A multi-zone coordinated new energy consumption control optimization method and system

Abstract

The application discloses a kind of new energy consumption control optimization method and system of multiple areas cooperation, it is related to new energy power generation technical field, method includes: first call multiple power supply area historical operation data, by source net load storage cooperativity analysis constructs mutual aid cooperativity matrix, according to this build mutual aid distribution topology;Each area collects and uploads dynamic operation data to consumption control central station, central station real-time optimization obtains consumption mutual aid area combination, reconstructs topological local authority and obtains real-time flexible interconnection topology, after sorting, executes cross-area power flexible control to combination.The application solves the technical problems that the prior art is difficult to deal with the intermittency and volatility of new energy, single-area regulation mode cannot realize cross-area resource optimal allocation, leading to low new energy consumption efficiency, achieves cross-area power optimization deployment, improves new energy consumption efficiency and grid operation stability and economic technology effect.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, and in particular to a method and system for optimizing the control of new energy consumption through multi-station coordinated operation. Background Technology

[0002] With the large-scale integration of renewable energy into the distribution network, multiple power supply areas face the challenge of renewable energy consumption. Existing technologies primarily focus on renewable energy regulation within a single distribution area, failing to consider the coordinated support between multiple distribution areas. While these methods have played a role in the stable operation of a single distribution area, as the penetration rate of renewable energy increases, the single-distribution-area regulation model struggles to cope with the intermittency and volatility of renewable energy. This leads to some distribution areas having surplus renewable energy that is abandoned, while adjacent distribution areas experience power shortages. This prevents the optimal allocation of resources across distribution areas and fails to meet the demands for efficient renewable energy consumption and the economical and stable operation of the power grid. Summary of the Invention

[0003] This application solves the technical problem that existing technologies are unable to cope with the intermittency and volatility of new energy sources, and that single-area control modes cannot achieve cross-area resource optimization and allocation, resulting in low efficiency of new energy consumption.

[0004] The first aspect of this application provides a method for optimizing the control of renewable energy consumption in multi-regional collaborative power distribution. The method includes: retrieving multiple historical operating data points from multiple power distribution areas and performing source-grid-load-storage synergy analysis based on the historical operating data to construct a mutual support synergy matrix; constructing a mutual support distribution topology for the multiple power distribution areas based on the mutual support synergy matrix; collecting and uploading multiple dynamic operating data points from the multiple power distribution areas to a consumption control center; performing real-time consumption mutual support optimization based on the dynamic operating data of the multiple power distribution areas to obtain multiple combinations of consumption mutual support distribution areas; reconstructing the local permissions of the mutual support distribution topology based on the multiple combinations of consumption mutual support distribution areas to obtain a real-time flexible interconnection topology; and performing cross-regional power flexible control on the real-time flexible interconnection topology after prioritizing the multiple combinations of consumption mutual support distribution areas.

[0005] A second aspect of this application provides a multi-regional collaborative new energy consumption control and optimization system, the system comprising: The data acquisition and collaborative analysis module is used to retrieve multiple historical operating data from multiple power supply areas, and to perform source-grid-load-storage synergy analysis based on the multiple historical operating data to construct a mutual assistance synergy matrix; The topology construction module is used to construct the mutual assistance distribution topology of the multiple power supply areas based on the mutual assistance and coordination matrix; The data acquisition and upload module is used to collect and upload dynamic operating data of the multiple power supply areas to the power consumption control center. The real-time optimization module is set in the consumption control center and is used to perform real-time consumption mutual assistance optimization based on the dynamic operation data of the multiple distribution areas to obtain multiple consumption mutual assistance distribution area combinations. The topology reconfiguration module is used to reconfigure the local permissions of the mutual aid distribution topology based on the combination of the multiple mutual aid distribution areas, so as to obtain a real-time flexible interconnection topology. The power control module is used to prioritize the multiple absorption and mutual assistance transformer area combinations and then perform cross-regional power flexible control on the real-time flexible interconnection topology.

[0006] This application proposes one or more technical solutions, which have at least the following technical effects: This application constructs a mutual assistance and coordination matrix and a mutual assistance distribution topology by retrieving historical operating data from multiple power supply areas. Each area uploads dynamic operating data to the absorption control center. The center obtains a combination of absorption and mutual assistance areas through real-time absorption and mutual assistance optimization, and then reconstructs the local permissions of the mutual assistance distribution topology to form a real-time flexible interconnected topology. After prioritizing the combination, cross-area power flexible control is performed to realize the optimization of new energy absorption control in multi-area collaboration, improve the efficiency of new energy absorption and the stability of grid operation, and achieve the technical effect of cross-area power optimization and allocation, improving the efficiency of new energy absorption and the stability and economy of grid operation. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 This is a flowchart illustrating a multi-regional collaborative new energy consumption control optimization method provided in an embodiment of this application.

[0009] Figure 2 This is a schematic diagram of a multi-regional collaborative new energy consumption control and optimization system provided in an embodiment of this application. Detailed Implementation

[0010] This application provides a method and system for optimizing the control of new energy consumption through multi-regional coordination, which addresses the technical problems of existing technologies being unable to cope with the intermittency and volatility of new energy, and the inability of single-regional control mode to achieve cross-regional resource optimization, resulting in low efficiency of new energy consumption.

[0011] 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. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0012] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0013] Example 1 like Figure 1 As shown, a multi-regional coordinated new energy consumption control optimization method is presented, wherein the method includes: Step A100: Retrieve multiple historical operating data from multiple power supply areas, and conduct source-grid-load-storage synergy analysis based on the multiple historical operating data to construct a mutual assistance synergy matrix.

