Energy storage power station comprehensive management system and method of power system

Abstract

The application discloses a kind of energy storage power station comprehensive management system and method of power system, it is related to power system automation and energy management technical field.The system can realize the whole-process closed-loop management from data synchronous acquisition, real-time identification of crossing condition, to settlement deviation accurate quantification, to active collaborative control by integrating deployment data synchronous acquisition module, tide flow crossing condition identification module, settlement electric quantity deviation modeling module and condition-deviation collaborative control module.This not only solves the problem of traditional method to tide flow crossing phenomenon response lag, analysis fragmentation, but also closely combines the traceability of settlement deviation and the dynamic identification of power grid condition, provides real-time, accurate data support and decision basis for energy storage power station (and new energy station) participating in power grid operation and market settlement, effectively improves the fairness and accuracy of electric quantity settlement under complex power grid environment.

Description

Technical Field

[0001] This application relates to the field of power system automation and energy management technology, specifically to a comprehensive management system and method for energy storage power stations in a power system. Background Technology

[0002] In the context of new power systems, especially in complex scenarios involving multi-voltage level grid connection and multi-power plant "combined grid connection," power flow ride-through phenomena (such as reverse ride-through, bypass ride-through, and circulating current ride-through) are becoming increasingly frequent. Power flow ride-through not only alters traditional power transmission paths and complicates electricity metering at the settlement point, but also leads to untraceable deviations in settled electricity amounts, seriously affecting the fairness of electricity transactions between the two parties and the economic benefits of grid companies.

[0003] Currently, when faced with settlement deviations under crossing conditions, the formulation of control strategies mainly relies on the personal experience of dispatchers or metering personnel. This makes the decision-making process slow and unable to achieve dynamic, accurate, and collaborative control based on real-time operating conditions. Summary of the Invention

[0004] In a first aspect, one embodiment of this application provides a comprehensive management system for energy storage power stations in a power system. The system includes: a data synchronization acquisition module configured to deploy multiple energy metering devices at new energy grid connection points, key lines, and settlement points in the power system to synchronously collect grid operation data from each node. The grid operation data includes at least the voltage, current, active power, reactive power, and metering point energy data of each node. The collected data is transmitted after noise reduction and filtering preprocessing; a power flow crossing condition identification module connected to the data synchronization acquisition module, configured to receive the preprocessed grid operation data, call pre-stored grid topology data, and, based on the grid operation data and grid topology, identify in real time whether a power flow crossing condition has occurred in the grid, and determine the type and impact path of the power flow crossing condition; and a settlement energy deviation modeling module connected to the data synchronization acquisition module and the power flow crossing condition identification module. The module is configured to receive power data and comprehensive error data of metering devices from each metering point transmitted by the data synchronization acquisition module, and to receive the power flow crossing condition type and impact path transmitted by the power flow crossing condition identification module. Based on the power data, comprehensive error of metering devices, and system line loss data of each metering point, and in accordance with the principle of power conservation, it establishes a quantitative relationship model between the power flow crossing condition and the settlement power deviation to calculate the settlement power deviation value within the current or future preset time period. The condition-deviation collaborative management module is connected to the power flow crossing condition identification module and the settlement power deviation modeling module, respectively. It is configured to receive the condition identification results transmitted by the power flow crossing condition identification module, call the deviation calculation results and quantitative relationship model transmitted by the settlement power deviation modeling module, and generate targeted deviation prevention and control strategies and condition early warning signals based on the power flow crossing condition type and impact path and the settlement power deviation value for distribution.

[0005] In conjunction with the first aspect, in some implementations of the first aspect, the operating condition-deviation coordinated management module includes: a spatiotemporal coupling risk assessment unit, configured to: receive the operating condition identification results from the power flow crossing operating condition identification module and the deviation calculation results from the settlement power deviation modeling module; calculate and generate a spatiotemporal coupling risk index based on the type and impact path of the power flow crossing operating condition, as well as the settlement power deviation value and its changing trend; a multi-level strategy decision-making unit, connected to the spatiotemporal coupling risk assessment unit, configured to: pre-store a multi-level strategy knowledge base; call the multi-level strategy knowledge base according to the spatiotemporal coupling risk index, match and generate a phased differentiated deviation prevention and control strategy corresponding to the risk level; and an executable early warning generation unit, connected to the multi-level strategy decision-making unit, configured to: receive the deviation prevention and control strategy generated by the multi-level strategy decision-making unit, and, in conjunction with the real-time operating status of the power grid, convert the prevention and control strategy into an operating condition early warning signal to trigger corresponding deviation prevention and control and operating condition management actions.

