A power distribution network multi-scenario energy storage method, device and medium

Abstract

The application discloses a power distribution network multi-scene energy storage method, device and medium, and belongs to the technical field of power distribution network energy storage, and comprises the following steps: establishing a unified data interface pool, continuously accessing and preprocessing energy storage data streams, selecting available resources, calculating the dynamic weight of each type of data stream, generating preliminary scheduling instructions, and constructing a dynamic capability image; a multi-objective matching algorithm based on fuzzy comprehensive evaluation selects the optimal energy storage unit combination from the available resources, outputs the optimal energy storage unit set list, assigns a response mode label, forms the final optimized scheduling plan, extracts the response mode label, triggers the construction of a different power level energy storage rapid response framework, and associates multiple energy storage units under different levels of energy storage framework. The application significantly improves the response capability and operation resilience of the power distribution network to uncertain events, and is suitable for various complex application scenarios such as disaster emergency, planned scheduling, and collaborative regulation.

Description

A method, device and medium for energy storage in multiple scenarios of power distribution network Technical Field

[0001] This invention relates to the field of energy storage technology for power distribution networks, specifically to an energy storage method, equipment, and medium for multiple scenarios in power distribution networks. Background Technology

[0002] With the increasing penetration of renewable energy and the large-scale deployment of distributed energy storage in power distribution networks, the operation and maintenance management of energy storage systems has become a key technology direction in the field of smart grids. Traditional energy storage management is mostly focused on monitoring and scheduling in single application scenarios, such as peak shaving, frequency regulation, or islanded operation. In recent years, researchers have gradually recognized the importance of multi-scenario collaborative management and have begun to explore data-driven methods for optimizing the allocation of energy storage resources. Related technological developments are mainly reflected in multi-source data access and fusion, resource scheduling algorithm design, and distributed control architecture. For example, cloud-edge collaborative energy storage management platforms have achieved preliminary collection and processing of massive amounts of data; heuristic search algorithms and reinforcement learning methods have been applied to the selection and combination optimization of energy storage units; and hierarchical control strategies have also solved the problem of delayed response in centralized scheduling to some extent. In addition, some advanced systems have introduced state assessment and health management (SOH, SOC monitoring) technologies to improve the reliability and economy of energy storage unit operation. These achievements have laid a preliminary foundation for the effective utilization of energy storage resources in complex power distribution network environments.

[0003] However, existing technologies still have significant limitations in practical applications. First, the access to multi-source heterogeneous data lacks unified standards and real-time processing capabilities. Inconsistencies in data stream format, timing, and semantics make information fusion difficult, hindering accurate scheduling decisions. Second, existing resource selection and weight allocation models largely rely on static rules or offline training parameters, failing to adapt to the dynamic and sudden changes in distribution networks across various scenarios. This is particularly true in disaster emergencies and scenarios with rapid load fluctuations, where the matching degree between dispatch command generation and real-time situation is insufficient. Furthermore, most systems do not fully consider the collaborative mechanisms and response characteristics of different types of energy storage (such as fixed and mobile), lacking an integrated framework for multi-mode requirements such as millisecond-level rapid response, minute-level collaborative adjustment, and hourly planned scheduling. At the control level, existing strategies often neglect circulating current suppression and impedance matching issues during parallel operation, easily leading to system oscillations or capacity losses, affecting overall operational efficiency and safety. These shortcomings limit the maximization of the comprehensive effectiveness of energy storage in complex distribution network environments, necessitating the introduction of more adaptive, collaborative, and refined operation and maintenance management methods. Summary of the Invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by this invention is to address the low resource utilization efficiency, slow response speed, and poor adaptability to multiple scenarios caused by data heterogeneity, rigid models, and single control modes in existing power distribution network energy storage operation and maintenance management technologies.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: an energy storage method for multiple scenarios in a power distribution network, comprising,

[0007] A unified data interface pool is established to continuously access energy storage data streams. These data streams are cleaned, formatted, and timestamped to output a standardized data set. An adaptive weight allocation model is constructed to select available resources. The standardized data set is used as input to the adaptive weight allocation model to calculate the dynamic weight of each type of data stream and generate preliminary scheduling instructions. The preliminary scheduling instructions are received, and key fields of the instruction format are extracted to form a task requirement profile. Based on this task requirement profile, real-time status data of all energy storage units are accessed in parallel to construct a dynamic capability profile. A multi-objective matching algorithm based on fuzzy comprehensive evaluation is used to select the optimal combination of energy storage units from available resources, outputting the optimal energy storage unit set list (VAS). Response mode labels are assigned according to the composition of the VAS of the initial scheduling instructions to form the final optimized scheduling plan. The optimized scheduling plan is received, and the response mode labels are extracted and used as a first-level instruction to trigger the construction of rapid response frameworks for energy storage at different power levels. After deploying different control frameworks to the corresponding local energy storage controllers, a framework ready instruction is sent, associating multiple energy storage units under different energy storage framework levels.

[0008] As a preferred embodiment of the energy storage method for multiple scenarios in a power distribution network described in this invention, the energy storage data stream includes: establishing a unified data interface pool, continuously accessing and preprocessing the energy storage data stream, including meteorological and geological disaster information stream, real-time status and vulnerability information stream of the power grid, geographical and transportation information stream, energy storage unit status information stream, and cleaning, unifying the format, and aligning the timestamps of the energy storage data stream to output a standardized data set.

[0009] An adaptive weight allocation model based on spatiotemporal urgency is constructed to determine available resources. A standardized dataset is used as input to the adaptive weight allocation model, which runs in real time to calculate dynamic weights. ;

[0010] The standardized dataset is weighted and averaged based on the calculated dynamic weights to generate preliminary scheduling instructions. The instruction format is [target location, core requirement profile, resource preference, path constraint].