[0014] In this embodiment of the application, a power supply area refers to a regional power supply unit with an independent power supply range, including power sources, grid facilities, loads and possible energy storage systems, and is a basic regional unit for power distribution and management in the power system.

[0015] Specifically, historical operating data from multiple power supply areas are retrieved, load power time series waveforms are constructed and peak-valley characteristic sequences are extracted, Pearson load complementarity index is calculated by enumerating the combined time series of power supply areas, and then it is filled into the constructed mutual assistance and synergy matrix. The specific steps are explained in detail in A110-A140.

[0016] Step A200: Construct a mutual assistance distribution topology for multiple power supply areas based on the mutual assistance and coordination matrix.

[0017] Optionally, based on the mutual assistance and synergy matrix, load complementarity indices are screened according to a preset load complementarity threshold range, effective mutual assistance combinations are extracted by mapping, and the load complementarity indices are quantified into topology connections to construct a mutual assistance distribution topology. Specific steps are explained in detail in A210-A230.

[0018] Step A300: Collect and upload dynamic operation data of multiple power supply areas to the consumption control center.

[0019] In this embodiment, the absorption control platform is the central hub for realizing coordinated absorption control across multiple distribution zones.

[0020] In one embodiment of this application, multiple power supply areas need to deploy corresponding real-time monitoring equipment. This real-time monitoring equipment includes new energy power generation sensors, load current transformers, energy storage system status monitors, etc., to collect dynamic operating data of the power supply areas. For example, the photovoltaic inverter in area 1 uploads real-time power generation data every 10 seconds, ranging from 0 to 800 kW; the smart meter in area 2 records the current load consumption every 10 seconds, ranging from 200 to 1200 kW; the energy storage battery management system in area 3 provides feedback on the remaining capacity and charging / discharging power every 10 seconds, with a capacity range of 0-500 kWh and a charging / discharging power range of -300 kW to 300 kW, where the negative sign indicates discharging.

[0021] Then, the collected dynamic operation data from multiple power distribution areas needs to be encapsulated according to a unified data protocol. The protocol includes the power distribution area number, data collection timestamp, data type, and specific values. The data type includes power generation, load, and energy storage status. For example, the encapsulated data packets are: Power Distribution Area 1, 14:00:00, Photovoltaic Power Generation, 650kW; Power Distribution Area 2, 14:00:00, Load Consumption, 900kW; Power Distribution Area 3, 14:00:00, Remaining Energy Storage Capacity, 300kWh, Discharge Power, 200kW. After encapsulation, each power distribution area uploads the data packets to the power consumption control center via the power communication network. The power consumption control center is equipped with a data receiving interface to receive and temporarily store the data uploaded by each power distribution area in real time, ensuring the timeliness and integrity of data transmission and avoiding delays or data loss that could affect subsequent processing.

[0022] By deploying monitoring equipment in each power supply area to collect dynamic data, which is then packaged in a standardized manner and uploaded to the power consumption control center via a communication network, the real-time operating status data of multiple power supply areas is aggregated, providing dynamic data basis for the real-time power consumption mutual assistance and optimization of the power consumption control center.

[0023] Step A400: The consumption control platform performs real-time consumption mutual assistance optimization based on the dynamic operation data of multiple distribution areas, and obtains multiple consumption mutual assistance distribution area combinations.

[0024] Specifically, the consumption control platform judges the real-time consumption demand based on the dynamic operating parameters of the distribution area, selects the distribution areas to be consumed, enumerates the effective mutual assistance combinations and calculates the consumption adaptability of the combined distribution area dynamic operating parameters, and obtains the consumption mutual assistance distribution area combination through mutual assistance conflict graph theory screening. The specific steps are explained in detail in A410-A450.

[0025] Step A500: Reconstruct the local permissions of the mutual assistance distribution topology based on the combination of multiple mutual assistance distribution areas to obtain a real-time flexible interconnection topology.

[0026] Optionally, based on the distribution area to be consumed, the flexible interconnection enabling authority of the mutual assistance consumption node is activated locally in the mutual assistance distribution topology, and then the flexible interconnection enabling authority between the mutual assistance consumption nodes is activated according to the combination of multiple mutual assistance consumption distribution areas to obtain the real-time flexible interconnection topology. The specific steps are explained in detail in A510-A520.

[0027] Step A600: After prioritizing multiple combinations of power absorption and mutual assistance distribution areas, perform cross-distribution area power flexible control in the real-time flexible interconnection topology.

[0028] Optionally, the dynamic operating parameters of the combined and reorganized distribution areas are reorganized and the absorption adaptability is quantified. The absorption priority sequence is obtained by sorting the absorption adaptability. Based on this, cross-distribution area power flexible control is performed in the real-time flexible interconnection topology. The specific steps are explained in detail in A610-A640.

[0029] Furthermore, step A100 in the method provided in this application embodiment includes: A110: After constructing multiple load power time series waveforms based on multiple historical operating data, the envelope decomposition algorithm is used to extract load peaks and valleys, resulting in multiple peak and valley feature sequences.

[0030] A120: Combine and enumerate multiple power supply areas to obtain multiple combinations of areas.

[0031] A130: Based on the time series combination of multiple transformer substations, align multiple peak and valley characteristic sequences to calculate the Pearson load complementarity index and output multiple load complementarity indices.

