Energy multi-source collaborative analysis and management method based on decentralized energy storage system

Abstract

The present application relates to the technical field of distributed energy storage system management, and particularly relates to an energy multi-source collaborative analysis management method based on a distributed energy storage system; the present application is through collecting real-time data of each distributed energy storage node, executing local optimization control, processing sudden operation events, guaranteeing single-node operation safety and response timeliness, helping to improve local autonomous control effect, taking grid-connected power fluctuation amplitude as a first-level target, ensuring energy storage output and associated energy, local load collaborative matching through global hierarchical screening optimization, further focusing on wind turbine abandoned wind rate control, preferentially consuming renewable energy, reducing conventional energy supplement cost, finally controlling SOC change rate to reduce equipment loss, and combining local autonomy and global collaboration to coordinate conflicts, timely call preset control decisions and visually display, so that operation and maintenance personnel can quickly solve contradictions and avoid energy storage "refusal" or "misoperation" caused by instruction conflicts.

Description

Technical Field

[0001] This invention relates to the field of distributed energy storage system management technology, and in particular to a method for multi-source collaborative analysis and management of energy based on distributed energy storage systems. Background Technology

[0002] With the large-scale integration of renewable energy sources (such as wind power and photovoltaics) and the rapid development of distributed energy storage technology, regional energy systems are gradually exhibiting characteristics of multi-source, distributed, and highly volatile.

[0003] Against this technological backdrop, distributed energy storage systems, due to their flexibility, scalability, and local consumption capabilities, have become a key technological means to enhance grid resilience and promote the consumption of new energy sources. However, traditional distributed energy storage systems still face many technical bottlenecks and challenges in actual operation and management, mainly in the following aspects:

[0004] The response capability to sudden scenarios (such as communication interruption, load change, and energy storage parameter exceeding limits) is weak, which can easily lead to system shutdown or equipment damage. Moreover, most energy storage control systems adopt a single-objective management mode, which makes it difficult to take into account multiple requirements such as grid security, energy consumption, and equipment lifespan. Furthermore, the lack of coordination between local control and global scheduling can lead to problems such as command conflicts and power change, affecting the stability of system operation. Therefore, a solution is proposed. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-source collaborative analysis and management method for energy based on distributed energy storage systems. It adopts a two-layer control architecture of "local autonomy + remote collaboration" to realize multi-source collaborative management of distributed energy storage systems, thereby solving the aforementioned technical defects.

[0006] The objective of this invention can be achieved through the following technical solution: a multi-source collaborative analysis and management method for energy based on distributed energy storage systems, comprising the following steps:

[0007] Step 1: Collect multi-dimensional data of distributed energy storage nodes in the target area in real time through local terminals and preprocess the data to obtain reliable input data;

[0008] Step 2: Set constraints and combine reliable input data to perform energy storage node area division analysis to obtain an energy storage state map and realize local energy collaborative control;

[0009] Step 3: Constructing the global energy data pool;

[0010] Step 4: Remote collaborative energy scheduling: Generate a dynamic set of equipment safety constraints for each energy storage node, and construct the local feasible solution space and global initial feasible solution matrix for each node. Through a greedy algorithm, traverse and combine the results to finally obtain the charging and discharging power command for each energy storage node in this control cycle.

[0011] Step 5: After verifying the charging and discharging power command, it is divided into three 5-minute sub-period power values ​​and sent to each energy storage node. At the same time, the local terminal starts the conflict detection process.

[0012] Preferably, the local energy coordinated control includes:

[0013] Set constraints, analyze reliable input data based on these constraints, and trigger a local autonomous control process when any constraint is met.

[0014] The energy storage nodes are initially divided and analyzed to obtain the energy storage SOC and the real-time output power of the associated energy devices of each energy storage node. The energy storage SOC and real-time output power are distinguished to output autonomous control energy storage nodes and non-autonomous control energy storage nodes.

[0015] An energy storage status map of the target area is constructed based on autonomously controlled energy storage nodes and non-autonomously controlled energy storage nodes. In the energy storage status map, autonomously controlled energy storage nodes are marked in green, and non-autonomously controlled energy storage nodes are marked in red.