[0006] In conjunction with the first aspect, in some implementations of the first aspect, the spatiotemporal coupling risk assessment unit is further configured as follows: determining the crossing risk level based on the type of power flow crossing condition and the crossing power level; determining the deviation risk level based on the settlement power deviation value and its changing trend, combined with a preset settlement threshold; superimposing the impact path of the crossing on the power grid topology map, and determining the network topology risk level based on the assessment of the power flow margin of the key transmission sections associated with the path and the voltage stability margin of the key nodes; and merging the crossing risk level, the deviation risk level, and the network topology risk level to generate a spatiotemporal coupling risk index.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, the knowledge base includes: a real-time response layer strategy for generating immediate control instructions for high-risk index scenarios; a short-term regulation layer strategy for generating operational mode adjustment suggestions for medium-risk index scenarios; and a long-term optimization and planning layer strategy for generating planning suggestions for measurement, structure, or protection for long-term, high-incidence crossing conditions.

[0008] In conjunction with the first aspect, in some implementations of the first aspect, the deviation prevention and control strategy includes at least one of the following: adjusting the metering scheme, optimizing the gate configuration, issuing deviation warnings, and suggesting adjustments to the operating mode. The operating condition warning signal includes at least the warning level, the scope of impact, the effective time of the warning trigger signal, the collaborative control instruction, the execution priority, and the quantitative expected effect evaluation information.

[0009] In conjunction with the first aspect, in some implementations of the first aspect, the current crossing condition identification module identifies at least one of the following types of current crossing conditions: reverse crossing, skipping level crossing, and circulation crossing.

[0010] In conjunction with the first aspect, in some implementations of the first aspect, in the quantitative relationship model established by the settlement electricity deviation modeling module, the comprehensive error of the metering device is the sum of the electricity meter error, the transformer synthesis error, the secondary circuit synthesis error, and the errors caused by other factors in the power system, and the system line loss is calculated by the root mean square current method.

[0011] In conjunction with the first aspect, in some implementations of the first aspect, the system further includes: a data storage and verification module, which is bidirectionally connected to the data synchronization acquisition module, the power flow crossing condition identification module, the settlement power deviation modeling module, and the condition-deviation collaborative control module. It is configured to store the input data and / or output data of all modules, power grid topology data, metering device calibration data, quantitative relationship model parameters, condition identification records, and deviation control records, and to perform real-time verification on the stored and flowing data.

[0012] In conjunction with the first aspect, in some implementations of the first aspect, the data storage and verification module includes: a database unit configured to store power grid topology data, historical operating condition database, metering device ledger and calibration database, quantitative relationship model parameter library, and historical operation and identification record database; a data interaction interface unit configured to interact with the power system metering center and energy storage power station operation and maintenance platform through standard protocols; and a data verification unit configured to verify the collected raw data, intermediate and final results of operating condition identification, and deviation calculation results.

[0013] Secondly, one embodiment of this application provides a comprehensive management method for energy storage power stations in a power system, applied to the system mentioned in the first aspect. The method includes: deploying multiple power metering devices at new energy grid connection points, key lines, and settlement points in the power system to synchronously collect grid operation data from each node. The grid operation data includes at least the voltage, current, active power, reactive power, and metering point power data of each node, and performing noise reduction and filtering preprocessing on the collected data; based on the preprocessed grid operation data and pre-stored grid topology data, identifying in real time whether a power flow crossing condition has occurred in the grid, and determining the type and impact path of the power flow crossing condition; based on the power data of each metering point, the comprehensive error of the metering device, and the system line loss data, and according to the power flow crossing condition type and impact path, and based on the principle of power conservation, establishing a quantitative relationship model between the power flow crossing condition and the settlement power deviation, and calculating the settlement power deviation value within the current or future preset time period; and generating targeted deviation prevention and control strategies and condition warning signals based on the identified power flow crossing condition type and impact path, and the calculated settlement power deviation value, and issuing them.

[0014] The integrated management system for energy storage power stations mentioned in this application, through the integrated deployment of a data synchronization acquisition module, a power flow crossing condition identification module, a settlement power deviation modeling module, and a condition-deviation collaborative control module, enables closed-loop management of the entire process, from data synchronization acquisition and real-time identification of crossing conditions to accurate quantification of settlement deviations and proactive collaborative control. This not only solves the problems of delayed response and fragmented analysis of power flow crossing phenomena in traditional methods, but also closely integrates the tracing of settlement deviations with the dynamic identification of grid conditions. It provides real-time and accurate data support and decision-making basis for energy storage power stations (and new energy power plants) to participate in grid operation and market settlement, effectively improving the fairness and accuracy of power settlement in complex grid environments. Attached Figure Description

[0015] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 The diagram shown is a structural schematic of an integrated management system for an energy storage power station provided in an exemplary embodiment of this application.

