A distributed source-load end-to-end matching control method, device, equipment and medium

Abstract

The present application belongs to the field of energy consumption and multi-source load collaborative control, and particularly relates to a distributed source-load end-to-end matching control method, device, equipment and medium. The method comprises: acquiring power data of all power sources and loads in a target area, and calculating a correlation coefficient between each power source and each load based on the power data; generating a correlation coefficient matrix based on the calculated correlation coefficients; sorting the correlation coefficients in the correlation coefficient matrix, and matching the sources and loads according to the sorting result; and generating an instruction for power interaction according to the result of source-load matching. The distributed source-load end-to-end matching control method provided by the present application proposes a source-load correlation-based method, comprehensively considers the correlation between different sources and loads in a distribution network, reflects the correlation degree between different sources and loads through the correlation, and determines the source-load matching order according to the source-load correlation degree level, so as to reasonably guide the orderly matching between distributed sources and loads.

Description

Technical Field

[0001] This invention belongs to the field of distributed new energy consumption and multi-source load coordinated control, specifically involving a distributed source load end-to-end matching control method, device, equipment and medium. Background Technology

[0002] The large-scale application of renewable energy has become a key solution to global energy shortages and environmental problems. However, the randomness and volatility of renewable energy generation make it difficult to absorb, leading to significant wind and solar power curtailment and a huge waste of resources. A source-load system can be defined as a group of interconnected loads and distributed energy sources, potentially consisting of photovoltaic power generation, wind power generation, battery storage, and electricity users. Source-load systems are considered ideal platforms for accommodating the high penetration of renewable energy. With the flexibility of their energy storage devices and loads, source-load systems can absorb and absorb renewable energy as much as possible, reducing energy exchange with the main grid. However, the uncertainty of renewable energy unit output makes it difficult to balance the supply and demand of electricity within a source-load system. Furthermore, the capacity and cost of energy storage batteries limit further improvements in renewable energy utilization. With the further improvement of clean energy development and utilization, the interconnected operation of multiple source-load systems within a region will become common in distribution networks in the future. The complementary advantages and energy mutual support of source-load systems with different characteristics will help to further absorb renewable energy both spatially and temporally. Wind power, photovoltaic power, and loads connected within the same region exhibit certain correlations, thus source-load power also shows correlation.

[0003] Existing power source-load trading matching mechanisms are generally based on distance, prioritizing power transactions between sources and loads that are closer together. However, this approach suffers from significant differences in net power output between sources and loads. Sources with larger net power outputs may not have their power needs met after trading with sources with smaller net power outputs that are closer together (e.g., sellers haven't sold their stock, and buyers haven't purchased enough). This necessitates frequent switching between sources and loads, which is cumbersome, costly, and leads to low participation rates and poor interaction between power sources and loads. Summary of the Invention

[0004] The purpose of this invention is to provide a distributed source-load end-to-end matching control method, device, equipment and medium to solve the problem that the source-load matching mechanism in the existing distributed source-load matching control is unclear, resulting in poor power interaction between source and load.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] In a first aspect, the present invention provides a distributed source-load end-to-end matching control method, comprising the following steps:

[0007] Acquire power data for all power sources and loads within the target area, and calculate the correlation coefficient between each power source and each load based on the power data;

[0008] Based on the calculated correlation coefficients, a correlation coefficient matrix is ​​generated;

[0009] The correlation coefficients in the correlation coefficient matrix are sorted, and source load matching is performed based on the sorting results;

[0010] Instructions for power interaction are generated based on the source-load matching results.

[0011] Furthermore, acquire power data for all power sources and loads within the target area, and calculate the correlation coefficient between each power source and each load based on the power data, including:

[0012] Acquire power data of source loads for K typical time periods within the target area on a given day; where the source loads consist of a set of power sources Y = {Y1,…,Y}. M} and a set of loads H = {H1, ..., H} N Composition; for power supply Y m (m∈M) and load H n The power data for each time period within the operating cycle (n∈N) are represented by Y. m =[Y m1 ,…Y mK ] and H n =[H n1 ,…H nK ];

[0013] For power data Y m =[Y m1 ,…Y mK ] and H n =[H n1 ,…H nK The Pearson correlation coefficient method was used to calculate the power supply Y. m (m∈M) and load H n The correlation coefficient (n∈N).

