all technical solutions such as steps S110-S160, such as... Figure 4 The diagram shown is a schematic of a real-time energy management system for a microgrid provided in an embodiment of this application. The system includes:
[0136] The microgrid energy monitoring system consists of power sensors and is used to acquire energy supply data from all power supply branches within the microgrid.
[0137] An energy prediction module, connected to the microgrid energy monitoring system, is used to predict the energy demand at the next energy supply time node.
[0138] The database is connected to the microgrid energy monitoring system to acquire and record energy supply data.
[0139] The historical data analysis module is connected to the database and is used to obtain the rate of change of energy supply data for each cycle of the microgrid.
[0140] The energy supply data analysis module, along with the historical data analysis module and the energy prediction module, are connected to obtain the energy supply data analysis benchmark.
[0141] The energy adjustment module is connected to the energy supply data analysis module and is used for real-time energy management of the microgrid.
[0142] The beneficial effects of this application include:
[0143] 1. It enables advance prediction of microgrid power supply information. In the technical solution of this application, in order to realize real-time management of microgrid power supply parameters, an advance prediction method for microgrid power supply parameters is adopted. Then, the parameter adjustments that the microgrid needs to make when the corresponding time point of power supply is reached are obtained. Therefore, under this situation, the adjustment speed and accuracy of microgrid power supply can be significantly improved.
[0144] 2. Improved accuracy of microgrid power supply information analysis. The technical solution of this application verifies the power supply status of the microgrid. In the specific processing, it analyzes two types of microgrid power supply information obtained based on short-term and long-term data, and selects and determines the microgrid power supply data accordingly. Based on the processing results, it determines the adjustment method for the microgrid power supply data to finalize the microgrid power supply scheme, thereby improving analysis accuracy.
[0145] 3. Improved robustness of microgrid power supply information processing methods. In the technical solution of this application, the analysis of microgrid power supply data and the real-time processing stage of microgrid energy are affected by various factors, which can lead to a decrease in the accuracy of the obtained data. In order to achieve good processing results, this application adopts a common analysis mode based on a combination of long-term and short-term analysis and the factors affecting accuracy, thereby processing these elements together and significantly improving the robustness of the entire system.
[0146] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to computer program instructions. The aforementioned computer program can be stored in a non-volatile storage medium, and when executed, it performs the steps of the above method embodiments. Alternatively, if the integrated unit of the present invention is implemented as a software-powered module and sold or used as an independent product, it can also be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the embodiments of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, network device, etc.) to execute all or part of the methods described in the various embodiments of the present invention.
[0147] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.
Claims
1. A real-time energy management method for microgrids, characterized in that, The management method includes: Acquire and record the microgrid's power supply data to update the microgrid's power supply information and obtain the microgrid's historical energy data; Based on historical energy data obtained during the current energy supply cycle of the microgrid, an energy supply analysis is performed on the next energy supply time node during the current energy supply cycle of the microgrid to obtain the expected energy supply data. Obtain the environment of the microgrid and acquire power supply fluctuation data of the microgrid based on the environmental information; To obtain the trend of energy supply data changes in the microgrid during each energy supply cycle, so as to obtain the energy demand of the microgrid in the current energy supply cycle; The expected energy supply data and energy supply fluctuation data are verified using energy demand data to obtain a benchmark for energy supply data analysis; Based on the aforementioned energy supply data analysis benchmark, the microgrid energy adjustment mode for the next energy supply time node is determined and executed when the next energy supply time node is reached.
2. The microgrid real-time energy management method according to claim 1, characterized in that, The process of acquiring and recording microgrid power supply data to update microgrid power supply information and obtain historical energy data of the microgrid includes: Based on the power sensors within the microgrid, acquire power supply data from the microgrid to all power supply branches; The energy supply data of all energy supply branches is sent to the database and recorded by the database. The energy supply data of the microgrid recorded in the database is the historical energy data of the microgrid.
3. The microgrid real-time energy management method according to claim 1, characterized in that, Based on historical energy data obtained during the current energy supply cycle of the microgrid, the energy supply at the next energy supply time node within the current energy supply cycle is analyzed to obtain expected energy supply data, including: Based on historical energy data obtained during the current energy supply cycle of the microgrid, the changing trends of energy supply data for all energy supply branches are obtained. Based on the trend of energy supply data changes, obtain the expected energy supply data at the next energy supply time point.