[0016] Preferably, the local energy coordinated control further includes:

[0017] Extract the energy storage SOC of autonomous control energy storage nodes and the real-time output power of associated energy devices from reliable input data in the energy storage state map;

[0018] The preset output power range [GLmin, GLmax] and preset energy storage SOC range [CNmin, CNmax] are retrieved, and the local autonomous control logic is used to evaluate and analyze the energy storage SOC and real-time output power to obtain the working mode of each autonomous control energy storage node. The working modes include charging mode, discharging mode and standby mode.

[0019] Preferably, the construction process of the global energy data pool is as follows:

[0020] Set the data acquisition cycle, receive real-time data uploaded by each local terminal based on the data acquisition cycle, and update the power prediction data of related energy equipment for the next 24 hours.

[0021] Real-time data, power prediction data, and energy storage status maps are processed in a unified manner to build a global energy data pool with a unified format and consistent logic.

[0022] Preferably, the process for obtaining the charging / discharging power command is as follows:

[0023] S1: Based on real-time data and predicted power data from the global energy data pool, extract a dynamic set of equipment safety constraints for each energy storage node;

[0024] The dynamic device safety constraint set includes constraints on the energy storage itself and constraints on associated devices;

[0025] S2: For each energy storage node, based on the dynamic equipment safety constraint set, generate a set of feasible intervals for charging and discharging power within the cost data acquisition period, remove invalid power values ​​that exceed the intervals, and form an independent local feasible solution space for each node;

[0026] S3: Summarize the local feasible solution space of all energy storage nodes and construct the initial feasible solution matrix at the global level;

[0027] S4: Obtain the priority target system for the target area, which includes primary targets, secondary targets, and tertiary targets;

[0028] S5: Based on the priority target system, the global grid-connected power fluctuation value, wind turbine curtailment rate and node SOC change rate corresponding to the first-level target, second-level target and third-level target are obtained respectively. Based on the layer-by-layer screening of the global grid-connected power fluctuation value, wind turbine curtailment rate and node SOC change rate, the charging and discharging power command of each energy storage node in the current control cycle is obtained.

[0029] Preferably, S5 further includes S51: based on the initial feasible solution matrix, a preset greedy algorithm is used to traverse all feasible power combinations of nodes, calculate the global grid-connected power fluctuation value of each combination, and select feasible power combinations whose global grid-connected power fluctuation value is ≤ a preset threshold to form a "first-level feasible solution subset". If the subset is empty, the preset threshold is adjusted and the selection is repeated.

[0030] S52: For all feasible power combinations in the first-level feasible solution subset, calculate the wind curtailment rate of the wind turbines, retain the feasible power combinations with a curtailment rate ≤ the preset curtailment rate, and form a "second-level feasible solution subset". If there are multiple combinations that meet the conditions, select the three combinations with the lowest curtailment rates to enter S53.

[0031] S53: For the three combinations in the second-level feasible solution subset, calculate the SOC change rate of each node, select the combination with the smallest SOC change rate as the final optimization result, and generate the final power command for charging and discharging of each energy storage node in this control cycle.

[0032] Preferably, the conflict detection process is as follows:

[0033] A second verification is performed on the final power command combination to confirm that all energy storage node power commands are within their local feasible solution space.

[0034] Based on the final determined combination of charging and discharging power commands, the power values ​​are broken down into three 5-minute sub-periods according to the time dimension, forming a time-segmented charging and discharging control command sequence within a 15-minute cycle;

[0035] The core parameters in the charge and discharge control command sequence are compared one by one with the current command parameters generated by the local autonomous control logic evaluation and analysis. If there is a parameter inconsistency that causes the energy storage nodes to be unable to execute simultaneously, it is determined to be a command conflict and the conflict coordination mechanism is triggered. The conflict type of the corresponding energy storage node is obtained. The conflict type includes safety constraint conflict and operation stability conflict coordination. Then, the conflict control decision of the current conflict type of the corresponding energy storage node is retrieved.

[0036] If the parameters are consistent or compatible, no coordination is required.