[0011] As a preferred embodiment of the energy storage method for multiple scenarios in a power distribution network described in this invention, the process of constructing a task requirement profile includes receiving a preliminary scheduling instruction and extracting key fields of the instruction format, including the target location. Power Requirement Energy demand Latest arrival time Create a task requirement profile;

[0012] Based on the task requirement profile, the system accesses the real-time status data of all energy storage units in parallel and constructs a dynamic capability profile for each energy storage unit, including static attributes, dynamic attributes, reachability attributes, and collaborative attributes.

[0013] The accessibility attribute refers to the ability of a mobile energy storage unit to integrate real-time traffic data and dynamically calculate the distance to the target location. Time required For stationary energy storage units, Consider it as zero.

[0014] As a preferred embodiment of the energy storage method for multiple scenarios in a distribution network as described in this invention, the output of the optimal energy storage unit set list (VAS) includes: selecting the optimal combination of energy storage units from available resources using a multi-objective matching algorithm based on fuzzy comprehensive evaluation; and calculating the comprehensive matching degree score of each energy storage unit j for the task of the current instruction. :

[0015] ;

[0016] in, It is a capacity matching function, used to determine whether the available energy of energy storage unit j can effectively contribute to total demand. ; It is a power matching function, used to determine whether the output capacity of energy storage unit j can effectively contribute to total demand. ; It is a spatiotemporal reachability function that comprehensively considers the geographical location of energy storage unit j and the estimated time to move to the target. The latest time required by the task The relationship between them; It is a health state function. , , , These are dynamic weighting coefficients, belonging to... ; The health status of the j-th energy storage unit, Let j be the current releaseable energy of the j-th energy storage unit. The maximum continuous discharge power that the j-th energy storage unit can currently provide;

[0017] according to Arrange the energy storage units in descending order and select them sequentially from high to low until the aggregate capacity and power of the energy storage units meet the task requirements of the initial scheduling command, and output the optimal energy storage unit set list VAS;

[0018] Based on the structure of the VAS in the initial scheduling instruction, assign a response mode label to the current schedule:

[0019] If the task requires millisecond or second-level power grid support, then the label is Mode A - Ultra-fast Response;

[0020] If the task requires collaborative operations on a minute-by-minute basis, then the label is Mode B - Collaborative Adjustment;

[0021] If the task requires continuous power supply for hours or more, then the label is Mode C - Planned Scheduling;

[0022] Integrate task requirement profiles, VAS, and corresponding matching response mode tag information to form the final optimized scheduling plan.

[0023] As a preferred embodiment of the energy storage method for multiple scenarios in a power distribution network described in this invention, the allocation response mode tag includes receiving an optimized scheduling plan, extracting the response mode tag from the plan, and using it as a first-level instruction to trigger the construction of a fast response framework for energy storage at different power levels. At the same time, the VAS member list, target location, and total output demand are extracted from the optimized scheduling plan as framework input parameters.

[0024] Based on the first-level instructions, the corresponding control framework is dynamically generated: Mode A - Rapid Response Generates Framework A' for operation and maintenance, decentralizes control, issues instructions to all energy storage units in the VAS list, elevates the local controller's authority to the highest level, and enables autonomous response;

[0025] Mode B – the collaborative regulation framework B' – is used for operation and maintenance. Based on the real-time status of each energy storage unit in the VAS inventory, the master unit is selected, and the election factor for the j-th energy storage unit is defined. for:

[0026] ;

[0027] in, and This represents the real-time state of charge and health status of the j-th energy storage unit. The current communication rate of the unit. For communication rate threshold, As the unit resource age factor, , , , The parameters were obtained through training and optimization using historical data to adjust them.

[0028] The main unit election condition is: when > and = n represents the number of energy storage units; the master unit is responsible for receiving instructions and, based on the real-time SOC reported by the units, calculating the optimal power allocation strategy and distributing the instructions. This is a preset threshold for eligibility to run for office.

[0029] Mode C - the planning and scheduling generation framework C' is used for operation and maintenance. The centralized scheduler is activated, and the scheduler dynamically accesses ultra-short-term load forecasting and renewable energy output forecasting data streams. Through multi-timescale rolling optimization, future power scheduling plan curves are generated, and the power adjustment amount at the k-th time scale at time t is defined. for:

[0030] ;

[0031] in, For ultra-short-term load forecasting power, For actual power measurement, , , These are the adjustment coefficients for the k-th time scale. This is the average of the election factors for all energy storage units;

[0032] After deploying different control frameworks onto corresponding hardware resources and storing energy in the local controller, a framework ready command is sent.

[0033] As a preferred embodiment of the energy storage method for multiple scenarios in a power distribution network described in this invention, the method of associating multiple energy storage units includes, after receiving a framework ready instruction, performing associated management and control of multiple energy storage units according to the different power level energy storage rapid response framework types in the instruction to perform energy storage scheduling services, and configuring optimizer parameters.

[0034] Based on optimizer parameter configuration, an association control model is established. This model is defined within a rolling time window, with the objective of minimizing the sum of outage risks for each load data point.

[0035] ;

[0036] The constraints are:

[0037] ;

[0038] in, Let be the power outage risk value of the i-th load data point at time t. Due to low battery power, For spatiotemporal decay memory function; The scheduling cost of energy storage unit j, and These represent spatial constraints and temporal constraints, respectively. This indicates summation over the time dimension. To optimize the time window length for scrolling, To represent the total number of all load data points in the area, Indicates the cost penalty factor; This indicates that the l-th spatial constraint must be satisfied at time t. Indicates the total number of spatial constraints; This indicates that the m-th time constraint must be satisfied at time t. Indicates the total number of time constraints; For the set of decision variables, The sequence of power dispatch instructions for all energy storage units. A sequence of target deployment locations for mobile energy storage units. This involves optimal path planning for a mobile energy storage unit to reach its target location, where t is a time variable and T is the start time of the current optimization cycle. This represents the total number of energy storage units participating in the scheduling.

[0039] As a preferred embodiment of the energy storage method for multiple scenarios in a power distribution network described in this invention, the method of associating multiple energy storage units further includes introducing a scheduling factor to expand the model and using an online rolling algorithm to solve the proposed optimization model for intelligent management and control of multiple energy storage systems.