[0017] Figure 2 The diagram shown is a structural schematic of an integrated management system for an energy storage power station provided in another exemplary embodiment of this application.

[0018] Figure 3 The diagram shown is a structural schematic of an integrated management system for an energy storage power station provided in another exemplary embodiment of this application.

[0019] Figure 4 The diagram shown is a flowchart illustrating an exemplary embodiment of this application of a comprehensive management method for an energy storage power station in a power system. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] Figure 1 The diagram shown is a structural schematic of an integrated management system for an energy storage power station provided in an exemplary embodiment of this application. Figure 1 As shown, a comprehensive management system for an energy storage power station in a power system includes: a data synchronization and acquisition module, a power flow crossing condition identification module, a settlement power deviation modeling module, and a condition-deviation collaborative control module. The power flow crossing condition identification module is connected to the data synchronization and acquisition module, the settlement power deviation modeling module is connected to both the data synchronization and acquisition module and the power flow crossing condition identification module, and the condition-deviation collaborative control module is connected to both the power flow crossing condition identification module and the settlement power deviation modeling module.

[0022] The data synchronization acquisition module is configured to deploy multiple power metering devices at new energy grid connection points, key lines and settlement points in the power system to synchronously collect grid operation data at each node. The collected data is transmitted after noise reduction and filtering preprocessing.

[0023] For example, an electric power system refers to an overall network for the production, transmission, distribution, and consumption of electrical energy, consisting of power generation, transmission, transformation, distribution, and consumption.

[0024] For example, the new energy grid connection point refers to the physical location of the common connection point for new energy power generation systems such as wind power generation and photovoltaic power generation to connect to the power grid.

[0025] It should be understood that in the electricity market, the settlement point, which is used for trade settlement and to determine the electricity charges for both the buyer and seller, is usually the boundary point of property rights and responsibilities.

[0026] For example, power grid operation data is a physical quantity used to reflect the real-time status of the power grid. Power grid operation data includes at least the voltage, current, active power, reactive power, and metering point electricity data of each node.

[0027] The power flow crossing condition identification module is configured to receive pre-processed power grid operation data, call pre-stored power grid topology data, and based on the power grid operation data and power grid topology, identify in real time whether the power flow crossing condition has occurred, and determine the type and impact path of the power flow crossing condition.

[0028] For example, the power grid topology is a model of the connection relationships between various electrical components (such as generators, lines, transformers, buses, loads, etc.) in the power grid, and is usually represented by a network diagram composed of nodes and branches.

[0029] The power flow crossing condition refers to a situation where, due to changes in grid structure, load distribution, or power output, the actual transmission path of electrical energy deviates from the preset or conventional path, crossing different zones, voltage levels, or settlement thresholds. The power flow crossing condition identification module identifies at least one of the following types: reverse crossing, bypass crossing, and circulating current crossing. The influencing path refers to the main electrical connection path through which actual power flows under the power flow crossing condition, and whose power value exceeds a preset threshold.

[0030] The power flow crossing condition identification module is further configured as follows: based on the power grid topology and node power measurements, it monitors the changes in electrical quantities of each grid-connected node in real time; it analyzes the changes in electrical quantities to identify the random fluctuation patterns of new energy power generation, which include at least the random change law of power direction and the random characteristics of power magnitude fluctuation; based on the identified random fluctuation patterns, it analyzes the current operating conditions of the power grid in combination with decision tree or support vector machine intelligent identification algorithms to determine whether a power flow crossing condition has occurred and to identify the crossing type of the power flow crossing condition; based on the power grid topology and real-time power data, it reconstructs and analyzes the actual power flow direction and power transmission path under the power flow crossing condition to determine the impact path of the power flow crossing condition.

[0031] In one specific embodiment, the system continuously and synchronously collects real-time data such as voltage, current, active power P, and reactive power Q at each node through synchronous power metering devices deployed at the main outlet M1 of the photovoltaic power station, the inlet M2 of line A, and the inlet M3 of line B. The system's backend analysis module performs statistical analysis on the real-time power sequence, identifying the rapid drop and rise in the magnitude of renewable energy generation power due to factors such as cloud cover, as well as the random reversal of power direction on specific lines (such as M2). Subsequently, the system uses intelligent recognition algorithms such as support vector machines to intelligently analyze the current power grid operating conditions. For example, when a sudden drop in power at M1 and a reversal of the power direction at M2 are identified, the algorithm can determine that a specific "local reverse flow" power flow crossing condition has occurred. Finally, based on the grid topology and real-time power data (such as M1=+150kW, M2=-50kW, M3=+200kW), the system can perform physical reconstruction analysis of the actual power flow direction and power transmission path under the crossing condition, thereby accurately determining its impact path. For example, it can identify the core impact path of "main grid -> M2 metering point -> local network on the photovoltaic power station side", providing key input for subsequent settlement power deviation quantification and compensation control.