[0037] The beneficial effects of this invention are as follows:

[0038] (1) This invention collects real-time data from each distributed energy storage node, performs local optimization control, handles sudden operational events, ensures the operational safety and timely response of a single node, and helps improve the effect of local autonomous control.

[0039] (2) This invention also focuses on controlling the wind curtailment rate of wind turbines, giving priority to the consumption of renewable energy, reducing the cost of conventional energy replenishment, which helps to improve energy utilization efficiency. At the same time, it avoids sudden changes in charging and discharging power and reduces equipment losses by controlling the SOC change rate and smoothing power transition. Furthermore, it combines local autonomy with global collaboration, and resolves control contradictions through a command conflict coordination mechanism, adapting to complex and ever-changing operating scenarios. Attached Figure Description

[0040] The invention will now be further described with reference to the accompanying drawings;

[0041] Figure 1 This is a reference diagram of the method of the present invention;

[0042] Figure 2 This is a reference diagram for analyzing charging and discharging power commands;

[0043] Figure 3 This is a flowchart of the analysis process of S5 in this invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments;

[0046] Example 1: Please refer to Figures 1 to 3 As shown, this invention is a multi-source collaborative analysis and management method for energy based on distributed energy storage systems, comprising the following steps:

[0047] Step 1: Collect multi-dimensional data of distributed energy storage nodes in the target area in real time through local terminals and preprocess the data to obtain reliable input data;

[0048] Step 2: Set constraints and combine reliable input data to perform energy storage node area division analysis to obtain an energy storage state map and realize local energy collaborative control;

[0049] Step 3: Constructing the global energy data pool;

[0050] Step 4: Remote collaborative energy scheduling: Generate a dynamic set of equipment safety constraints for each energy storage node, and construct the local feasible solution space and global initial feasible solution matrix for each node. Through a greedy algorithm, traverse and combine the results to finally obtain the charging and discharging power command for each energy storage node in this control cycle.

[0051] Step 5: After verifying the charging and discharging power command, it is divided into three 5-minute sub-period power values ​​and sent to each energy storage node. At the same time, the local terminal starts the conflict detection process.

[0052] The process of obtaining reliable input data in step one is as follows:

[0053] The local terminal collects multi-dimensional data of distributed energy storage nodes in the target area in real time. The multi-dimensional data includes core parameters, local load operation data and related energy equipment data. Outlier removal and moving average data smoothing are performed on the collected multi-dimensional data to obtain reliable input data.

[0054] The core parameters include State of Charge (SOC), charging and discharging power, voltage, and temperature; the local load operation data includes load power and connection / disconnection status; and the associated energy equipment (wind turbine) data represents the real-time output power.

[0055] The specific implementation process of step two:

[0056] Set constraints, including exceeding limits for energy storage node parameters (such as charging power exceeding the rated charging power of the converter PCS, discharging power exceeding the rated discharging power, or SOC exceeding the 20%-90% safe range), sudden changes in local load status (such as load power fluctuation exceeding the set threshold, adding / cutting off large-capacity loads), and remote communication interruption (triggering local independent operation mode to avoid system shutdown).

[0057] Based on the analysis of reliable input data under constraints, a local autonomous control process is triggered when any constraint is met:

[0058] A preliminary analysis of energy storage nodes is performed to obtain the energy storage SOC and the real-time output power of associated energy devices for each energy storage node. If the energy storage SOC is within the preset energy storage SOC range and the grid-connected power of the associated energy devices is less than the rated power, the corresponding energy storage node is determined to be an autonomous control energy storage node. If the energy storage SOC is not within the preset energy storage SOC range, or the grid-connected power of the associated energy devices is greater than or equal to the rated power, the corresponding energy storage node is determined to be a non-autonomous control energy storage node.

[0059] An energy storage status map of the target area is constructed based on autonomous and non-autonomous energy storage nodes. The energy storage status map is constructed by marking autonomous energy storage nodes in green and non-autonomous energy storage nodes in red. The energy storage status map is displayed immediately so that the non-autonomous energy storage nodes in the energy storage status map can be adjusted to ensure that the non-autonomous energy storage nodes can complete the local autonomous control logic evaluation and analysis, and to ensure that all energy storage nodes in the target area can achieve local autonomy.