[0040] The scheduling factor is set as the scheduling urgency factor. :

[0041] ;

[0042] in, The total power outage risk is calculated through a comprehensive assessment of power outage risk values. To adjust the parameters, t is the current time point. The total risk change rate represents the total power outage risk of the power grid. rate of change over time;

[0043] The optimizer uses an online rolling solution with an association control model. At the beginning of each optimization cycle, based on the latest system state, it uses the association control model to perform optimization within a finite time domain. It only executes the first step of the control instruction for the solution result. Based on the online rolling calculation, it dynamically adjusts the time length of the next optimization cycle and moves to the next time point.

[0044] The control commands obtained from the optimization solution are converted into a standard format to generate urgent dispatch commands. These urgent dispatch commands are then sent to the corresponding master units through a fast response framework for energy storage of different power levels. The execution status of the commands is monitored in real time, and the execution deviations are fed back to the optimizer as the state correction amount for the next rolling optimization.

[0045] As a preferred embodiment of the energy storage method for multiple scenarios in a power distribution network as described in this invention, the method of associating multiple energy storage units includes, after associating multiple energy storage units under different levels of energy storage framework, high-speed acquisition of electrical quantities of each energy storage unit and monitoring of circulating current components between parallel units.

[0046] Key features extracted from the collected electrical quantities include the active and reactive components of the output current of each energy storage unit, the magnitude and phase of the circulating current between energy storage units, the trend of output impedance characteristics, and the degree of power distribution deviation.

[0047] Based on the extracted key features, the system's operating status is evaluated, including the severity of circulating current, the degree of power distribution balance, and the system stability margin.

[0048] The parallel circulating current problem is addressed through online identification and adaptive correction. A small-signal injection method is used to identify the output impedance characteristics of each energy storage unit online, including impedance amplitude and phase. Based on the impedance identification results, a circulating current suppression strategy is generated.

[0049] For circulating current caused by output impedance mismatch, a virtual impedance compensation strategy is adopted.

[0050] For circulating current caused by unsuitable control parameters, an adaptive adjustment strategy for controller parameters is adopted.

[0051] For circulating currents caused by differences in line impedance, a line voltage drop compensation strategy is adopted.

[0052] Based on the generated suppression strategy, the corresponding key features are corrected.

[0053] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the energy storage method for multiple scenarios in a power distribution network.

[0054] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the energy storage method for multiple scenarios in a power distribution network.

[0055] The beneficial effects of this invention are as follows: By establishing a unified data interface pool and cleaning, aligning, and standardizing multi-source heterogeneous data, this invention provides a high-quality data foundation for subsequent decision-making, achieving reliability and consistency in information fusion. Furthermore, by constructing an adaptive weight allocation model, it dynamically assesses the spatiotemporal urgency of various data types and generates concrete preliminary scheduling instructions, realizing intelligent generation and precise focusing of scheduling strategies under different emergency scenarios. Subsequently, by constructing task requirement profiles and dynamic capability profiles of energy storage units in parallel, it provides clear bidirectional objectives and constraints for resource matching, realizing dynamic mapping between tasks and resources. Based on this, a multi-objective matching algorithm based on fuzzy comprehensive evaluation is used to select the optimal energy storage unit combination (VAS) and automatically assign response mode labels, achieving global optimization of resource selection and adaptive classification of application scenarios. Then, by parsing the response mode labels and dynamically constructing three hierarchical response control frameworks (rapid response, coordinated adjustment, and planned scheduling), it achieves precise deployment of control strategies and hardware resources and a balance between response speed and control stability. Finally, by associating multiple energy storage units and introducing an integrated online rolling optimization and adaptive circulation suppression strategy-based associated management and control model, it achieves collaborative operation and stable intelligent management and control of multiple energy storage systems. Attached Figure Description

[0056] To more clearly illustrate the technical solutions of 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.

[0057] Figure 1 is a general flowchart of an energy storage method for multiple scenarios in a power distribution network according to an embodiment of the present invention. Detailed Implementation

[0058] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0059] Example 1, referring to Figure 1, is an embodiment of the present invention. This embodiment provides an energy storage method for multiple scenarios in a power distribution network, including:

[0060] Based on its objectives, this invention specifically addresses the challenges of data fusion in multi-source heterogeneous energy storage and the lack of real-time and accuracy in decision-making; the poor adaptability of resource scheduling models and their disconnect from dynamic emergency scenarios; and the problem of a singular control framework that cannot meet the multi-timescale response requirements of the distribution network.

[0061] A unified data interface pool is established to continuously access energy storage data streams. The energy storage data streams are cleaned, formatted uniformly, and timestamp-aligned to output standardized data sets. In summary, this improves data availability and decision reliability, avoids scheduling errors caused by low data quality, and is especially suitable for scenarios with extremely high requirements for data real-time performance and accuracy, such as disaster emergency response.

[0062] An adaptive weight allocation model is constructed to select available resources. A standardized dataset is used as the input to the adaptive weight allocation model to calculate the dynamic weight of each type of data stream and generate preliminary scheduling instructions. In summary, dynamic optimization and situational adaptability of scheduling instructions are achieved, which improves the accuracy of response to emergencies and scheduling efficiency, and avoids problems such as resource misallocation or infeasible scheduling instructions.

[0063] The system receives initial scheduling instructions and extracts key fields from the instruction format to construct a task requirement profile. Based on this profile, it accesses real-time status data of all energy storage units in parallel to build a dynamic capability profile. In summary, it achieves bidirectional visualization and dynamic mapping between tasks and resources, providing high-precision input for subsequent multi-objective matching algorithms and significantly improving the rationality and executability of the matching results.

[0064] The multi-objective matching algorithm based on fuzzy comprehensive evaluation selects the optimal combination of energy storage units from available resources and outputs the optimal energy storage unit set list (VAS). It realizes intelligent and scenario-based adaptive resource selection, which not only improves resource utilization efficiency, but also ensures the system's response speed and stability in different scenarios.