[0014] Furthermore, based on the calculated correlation coefficients, a correlation coefficient matrix is ​​generated, including:

[0015] Obtain the correlation coefficients of all power sources and loads within the target area. Then, statistically analyze all correlation coefficients, with each row representing the correlation coefficient between the same power source and different loads, to obtain the correlation coefficient matrix ρ. M×N .

[0016] Furthermore, the correlation coefficients in the correlation coefficient matrix are sorted, and source-load matching is performed based on the sorting results, including:

[0017] The data in each row of the correlation coefficient matrix are arranged according to their size. When a column in the correlation coefficient matrix shows correlation coefficients between different power sources and the same load, the correlation coefficients between different power sources and the load are further compared, and source-load matching is performed according to the magnitude of the correlation coefficients.

[0018] Furthermore, instructions for power interaction are generated based on the source-load matching results, including:

[0019] Based on the sorted source-load correlation coefficient matrix, the source loads represented by the correlation coefficients in each column of the source-load correlation coefficient matrix are matched and interacted with in the same batch, and the source-load matching batches are ordered in descending order of correlation coefficients.

[0020] Furthermore, it also includes the following steps:

[0021] When all power sources in the same batch of matched source loads are matched to different loads, all matched source loads perform power interaction simultaneously; when two or more power sources in the same batch of matched source loads are matched to the same load, the correlation coefficients between different power sources and this load are compared, and power interaction is performed in sequence according to the correlation coefficient.

[0022] Furthermore, the process also includes the following steps: during the power interaction phase of matching source and load, time-segmented power interaction is carried out between source and load within the source and load scheduling cycle, and the source and load power information is updated after the power interaction is achieved in each time period; the source and load after updating the power information will carry out the next batch of source and load matching and power interaction, until all batches of source and load have achieved matching and power interaction, and the distributed source and load end-to-end matching control is completed.

[0023] In a second aspect, the present invention provides a distributed source-load end-to-end matching control device, comprising:

[0024] The calculation module is used to acquire power data of all power sources and loads within the target area, and calculate the correlation coefficient between each power source and each load based on the power data.

[0025] A matrix generation module is used to generate a correlation coefficient matrix based on the calculated correlation coefficients;

[0026] The sorting and matching module is used to sort the correlation coefficients in the correlation coefficient matrix and perform source load matching based on the sorting results.

[0027] The instruction generation module is used to generate instructions for power interaction based on the source-load matching results.

[0028] In a third aspect, the present invention provides an electronic device, characterized in that it includes a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the distributed source-load end-to-end matching control method as described above.

[0029] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the distributed source-load end-to-end matching control method as described above.

[0030] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0031] The distributed source-load end-to-end matching control method provided by this invention considers the correlation between source loads during the distributed source-load matching process. It achieves distributed source-load end-to-end matching control by developing energy mutual assistance schemes between source loads based on their correlation and interaction. In the distributed source-load end-to-end matching control process, considering the correlation between source loads has strong guiding significance for studying the complementary and synergistic characteristics of source loads, and for quickly and accurately identifying the optimal aggregation form of multiple source loads, thus achieving optimal source-load synergistic control.

[0032] The distributed source-load end-to-end matching control method provided by this invention proposes a method based on source-load correlation, which comprehensively considers the correlation between different source loads in the distribution network, reflects the degree of correlation between different source loads through the correlation, and determines the source-load matching order according to the degree of source-load correlation, so as to reasonably guide the orderly matching between distributed source loads.

[0033] The distributed source-load end-to-end matching control method provided by this invention constructs a multi-batch, time-segmented power interaction model between sources and loads. This model performs multi-batch matching and power interaction between sources and loads according to different correlation coefficient levels between power sources and loads, fully considering the correlation between sources and loads, and realizing energy mutual assistance between sources and loads. Attached Figure Description

[0034] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0035] Figure 1 This is a flowchart of a distributed source-load end-to-end matching control method according to an embodiment of the present invention;

[0036] Figure 2 This is a flowchart illustrating the source-load pair matching and source-load power interaction in an embodiment of the present invention.