[0060] Extract the energy storage SOC of autonomous control energy storage nodes and the real-time output power of associated energy devices from reliable input data in the energy storage state map;

[0061] Retrieve the preset output power range [GLmin, GLmax] and the preset energy storage SOC range [CNmin, CNmax], and perform local autonomous control logic evaluation and analysis on the energy storage SOC and real-time output power:

[0062] If the real-time output power is ≥GLmax and the energy storage SOC is ≤CNmin, then the charging mode is activated to store the excess electrical energy of the associated energy devices into the autonomous control energy storage node.

[0063] If the real-time output power is ≤GLmin and the energy storage SOC is ≥CNmax, then the discharge mode is activated to supplement the power gap of the grid through energy storage discharge.

[0064] If the charging and discharging modes above are not met, the standby mode will be activated to maintain the energy storage SOC stable within the preset energy storage SOC range, ensuring emergency response capability.

[0065] Example 2: The construction process of the global energy data pool is as follows:

[0066] Set the data acquisition cycle (e.g., every 15 minutes), and receive real-time data (e.g., energy storage SOC, charge and discharge status, load power, and actual wind turbine power) uploaded by each local terminal based on the data acquisition cycle, while updating the power prediction data of related energy equipment for the next 24 hours.

[0067] Real-time data, power prediction data, energy storage status maps, and constraint threshold parameters are processed in a unified manner to build a global energy data pool with a unified format and consistent logic. This provides comprehensive and accurate data support for multi-source energy collaborative scheduling. The data pool is updated in real time to ensure the timeliness of scheduling decisions.

[0068] The process of obtaining the charging and discharging power command in step four is as follows:

[0069] S1: Based on real-time data and predicted power data from the global energy data pool, extract a dynamic set of equipment safety constraints for each energy storage node;

[0070] The dynamic device safety constraint set includes constraints on the energy storage itself and constraints on associated devices;

[0071] Energy storage self-constraints (dynamic power boundary):

[0072] If the current SOC (S0) is less than the preset safety lower limit buffer value: the lower limit of charging power is limited to the preset charging ratio coefficient 0.3 × rated charging power, and the upper limit of discharging power is the preset discharging ratio coefficient 0.2 × rated discharging power, to prevent SOC from decreasing further;

[0073] If S0 > preset safety upper limit buffer value: limit the upper limit of charging power to the preset charging ratio coefficient 0.2 × rated charging power, and limit the upper limit of discharging power to the preset discharging ratio coefficient 0.3 × rated discharging power, to avoid excessively high SOC;

[0074] If the preset lower safety buffer value ≤ S0 ≤ the preset upper safety buffer value: maintain the original constraint, that is, the charging and discharging power satisfies - rated charging power ≤ energy storage power ≤ rated discharging power;

[0075] Related equipment constraints (energy output - load balance constraints):

[0076] Based on the current actual output of associated energy equipment and the predicted power range for the next 15 minutes, the predicted power range [predicted minimum output of wind turbines, predicted maximum output of wind turbines] is matched with dynamic balance constraints:

[0077] If the predicted output of associated energy equipment continues to exceed the local load (predicted output of associated energy equipment ≥ preset proportional coefficient 1.2 × local load power): the lower limit of charging power is relaxed to preset proportional coefficient 0.4 × rated charging power, and wind power is given priority.

[0078] If the predicted output of associated energy equipment is lower than the local load (predicted output of associated energy equipment ≤ preset proportional coefficient 0.8 × local load power): the lower limit of discharge power is relaxed to the preset proportional coefficient 0.4 × rated discharge power, and priority is given to supplementing the load gap;

[0079] S2: For each energy storage node, based on the dynamic device safety constraint set, generate a set of feasible intervals for charging and discharging power within the cost data acquisition period (e.g., node 1 is [node 1 power minimum, node 1 power maximum], node 2 is [node 2 power minimum, node 2 power maximum]), and eliminate invalid power values ​​that exceed the intervals to form an independent local feasible solution space for each node;

[0080] S3: Summarize the local feasible solution space of all energy storage nodes, construct the initial feasible solution matrix at the global level, clarify the power adjustment boundary of each node, and provide basic constraints for subsequent global optimization;

[0081] S4: Obtain the priority target system of the target area. The priority target system includes primary target (security assurance), secondary target (core basic target) and tertiary target (economic energy saving).