[0032] The settlement power deviation modeling module is configured to receive power data and comprehensive error data of metering devices transmitted by the data synchronization acquisition module, and receive the power flow crossing condition type and influence path transmitted by the power flow crossing condition identification module. Based on the power data, comprehensive error of metering devices and system line loss data of each metering point, and in accordance with the principle of power conservation, a quantitative relationship model between power flow crossing condition and settlement power deviation is established to calculate the settlement power deviation value in the current or future preset time period.

[0033] The comprehensive error of the metering device refers to the total error generated by the electricity metering device during the measurement process. During the transmission of electricity from the generator to the user, energy losses due to resistance, reactance, etc., in the transmission, transformation, and distribution stages constitute system line losses, which can be calculated using the root mean square current method. In the quantitative relationship model established by the settlement electricity deviation modeling module, the comprehensive error of the metering device is the sum of the electricity meter error, the transformer combined error, the secondary circuit combined error, and errors caused by other factors in the power system.

[0034] In one specific embodiment, the error of the electricity meter is mainly caused by the meter structure and operating principle. The error of the current transformer is mainly determined by the ratio difference and phase difference generated by the current transformer during measurement; the secondary circuit will create additional ratio difference and phase difference for the current transformer, thus affecting the error of the current transformer. The voltage at the secondary port of the voltage transformer should be equal to the voltage on the voltage coil of the electricity meter. The error in the secondary circuit is caused by factors such as fuse switches, terminal blocks, wires, test junction boxes, and contact resistance in the secondary circuit, resulting in inconsistencies in their values ​​and phases. This error is related to the secondary load of the voltage circuit, power factor, and connection method. At the same time, other factors such as high-order harmonics of the power system and the field environment will also bring certain measurement errors to the electricity metering device. Therefore, the actual calculated error mainly includes four aspects: the error of the electricity meter, the combined error of the current transformer, the combined error of its secondary circuit, and the error caused by other factors of the power system. ; In the formula, ε s This refers to the error of the electricity meter. ε h This refers to the synthesized error of the mutual inductors; ε d This refers to the synthesis error of the secondary circuit; ε p Errors caused by other factors in the power system.

[0035] On the other hand, in the process of electricity trading and settlement, it is necessary to accurately calculate the actual electricity consumption and to make reasonable and effective calculations of line losses. Taking line losses into account when applying the principle of conservation of settled electricity is essential to ensure the accuracy of power calculations.

[0036] The root mean square (RMS) current method, as the most basic and simplest method for theoretical calculation of distribution network line losses, works by equating the energy loss caused by the RMS current flowing through the transmission line with the energy loss generated by the actual load over the same period. The calculation of the energy loss is shown below: ; In the formula, Δ w The calculated power loss; I ifThis is the root mean square current of the line; R This is the sum of the total resistance of the circuit and the electrical equipment. t The time during which the loss occurs.

[0037] in I if The following formula can be used for calculation: ; In the formula, I if This is the root mean square current flowing through the line; I i This represents the current flowing through the corresponding period of the selected representative daily load.

[0038] If the active and reactive power and voltage parameters of the load carried by the line are known, then the root mean square current flowing through the line is... I if It can be expressed by the following formula: ; In the formula, P i For the hour i Active power at that time; Q i For the hour i Reactive power at that time; U i For the hour i Line voltage at that time.

[0039] From the perspective of electrical principles, the distribution area within the region where new energy is connected to the grid satisfies the principle of energy conservation, that is, the amount of new energy generated is equal to the sum of the amount of electricity fed into the grid, the amount of electricity consumed by users, plus the system line loss. ; In the formula, w j , w 0、 w i These represent the electricity consumption values ​​of the new energy generation sub-meter, the grid-connected electricity meter, and the user sub-meter, respectively. ε j , ε 0, ε i These represent the measurement errors of the corresponding electricity consumption values ​​for the new energy power generation sub-meters, the main meter, and each user's sub-meter; Δ w This is for system line loss.

[0040] The Support Vector Machine (SVM) algorithm is used to transform the problem into a dual problem for solution. By selecting an appropriate kernel function K(x, z) and parameter C, the power flow direction and power flow type are identified.

[0041] ; : It indicates that the Lagrange multiplier vector α to be optimized (one for each sample) ).

[0042] : The sum of all Lagrange multipliers.

[0043] : This is the core margin maximization term.