[0037] Figure 3 This is a schematic diagram of the output of each distributed power source in an embodiment of the present invention;

[0038] Figure 4 This is a schematic diagram of the power demand of each load in an embodiment of the present invention;

[0039] Figure 5 This is a schematic diagram of the correlation coefficient matrix between different source loads in an embodiment of the present invention;

[0040] Figure 6 This is a schematic diagram of the correlation coefficient matrix after sorting by size in an embodiment of the present invention;

[0041] Figure 7 This is a schematic diagram of the first batch of matched source-load pairs and the electrical interaction between source-loads in an embodiment of the present invention.

[0042] Figure 8 This is a schematic diagram of the second batch of matched source-load pairs and the power interaction between source-loads in an embodiment of the present invention;

[0043] Figure 9 This is a schematic diagram of the third batch of matched source-load pairs and the power interaction between source-loads in this embodiment of the invention.

[0044] Figure 10 This is a schematic diagram of the fourth batch of matched source-load pairs and the power interaction between source and load in this embodiment of the invention.

[0045] Figure 11 This is a schematic diagram of the fifth batch of matched source-load pairs and the power interaction between source-loads in this embodiment of the invention.

[0046] Figure 12 This is a schematic diagram of the remaining power of each distributed new energy source in an embodiment of the present invention;

[0047] Figure 13 This is a schematic diagram illustrating the unmet power demands of each load in an embodiment of the present invention;

[0048] Figure 14 This is a structural block diagram of a distributed source-load end-to-end matching control device according to an embodiment of the present invention;

[0049] Figure 15 This is a structural block diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0050] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0051] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0052] Example 1

[0053] This proposal suggests a distributed source-load end-to-end matching control method to achieve reasonable matching of distributed sources and loads in the distribution network and realize power interaction between sources and loads. First, this method establishes a source-load matching control mechanism, which guides reasonable matching between sources and loads by analyzing the correlation between different sources and loads. Second, it establishes a multi-batch, time-segmented power interaction mechanism between sources and loads to achieve a balance between power supply and demand.

[0054] Specifically, the correlation between different source loads is first clarified by calculating the correlation coefficient between power data within each source and load scheduling cycle to examine the degree of correlation between different source loads. Secondly, the correlation coefficient data between different source loads are sorted according to their magnitude to determine the order of matching between source loads. Finally, based on the determined source load matching order, a multi-batch, time-segmented source load power interaction mechanism is adopted to achieve mutual power support between source loads.

[0055] The solution provided in this embodiment explores the preference for power interaction between neighboring source loads, demonstrating a trend towards supporting local power generation and eliminating unnecessary power transfer. The method involved in this invention can ensure precise matching and energy mutual assistance between source loads, achieving good economic efficiency while meeting power system constraints, and can be widely applied in diverse source load groups (such as distributed energy systems within urban industrial parks and government functional zones).

[0056] In one alternative embodiment, such as Figure 1 As shown, a distributed source-load end-to-end matching control method includes the following steps:

[0057] S1. Obtain power data for all power sources and loads within the target area, and calculate the correlation coefficient between each power source and each load based on the power data.

[0058] In one optional embodiment, power data of all power sources and loads within the target area are acquired, and the correlation coefficient between each power source and each load is calculated based on the power data, including:

[0059] Acquire power data of source loads for K typical time periods within the target area on a given day; where the source loads consist of a set of power sources Y = {Y1,…,Y}. M} and a set of loads H = {H1, ..., H} NComposition; for power supply Y m (m∈M) and load H n The power data for each time period within the operating cycle (n∈N) are represented by Y. m =[Y m1 ,…Y mK ] and H n =[H n1 ,…H nK For power data Y m =[Y m1 ,…Y mK ] and H n =[H n1 ,…H nK The Pearson correlation coefficient method was used to calculate the power supply Y. m (m∈M) and load H n The correlation coefficient (n∈N).

[0060] S2. Based on the calculated correlation coefficients, generate a correlation coefficient matrix.

[0061] In one optional embodiment, generating a correlation coefficient matrix based on the calculated correlation coefficient includes:

[0062] Obtain the correlation coefficients of all power sources and loads within the target area. Then, statistically analyze all correlation coefficients, with each row representing the correlation coefficient between the same power source and different loads, to obtain the correlation coefficient matrix ρ. M×N .

[0063] S3. Sort the correlation coefficients in the correlation coefficient matrix and perform source-load matching based on the sorting results.