[0032] A140: After constructing a mutual support and coordination matrix based on multiple power supply areas, multiple load complementarity indices are filled into the mutual support and coordination matrix according to the combination of multiple areas.

[0033] Specifically, the historical operating data of multiple power supply areas is first obtained. For example, hourly load data from three power supply areas over the past 30 days are selected to construct three time series containing 720 data points, fully recording the power changes in each period. An envelope decomposition algorithm is applied to these time series to accurately extract the peak-valley characteristic sequences of each area by separating the trend and fluctuation components of the load data. After decomposition, characteristic data such as 800kW load peak at 8:00 AM and 1200kW load peak at 6:00 PM for area A, and 700kW load peak and 300kW load trough at 2:00 AM for area B are obtained. These are then organized according to the time dimension to form a load power time series waveform.

[0034] Next, multiple power distribution areas are enumerated according to permutation and combination logic. If there are 3 distribution areas, 3 combinations are generated: AB, AC, and BC. For each combination, the peak-valley characteristic sequences of the corresponding two distribution areas are time-series aligned to ensure consistency in the time dimension before calculating the Pearson load complementarity index. For a combination of two transformer substations, after ensuring consistency in the time dimension, based on the calculation logic of the Pearson correlation coefficient, the relationship between the characteristic values ​​of corresponding time points in the two aligned peak-valley characteristic sequences is analyzed to quantify the complementarity of the two substations in terms of the timing and magnitude of load peaks and valleys. That is, when one substation experiences a load peak, is the other substation more likely to experience a load trough, or vice versa? The final value obtained is the Pearson load complementarity index, which reflects the strength of the load complementarity of the combination of substations.

[0035] For example, the evening peak of transformer area A and the evening trough of transformer area B have a high time sequence matching degree, and the load complementarity index of the AB combination is calculated to be 0.75; the peak and valley overlap of transformer area A and transformer area C is high, and the complementarity index is 0.3.

[0036] Finally, an initial mutual support and coordination matrix is ​​constructed using the power supply substations as the rows and columns of the matrix, forming a 3×3 matrix framework for the three substations. Based on the enumeration results of the substation combinations, the calculated load complementarity index is filled into the corresponding positions in the matrix. That is, the index 0.75 for combination AB is filled into the first row and second column of the matrix, the index 0.3 for combination AC is filled into the first row and third column, and the index 0.6 for combination BC is filled into the second row and third column, thus forming a complete mutual support and coordination matrix.

[0037] Through a coherent process of historical data modeling, feature extraction, combined analysis, and matrix construction, the potential for mutual assistance between multiple power supply stations was quantitatively characterized, providing data support for the subsequent construction of mutual assistance distribution topologies.

[0038] Furthermore, step A200 in the method provided in this application embodiment includes: A210: Based on a preset load complementarity threshold range, traverse multiple load complementarity indices and filter to obtain P load complementarity indices.

[0039] A220: Extract P effective mutual assistance combinations from multiple transformer area combinations based on P load complementarity indices.

[0040] A230: Quantify P load complementarity indices into P effective mutual assistance combinations of topology connections to construct a mutual assistance distribution topology.

[0041] Optionally, based on a preset load complementarity threshold range, multiple load complementarity indices contained in the mutual assistance and synergy matrix are iterated and filtered. Assuming the preset load complementarity threshold range is 0.6 to 1.0, that is, only indices with strong complementarity are retained, then in the constructed mutual assistance and synergy matrix, if there are three load complementarity indices of 0.75, 0.3, and 0.6, after iterative judgment, 0.75 and 0.6 meet the threshold requirements, thus obtaining P=2 load complementarity indices.

[0042] Next, based on the selected P load complementarity indices, the corresponding effective mutual assistance combinations are extracted from multiple transformer area combinations. For example, a load complementarity index of 0.75 corresponds to transformer area combination AB, and a load complementarity index of 0.6 corresponds to transformer area combination BC. Then, the two effective mutual assistance combinations AB and BC are extracted from all transformer area combinations.

[0043] Finally, the P load complementarity indices obtained from the screening are quantified into the topology connections of the corresponding effective mutual assistance combinations. Taking 0.75 for combination AB and 0.6 for combination BC as examples, the index values ​​can be converted into weight parameters for the connections. For example, 0.75 corresponds to thicker connections and 0.6 corresponds to thinner connections, thus intuitively reflecting the differences in mutual assistance strength between different combinations. Finally, a mutual assistance distribution topology containing connections between A and B, and between B and C is constructed.

[0044] By setting thresholds to screen effective complementary indices, mapping and extracting corresponding combinations, and quantifying the indices into topological connections, a mutually supportive distribution topology that reflects the actual mutual support potential between distribution stations is constructed, providing basic structural support for subsequent real-time absorption and optimization of mutual support.

[0045] Furthermore, step A400 in the method provided in this application embodiment includes: A410: Real-time assessment of the demand for power consumption from multiple transformer substations based on their dynamic operating parameters, and selection of M dynamic operating parameters from M transformer substations to be consumed.

[0046] A420: Based on M distribution areas to be consumed, enumerate P effective mutual assistance combinations in the mutual assistance distribution topology.

[0047] A430: Based on the configuration of P effective mutual support combinations of transformer substations, combine the dynamic operating parameters of M transformer substations to obtain P sets of dynamic operating parameters for transformer substations.

[0048] A440: Based on the dynamic operating parameters of P groups of transformer substations, the absorption and adaptation quantification is performed, and P absorption and adaptation degrees are output.