[0065] Based on the composition of the VAS of the initial scheduling instruction, response mode labels are assigned to form the final optimized scheduling plan; the optimized scheduling plan is received, the response mode labels in the plan are extracted, and they are used as first-level instructions to trigger the construction of the fast response framework for energy storage of different power levels; in summary, the flexible reconfiguration of the control architecture and the significant improvement of response speed are achieved, especially in the ultra-fast response scenario, the millisecond-level response capability is achieved through local autonomy and parameter preset.

[0066] After deploying different control frameworks to the corresponding local energy storage controllers, framework ready commands are sent to associate multiple energy storage systems under different levels of energy storage frameworks. In summary, online intelligent collaboration and stable operation of multiple energy storage systems are realized. Through adaptive compensation and parameter adjustment, the risk of system oscillation and circulating current hazards are significantly reduced, and the system reliability is improved.

[0067] Example 2, an embodiment of the present invention, provides an energy storage method for multiple scenarios in a power distribution network based on the previous embodiment, including:

[0068] S100. Establish a unified data interface pool, continuously access energy storage data streams, clean the energy storage data streams, unify the format, align the timestamps, and output a standardized data set.

[0069] The purpose of this step is to overcome the limitations of traditional simple data stacking and transform massive amounts of chaotic data into clearly structured data that can directly drive subsequent decision-making instructions. Therefore, specifically, this involves establishing a unified data interface pool to continuously access and preprocess energy storage data streams, including:

[0070] Meteorological and geological disaster information streams include, but are not limited to, real-time access to data streams containing predicted paths, intensities, impact ranges, and estimated arrival times of disasters such as typhoons, rainstorms, and hail.

[0071] The real-time status and vulnerability information flow of the power grid includes, but is not limited to, real-time load rate, temperature, and voltage deviation of key nodes of distribution lines and transformers obtained synchronously from the distribution management system, as well as the predicted value of equipment failure probability obtained based on historical data analysis.

[0072] Geographic and traffic information flow includes, but is not limited to, obtaining information from geographic information systems and real-time traffic information platforms on the location of energy storage units, the location of potential risk points, as well as the road network topology, road grade, real-time traffic speed, and physical constraints such as bridge and tunnel height restrictions.

[0073] The energy storage unit's status information flow includes, but is not limited to, continuously collecting real-time status data of each energy storage unit through IoT protocols, including current state of charge (SOC), state of health (SOH), rated capacity and power, currently available maximum charge and discharge rate, and information on whether mobile energy storage is currently in a deployable state.

[0074] Furthermore, the collected energy storage data streams are cleaned, formatted uniformly, and timestamped to output a standardized data set.

[0075] S200. Construct an adaptive weight allocation model to select available resources. Use a standardized data set as input to the adaptive weight allocation model, calculate the dynamic weight of each type of data stream, and generate preliminary scheduling instructions.

[0076] Furthermore, an adaptive weight allocation model based on spatiotemporal urgency is constructed to determine which type of information is most critical to the current decision instruction. A standardized dataset is used as the input to the adaptive weight allocation model based on spatiotemporal urgency. The model runs in real time, calculating dynamic weights. This reflects the importance of various types of data in the current emergency situation; the higher the value of the dynamic weight, the greater the importance.

[0077] Specifically, at the current time t, for the i-th data factor, its weight coefficient at the current time t is defined. for:

[0078] ;

[0079] in: This represents the prediction confidence level of current data i at the current time t. This represents the recent rate of change of the current data i. This indicates the spatial distance between the event described by the current data i and the core protected target. , , An adjustable balance coefficient is used to adjust the overall importance of the three types of influencing factors, and is derived through training with historical data; , , To achieve the normalization function corresponding to each data factor, inputs with different dimensions are mapped to the interval [0,1] to ensure their comparability.

[0080] The standardized dataset is weighted and averaged based on the calculated dynamic weights to generate a preliminary dispatch instruction. This instruction is formatted as [target location, core demand profile, resource preference, path constraint]. The target location is the precise geographical location where power support is needed; the core demand profile is the required supporting power capacity, energy capacity, and latest arrival time requirement; the resource preference indicates, based on weight analysis, whether fixed or mobile energy storage should be prioritized; and the path constraint indicates the critical path constraints leading to the target location.

[0081] For ease of understanding, here is an example: "Emergency dispatch of at least 4MWh of available capacity and 2MW of continuous power energy storage resources that can arrive within 45 minutes within a 20km radius of the latitude and longitude [X, Y] (fault prediction point). The roads in this area are currently open, but the bridges have a weight limit of 20 tons."

[0082] S300 receives the initial scheduling instruction and extracts the key fields of the instruction format to form a task requirement profile. Based on the task requirement profile, it accesses the real-time status data of all energy storage units in parallel to build a dynamic capability profile.

[0083] Receive initial scheduling instructions and extract the key field of the instruction format: target location. Power Requirement Energy demand Latest arrival time The task requirements profile, along with potential soft constraints (such as resource type preferences), defines what the task needs to do. At this point, a dynamic capability profile needs to be constructed to describe what resources are available based on what needs to be done.

[0084] Based on the task requirement profile, real-time status data of all energy storage units are accessed in parallel, including both fixed and mobile units; a dynamic capability profile is constructed for each energy storage unit, including:

[0085] ①Static attributes include rated capacity, rated power, and geographical location;

[0086] ② Dynamic attributes include real-time state of charge (SOC), estimated state of health (SOH), and current maximum sustainable charge / discharge rate;

[0087] ③ Accessibility attribute: For mobile energy storage, the system integrates real-time traffic data and dynamically calculates its arrival time at the target location. Time required This value is continuously updated. For stationary energy storage, Consider it as 0;

[0088] ④ Collaboration attributes include recording the communication protocol of the energy storage unit and the controller model, and assessing the ability of the energy storage unit to participate in collaborative control.