[0064] In one optional embodiment, the correlation coefficients in the correlation coefficient matrix are sorted, and source load matching is performed based on the sorting results, including:

[0065] The data in each row of the correlation coefficient matrix are arranged according to their size. When a column in the correlation coefficient matrix shows correlation coefficients between different power sources and the same load, the correlation coefficients between different power sources and the load are further compared, and source-load matching is performed according to the magnitude of the correlation coefficients.

[0066] S4. Generate instructions for power interaction based on the source-load matching results.

[0067] In one alternative embodiment, instructions for power interaction are generated based on the source-load matching results, including:

[0068] Based on the sorted source-load correlation coefficient matrix, the source loads represented by the correlation coefficients in each column of the source-load correlation coefficient matrix are matched and interacted with in the same batch, and the source-load matching batches are ordered in descending order of correlation coefficients.

[0069] In a preferred embodiment, when all power sources in the same batch of matched source loads are matched with different loads, all matched source loads perform power interaction simultaneously; when two or more power sources in the same batch of matched source loads match the same load, the correlation coefficients between different power sources and this load are compared, and power interaction is performed sequentially according to the correlation coefficients.

[0070] In another preferred embodiment, during the power interaction phase of matching source and load, power interaction between source and load is carried out in different time periods within the source and load scheduling cycle, and the power information of source and load is updated after the power interaction is achieved in each time period. The source and load after updating the power information will carry out the next batch of source and load matching and power interaction until all batches of source and load have achieved matching and power interaction, thus completing the distributed source and load end-to-end matching control.

[0071] In one optional embodiment, a distributed source-load end-to-end matching control method is also provided. This method analyzes the correlation between source loads to determine the matching order and achieves time-segmented power interaction between matched source-load pairs. The specific implementation steps are as follows:

[0072] Step 1: Construct a distributed source-load matching model based on source-load correlation.

[0073] It should be noted that the distributed power sources in this scheme include wind power generation, photovoltaic power generation, and user loads. To fully consider the correlation between multiple power sources, power data from K typical time periods on a given day are selected to analyze the correlation between power sources. The power sources within the target area consist of a set of power sources Y = {Y1,…,Y}. M} and a set of loads H = {H1, ..., H} N It consists of}, where M and N are the number of power sources and loads in the area, respectively.

[0074] It is understandable that, for each selected time period, each power source can generate a certain amount of electricity, and each load requires a certain amount of electricity to meet its own needs. Any power source Y m (m∈M) and load H n The power data for each time period within the operating cycle (n∈N) are represented by Y. m =[Y m1 ,…Y mK ] and H n =[H n1 ,…H nK ].

[0075] 1) Calculation of correlation coefficient between source and load

[0076] In this scheme, the Pearson correlation coefficient is used to measure the linear correlation between random power output from different sources and loads and electricity demand. For any source and load Y m H n Y m H n The correlation coefficient is expressed as in equation (1):

[0077]

[0078] In the above formula, the numerator cov represents the covariance between the source and load power data, and the denominator σ represents the standard deviation of the source and load power data, which can be calculated by the following formulas (2), (3), and (4):

[0079]

[0080]

[0081]

[0082] In the formula and These are the average values ​​of power data for each time period within the source-load operating cycle.

[0083] When the Pearson correlation coefficient is 0, it indicates that the two source loads are independent. A positive Pearson correlation coefficient, closer to 1, indicates a stronger positive linear correlation between the two source loads; conversely, a coefficient closer to -1 indicates a stronger negative linear correlation. In this scheme, the correlation coefficient ranges from -1 to 1, with a positive linear correlation defined as a coefficient between 0 and 1. A larger correlation coefficient indicates a stronger correlation, and source loads with stronger correlations are matched first, meaning they have a higher matching priority.

[0084] 2) Generation and sorting of source-load correlation coefficient matrix

[0085] First, after calculating the correlation coefficients between any source loads, a source load correlation coefficient matrix is ​​constructed.

[0086] In one optional embodiment, for a set of power sources Y = {Y1, ..., Y} within the target area M} and a set of loads H = {H1, ..., H} N}, its initial correlation coefficient matrix can be expressed as ρ as shown in equation (5) below. M×N .

[0087]

[0088] It is understandable that in the correlation coefficient matrix ρ M×N In this matrix, each row represents the correlation coefficient between the same power source and different loads. To maximize the utilization of renewable energy output, a reasonable source-load matching order needs to be determined. In this invention, the matching order between sources and loads is determined based on the magnitude of the correlation coefficients. Therefore, it is first necessary to process the initial correlation coefficient matrix ρ. M×N The correlation coefficients in the data are sorted.