[0049] A450: Based on P absorption fitness values, P effective mutual assistance combinations are screened using the mutual assistance conflict graph theory to obtain multiple absorption mutual assistance combination zones.

[0050] Specifically, the consumption control platform first receives dynamic operating parameters uploaded by multiple power supply substations and performs real-time consumption demand assessment on these parameters. For example, it is determined that there is consumption demand when the renewable energy generation power of a power supply substation continuously exceeds the sum of the load power and the maximum charging power of energy storage for 1 minute. Assuming there are 6 substations, substation 1 has a power generation power of 900kW, a load power of 600kW, and a maximum energy storage charging power of 200kW. Since 900kW > 600 + 200 = 800kW, it is determined to be in need of consumption. Substation 3 has a power generation power of 700kW, a load power of 400kW, and a maximum energy storage charging power of 250kW. Since 700kW > 400 + 250 = 650kW, it is determined to be in need of consumption. The remaining 4 substations have no consumption demand. Thus, M = 2 substations in need of consumption are selected, namely substation 1 and substation 3, and their corresponding dynamic operating parameters are extracted.

[0051] Next, based on the two distribution areas to be consumed mentioned above, all effective mutual assistance combinations containing distribution area 1 or distribution area 3 are enumerated in the constructed mutual assistance distribution topology. Assuming that the effective mutual assistance combinations in the mutual assistance distribution topology include 1-2, 1-4, 1-5, 3-2, 3-4, and 3-6, then after enumeration, P=6 effective mutual assistance combinations are obtained.

[0052] Then, based on the six effective mutual assistance combinations of the above-mentioned transformer substations, the corresponding dynamic operating parameters are combined respectively. For example, combination 1-2 corresponds to the dynamic parameters of transformer substation 1 and transformer substation 2, combination 1-4 corresponds to the dynamic parameters of transformer substation 1 and transformer substation 4, and so on, to obtain six sets of dynamic operating parameters for the transformer substations.

[0053] Then, based on these 6 sets of parameters, the absorption and adaptation quantification is performed, and the absorption and adaptation degree is calculated. Taking combination 1-4 as an example, the amount to be absorbed in area 1 is 900-600-200=100kW, and the load gap in area 4 is 500-300-100=100kW, of which the load is 500kW, the generation is 300kW, and the energy storage can discharge 100kW, so the absorption and adaptation degree is 100 / 100=1.0; in combination 3-6, the amount to be absorbed in area 3 is 700-400-250=50kW, and the load gap in area 6 is 450-200-150=100kW, so the absorption and adaptation degree is 50 / 100=0.5; assuming that the absorption and adaptation degrees of the other combinations are calculated to be 0.8, 0.3, 0.6, and 0.7 respectively, 6 absorption and adaptation degrees are finally output.

[0054] Finally, based on these six absorption suitability scores, the mutual assistance conflict graph theory is used to screen effective combinations. If combinations 1-2 and 1-4 both involve transformer area 1, there is a conflict. Since 1.0 > 0.8, combination 1-4, with its higher suitability, is retained first. If combinations 3-2 and 3-6 both involve transformer area 3, since 0.5 > 0.3, combination 3-6, with its higher suitability, is retained. Combinations 1-5 and 1-4 conflict, and combinations 3-4 and 3-6 conflict, and are therefore excluded. This results in several conflict-free absorption mutual assistance transformer area combinations with high suitability, as exemplified by combinations 1-4 and 3-6.

[0055] By employing a coherent process of real-time assessment of absorption demand, enumeration of effective combinations, combination of dynamic parameters, quantification of fit, and graph theory screening of conflicts, a combination of absorption and mutual assistance distribution areas that conforms to the real-time operating status was obtained, providing a precise combination basis for subsequent mutual assistance distribution topology reconfiguration and cross-distribution area power control.

[0056] Furthermore, step A500 in the method provided in this application embodiment includes: A510: Based on M distribution areas to be consumed, activate the flexible interconnection enablement authority of M mutual consumption nodes in the mutual distribution topology local area.

[0057] A520: Based on the combination of multiple mutual aid and mutual assistance substations, activate the flexible interconnection enable permissions between M mutual aid and mutual assistance nodes to obtain the real-time flexible interconnection topology.

[0058] Specifically, the absorption control platform first identifies M distribution areas to be absorbed. Based on these areas, it locates the corresponding mutual absorption nodes in the existing mutual absorption distribution topology and activates the flexible interconnection enabling permissions of these nodes. For example, if M=2, and the distribution areas to be absorbed in the aforementioned steps are distribution area 1 and distribution area 3, then in the mutual absorption distribution topology, the nodes corresponding to distribution area 1 and distribution area 3 will switch from the initial disabled state to the enabled state. At this time, these two nodes have the basic conditions to establish flexible interconnection with other nodes and can respond to subsequent interconnection commands.

[0059] After activating the flexible interconnection enablement permissions of M mutual absorption nodes, the flexible interconnection enablement permissions between these nodes are further activated based on the previously obtained multiple absorption mutual absorption distribution area combinations. Assuming the absorption mutual absorption distribution area combinations are 1-4 and 3-6, then based on the already activated nodes in distribution areas 1 and 3, the flexible interconnection enablement permissions between distribution areas 1 and 4, and between distribution areas 3 and 6, will be activated. The connections between these nodes in the original mutual absorption distribution topology might have been in a locked state. After activation, the physical switches or control modules corresponding to the connections enter an operable state, forming a path capable of real-time power transmission. After the above operations, the nodes and connections between nodes in the mutual absorption distribution topology related to the current absorption demand are selectively activated, ultimately forming a real-time flexible interconnection topology.