[0089] S400: The multi-objective matching algorithm based on fuzzy comprehensive evaluation selects the optimal combination of energy storage units from available resources and outputs the optimal energy storage unit set list (VAS). According to the composition of the VAS of the initial scheduling instruction, response mode labels are assigned to form the final optimized scheduling plan.

[0090] Specifically, a multi-objective matching algorithm based on fuzzy comprehensive evaluation selects the optimal combination of energy storage units from available resources, and calculates the comprehensive matching degree score of each energy storage unit j with respect to the task of the current instruction. :

[0091] ;

[0092] in, It is a capacity matching function, used to determine whether the available energy of energy storage unit j can effectively contribute to total demand. ; It is a power matching function, used to determine whether the output capacity of energy storage unit j can effectively contribute to total demand. ; It is a spatiotemporal reachability function that comprehensively considers the geographical location of energy storage unit j and the estimated time to move to the target. The latest time required by the task The relationship between the spatiotemporal reachability function values ​​in much smaller At its highest point, with Exceed And it dropped sharply; It is a health state function. , , , For dynamic weighting coefficients, The health status of the j-th energy storage unit, The current releaseable energy of the j-th energy storage unit is calculated by multiplying the rated capacity by the current SOC; The maximum continuous discharge power that the j-th energy storage unit can currently provide;

[0093] Among them, the capacity matching degree function can be a saturation function, the power matching degree function can be a linear function with a penalty factor, the spatiotemporal reachability function can be an exponential decay function, and the health state function can be a sigmoid function.

[0094] according to The units are sorted in descending order and selected sequentially from high to low until their aggregate capacity and power meet the task requirements of the initial scheduling instruction. The optimal energy storage unit set list (VAS) is output. Based on the composition of the VAS of the initial scheduling instruction, a response mode label for different scenarios is automatically assigned to the current scheduling: if the task requirement is millisecond or second-level grid support, the label is Mode A - Ultra-fast response.

[0095] If the task requires collaborative operations on a minute-by-minute basis, then the label is Mode B - Collaborative Adjustment;

[0096] If the task requires continuous power supply for hours or more, then the label is Mode C - Planned Scheduling;

[0097] Integrate task requirement profiles, VAS, and corresponding matching response mode tag information to form the final optimized scheduling plan.

[0098] In summary, by receiving preliminary scheduling instructions and generating optimized scheduling plans that can directly drive a hierarchical response framework through multi-dimensional dynamic matching and virtual aggregation mechanisms, the problem of rigid resource utilization and disconnect between response and demand in traditional energy storage scheduling is solved.

[0099] S500 receives the optimized scheduling plan, extracts the response mode tag in the plan, and uses it as a first-level instruction to trigger the construction of a fast response framework for energy storage at different power levels.

[0100] The system receives optimized scheduling plans, extracts response mode tags from these plans, and uses them as primary commands to trigger the construction of rapid response frameworks for energy storage at different power levels. Simultaneously, it extracts detailed information from the optimized scheduling plans, such as the VAS member list, target locations, and total output requirements, as input parameters for the frameworks. Based on the primary commands, it dynamically generates the corresponding control framework.

[0101] Mode A - Rapid Response Generation Framework A' is used for operation and maintenance, control is decentralized, instructions are issued to all energy storage units in the VAS list, the permissions of the corresponding local controllers are elevated to the highest level, and authorization is granted to respond autonomously based on local measurement values;

[0102] Meanwhile, to avoid suboptimal oscillations or conflicts that may be caused by fully autonomous response, the framework A' is not simply left unattended. Based on the overall characteristics of the VAS list, a set of optimized local control parameters are pre-calculated and issued, such as the slope of the voltage-reactive power droop curve and the frequency-active power droop coefficient. At this time, the framework A' only plays the role of a monitor and no longer intervenes in the real-time control loop. When multiple energy storage units are running in the future, the framework A' does not actively intervene but only performs status monitoring.

[0103] Mode B – the collaborative regulation framework B' – is used for operation and maintenance. Based on the real-time status of each energy storage unit in the VAS inventory, the master unit is selected, and the election factor for the j-th energy storage unit is defined. for:

[0104] ;

[0105] in, and This represents the real-time state of charge and health status of the j-th energy storage unit. The current communication rate of the unit. For communication rate threshold, As the unit resource age factor, , , , The parameters were obtained through training and optimization using historical data to adjust them.

[0106] The main unit election condition is: when > and = n is the number of energy storage units; the master unit is responsible for receiving instructions and calculating the optimal power allocation strategy based on the real-time SOC reported by the units, and then distributing the instructions. The pre-set eligibility threshold is determined through historical data simulation and stability requirements, such as all currently online energy storage units. Any proportion of the average; For all campaign factors.

[0107] Mode C - Plan and Scheduling Generation Framework C' is used for operation and maintenance. The centralized scheduler is activated. The scheduler dynamically accesses the ultra-short-term load forecast and renewable energy output forecast data streams, and generates detailed power scheduling plan curves for the next few hours to 24 hours through rolling optimization at multiple time scales with a period of 15 minutes to 1 hour.

[0108] Specifically, multi-timescale rolling optimization defines the power adjustment amount at the k-th time scale at time t. for:

[0109] ;

[0110] in, For ultra-short-term load forecasting power, For actual power measurement, , , These are the adjustment coefficients for the k-th time scale. This is the average of the election factors for all energy storage units;

[0111] Obtain the final power plan based on the power adjustment amount. for:

[0112] ;

[0113] in, K represents the initial power, and K represents the total number of time scales divided. Within the framework C', the energy storage unit adjusts its power according to the final power plan.

[0114] After deploying different control frameworks onto corresponding hardware resources and storing energy in the local controller, a framework ready command is sent, which contains key information about the framework type (A' / B' / C').

[0115] S600: After deploying different control frameworks to the corresponding local energy storage controllers, it sends a framework ready command to associate multiple energy storage units under different levels of energy storage frameworks.