[0089] In one optional embodiment, the correlation coefficients are sorted, and the specific sorting method is as follows:

[0090] First, the data in each row of the correlation coefficient matrix is ​​sorted by size. This sorting result indicates the degree of correlation between the power source represented by a row and different loads, and also indicates the matching order of the power source and load. The data in all columns of the correlation coefficient matrix are sorted according to the magnitude of the correlation coefficient. After obtaining the sorted correlation coefficient matrix, multi-batch source-load matching control is performed based on the data in each column of the correlation coefficient matrix.

[0091] When a column in the correlation coefficient matrix shows correlation coefficients between different power sources and the same load, that is, during the same batch of source-load matching, a certain load needs to be matched with multiple power sources, in such cases, it is necessary to further compare the correlation coefficients between different power sources and this load, and perform source-load matching according to the magnitude of the correlation coefficients, so as to strictly ensure that the matching between source loads is based on the degree of correlation between source loads.

[0092] It should be noted that in this scheme, the higher the correlation coefficient, the higher the matching priority.

[0093] Step 2: Establish a multi-batch, time-segmented power interaction model between power sources and loads;

[0094] Understandably, in step 1 of this scheme, the correlation coefficients between source loads are calculated, a source load correlation coefficient matrix is ​​generated, and the correlation coefficient matrix is ​​sorted to determine the matching order between source loads. After obtaining successfully matched source load pairs, power interaction between source loads is required to meet the power demand of the source loads.

[0095] Specifically, this invention constructs a multi-batch, time-segmented power interaction model between sources and loads. Based on the sorted source-load correlation coefficient matrix, the source loads represented by the correlation coefficients in each column of the source-load correlation coefficient matrix are matched and interacted with in the same batch, and the source-load matching batches are arranged in descending order of correlation coefficients.

[0096] In a preferred embodiment, when all power sources in the same batch of matched source loads are matched to different loads, all matched source load pairs perform power interaction simultaneously.

[0097] In another preferred embodiment, when two or more power sources match the same load in the same batch of matched source loads, the correlation coefficients between different power sources and this load are compared, and power interaction is performed sequentially according to the correlation coefficients.

[0098] During the power interaction phase of matching source and load, time-segmented power interactions are performed between source and load within the source and load scheduling cycle, and the source and load power information is updated after each power interaction is completed. The source and load with updated power information will then proceed to the next batch of source and load matching and power interaction, until all batches of source and load have achieved matching and power interaction, thus completing the distributed source and load end-to-end matching control.

[0099] The distributed source-load end-to-end matching control method provided by the above scheme considers the correlation between sources and loads in the distributed source-load matching process, and realizes the distributed source-load end-to-end matching control by carrying out the energy mutual assistance scheme between sources and loads based on the correlation and interaction between sources and loads.

[0100] Specifically, this scheme comprehensively considers the correlation between different source loads in the distribution network, reflects the degree of correlation between different source loads through the correlation relationship, and determines the source load matching order according to the correlation level of the source loads, so as to reasonably guide the orderly matching between distributed source loads. Furthermore, it constructs a multi-batch, time-segmented power interaction model between source loads, which can perform multi-batch matching and power interaction between source loads according to the correlation coefficient levels of different power sources and loads, fully considering the correlation between source loads and realizing energy mutual assistance between source loads.

[0101] like Figure 2 As shown, in one optional embodiment, a power interaction method for source-load matching is provided, specifically including:

[0102] Based on the power data of the power sources, the correlation coefficients between all different power sources within the target area are calculated, and a correlation coefficient matrix is ​​generated. The coefficients in the correlation coefficient matrix are then sorted. Specifically, in the correlation coefficient matrix, each row represents the correlation coefficient between the same power source and different loads. The data in each row of the correlation coefficient matrix are arranged according to their size, which serves as the matching order between the power source and the load. The data in all columns of the correlation coefficient matrix are sorted according to the size of the correlation coefficient.

[0103] Select the correlation coefficient in the i-th column of the correlation coefficient matrix. When i is greater than the number of loads, it means that all loads have completed the transaction, and the power exchange ends. Otherwise, the power exchange in the i-th column is performed. It can be understood that there are i columns of correlation coefficients, and the number of loads is also i.