[0044] : The kernel function. It is responsible for mapping the original power grid operation data (such as voltage, current, power) to a high-dimensional feature space, making the power flow patterns that are "intertwined" in the two-dimensional plane linearly separable in the high-dimensional space. For example, the Gaussian kernel can capture the non-linear features of the power flow crossing.

[0045] : The class label of the sample (e.g., +1 represents "power flow crossing occurs", -1 represents "not occurs").

[0046] : The product of this term ensures that only the inner products between samples of the same class (or support vectors) contribute to the margin, essentially calculating the distance between sample points in the high-dimensional feature space.

[0047] By maximizing this expression, the SVM tries to find a hyperplane that pushes the sample points of different classes as far apart as possible (i.e., the larger the margin), thereby improving the generalization ability of the model.

[0048] By solving the above optimization problem, a set of optimal Lagrange multipliers can be obtained .

[0049] Select α * A positive component 0 < <C: Select a <C from the optimal solution that satisfies 0 < α * , which means the corresponding sample is a support vector and lies on the margin boundary.

[0050] Using the selected support vectors and the optimal multipliers , calculate the bias term: ; α and b Then, the classification decision function of SVM can be determined. This function can determine which category a sample belongs to based on its features.

[0051] Based on the principle of conservation of settlement power, and taking into account line loss and meter error, a quantitative relationship model between power flow crossing and settlement power deviation is determined.

[0052] It should be understood that within a specific power grid area, the electrical energy input by all power sources is equal to the sum of the electrical energy consumed by all loads and the internal losses of the system, which satisfies the principle of energy conservation.

[0053] The working condition-deviation collaborative management module is configured to receive the working condition identification results transmitted by the power flow crossing working condition identification module, call the deviation calculation results and quantitative relationship model transmitted by the settlement power deviation modeling module, and generate targeted deviation prevention and control strategies and working condition early warning signals based on the power flow crossing working condition type and influence path and the settlement power deviation value, so as to issue them.

[0054] Among them, the quantitative relationship model is used to describe the quantitative relationship between the characteristic parameters of the tidal flow crossing condition and the resulting deviation in the settlement electricity value.

[0055] For example, deviation control strategies include at least one of the following: adjusting the metering scheme, optimizing the gate configuration, issuing deviation warnings, and suggesting adjustments to the operating mode.

[0056] For example, the working condition early warning signal shall include at least the early warning level, the scope of impact, the early warning trigger signal with the effective time, the coordinated control command, the execution priority, and the quantitative expected effect assessment information.

[0057] The integrated management system for power storage power stations provided in this application integrates a data synchronization acquisition module, a power flow crossing condition identification module, a settlement power deviation modeling module, and a condition-deviation collaborative control module. This system enables closed-loop management of the entire process, from data synchronization acquisition and real-time identification of crossing conditions to precise quantification of settlement deviations and proactive collaborative control. This not only solves the problems of delayed response and fragmented analysis in traditional methods for power flow crossing phenomena, but also closely integrates the tracing of settlement deviations with the dynamic identification of grid conditions. It provides real-time and accurate data support and decision-making basis for energy storage power stations (and new energy power plants) to participate in grid operation and market settlement, effectively improving the fairness and accuracy of power settlement in complex grid environments.

[0058] Figure 2 The diagram shown is a structural schematic of an integrated management system for an energy storage power station in a power system, provided by another exemplary embodiment of this application. Figure 2As shown, the operating condition-deviation collaborative control module includes: a spatiotemporal coupled risk assessment unit, a multi-level strategy decision-making unit, and an executable early warning generation unit. The multi-level strategy decision-making unit is connected to the spatiotemporal coupled risk assessment unit, and the executable early warning generation unit is connected to the multi-level strategy decision-making unit.

[0059] The spatiotemporal coupling risk assessment unit is configured to: receive the operating condition identification results from the tidal current crossing operating condition identification module and the deviation calculation results from the settlement power deviation modeling module; and calculate and generate the spatiotemporal coupling risk index based on the type and impact path of the tidal current crossing operating condition, as well as the settlement power deviation value and its changing trend.

[0060] Specifically, the calculation of the spatiotemporal coupling risk index includes the following steps: (1) Determine the crossing risk level based on the type of tidal crossing conditions and the crossing power level.

[0061] Different types of crossing events have different impact patterns on grid operation and electricity metering. The crossing power level quantifies the scale of the crossing event. Based on these two factors, the system will pre-set or calculate a comprehensive "crossing risk level." For example, a high-power circulating current crossing may have a higher risk level than a low-power reverse crossing. The purpose of this assessment is to quantify the potential threat of the crossing event itself.

[0062] (2) Determine the deviation risk level based on the settlement electricity deviation value and its trend, combined with the preset settlement threshold.