[0082] For example: Level 1 (Safety Assurance): Grid-connected power fluctuation range ≤5%, ensuring grid operation stability (core basic objective); Level 2 (Economic and Energy Saving): Wind curtailment rate (related energy equipment is wind turbines) ≤3%, maximizing the absorption of renewable energy and reducing the cost of conventional energy supplementation; Level 3 (Energy Storage Efficiency): SOC change rate of each node ≤5% / 15min, avoiding sudden changes in charging and discharging power and extending the service life of energy storage equipment;

[0083] S5: Based on the priority target system, the global grid-connected power fluctuation value, wind turbine curtailment rate and node SOC change rate corresponding to the first-level target, second-level target and third-level target are obtained respectively. Based on the layer-by-layer screening of the global grid-connected power fluctuation value, wind turbine curtailment rate and node SOC change rate, the charging and discharging power command of each energy storage node in this control cycle is obtained.

[0084] S51: Based on the initial feasible solution matrix, a preset greedy algorithm is used to traverse all feasible power combinations of nodes, calculate the global grid-connected power fluctuation value of each combination (|actual combined output - predicted grid-connected power in this cycle| / predicted grid-connected power in this cycle × 100%), and select feasible power combinations with global grid-connected power fluctuation value ≤ preset threshold to form a "first-level feasible solution subset". If the subset is empty, the preset threshold is adjusted (to retain the basic stability of the power grid) and then the selection is re-selected.

[0085] S52: For all feasible power combinations in the first-level feasible solution subset, calculate the wind curtailment rate (formula: (predicted wind turbine output - energy storage charging power - load power consumption) / predicted wind turbine output × 100%), retain feasible power combinations with a curtailment rate ≤ preset curtailment rate to form a "second-level feasible solution subset". If there are multiple combinations that meet the conditions, select the top 3 combinations with the lowest curtailment rates to enter S53.

[0086] S53: For the three combinations in the second-level feasible solution subset, calculate the SOC change rate of each node (formula: |SOC at the end of this cycle - initial SOC| / initial SOC×100%), select the combination with the smallest SOC change rate as the final optimization result, and generate the final power command for charging and discharging of each energy storage node in this control cycle (e.g., the optimal power for node 1 and the optimal power for node 2).

[0087] In summary, based on SOC buffer space and wind turbine-load power prediction, the charging and discharging power boundaries are dynamically adjusted to balance equipment safety and energy consumption requirements. A three-level priority target system balances grid security, economic energy saving, and energy storage lifespan, overcoming the limitations of single-target management. Local constraint verification narrows the feasible solution range, while global hierarchical iterative solving improves optimization efficiency and adapts to 15-minute short-cycle control requirements. The combination of constraint-triggered local autonomous control and global collaborative optimization addresses sudden scenarios such as communication interruptions and load surges, enhancing system robustness.

[0088] Step 5: After verifying the charging and discharging power command, it is divided into three 5-minute sub-periods of power value and sent to each energy storage node. At the same time, the local terminal initiates a conflict detection process, which includes:

[0089] A second verification is performed on the final power command combination to confirm that all energy storage node power commands are within their local feasible solution space (e.g., the optimal power of node 1 ∈ [the minimum power of node 1, the maximum power of node 1]).

[0090] Based on the final determined combination of charging and discharging power commands, the power values ​​are broken down into three 5-minute sub-periods according to the time dimension (e.g., the optimal power of node 1 is broken down into the power of the first sub-period of node 1, the power of the second sub-period of node 1, and the power of the third sub-period of node 1) to ensure a smooth transition of power between adjacent sub-periods and avoid power abrupt changes from impacting the equipment and the power grid.