[0116] Upon receiving the framework ready instruction, based on the different power level energy storage rapid response framework types specified in the instruction, multiple energy storage units are correlated and managed for energy storage scheduling services. The first step is to configure the optimizer parameters:

[0117] Under framework A', the optimizer enters a dormant monitoring state and does not actively intervene; under framework B', the optimizer sets the master unit as the direct optimization object and adjusts the optimization cycle to the minute level; under framework C', the optimizer sets itself as the centralized planner and adjusts the optimization cycle to the hour level.

[0118] Based on optimizer parameter configuration, the correlation control specifically involves establishing a correlation control model. This model is defined within a rolling time window, with the objective of minimizing the sum of outage risks for each load data point.

[0119] ;

[0120] The constraints are:

[0121] ;

[0122] in, Let be the power outage risk value of the i-th load data point at time t, and the power shortage value. Related; For spatiotemporal decay memory function, This is used to enhance the algorithm's ability to remember recent decision biases and to introduce periodic oscillations to avoid getting trapped in local optima; The scheduling cost of energy storage unit j, and These represent spatial constraints (such as road capacity and deployment site conditions) and temporal constraints (such as emergency response time limits and energy storage charging and discharging duration), respectively. This represents summing over the time dimension, where t=T, meaning summing from the current moment to future moments. Finish; To optimize the time window length for scrolling, To represent the total number of all load data points in the area, Indicates the cost penalty factor; This indicates that the l-th spatial constraint must be satisfied at time t. Indicates the total number of spatial constraints; This indicates that the m-th time constraint must be satisfied at time t. Indicates the total number of time constraints; For the set of decision variables, The sequence of power dispatch instructions for all energy storage units, i.e. when and how much power to emit; A sequence of target deployment locations for mobile energy storage units. This is the optimal path planning for a mobile energy storage unit to reach a target location, where i is the variable index; t is the time variable, representing a specific moment within the optimization time window; and T is the start time of the current optimization cycle. This represents the total number of energy storage units participating in the scheduling.

[0123] Furthermore, to address the issue that time constraints for urgent energy storage scheduling services may not be met in practice, a scheduling factor is introduced to expand the model, and an online rolling algorithm is used to solve the proposed optimization model, thereby achieving intelligent management and control of multiple energy storage systems.

[0124] Specifically, in this invention, the scheduling factor is set as the scheduling urgency factor. :

[0125] ;

[0126] in, The total power outage risk is calculated through a comprehensive assessment of power outage risk values. To adjust the parameters, t is the current time point. The total risk change rate represents the total power outage risk of the power grid. Rate of change over time; rolling optimization cycle , This is the original rolling cycle;

[0127] The optimizer uses an online rolling solution model. At the beginning of each optimization cycle, based on the latest system state, it uses the online rolling solution model to perform optimization within a finite time domain. It only executes the first step of the control instruction for the solution result. Based on the online rolling calculation, it dynamically adjusts the time length of the next optimization cycle, moves to the next time point, and repeats the above process.

[0128] The control commands obtained from the optimization solution are converted into a standard format to generate urgent dispatch commands. These urgent dispatch commands are then sent to the corresponding master units through a fast response framework for energy storage at different power levels. The execution status of the commands is monitored in real time, including the actual output, movement status, and route compliance of the energy storage units. The execution deviations are fed back to the optimizer as the state correction amount for the next rolling optimization.

[0129] It should be noted that after connecting multiple energy storage units, the output current, output voltage, power, frequency and other electrical quantities of each energy storage unit are collected at high speed, with a focus on monitoring the circulating current component between parallel units.

[0130] Key features extracted from the collected electrical quantities include the active and reactive components of the output current of each energy storage unit, the magnitude and phase of the circulating current between energy storage units, the trend of output impedance characteristics, and the degree of power distribution deviation.

[0131] Based on the extracted key features, the system's operating status is evaluated, including the severity of circulating current, the degree of power distribution balance, and the system stability margin.

[0132] In an embodiment of the present invention, the assessment of the severity of the circulating current is to calculate the ratio of the total harmonic distortion rate of the circulating current component between each parallel branch to the effective value of the fundamental wave, and compare the current ratio with a threshold obtained by adding twice the standard deviation of the statistical mean of historical safe operation data. When the ratio is greater than the threshold, the current ratio is regarded as the severity index of the circulating current, which quantitatively characterizes the level of harm of the circulating current to the current operating state of the system.

[0133] The assessment of power distribution balance is to calculate the deviation between the actual output power of each energy storage unit and the expected power distribution determined by factors such as its capacity and SOC status. This deviation is regarded as the power balance deviation index, which reflects the fairness and rationality of the current power distribution. The larger the value, the more unbalanced the distribution.

[0134] The assessment of the system stability margin involves substituting the output impedance characteristics (amplitude and phase) of each unit obtained online into a stability analyzer based on the impedance ratio criterion. By analyzing the phase margin and amplitude margin of the impedance overlap region, rather than the simple Nyquist criterion, the small-signal stability of the system at a specific operating point is predicted, generating a stability margin parameter that indicates how much margin the system has left before oscillation instability. The larger the stability margin parameter, the smaller the margin the system has before oscillation instability.

[0135] The parallel circulating current problem is addressed through online identification and adaptive correction. A small-signal injection method is used to identify the output impedance characteristics of each energy storage unit online, including impedance amplitude and phase. Based on the impedance identification results, a circulating current suppression strategy is generated.

[0136] For circulating current caused by output impedance mismatch, a virtual impedance compensation strategy is adopted.

[0137] For circulating current caused by unsuitable control parameters, an adaptive adjustment strategy for controller parameters is adopted.

[0138] For circulating currents caused by differences in line impedance, a line voltage drop compensation strategy is adopted.