[0063] A settlement threshold is a pre-set value or range used as a benchmark to determine the severity and extent of deviations in settled electricity amounts. In actual systems, the preset settlement threshold is typically determined by electricity market rules, internal management regulations of the power grid company, or technical agreements.

[0064] (3) Overlay the impact path on the power grid topology map, and determine the network topology risk level based on the assessment of the power flow margin of the key transmission sections associated with the path and the voltage stability margin of the key nodes.

[0065] The power flow margin of critical transmission sections is used to assess whether a crossing path causes the power transmission of a line or section to approach its limit capacity. The smaller the power flow margin, the higher the risk.

[0066] The critical node voltage stability margin is used to assess whether a crossing path will cause voltage overshoot (too high or too low) on important buses or nodes. The smaller the voltage stability margin, the higher the risk.

[0067] (4) Generate a spatiotemporal coupling risk index based on the traversal risk level, deviation risk level, and network topology risk level.

[0068] Crossing risk level, deviation risk level, and network topology risk level can be graded values ​​(e.g., low, medium, high) or quantified scores. The spatiotemporal coupling risk index, on the other hand, can be a single, comprehensive quantified value generated by fusing the risk levels from these three different dimensions. This generated spatiotemporal coupling risk index is more scientific and has practical guiding significance, providing refined input for subsequent graded, zoned, and time-based control strategies.

[0069] In one specific embodiment, a high-risk index can trigger a "real-time response layer strategy" (generating immediate control instructions), a medium-risk index can trigger a "short-term regulation layer strategy" (generating operational mode adjustment suggestions), and a long-term / high-incidence risk can trigger a "long-term optimization and planning layer strategy" (generating planning suggestions for measurement, structure, or protection).

[0070] The multi-level strategy decision-making unit is configured as follows: a multi-level strategy knowledge base is pre-stored; based on the spatiotemporal coupling risk index, the multi-level strategy knowledge base is called to match and generate a phased differentiated deviation prevention and control strategy corresponding to the risk level.

[0071] For example, the knowledge base includes real-time response layer strategies, short-term control layer strategies, and long-term optimization and planning layer strategies.

[0072] The real-time response layer strategy generates immediate control commands for high-risk scenarios. The short-term adjustment layer strategy generates operational mode adjustment suggestions for medium-risk scenarios. The long-term optimization and planning layer strategy generates planning suggestions for metering, structure, or protection for long-term, high-incidence crossing conditions.

[0073] The executable early warning generation unit is configured to receive deviation prevention and control strategies generated by the multi-level strategy decision-making unit, and combine them with the real-time operating status of the power grid to convert the prevention and control strategies into operating condition early warning signals in order to trigger corresponding deviation prevention and control and operating condition management actions.

[0074] Operating condition warning signals should include at least the following: Warning levels: such as Emergency, Severe, Caution, Alert; Scope of impact: Specify the specific equipment, lines, and areas affected; Effective time: Immediate, Scheduled, Conditional; Warning trigger signal: The basic signal to activate a warning; Coordinated control instructions: Instructions that require multiple devices or systems to execute in coordination; Execution priority: the order in which multiple instructions are executed when they exist simultaneously; Quantitative assessment of expected results: Quantitative indicators of the expected results after implementing this strategy.

[0075] After the prevention and control strategy is transformed into an early warning signal for the working condition, it will be further triggered as follows: the automatic control system will switch the metering mode, the monitoring system will start enhanced monitoring of the designated line, the alarm system will send alarm information to relevant personnel, and the recording system will record the event and the handling process.

[0076] By establishing a spatiotemporally coupled risk assessment unit, a multi-level strategy decision-making unit, and an executable early warning generation unit, a high degree of automation and intelligence is achieved from risk assessment and strategy matching to signal generation. The spatiotemporally coupled risk assessment breaks through the limitation of considering only a single risk source, integrating analysis of crossing, deviation, and topology to more comprehensively reveal the nature of the risk. The multi-level strategy knowledge base ensures that the most suitable and executable control measures can be quickly matched for different levels of risk, significantly improving the efficiency and accuracy of risk response and decision-making.

[0077] Figure 3 The diagram shown is a structural schematic of an integrated management system for an energy storage power station in a power system, provided in another exemplary embodiment of this application. Figure 3 As shown, the system also includes: The data storage and verification module is bidirectionally connected to the data synchronization acquisition module, the power flow crossing condition identification module, the settlement power deviation modeling module, and the condition-deviation collaborative management module. It is configured to store the input and / or output data of all modules, power grid topology data, metering device calibration data, quantitative relationship model parameters, condition identification records, and deviation management records, and to perform real-time verification on the stored and flowing data.

[0078] The data storage and verification module includes: a database unit, a data interaction interface unit, and a data verification unit.