[0091] A time-segmented charging and discharging control command sequence is formed within a 15-minute cycle, and the specific power execution value of each node in each 5-minute sub-segment is clarified, providing a standardized basis for command extraction, issuance and execution in the subsequent execution phase;

[0092] Based on the charge / discharge control command sequence, the local terminal immediately initiates the conflict detection process:

[0093] The core parameters in the charge and discharge control command sequence (such as target charge and discharge power, SOC control threshold, and execution time) are compared one by one with the current command parameters generated by the local autonomous control logic evaluation and analysis. If there are parameter inconsistencies that prevent energy storage nodes from executing simultaneously, it is determined to be a command conflict, and the conflict coordination mechanism is triggered. The conflict type of the corresponding energy storage node is obtained. The conflict type includes safety constraint conflict and operation stability conflict coordination. Then, the conflict control decision of the current conflict type of the corresponding energy storage node is retrieved and displayed immediately so that the operation and management personnel can control the corresponding energy storage node based on the conflict control decision to ensure timely resolution of the conflict.

[0094] For example, safety constraint conflicts include:

[0095] Charge / discharge power conflict: The charge / discharge power required by the remote command (charge / discharge control command sequence) is greater than the rated charge / discharge power of the converter PCS; SOC threshold conflict: The lower limit of SOC control required by the remote command is less than CNmin or the upper limit is greater than the safe range of CNmax; Related energy constraint conflict: The remote command indirectly causes the grid-connected power of the wind turbine to be greater than the rated power;

[0096] Smooth operation type conflict coordination includes:

[0097] Power change rate conflict: The rate of change of charging and discharging power required by the remote command is greater than the local load tolerance threshold; a sudden increase in local load (exceeding the corresponding rate threshold) during remote charging leads to a power supply gap, or a sudden increase in fan output (exceeding the corresponding rate threshold) during remote discharging leads to excess power.

[0098] If the parameters are consistent or compatible (e.g., the remote command is the SOC maintenance range, and the local command is the specific charging and discharging power within that range), then no coordination is required.

[0099] In summary, by collecting real-time data from each distributed energy storage node, executing local optimized control, and handling sudden operational events, the system ensures the operational safety and timely response of individual nodes. By focusing on wind turbine curtailment control, it prioritizes the consumption of renewable energy, reduces the cost of conventional energy replenishment, and helps improve energy utilization efficiency. At the same time, by controlling the rate of change of SOC and smoothing power transition, it avoids sudden changes in charging and discharging power, reduces equipment losses, and combines local autonomy with global collaboration. The command conflict coordination mechanism resolves control contradictions and adapts to complex and ever-changing operating scenarios.

[0100] The threshold is set for comparative analysis of results to determine whether they are good or bad. The value of the threshold is determined by a combination of large-scale model analysis of sample data and human experience. It can also be adjusted appropriately based on seasonal or common-sense influencing factors.