[0060] By first activating the permissions of the nodes in the distribution area to be consumed, and then activating the permissions between the corresponding combined nodes, a flexible interconnection path that adapts to real-time consumption needs is constructed, providing operable physical topology support for flexible power control across distribution areas.

[0061] Furthermore, step A600 in the method provided in this application embodiment includes: A610: Based on the combination of multiple absorption and mutual assistance transformer areas, reorganize the dynamic operating parameters of M transformer areas to obtain multiple sets of coupled operating parameters.

[0062] A620: Based on multiple sets of coupled operating parameters, the absorption control requirements are quantified to obtain multiple absorption adaptability.

[0063] A630: Arrange multiple absorption and mutual assistance transformer area combinations in descending order based on multiple absorption adaptability, and output the absorption priority sequence.

[0064] A640: Using the absorption priority sequence as a timing constraint, it performs cross-regional power flexible control in a real-time flexible interconnection topology.

[0065] Specifically, firstly, based on multiple combinations of power absorption and mutual assistance distribution areas, corresponding data are extracted from the dynamic operating parameters of M distribution areas to be absorbed and recombined to form multiple sets of coupled operating parameters. For example, if the combinations of power absorption and mutual assistance distribution areas are 1-4 and 3-6, and M=2, that is, distribution areas 1 and 3 to be absorbed, then data such as the power to be absorbed, load gap, and energy storage status are extracted from the dynamic operating parameters of distribution areas 1 and 4 and recombined into the coupled operating parameter set of combination 1-4, including information such as the power to be absorbed by distribution area 1 is 100kW, the load gap of distribution area 4 is 100kW, and the energy storage of distribution area 4 can support 100kW transmission; similarly, the coupled operating parameter set of combination 3-6 is recombined, including information such as the power to be absorbed by distribution area 3 is 50kW, the load gap of distribution area 6 is 100kW, and the transmission limit of distribution area 6 is 150kW, thus obtaining multiple sets of coupled operating parameters.

[0066] Next, based on the obtained sets of multiple sets of coupled operating parameters, the absorption control requirements are quantified by calculating the matching degree between the amount to be absorbed and the receiving capacity, the load margin of the transmission path, etc., resulting in multiple absorption adaptability scores. Taking combination 1-4 as an example, the amount to be absorbed of 100kW is completely matched with the gap and transmission capacity of area 4, and the absorption adaptability score is 1.0; in combination 3-6, the amount to be absorbed of 50kW only accounts for 50% of the gap in area 6, and the absorption adaptability score is 0.5.

[0067] Then, the combinations of mutual aid distribution stations are sorted in descending order according to multiple absorption adaptability scores, with combinations with higher adaptability scores given priority, forming an absorption priority sequence. For example, combination 1-4 with an adaptability score of 1.0 is ranked first, and combination 3-6 with an adaptability score of 0.5 is ranked second, resulting in the output sequence 1-4, 3-6.

[0068] Ultimately, based on the priority sequence of power absorption, cross-regional power flexible control is executed sequentially in the real-time flexible interconnection topology. First, based on the topology path of combination 1-4, the 100kW excess power of region 1 is transferred to region 4; after the control stabilizes, based on the path of combination 3-6, the 50kW excess power of region 3 is transferred to region 6, ensuring orderly and efficient power allocation.

[0069] By reorganizing parameter sets, quantifying adaptability, sorting priorities, and controlling in sequence, an orderly and efficient allocation of power across power distribution areas was achieved, improving the real-time performance and accuracy of renewable energy consumption.

[0070] Furthermore, step A600 in the method provided in this application embodiment includes: A650: Real-time flexible interconnection topology tracking acquires tracking operation data for M distribution areas to be consumed.

[0071] A660: Based on the real-time operation data of M distribution areas, determine the consumption demand in real time until the consumption demand is set to 0 after W tracking cycles, and then reclaim the cross-distribution mutual assistance authority of the real-time flexible interconnection topology.

[0072] In one embodiment, during the real-time flexible interconnection topology operation, the system continuously tracks and monitors M distribution areas to be consumed, acquiring their tracking operation data. For example, if each tracking cycle is set to 2 minutes, when M=2, real-time data is collected for distribution areas 1 and 3 every 2 minutes, including renewable energy generation power, current load power, and energy storage system charging and discharging status. If distribution area 1 has a generation power of 800kW, a load power of 600kW, and a maximum energy storage charging power of 200kW in the first cycle; and distribution area 3 has a generation power of 650kW, a load power of 400kW, and a maximum energy storage charging power of 250kW, this data will be transmitted to the consumption control center in real time.

[0073] Next, the consumption control platform performs real-time evaluation based on the acquired tracking data from M distribution areas, according to preset consumption demand judgment criteria. The judgment criteria are consistent with those used in the previous steps for selecting distribution areas to be consumed; that is, when the renewable energy generation power of a distribution area does not exceed the sum of its load power and the maximum charging power of its energy storage, the consumption demand is determined to be 0. Taking the above data as an example, for distribution area 1, 800kW = 600kW + 200kW, the consumption demand is 0; for distribution area 3, 650kW = 400kW + 250kW, the consumption demand is also 0. If, for a subsequent continuous period of W = 3 tracking cycles (6 minutes), the consumption demand of both distribution areas remains 0, then the current consumption task is determined to be completed.