[0139] Based on the generated suppression strategy, the corresponding key features are corrected. Specifically, in the embodiments of the present invention, the correction for the impedance characteristic change trend is as follows: if the evaluation finds that the circulating current is mainly caused by impedance mismatch and the stability margin is insufficient, a virtual impedance compensation strategy is activated. At the command output of the controller, a compensation amount with the opposite sign and proportional magnitude to the measured circulating current component is superimposed. This compensation amount is equivalent to introducing a virtual impedance into the control loop of the power electronic converter. Its amplitude and phase are dynamically adjusted according to the circulating current severity index and the stability margin.

[0140] For the correction of circulating current magnitude and phase, if the evaluation shows that the power distribution deviation is large and the dynamic response oscillates, the adaptive adjustment strategy of the controller parameters is initiated. Based on the parameters of a PI controller, the proportional and integral coefficients of the controller are finely adjusted online according to the power balance deviation index and the performance indicators such as the overshoot and settling time of the system dynamic response.

[0141] To correct for the degree of power distribution deviation, if the evaluation results show that there is a steady-state power deviation that is related to the line parameters, the line voltage drop compensation strategy is activated. By measuring and calculating the voltage drop across the known line impedance, a compensation voltage value is fed forward in the voltage reference command to offset the adverse effects of the line impedance.

[0142] Example 3 is an embodiment of the present invention. This embodiment provides an electronic device applicable to an energy storage method in multiple scenarios of a power distribution network, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the energy storage method in multiple scenarios of a power distribution network as proposed in the above embodiments.

[0143] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, it implements an energy storage method for multiple scenarios in a power distribution network as proposed in the above embodiments.

[0144] The storage medium proposed in this embodiment and the energy storage method for multiple scenarios in a power distribution network proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0145] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0146] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An energy storage method for multiple scenarios in a power distribution network, characterized in that: This includes: establishing a unified data interface pool to continuously access energy storage data streams; cleaning, formatting, and aligning the data streams with timestamps to output a standardized data set; constructing an adaptive weight allocation model to select available resources, using the standardized data set as input to the adaptive weight allocation model, calculating the dynamic weight of each type of data stream, and generating preliminary scheduling instructions; receiving the preliminary scheduling instructions and extracting key fields of the instruction format to construct a task requirement profile; using the task requirement profile to access the real-time status data of all energy storage units in parallel to construct a dynamic capability profile; and using a multi-objective matching algorithm based on fuzzy comprehensive evaluation to select the optimal combination of energy storage units from available resources and output the optimal energy storage unit set list VA. S; Based on the composition of the VAS of the initial scheduling instruction, allocate response mode labels to form the final optimized scheduling plan; receive the optimized scheduling plan, extract the response mode labels from the plan, and use the response mode labels as a first-level instruction to trigger the construction of fast response frameworks for energy storage at different power levels; after deploying different control frameworks to the corresponding local energy storage controllers, send a framework ready instruction to associate multiple energy storage units under different energy storage frameworks; the output of the optimal energy storage unit set list VAS includes: using a multi-objective matching algorithm based on fuzzy comprehensive evaluation to select the optimal combination of energy storage units from available resources, and calculating the comprehensive matching degree score for each energy storage unit j relative to the task of the current instruction. ： in, It is a capacity matching function, used to determine whether the available energy of energy storage unit j can effectively contribute to total demand. ； It is a power matching function, used to determine whether the output capacity of energy storage unit j can effectively contribute to total demand. ； It is a spatiotemporal reachability function that comprehensively considers the geographical location of energy storage unit j and the estimated time to move to the target. The latest time required by the task The relationship between them; It is a health state function. 、 、 、 These are dynamic weighting coefficients, belonging to... ， The health status of the j-th energy storage unit, Let j be the current releaseable energy of the j-th energy storage unit. Let j be the maximum continuous discharge power that the j-th energy storage unit can currently provide; according to The energy storage units are sorted in descending order, and selected sequentially from high to low until the aggregate capacity and power of the energy storage units meet the task requirements of the initial scheduling command. The optimal energy storage unit set list (VAS) is output. Based on the composition of the VAS of the initial scheduling command, a response mode label is assigned to the current scheduling: if the task requirement is millisecond or second-level grid support, the label is Mode A - Rapid Response; if the task requirement is minute-level coordinated operation, the label is Mode B - Coordinated Regulation; if the task requirement is hour-level or longer continuous power supply, the label is Mode C - Planned Scheduling. The task requirement profile, VAS, and corresponding matching response mode label information are integrated to form the final optimized scheduling plan.

2. The energy storage method for multiple scenarios in a power distribution network as described in claim 1, characterized in that: The energy storage data stream includes: establishing a unified data interface pool; continuously accessing and preprocessing energy storage data streams, including meteorological and geological disaster information streams, real-time grid status and vulnerability information streams, geographical and transportation information streams, and energy storage unit status information streams; cleaning, formatting, and timestamp-aligning the energy storage data streams to output a standardized data set; and constructing an adaptive weight allocation model based on spatiotemporal urgency to determine available resources, using the standardized data set as input to the adaptive weight allocation model, which runs in real time to calculate dynamic weights. ； Define the weight coefficient of the i-th data factor at the current time t. for: in, This represents the prediction confidence level of current data i at the current time t. This represents the recent rate of change of the current data i. This indicates the spatial distance between the event described by the current data i and the core protected target. 、 、 For adjustable balance coefficients, 、 、 For each data factor, a normalization function is used; the standardized data set is weighted and averaged according to the calculated dynamic weights to generate preliminary scheduling instructions. The instruction format is [target location, core demand profile, resource preference, path constraint].

3. The energy storage method for multiple scenarios in a distribution network as described in claim 2, characterized in that: The task requirement profile includes receiving preliminary scheduling instructions and extracting key fields from the instruction format, including the target location. Power Requirement Energy demand Latest arrival time Create a task requirement profile; Based on the task requirement profile, the system accesses the real-time status data of all energy storage units in parallel, constructing a dynamic capability profile for each unit, including static attributes, dynamic attributes, accessibility attributes, and collaborative attributes. The accessibility attribute, for mobile energy storage units, integrates real-time traffic data to dynamically calculate the distance to the target location. Time required For stationary energy storage units, Consider it as zero.