[0104] When conducting electricity transactions, it is determined whether the source-load pair represented by the correlation coefficient in the j-th row of the i-th column has already been traded. If so, j+1 is used to proceed to the next row. If not, it is further determined whether the current load is matched with other power sources. If the current load is matched with other power sources, multiple power sources with the same load are matched simultaneously, and time-segmented power exchanges between the source and load are performed according to the magnitude of the source-load correlation coefficient. If the current load is not matched with other power sources, the power exchange of the current source-load pair is executed, the power data of the exchanged source-load is updated, and the correlation coefficient of the next row is determined until j is greater than the number of power sources. Then, the next column of correlation coefficients is used to perform source-load matching power exchanges.

[0105] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0106] This invention is illustrated using distributed multi-source heterogeneous source-load end-to-end matching control located at different positions in a power distribution network as an example.

[0107] This invention is implemented in a matching control scenario involving nine randomly generated source loads within a power distribution network area. These nine source loads include four renewable energy sources and five loads, all connected by power lines and connected to the main grid via a single distribution substation. Considering the different power generation characteristics of various renewable energy sources and to account for the correlation between multiple source loads as much as possible, a typical daily period of 9:00-16:00 (eight time periods) is used as a scheduling cycle. During any time period within the scheduling cycle, distributed power sources can generate a certain amount of electricity, while loads require a certain amount of electricity to meet their needs. The power output of each renewable energy source and the power demand of each load within the scheduling cycle are as follows: Figure 3 and Figure 4 As shown.

[0108] Calculate the correlation coefficients between each source load within the scheduling cycle, and generate the following: Figure 5 The initial correlation coefficient matrix ρ of the source load is shown. M×N Where M is the number of power sources and N is the number of loads. In the initial correlation coefficient matrix, the correlation coefficient in the m-th (m∈M) row and n-th (n∈N) column is the correlation coefficient between power source m and load n. To determine the order of matching between source and load, the data in each row of the initial correlation coefficient matrix is ​​arranged from largest to smallest, resulting in a sorted correlation coefficient matrix. Source and load matching is then performed in batches based on this sorted correlation coefficient matrix. The ρ(Y) in the i-th column of the sorted correlation coefficient matrix... m H n ) represents the power supply Y during the i-th batch matching process. m Matched to load H n . Figure 6The figure shows the correlation coefficient matrix after sorting the nine distributed source loads in this embodiment. It can be seen from the correlation coefficient matrix that a total of five batches of source load matching were performed.

[0109] In the first batch of source-load matching, source 1 matches load 3, source 2 matches load 2, source 3 matches load 4, and source 4 matches load 1.

[0110] In the second batch of source-load matching, source 1 is matched with load 1, and source 2, source 3, and source 4 are all matched with load 3.

[0111] In the third batch of source-load matching, source 1, source 3, and source 4 are all matched with load 2, and source 2 is matched with load 5.

[0112] In the fourth batch of source-load matching, source 1, source 3, and source 4 are all matched with load 5, while source 2 is matched with load 4.

[0113] In the fifth batch of source-load matching, source 1 and source 4 were both matched with load 4, and source 2 and source 3 were both matched with load 1.

[0114] The first batch of source-load matching results and the power interaction volume of matched source-loads in each time period during the dispatch cycle are as follows: Figure 7 As shown. In the first batch of source-load matching, all power sources were matched to different loads.

[0115] Source 1 and load 3 engage in power interaction. During this interaction, 25.05% of the total output of source 1 during the scheduling cycle is supplied to load 3, and the power demand of load 3 during each period of the scheduling cycle is met.

[0116] Source 2 and Load 2 engage in power interaction. During this interaction, 63.13% of the total output of Source 2 during its scheduling cycle is supplied to Load 2, thus meeting the power demand of Load 2 at each time period during its scheduling cycle.

[0117] Source 3 interacts with load 4, and all output of source 3 at any given time is supplied to load 4, satisfying 57.43% of the total power demand of load 4 during the dispatch cycle.

[0118] Finally, source 4 interacts with load 1. At every moment, all output of source 4 is supplied to load 1 and meets 81.80% of the total power demand of load 4 within the scheduling cycle.