[0074] Once the absorption demand is set to 0 for W tracking cycles, the absorption control platform issues a permission revocation command to the real-time flexible interconnection topology, disabling the previously activated cross-regional mutual assistance permissions. In the aforementioned example, the flexible interconnection enable permissions between regions 1 and 4, and between regions 3 and 6, will be terminated, and the relevant nodes and connections will revert to their initial state, ceasing cross-regional power transmission.

[0075] By continuously tracking the operation data of the distribution areas to be consumed, judging the consumption demand in real time, and revoking the mutual assistance authority after the target is met, a complete consumption control closed loop has been formed. This ensures that the cross-distribution mutual assistance is terminated in a timely manner after the new energy consumption is completed, thereby improving the economy and safety of the system operation.

[0076] In summary, the multi-regional coordinated new energy consumption control optimization method provided in this application has the following technical effects: This application analyzes the synergy between power generation, grid, load, and energy storage by retrieving historical operating data from multiple power supply areas, constructs a mutual assistance synergy matrix and a mutual assistance distribution topology, collects and uploads dynamic operating data from each area to the consumption control center, obtains a combination of consumption mutual assistance areas through real-time consumption mutual assistance optimization, and then reconstructs the local permissions of the mutual assistance distribution topology to form a real-time flexible interconnected topology. After prioritizing the combination, cross-area power flexible control is performed, and the data of the areas to be consumed is tracked until the consumption demand is met, after which the mutual assistance permissions are revoked. This achieves multi-area collaborative optimization of new energy consumption control, effectively improving the efficiency of new energy consumption and the stability of grid operation, and achieving the technical effect of cross-area power optimization and allocation, improving the efficiency of new energy consumption, grid operation stability, and economy.

[0077] Example 2 like Figure 2 As shown, based on the same inventive concept as Embodiment 1 above, Embodiment 2 of this application provides a multi-regional collaborative new energy consumption control and optimization system, wherein the system includes: The data acquisition and collaborative analysis module is used to retrieve multiple historical operating data from multiple power supply areas, and to perform source-grid-load-storage collaborative analysis based on multiple historical operating data to construct a mutual assistance collaborative matrix.

[0078] Specifically, the data acquisition and collaborative analysis module includes: The historical data acquisition submodule is used to retrieve historical operating data of multiple power supply areas from storage devices, such as time series data of load power and new energy power generation.

[0079] The feature extraction submodule is used to construct multiple load power time series waveforms based on multiple historical operating data, and then extract load peaks and valleys through the envelope decomposition algorithm to obtain multiple peak and valley feature sequences.

[0080] The combined enumeration submodule is used to enumerate multiple power supply areas in combination to obtain multiple area combinations.

[0081] The complementary index calculation submodule is used to calculate the Pearson load complementary index based on the time sequence combination of multiple transformer areas and align multiple peak and valley characteristic sequences, and output multiple load complementary indices.

[0082] The matrix construction submodule is used to construct a mutual assistance and coordination matrix based on multiple power supply areas, and then fill multiple load complementarity indices into the mutual assistance and coordination matrix according to the combination of multiple areas.

[0083] The topology building module is used to construct a mutual assistance distribution topology for multiple power supply areas based on a mutual assistance and coordination matrix. Optionally, the topology building module includes: The index filtering submodule is used to traverse multiple load complementarity indices based on a preset load complementarity threshold range and filter out P load complementarity indices.

[0084] The combined mapping submodule is used to extract P effective mutual aid combinations from multiple transformer area combined mappings based on P load complementarity indices.

[0085] The topology generation submodule is used to quantify P load complementarity indices into P effective mutual assistance combinations of topology connections, and construct mutual assistance distribution topologies.

[0086] The data acquisition and upload module is used to collect and upload dynamic operation data of multiple power supply areas to the consumption control center.

[0087] In this embodiment, the absorption control platform is the central hub for realizing coordinated absorption control of multiple distribution zones, including a data receiving interface, a data processing unit, and a control command generation unit.

[0088] Specifically, the data acquisition and uploading module includes: The monitoring equipment deployment submodule is used to deploy real-time monitoring equipment in multiple power supply areas, including new energy power generation sensors, load current transformers, energy storage system status monitors, etc., to collect dynamic operation data of the areas.

[0089] The data encapsulation submodule is used to encapsulate the collected dynamic operation data of multiple transformer substations according to a unified data protocol. The protocol includes the transformer substation number, data collection timestamp, data type, and specific value.

[0090] The communication transmission submodule is used to upload the encapsulated data packets to the power consumption control center via the power communication network, ensuring the timeliness and integrity of data transmission.

[0091] The real-time optimization module, located in the power consumption control center, is used to perform real-time power consumption mutual assistance optimization based on the dynamic operating data of multiple power distribution areas, resulting in multiple combinations of power consumption mutual assistance areas. Specifically, the real-time optimization module includes: The demand assessment submodule is used to assess the demand for power consumption in real time based on the dynamic operating parameters of multiple power distribution areas, and to filter the dynamic operating parameters of M power distribution areas to be consumed.

[0092] The combination enumeration submodule is used to enumerate P effective mutual assistance combinations in the mutual assistance distribution topology based on M distribution areas to be consumed.

[0093] The parameter combination submodule is used to combine the dynamic operating parameters of M substations based on the configuration of P effective mutual assistance combinations, and obtain P sets of dynamic operating parameters for the substations.