[0079] The database unit is configured to store power grid topology data, historical operating condition database, metering device ledger and calibration database, quantitative relationship model parameter library, and historical operation and identification record database.

[0080] The data interaction interface unit is configured to interact with the power system metering center and the energy storage power station operation and maintenance platform through standard protocols.

[0081] The data verification unit is configured to perform integrity verification, consistency verification, and timeliness verification on the collected raw data, intermediate and final results of working condition identification, and deviation calculation results.

[0082] By establishing structured database units, key information such as power grid topology, historical operating conditions, metering device ledgers, and model parameters are categorized and stored, ensuring well-organized data management and efficient and convenient querying and retrieval. The data interaction interface unit ensures seamless integration with external systems such as higher-level metering centers and energy storage power station operation and maintenance platforms, enabling information sharing. The specialized design of the data verification unit allows for multi-level quality checks on raw data, intermediate results, and final output, ensuring data credibility throughout the entire chain from source to decision-making, and serving as a crucial cornerstone for reliable system operation.

[0083] Figure 4 The diagram shown is a flowchart illustrating an exemplary embodiment of this application of a comprehensive management method for an energy storage power station in a power system. Figure 4 As shown, this integrated management method is applied to the integrated management system of the energy storage power station mentioned in any of the above embodiments, and the method includes the following steps: Step 100: Deploy multiple power metering devices at new energy grid connection points, key lines, and settlement checkpoints in the power system to synchronously collect grid operation data at each node.

[0084] The power grid operation data includes at least the voltage, current, active power, reactive power and metering data of each node, and the collected data is preprocessed by noise reduction and filtering.

[0085] Step 200: Based on the preprocessed power grid operation data and the pre-stored power grid topology data, identify in real time whether the power flow crossing condition has occurred in the power grid, and determine the type and impact path of the power flow crossing condition.

[0086] Step 300: Based on the power data of each metering point, the comprehensive error of the metering device and the system line loss data, and according to the power flow crossing condition type and the influence path, and based on the principle of power conservation, establish a quantitative relationship model between the power flow crossing condition and the settlement power deviation, and calculate the settlement power deviation value in the current or future preset time period.

[0087] Step 400: Based on the identified power flow crossing condition type and impact path, as well as the calculated settlement power deviation value, generate targeted deviation prevention and control strategies and condition early warning signals, and issue them.

[0088] The integrated management method for power storage power stations provided in this application covers a complete logical closed loop from data acquisition and preprocessing, real-time identification of operating conditions, deviation modeling and calculation to the generation of collaborative control strategies. It provides a clear and replicable operation guide for implementing such integrated management in energy storage power stations and even more new energy power stations, ensuring the stability and sustainability of management results.

[0089] It should be understood that the various processes shown above can be used, with steps rearranged, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein. The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A comprehensive management system for energy storage power stations in a power system, characterized in that, include: The data synchronization acquisition module is configured to deploy multiple power metering devices at the new energy grid connection points, key lines and settlement points of the power system to synchronously collect grid operation data at each node. The grid operation data includes at least the voltage, current, active power, reactive power and metering point power data of each node. The collected data is transmitted after noise reduction and filtering preprocessing. The power flow crossing condition identification module is connected to the data synchronization acquisition module and is configured to receive pre-processed power grid operation data, call pre-stored power grid topology data, and based on the power grid operation data and power grid topology, identify in real time whether the power flow crossing condition has occurred in the power grid, and determine the type and impact path of the power flow crossing condition. The settlement power deviation modeling module is connected to the data synchronization acquisition module and the tidal current crossing condition identification module, respectively. It is configured to receive power data and comprehensive error data of each metering point transmitted by the data synchronization acquisition module, and receive the tidal current crossing condition type and influence path transmitted by the tidal current crossing condition identification module. Based on the power data of each metering point, the comprehensive error of the metering device and the system line loss data, and in accordance with the principle of power conservation, it establishes a quantitative relationship model between tidal current crossing condition and settlement power deviation to calculate the settlement power deviation value in the current or future preset time period. The working condition-deviation collaborative management module is connected to the current flow crossing working condition identification module and the settlement power deviation modeling module, respectively. It is configured to receive the working condition identification result transmitted by the current flow crossing working condition identification module, call the deviation calculation result and the quantitative relationship model transmitted by the settlement power deviation modeling module, and generate targeted deviation prevention and control strategies and working condition early warning signals based on the current flow crossing working condition type and influence path and the settlement power deviation value, so as to issue them.