[0119] The results of the second batch of source-load matching and the power interaction volume of the matched source-loads in each time period during the dispatch cycle are as follows: Figure 8 As shown.

[0120] During the second batch of source-load matching, source 1 and load 1 engage in power interaction. During this interaction, 18.53% of the total output of source 1 during its scheduling cycle is supplied to load 1, and the remaining power demand of load 1 during each period of its scheduling cycle is met.

[0121] Sources 2, 3, and 4 are all matched to load 3. The initial source-load correlation coefficient matrix shows that source 3 has the highest correlation with load 3, followed by source 2, and source 4 has the lowest. Therefore, according to the settings, sources 3, 2, and 4 sequentially interact with load 3. Since load 3 has fully met its needs in the first batch of source-load matching control, the actual amount of power interaction between sources 3, 2, 4, and load 3 in the second batch is 0.

[0122] The results of the third, fourth, and fifth batches of source-load matching and the power interaction volume of the matched source-loads in each time period within the dispatch cycle are as follows: Figure 9 , 10 As shown in Figure 11.

[0123] During the third batch of source-load matching process, source 2 and load 5 interact electrically;

[0124] During the fourth batch of source-load matching, source 1 and load 5 exchange power, and source 2 and load 4 exchange power.

[0125] During the fifth batch of source-load matching, source 1 and load 4 interacted electrically.

[0126] After five rounds of source-load matching control, the remaining output of each power source and the unmet power demand of each load are as follows: Figure 12 and 13 As shown. In this embodiment, a total of 7 source loads fully meet their own power needs through power interaction with other source loads, and 97.31% of the new energy output is consumed locally through loads, effectively promoting mutual cooperation among source loads, realizing regional energy complementarity, and improving the utilization rate of new energy.

[0127] In summary, the model proposed in this invention has certain reference value for achieving efficient matching control between distributed source loads.

[0128] It is understood that this invention proposes a correlation-based source-load matching method to address how to achieve distributed source-load end-to-end matching control. The method calculates correlation coefficients between different types of source loads based on power data within the scheduling cycle, used to specifically analyze the degree of correlation between source loads. After calculating the correlation coefficients between each power source and different loads, a source-load correlation coefficient matrix is ​​generated, and the data in each row of the correlation coefficient matrix is ​​sorted from largest to smallest. Each power source is matched sequentially according to the magnitude of its correlation coefficient with different loads. Determining the source-load matching order is a prerequisite for end-to-end source-load matching control, and the calculation of correlation coefficients between source loads, the generation and sorting of the correlation coefficient matrix are crucial to determining the source-load matching order. Therefore, the distributed source-load correlation coefficient calculation, correlation coefficient matrix generation and sorting, and the determination of the source-load matching order proposed in this invention are key points and areas for protection in this application. After determining the matching order between source loads, power interaction is required between the matched source-load pairs to meet the power demand of the source loads. This invention proposes a multi-batch, time-segmented power interaction mechanism between source loads. Source-load matching is carried out in batches according to the source-load correlation coefficient level, thereby further ensuring that the matching between source-loads is based on the correlation between source-loads; within the scheduling cycle, the matched source-load pairs carry out time-segmented power interaction to realize energy mutual assistance between source-loads.

[0129] Example 2

[0130] like Figure 14 As shown, based on the same inventive concept as the above embodiments, the present invention also provides a distributed source-load end-to-end matching control device, comprising:

[0131] The calculation module is used to acquire power data of all power sources and loads within the target area, and calculate the correlation coefficient between each power source and each load based on the power data.

[0132] A matrix generation module is used to generate a correlation coefficient matrix based on the calculated correlation coefficients;

[0133] The sorting and matching module is used to sort the correlation coefficients in the correlation coefficient matrix and perform source load matching based on the sorting results.

[0134] The instruction generation module is used to generate instructions for power interaction based on the source-load matching results.

[0135] Example 3

[0136] like Figure 15 As shown, the present invention also provides an electronic device 100 for implementing a distributed source-load end-to-end matching control method according to the above embodiments;

[0137] The electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on at least one processor 102, and at least one communication bus 104.

[0138] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the distributed source-load end-to-end matching control method of Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0139] The memory 101 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created based on the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0140] At least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 102 may be a microprocessor or any conventional processor. Processor 102 is the control center of electronic device 100, connecting various parts of electronic device 100 via various interfaces and lines.