[0094] The adaptability calculation submodule is used to quantify the absorption adaptability based on the dynamic operating parameters of P groups of transformer areas and output P absorption adaptability values.

[0095] The conflict screening submodule is used to perform graph theory screening of P effective mutual assistance combinations based on P absorption and fitness levels, resulting in multiple absorption and mutual assistance combination zones.

[0096] The topology reconfiguration module is used to reconfigure the local permissions of the mutual aid distribution topology based on the combination of multiple mutual aid distribution areas, so as to obtain a real-time flexible interconnection topology.

[0097] Optionally, the topology reconfiguration module includes: The node activation submodule is used to activate the flexible interconnection enablement authority of M mutual aid and absorption nodes in the mutual aid distribution topology local area based on M distribution areas to be absorbed.

[0098] The permission activation submodule is used to activate the flexible interconnection enable permissions between M mutual aid and consumption nodes based on the combination of multiple mutual aid and consumption areas, so as to obtain the real-time flexible interconnection topology.

[0099] A power control module is used to prioritize multiple combinations of mutually supportive distribution transformer areas and then perform cross-area power flexibility control in a real-time flexible interconnection topology. Optionally, the power control module includes: The parameter reorganization submodule is used to reorganize the dynamic operating parameters of M transformer substations based on the combination of multiple absorption and mutual assistance substations, and obtain multiple sets of coupled operating parameters.

[0100] The quantization submodule is used to quantify the absorption control requirements based on multiple sets of coupled operating parameters, and obtain multiple absorption fit degrees.

[0101] The sorting submodule is used to sort multiple combinations of mutual aid distribution areas in descending order based on multiple absorption adaptability, and output the absorption priority sequence.

[0102] The control execution submodule is used to perform cross-regional power flexible control in a real-time flexible interconnection topology, with the absorption priority sequence as the timing constraint.

[0103] Furthermore, the power control module also includes: The tracking submodule is used to acquire tracking operation data of M distribution areas to be consumed in real-time flexible interconnection topology tracking.

[0104] The permission revocation submodule is used to determine the real-time consumption demand based on the tracking operation data of M distribution areas, until the consumption demand is set to 0 after W tracking cycles, and then revoke the cross-distribution mutual assistance permission of the real-time flexible interconnection topology.

[0105] The absorption control platform, as the central hub of the system, coordinates the operation of various modules, including the data receiving interface, real-time data processing unit, optimization algorithm unit, and control command issuing unit. The absorption control platform is connected to the data acquisition and uploading module, real-time optimization module, topology reconstruction module, and power control module via a communication network to achieve integrated management of data flow and control flow.

[0106] In summary, the multi-regional collaborative new energy consumption control and optimization system provided in this application has the following technical effects: This application constructs a mutual support and synergy matrix through a data acquisition and collaborative analysis module, generates a mutual support distribution topology through a topology construction module, uploads dynamic data in real time through a data acquisition and upload module, obtains a combination of mutual support distribution areas through a real-time optimization module in the absorption control center, forms a real-time flexible interconnected topology through a topology reconstruction module, and executes cross-area power flexible control and tracks and reclaims permissions through a power control module. This achieves multi-area collaborative optimization of new energy absorption control, effectively improving the efficiency of new energy absorption and the stability of grid operation. It achieves the technical effect of cross-area power optimization and allocation, improving the efficiency of new energy absorption and the stability and economy of grid operation.

[0107] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0108] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for optimizing the control of renewable energy consumption through multi-regional collaboration, characterized in that, The method includes: Retrieve multiple historical operating data from multiple power supply areas, and conduct source-grid-load-storage synergy analysis based on the multiple historical operating data to construct a mutual assistance synergy matrix; The mutual assistance distribution topology of the multiple power supply areas is constructed based on the mutual assistance and synergy matrix; The multiple power supply areas collect and upload dynamic operation data of multiple areas to the power consumption control center; The absorption control platform performs real-time absorption mutual assistance optimization based on the dynamic operation data of the multiple distribution areas, and obtains multiple absorption mutual assistance distribution area combinations. Based on the combination of multiple mutual aid distribution areas, the local access control of the mutual aid distribution topology is reconstructed to obtain a real-time flexible interconnection topology; After prioritizing the multiple combinations of mutually supportive distribution areas, cross-area power flexible control is performed on the real-time flexible interconnection topology. The method involves retrieving multiple historical operating data points from multiple power supply areas, performing source-grid-load-storage synergy analysis based on the historical operating data, and constructing a mutual assistance synergy matrix. After constructing multiple load power time series waveforms based on the aforementioned historical operating data, the envelope decomposition algorithm is used to extract load peaks and valleys, resulting in multiple peak and valley feature sequences. By combining and enumerating the multiple power supply areas, multiple combinations of areas are obtained; Based on the time series combination of the multiple transformer areas, the multiple peak-valley characteristic sequences are aligned to calculate the Pearson load complementarity index, and multiple load complementarity indices are output. After constructing a mutual support and coordination matrix based on the multiple power supply areas, the multiple load complementarity indices are filled into the mutual support and coordination matrix according to the combination of the multiple areas. The power consumption control platform performs real-time power consumption mutual assistance optimization based on the dynamic operation data of the multiple power distribution areas, resulting in multiple combinations of power consumption mutual assistance power distribution areas. The method includes: Real-time demand assessment is performed on the dynamic operating parameters of the multiple transformer substations to determine the demand for power consumption, and M dynamic operating parameters of M transformer substations to be consumed are selected. Based on the M distribution areas to be consumed, P effective mutual assistance combinations are enumerated in the mutual assistance distribution topology; Based on the configuration of the P effective mutual support combinations of transformer substations, the dynamic operating parameters of the M transformer substations are combined to obtain P sets of dynamic operating parameters for transformer substations. Based on the dynamic operating parameters of the P groups of transformer substations, the absorption and adaptation quantification is performed, and P absorption and adaptation degrees are output. Based on the P absorption fit degrees, the mutual assistance conflict graph theory screening of the P effective mutual assistance combinations is performed to obtain the multiple absorption mutual assistance platform combinations; The method involves reconstructing the local access permissions of the mutual aid distribution topology based on the combination of multiple mutual aid distribution areas to obtain a real-time flexible interconnection topology. Based on the M distribution areas to be consumed, activate the flexible interconnection enablement authority of the M mutual consumption nodes in the mutual distribution topology local area. The real-time flexible interconnection topology is obtained by activating the flexible interconnection enable permissions between the M mutual aid and absorption nodes according to the combination of the multiple absorption and mutual aid stations.