4. The energy storage method for multiple scenarios in a power distribution network as described in claim 3, characterized in that: The allocation response mode label includes receiving the optimized scheduling plan, extracting the response mode label from the plan, and using it as a first-level instruction to trigger the construction of a rapid response framework for energy storage at different power levels. Simultaneously, it extracts the VAS member list, target location, and total output demand from the optimized scheduling plan as framework input parameters. Based on the first-level instruction, it dynamically generates the corresponding control framework: Mode A – Rapid Response Generation Framework A' performs operation and maintenance, decentralizing control and issuing instructions to all energy storage units in the VAS list, elevating the local controller's authority to the highest level for autonomous response; Mode B – Collaborative Regulation Generation Framework B' performs operation and maintenance, selecting the master unit based on the real-time status of each energy storage unit in the VAS list, and defining the election factor for the j-th energy storage unit. for: in, and This represents the real-time state of charge and health status of the j-th energy storage unit. The current communication rate of the unit. For communication rate threshold, As the unit resource age factor, 、 、 、 The parameters were optimized through training on historical data to adjust them; the principal unit election condition was: when > and = n represents the number of energy storage units; the master unit is responsible for receiving commands and, based on the real-time SOC reported by the slave units, calculating the optimal power allocation strategy and distributing the commands. The preset eligibility threshold is used; Mode C - the planning and scheduling generation framework C' is used for operation and maintenance, the centralized scheduler is activated, the scheduler dynamically accesses the ultra-short-term load forecast and renewable energy output forecast data streams, and generates future power scheduling plan curves through multi-timescale rolling optimization, defining the power adjustment amount at the k-th time scale at time t. for: in, For ultra-short-term load forecasting power, For actual power measurement, 、 、 These are the adjustment coefficients for the k-th time scale. The average value of the election factor for all energy storage units; after deploying different control frameworks to the corresponding hardware resources, the local energy storage controller sends a framework ready command.

5. The energy storage method for multiple scenarios in a distribution network as described in claim 4, characterized in that: The association of multiple energy storage units includes, upon receiving a framework ready command, performing associated management and control of multiple energy storage units according to the different power levels of the energy storage fast response framework type in the command to provide energy storage scheduling services, and configuring optimizer parameters; based on the optimizer parameter configuration, establishing an associated management and control model, defining the associated management and control model within a rolling time window with the objective of minimizing the sum of outage risks for each load data point. The constraints are: in, Let be the power outage risk value of the i-th load data point at time t. Due to low battery power, For spatiotemporal decay memory function; The scheduling cost of energy storage unit j, and These represent spatial constraints and temporal constraints, respectively. This indicates summation over the time dimension. To optimize the time window length for scrolling, To represent the total number of all load data points in the area, Indicates the cost penalty factor; This indicates that the l-th spatial constraint must be satisfied at time t. Indicates the total number of spatial constraints; This indicates that the m-th time constraint must be satisfied at time t. Indicates the total number of time constraints; For the set of decision variables, The sequence of power dispatch instructions for all energy storage units. A sequence of target deployment locations for mobile energy storage units. This is the optimal path planning for a mobile energy storage unit to reach a target location, where i is the variable index, t is the time variable, and T is the start time of the current optimization cycle. This represents the total number of energy storage units participating in the scheduling.

6. The energy storage method for multiple scenarios in a power distribution network as described in claim 5, characterized in that: The association of multiple energy storage units also includes introducing a scheduling factor to expand the model and using an online rolling algorithm to solve the proposed optimization model for intelligent management and control of multiple energy storage systems; wherein, the scheduling factor is set as a scheduling urgency factor. ： in, The total power outage risk is calculated through a comprehensive assessment of power outage risk values. To adjust the parameters, t is the current time point. The total risk change rate represents the total power outage risk of the power grid. The rate of change over time; the optimizer uses an associated control model for online rolling solution. At the beginning of each optimization cycle, based on the latest system state, it uses the associated control model to perform optimization within a finite time domain, executing only the first step of the control command of the solution result. Based on the online rolling calculation, it dynamically adjusts the time length of the next optimization cycle and moves to the next time point; the control commands obtained from the optimization solution are converted into a standard format to generate urgent scheduling commands. Through the fast response framework of energy storage of different power levels, the urgent scheduling commands are issued to the corresponding master units, and the execution status of the commands is monitored in real time. The execution deviation is fed back to the optimizer as the state correction amount for the next rolling optimization.

7. The energy storage method for multiple scenarios in a distribution network as described in claim 6, characterized in that: The association of multiple energy storage units includes, after associating multiple energy storage units under different levels of energy storage framework, collecting the electrical quantities of each energy storage unit and monitoring the circulating current components between parallel units; Key features are extracted from the collected electrical quantities, including the active and reactive components of the output current of each energy storage unit, the magnitude and phase of the circulating current between energy storage units, the trend of output impedance characteristics, and the degree of power distribution deviation. Based on the extracted key features, the operating status of the system is evaluated, including the severity of circulating current, the balance of power distribution, and the system stability margin. Parallel circulating current problems are solved through online identification and adaptive correction. The small signal injection method is used to identify the output impedance characteristics of each energy storage unit online, including impedance amplitude and phase. Based on the impedance identification results, a circulating current suppression strategy is generated: for circulating current caused by output impedance mismatch, a virtual impedance compensation strategy is adopted. For circulating current caused by unsuitable control parameters, an adaptive adjustment strategy for controller parameters is adopted. For circulating currents caused by line impedance differences, a line voltage drop compensation strategy is adopted; based on the generated suppression strategy, the corresponding key characteristics are corrected.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the energy storage method for multiple scenarios in a power distribution network as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the energy storage method for multiple scenarios in a power distribution network as described in any one of claims 1 to 6.

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