4. The microgrid real-time energy management method according to claim 1, characterized in that, The process of acquiring the environment of the microgrid and obtaining power supply fluctuation data of the microgrid based on the environmental information includes: The environment in which the microgrid is located is obtained and quantified to obtain environmental data; The output energy fluctuations of all power supply branches within the microgrid are obtained under the influence of environmental data to obtain the energy fluctuation data of the microgrid.
5. The microgrid real-time energy management method according to claim 1, characterized in that, The process of acquiring the energy supply data change trend of the microgrid in each power supply cycle to obtain the energy demand of the microgrid in the current power supply cycle includes: Based on the energy supply cycle time length set within the energy relationship system, historical energy data of the microgrid within each energy supply cycle is obtained; Historical energy data of the microgrid during each power supply cycle is analyzed to obtain cycle energy data; Consistency testing is performed on the microgrid energy supply data for the current energy supply cycle to determine the microgrid energy demand during the current energy supply cycle.
6. The microgrid real-time energy management method according to claim 5, characterized in that, The process of performing consistency checks on microgrid power supply data for the current power supply cycle to determine the microgrid energy demand within the current power supply cycle includes: Obtain the rate of change of energy supply data between the energy supply data collection time points that have passed in the current energy supply cycle and the same energy supply data collection time points in the previous energy supply cycle, so as to obtain the rate of change of energy supply at the corresponding time points. The energy supply change rate at the corresponding time point is compared with the preset change rate. If the corresponding energy supply change rate is not higher than the preset change rate, the energy supply data collection time point passes the consistency test; otherwise, it fails. When the number of energy supply data collection time points that pass the consistency test is not less than the preset percentage of the number of data collection time points that have already passed, the energy supply data in the previous energy supply cycle will be used as the microgrid energy demand in the current energy supply cycle. When the number of energy supply data collection time points that pass the consistency test is lower than the percentage of the number of data collection time points that have already passed, the system searches for the same periodic energy data as the energy supply data corresponding to the data collection time points that have already passed in order to obtain the energy demand of the microgrid.
7. The microgrid real-time energy management method according to claim 1, characterized in that, The process of using energy demand to verify expected energy supply data and energy supply fluctuation data to obtain a benchmark for energy supply data analysis includes: The expected energy supply data and the energy supply fluctuation data are summed to obtain the total energy supply at the next energy supply time node; Within the obtained energy demand, obtain the energy demand for the next energy supply time node; The total energy supply and energy demand corresponding to the next energy supply time node are compared, and standard data for energy management are obtained based on the comparison results to obtain the energy supply data analysis benchmark.
8. A microgrid real-time energy management method according to claim 7, characterized in that, The process of comparing the total energy supply and energy demand corresponding to the next energy supply time node, and obtaining standard data for energy management based on the comparison results to obtain a benchmark for energy supply data analysis, includes: Obtain the total energy supply and energy demand matching degree corresponding to the next energy supply time node; When the matching degree is not lower than the preset matching degree, the energy supply data analysis benchmark is the energy supply demand at the next energy supply time node; When the matching degree is lower than the preset matching degree, the energy supply data analysis benchmark is the larger value of the total energy supply and the energy supply demand corresponding to the next energy supply time node.
9. A microgrid real-time energy management method according to claim 1, characterized in that, The process of determining the microgrid energy adjustment mode for the next energy supply time node based on the energy supply data analysis benchmark, and executing it upon reaching the next time node, includes: Based on the aforementioned energy supply data analysis benchmark, the theoretical power supply of all energy supply branches in the microgrid at the next energy supply time node is obtained; Based on the theoretical power supply of all power supply branches, determine the operating actions of the controllers corresponding to all power supply branches; When the next power supply time arrives, the controller will perform its operation to adjust the energy of the microgrid.
10. A microgrid real-time energy management system, used to execute a microgrid real-time energy management method as described in any one of claims 1 to 9, characterized in that, The system includes: The microgrid energy monitoring system consists of power sensors and is used to acquire energy supply data from all power supply branches within the microgrid. An energy prediction module, connected to the microgrid energy monitoring system, is used to predict the energy demand at the next energy supply time node. The database is connected to the microgrid energy monitoring system to acquire and record energy supply data. The historical data analysis module is connected to the database and is used to obtain the rate of change of energy supply data for each cycle of the microgrid. The energy supply data analysis module, along with the historical data analysis module and the energy prediction module, are connected to obtain the energy supply data analysis benchmark. The energy adjustment module is connected to the energy supply data analysis module and is used for real-time energy management of the microgrid.