2. The multi-regional coordinated new energy consumption control optimization method as described in claim 1, characterized in that, The method for constructing the mutual assistance distribution topology of the multiple power supply areas based on the mutual assistance and cooperation matrix includes: Based on a preset load complementarity threshold range, the multiple load complementarity indices are traversed to obtain P load complementarity indices; Based on the P load complementarity indices, P effective mutual aid combinations are extracted from the multiple transformer area combinations. The P load complementarity indices are quantified into the topology connections of the P effective mutual assistance combinations to construct the mutual assistance distribution topology.

3. The multi-regional coordinated new energy consumption control optimization method as described in claim 1, characterized in that, After prioritizing the multiple combinations of mutually supportive distribution areas, cross-area power flexibility control is performed on the real-time flexible interconnection topology. The method includes: Based on the combination of multiple absorption and mutual assistance transformer areas, the dynamic operating parameters of the M transformer areas are reorganized to obtain multiple sets of coupled operating parameters; Based on the multiple sets of coupled operating parameters, the absorption control requirements are quantified to obtain multiple absorption adaptability degrees. Based on the multiple absorption adaptability, the multiple absorption mutual assistance transformer area combinations are sorted in descending order, and the absorption priority sequence is output; Using the absorption priority sequence as a timing constraint, cross-regional power flexible control is performed on the real-time flexible interconnection topology.

4. The multi-regional coordinated new energy consumption control optimization method as described in claim 1, characterized in that, The method further includes: The real-time flexible interconnection topology tracking acquires the tracking operation data of the M distribution areas to be eliminated; Based on the real-time consumption demand judgment of the M transformer area tracking operation data, until the consumption demand is set to 0 after W tracking cycles, the cross-transformer area mutual assistance permission of the real-time flexible interconnection topology is revoked.

5. A multi-regional collaborative new energy consumption control and optimization system, characterized in that, The system is used to perform the method according to any one of claims 1-4, the system comprising: The data acquisition and collaborative analysis module is used to retrieve multiple historical operating data from multiple power supply areas, and to perform source-grid-load-storage synergy analysis based on the multiple historical operating data to construct a mutual assistance synergy matrix; The topology construction module is used to construct the mutual assistance distribution topology of the multiple power supply areas based on the mutual assistance and coordination matrix; The data acquisition and upload module is used to collect and upload dynamic operating data of the multiple power supply areas to the power consumption control center. The real-time optimization module is set in the consumption control center and is used to perform real-time consumption mutual assistance optimization based on the dynamic operation data of the multiple distribution areas to obtain multiple consumption mutual assistance distribution area combinations. The topology reconfiguration module is used to reconfigure the local permissions of the mutual aid distribution topology based on the combination of the multiple mutual aid distribution areas, so as to obtain a real-time flexible interconnection topology. The power control module is used to prioritize the multiple absorption and mutual assistance transformer area combinations and then perform cross-regional power flexible control on the real-time flexible interconnection topology.

6. The multi-regional collaborative new energy consumption control and optimization system according to claim 5, characterized in that, The data acquisition and collaborative analysis module includes: The historical data acquisition submodule is used to retrieve the multiple historical operation data. The feature extraction submodule is used to construct multiple load power time series waveforms based on the multiple historical operating data, and to extract load peaks and valleys through the envelope decomposition algorithm to obtain multiple peak and valley feature sequences. The combined enumeration submodule is used to combine and enumerate the multiple power supply areas to obtain multiple combinations of areas. The complementarity index calculation submodule is used to calculate the Pearson load complementarity index based on the time sequence combination of the multiple transformer area combinations, and output multiple load complementarity indices. The matrix construction submodule is used to construct a mutual assistance and coordination matrix based on the multiple power supply areas, and then fill the multiple load complementarity indices into the mutual assistance and coordination matrix according to the combination of the multiple areas.

7. The multi-regional collaborative new energy consumption control and optimization system according to claim 5, characterized in that, The power control module also includes: The tracking submodule is used to acquire tracking operation data of the M distribution areas to be eliminated in the real-time flexible interconnection topology. The permission revocation submodule is used to determine the real-time consumption demand based on the tracking operation data of the M distribution areas, until the consumption demand is set to 0 after W tracking cycles, and then revoke the cross-distribution mutual assistance permission of the real-time flexible interconnection topology.

Images