[0141] The memory 101 in the electronic device 100 stores multiple instructions to implement a distributed source-load end-to-end matching control method, and the processor 102 can execute multiple instructions to achieve the following:

[0142] S1. Obtain power data for all power sources and loads within the target area, and calculate the correlation coefficient between each power source and each load based on the power data.

[0143] S2. Based on the calculated correlation coefficients, generate a correlation coefficient matrix.

[0144] S3. Sort the correlation coefficients in the correlation coefficient matrix and perform source-load matching based on the sorting results.

[0145] S4. Generate instructions for power interaction based on the source-load matching results.

[0146] Example 4

[0147] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, and read-only memory (ROM).

[0148] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0149] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0150] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0151] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0152] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distributed source-load end-to-end matching control method, characterized in that, Includes the following steps: Acquire power data for all power sources and loads within the target area, and calculate the correlation coefficient between each power source and each load based on the power data; Based on the calculated correlation coefficients, a correlation coefficient matrix is ​​generated; The correlation coefficients in the correlation coefficient matrix are sorted, and source load matching is performed based on the sorting results; Based on the source-load matching results, generate instructions for power interaction; During the power interaction phase of matching source and load, power interaction between source and load is carried out in time periods within the source and load scheduling cycle, and the source and load power information is updated after the power interaction is completed in each time period. After the power information is updated, the source load will undergo the next batch of source load matching and power interaction until all batches of source loads have achieved matching and power interaction, thus completing the distributed source load end-to-end matching control. Based on the source-load matching results, instructions for power interaction are generated, including: according to the sorted source-load correlation coefficient matrix, instructions for matching and power interaction of the source loads represented by the correlation coefficients in each column of the source-load correlation coefficient matrix in the same batch, and instructions for the source-load matching batches to be performed in descending order of correlation coefficients. It also includes the following steps: when all power sources in the same batch of matched source loads are matched to different loads, all matched source loads perform power interaction simultaneously; when two or more power sources in the same batch of matched source loads match the same load, the correlation coefficients between different power sources and this load are compared, and power interaction is performed sequentially according to the correlation coefficients.

2. The distributed source-load end-to-end matching control method according to claim 1, characterized in that, Acquire power data for all power sources and loads within the target area, and calculate the correlation coefficient between each power source and each load based on the power data, including: Obtain typical days within the target area Power data of source load for each time period; where the source load consists of a group of power sources. and a set of loads Composition; for power supply and load The power data for each time period within the operating cycle are represented as follows: and ; For power data and The Pearson correlation coefficient method was used to calculate the power supply. and load The correlation coefficient.

3. The distributed source-load end-to-end matching control method according to claim 1, characterized in that, Based on the calculated correlation coefficients, a correlation coefficient matrix is ​​generated, including: Obtain the correlation coefficients of all power sources and loads within the target area. Then, statistically analyze all correlation coefficients, with each row representing the correlation coefficient between the same power source and different loads, to obtain a correlation coefficient matrix. .

4. The distributed source-load end-to-end matching control method according to claim 1, characterized in that, The correlation coefficients in the correlation coefficient matrix are sorted, and source load matching is performed based on the sorting results, including: The data in each row of the correlation coefficient matrix are arranged according to their size. When a column in the correlation coefficient matrix shows correlation coefficients between different power sources and the same load, the correlation coefficients between different power sources and the load are further compared, and source-load matching is performed according to the magnitude of the correlation coefficients.

5. A distributed source-load end-to-end matching control device, used to implement the distributed source-load end-to-end matching control method of claim 1, characterized in that, include: The calculation module is used to acquire power data of all power sources and loads within the target area, and calculate the correlation coefficient between each power source and each load based on the power data. A matrix generation module is used to generate a correlation coefficient matrix based on the calculated correlation coefficients; The sorting and matching module is used to sort the correlation coefficients in the correlation coefficient matrix and perform source load matching based on the sorting results. The instruction generation module is used to generate instructions for power interaction based on the source-load matching results.

6. An electronic device, characterized in that, It includes a processor and a memory, the processor being used to execute a computer program stored in the memory to implement the distributed source-load end-to-end matching control method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, which, when executed by a processor, implements the distributed source-load end-to-end matching control method as described in any one of claims 1 to 4.

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