2. The system according to claim 1, characterized in that, The operating condition-deviation coordinated management module includes: The spatiotemporal coupling risk assessment unit is configured to: receive the condition identification result from the tidal current crossing condition identification module and the deviation calculation result from the settlement power deviation modeling module; and calculate and generate a spatiotemporal coupling risk index based on the type and influence path of the tidal current crossing condition, as well as the settlement power deviation value and its changing trend. The multi-level strategy decision-making unit, connected to the spatiotemporal coupling risk assessment unit, is configured to: pre-store a multi-level strategy knowledge base; and, based on the spatiotemporal coupling risk index, call the multi-level strategy knowledge base to match and generate phased differentiated deviation prevention and control strategies corresponding to the risk level. An executable early warning generation unit is connected to the multi-level strategy decision-making unit and configured to: receive the deviation prevention and control strategy generated by the multi-level strategy decision-making unit, and, in conjunction with the real-time operating status of the power grid, convert the prevention and control strategy into an operating condition early warning signal so as to trigger corresponding deviation prevention and control and operating condition management actions.

3. The system according to claim 2, characterized in that, The spatiotemporal coupling risk assessment unit is further configured as follows: The crossing risk level is determined based on the type of tidal current crossing condition and the crossing power level. Based on the settlement electricity deviation value and its changing trend, and in conjunction with the preset settlement threshold, the deviation risk level is determined; The impact path of the crossing is superimposed on the power grid topology map, and the network topology risk level is determined based on the assessment of the power flow margin of the key transmission sections associated with the path and the voltage stability margin of the key nodes. The spatiotemporal coupling risk index is generated by integrating the traversal risk level, the deviation risk level, and the network topology risk level.

4. The system according to claim 2, characterized in that, The knowledge base includes: Real-time response layer strategy, used to generate immediate control commands for high-risk scenarios; Short-term regulatory strategies are used to generate recommendations for adjusting operational methods in medium-risk index scenarios. Long-term optimization and planning strategies are used to generate planning recommendations for measurement, structure, or protection for long-term, high-frequency crossing conditions.

5. The system according to claim 2, characterized in that, The deviation prevention and control strategy includes at least one of the following: adjusting the metering scheme, optimizing the gate configuration, issuing deviation warnings, and suggesting adjustments to the operating mode. The operating condition warning signal includes at least the warning level, the scope of impact, the warning trigger signal with the effective time, the collaborative control command, the execution priority, and the quantitative expected effect evaluation information.

6. The system according to any one of claims 1 to 5, characterized in that, In the current traversal condition identification module, the identification type of the current traversal condition includes at least one of reverse traversal, cross-level traversal, and circulation traversal.

7. The system according to any one of claims 1 to 5, characterized in that, In the quantitative relationship model established by the settlement electricity deviation modeling module, the comprehensive error of the metering device is the sum of the electricity meter error, the transformer combined error, the secondary circuit combined error, and the errors caused by other factors in the power system. The system line loss is calculated by the root mean square current method.

8. The system according to any one of claims 1 to 5, characterized in that, Also includes: The data storage and verification module is bidirectionally connected to the data synchronization acquisition module, the power flow crossing condition identification module, the settlement power deviation modeling module, and the condition-deviation collaborative management module. It is configured to store the input data and / or output data of all modules, power grid topology data, metering device calibration data, quantitative relationship model parameters, condition identification records, and deviation management records, and to perform real-time verification on the stored and flowing data.

9. The system according to any one of claims 1 to 5, characterized in that, The data storage and verification module includes: The database unit is configured to store the power grid topology data, historical operating condition database, metering device ledger and calibration database, quantitative relationship model parameter library, and historical operation and identification record database. The data interaction interface unit is configured to interact with the power system metering center and the energy storage power station operation and maintenance platform through standard protocols. The data verification unit is configured to verify the collected raw data, intermediate and final results of working condition identification, and deviation calculation results.

10. A comprehensive management method for energy storage power stations in a power system, characterized in that, Applied to the system as described in any one of claims 1-9, the method comprises: Multiple power metering devices are deployed at the new energy grid connection points, key lines and settlement points of the power system to synchronously collect grid operation data of each node. The grid operation data includes at least the voltage, current, active power, reactive power and electricity data of each node, and the collected data is preprocessed by noise reduction and filtering. Based on preprocessed power grid operation data and pre-stored power grid topology data, the system can identify in real time whether a power flow crossing condition occurs in the power grid and determine the type and impact path of the power flow crossing condition. Based on the electricity data of each metering point, the comprehensive error of the metering device and the system line loss data, and according to the power flow crossing condition type and the influence path, and based on the principle of power conservation, a quantitative relationship model between the power flow crossing condition and the settlement electricity deviation is established to calculate the settlement electricity deviation value in the current or future preset time period. Based on the identified power flow crossing condition type and impact path, and the calculated settlement power deviation value, a targeted deviation prevention and control strategy and condition early warning signal are generated and issued.

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