[0101] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for multi-source collaborative analysis and management of energy based on distributed energy storage systems, characterized in that, Includes the following steps: Step 1: Collect multi-dimensional data of distributed energy storage nodes in the target area in real time through local terminals and preprocess the data to obtain reliable input data; Step 2: Set constraints and combine reliable input data to perform energy storage node area division analysis to obtain an energy storage state map and realize local energy collaborative control; Step 3: Constructing the global energy data pool; Step 4: Remote collaborative energy scheduling: Generate a dynamic set of equipment safety constraints for each energy storage node, and construct the local feasible solution space and global initial feasible solution matrix for each node. Through a greedy algorithm, traverse and combine the results to finally obtain the charging and discharging power command for each energy storage node in this control cycle. Step 5: After verifying the charging and discharging power command, it is divided into three 5-minute sub-period power values ​​and sent to each energy storage node. At the same time, the local terminal starts the conflict detection process. The local terminal collects multi-dimensional data of distributed energy storage nodes in the target area in real time. The multi-dimensional data includes core parameters, local load operation data and related energy equipment data. The local energy coordination control includes: Set constraints, including exceeding limits for energy storage node parameters, sudden changes in local load status, and remote communication interruptions. Analyze reliable input data based on these constraints. If any constraint is met, the local autonomous control process is triggered. The energy storage nodes are initially divided and analyzed to obtain the energy storage SOC and the real-time output power of the associated energy devices of each energy storage node. The energy storage SOC and real-time output power are distinguished to output autonomous control energy storage nodes and non-autonomous control energy storage nodes. An energy storage status map of the target area is constructed based on autonomously controlled energy storage nodes and non-autonomous controlled energy storage nodes. In the energy storage status map, autonomously controlled energy storage nodes are marked in green, and non-autonomous controlled energy storage nodes are marked in red. The local energy coordination control also includes: Extract the energy storage SOC of autonomous control energy storage nodes and the real-time output power of associated energy devices from reliable input data in the energy storage state map; The preset output power range [GLmin, GLmax] and preset energy storage SOC range [CNmin, CNmax] are retrieved, and the local autonomous control logic is used to evaluate and analyze the energy storage SOC and real-time output power to obtain the working mode of each autonomous control energy storage node. The working modes include charging mode, discharging mode and standby mode. The construction process of the global energy data pool is as follows: Set the data acquisition cycle, receive real-time data uploaded by each local terminal based on the data acquisition cycle, and update the power prediction data of related energy equipment for the next 24 hours. Real-time data, power prediction data, and energy storage status maps are processed in a unified manner to build a global energy data pool with a unified format and consistent logic. The process for obtaining the charging / discharging power command is as follows: S1: Based on real-time data and predicted power data from the global energy data pool, extract a dynamic set of equipment safety constraints for each energy storage node; The dynamic device safety constraint set includes constraints on the energy storage itself and constraints on associated devices; S2: For each energy storage node, based on the dynamic equipment safety constraint set, generate a set of feasible intervals for charging and discharging power within the cost data acquisition period, remove invalid power values ​​that exceed the intervals, and form an independent local feasible solution space for each node; S3: Summarize the local feasible solution space of all energy storage nodes and construct the initial feasible solution matrix at the global level; S4: Obtain the priority target system for the target area, which includes primary targets, secondary targets, and tertiary targets; S5: Based on the priority target system, the global grid-connected power fluctuation value, wind turbine curtailment rate and node SOC change rate corresponding to the first-level target, second-level target and third-level target are obtained respectively. Based on the layer-by-layer screening of the global grid-connected power fluctuation value, wind turbine curtailment rate and node SOC change rate, the charging and discharging power command of each energy storage node in this control cycle is obtained. S5 further includes S51: Based on the initial feasible solution matrix, a preset greedy algorithm is used to traverse all feasible power combinations of nodes, calculate the global grid-connected power fluctuation value of each combination, and select feasible power combinations with global grid-connected power fluctuation values ​​≤ preset thresholds to form a "first-level feasible solution subset". If the subset is empty, the preset threshold is adjusted and the selection is repeated. S52: For all feasible power combinations in the first-level feasible solution subset, calculate the wind curtailment rate of the wind turbines, retain the feasible power combinations with a curtailment rate ≤ the preset curtailment rate, and form the "second-level feasible solution subset". If there are multiple combinations that meet the conditions, select the three combinations with the lowest curtailment rates to enter S53. S53: For the three combinations in the second-level feasible solution subset, calculate the SOC change rate of each node, select the combination with the smallest SOC change rate as the final optimization result, and generate the final power command for charging and discharging of each energy storage node in this control cycle.

2. The energy multi-source collaborative analysis and management method based on a distributed energy storage system according to claim 1, characterized in that, The conflict detection process is as follows: A second verification is performed on the final power command combination to confirm that all energy storage node power commands are within their local feasible solution space. Based on the final determined combination of charging and discharging power commands, the power values ​​are broken down into three 5-minute sub-periods according to the time dimension, forming a time-segmented charging and discharging control command sequence within a 15-minute cycle; The core parameters in the charge and discharge control command sequence are compared one by one with the current command parameters generated by the local autonomous control logic evaluation and analysis. If there is a parameter inconsistency that causes the energy storage nodes to be unable to execute simultaneously, it is determined to be a command conflict and the conflict coordination mechanism is triggered. The conflict type of the corresponding energy storage node is obtained. The conflict type includes safety constraint conflict and operation stability conflict coordination. Then, the conflict control decision of the current conflict type of the corresponding energy storage node is retrieved. If the parameters are consistent or compatible, no coordination is required.

Images