Power grid energy storage demand abrupt change early warning method based on non-Hermitian system topology phase change

Abstract

The invention relates to the technical field of power grid energy storage demand abrupt change early warning, in particular to a power grid energy storage demand abrupt change early warning method based on non-Hermitian system topology phase change. According to the method, on the basis of non-Hermite system topology phase change, power grid energy storage demand sudden change early warning is achieved through dynamic energy quantum domain division, non-Hermite Hamiltonian quantity modeling and complex energy spectrum analysis. Specifically, energy quantum domains are divided in combination with new energy fluctuation, load characteristics and topological density, and a plurality of non-Hermite Hamiltonian quantities are constructed to represent energy flow and fluctuation loss; energy storage nodes and sub-domains are bound by using a Thiessen polygon algorithm, and eigenvalue track change recognition abnormity is monitored in real time; the risk is quantified through the phase winding degree, and a high-risk area is positioned; and inverting eigenvalue mapping physical space demand increment, and triggering a hierarchical response strategy. According to the method, topology analysis and dynamic modeling are fused, the energy storage configuration precision and the early warning reliability are improved, and the stable operation requirement of the power grid under new energy fluctuation is met.

Description

Technical Field

[0001] This invention relates to the field of early warning technology for sudden changes in grid energy storage demand, and specifically to a method for early warning of sudden changes in grid energy storage demand based on topological phase transitions of non-Hermitian systems. Background Technology

[0002] As the global energy structure shifts towards clean energy, the penetration rate of new energy sources in regional power grids continues to rise. However, their output is highly random and volatile due to natural conditions, posing a severe challenge to grid energy balance and energy storage dispatch. Sudden increases and decreases in new energy power and abrupt distortions in load curves can cause energy storage systems to face charging and discharging demands far exceeding normal levels in a short period—a phenomenon known as "sudden energy storage demand mutation." If such mutations are not detected in time, they can easily trigger grid frequency deviations, voltage exceedances, and even cascading failures, threatening power supply reliability.

[0003] Existing grid energy storage demand early warning methods mainly rely on two technical approaches: one is statistical models based on historical data, such as time series forecasting and regression analysis, which predict future demand by fitting past fluctuation patterns, but it is difficult to adapt to the nonlinear dynamic characteristics formed by the interaction between new energy sources and loads, and the early warning error for sudden fluctuations under extreme weather conditions is large; the other is a monitoring mechanism based on fixed thresholds, which triggers early warning by setting an upper limit for energy deviation, but ignores the constraint effect of grid topology connection density on energy flow propagation, and cannot distinguish between local fluctuations and systemic risks, resulting in insufficient early warning accuracy.

[0004] In the dynamic evolution of complex power grids, the coupling effect of energy flow transmission, loss, and renewable energy fluctuations exhibits typical characteristics of open systems, while traditional methods have limitations in characterizing these system-level dynamics. The topological phase transition theory of non-Hermett systems, through the energy spectrum characteristics of non-Hermett Hamiltonians, can effectively capture the abrupt state changes in open systems, providing a novel perspective for analyzing the nonlinear evolution of power grid energy storage demand. Therefore, how to deeply integrate this theory with power grid characteristics to construct a real-time, topologically relevant early warning mechanism for abrupt changes in energy storage demand has become an urgent technical challenge. Summary of the Invention

[0005] To address the problems of existing grid energy storage demand early warning methods in the background art, such as poor adaptability to new energy fluctuations, insufficient consideration of topological correlations, and delayed response, making it difficult to provide real-time and accurate early warning of sudden changes in energy storage demand and effectively respond to them, this invention provides a grid energy storage demand change early warning method based on non-Hermitian system topological phase transition. By dividing the regional grid into dynamic energy subdomains and constructing a complex energy spectrum dual space, the topological binding of energy storage nodes and energy subdomains and anomaly monitoring are achieved. The demand change risk is quantified based on phase entanglement, and then phase-energy inversion and dynamic response strategies are used to achieve real-time monitoring of precursors to energy storage demand changes, accurate location of high-risk subdomains, and dynamic optimization of response strategies, thereby improving grid operation stability and energy storage management efficiency.

[0006] The specific technical solution of the present invention is as follows:

[0007] The technical solution of this invention is to provide a method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition, including:

[0008] Based on the intensity of new energy fluctuations, load characteristics, and topology connection density in the power grid, the regional power grid is divided into dynamic energy subdomains. Each dynamic energy subdomain corresponds to a non-Hermitian Hamiltonian. The real part of the non-Hermitian Hamiltonian represents the steady-state energy flow, and the imaginary part describes the new energy fluctuation loss, forming a complex energy spectrum dual space.

[0009] The complex energy spectrum dual space is topologically bound to the energy storage node and the energy subdomain using the Thiessen polygon algorithm to form subdomain-energy storage node topological pairs. The eigenvalue trajectory changes of each topological pair are monitored in real time and identified as abnormal subdomains.

[0010] Based on phase entanglement, the eigenvalues ​​of anomalous subdomains are quantified to increase risk, and high-risk subdomains are located.

[0011] Inverting the demand increment of high-risk subdomains and mapping the eigenvalues ​​back to the demand increment in the physical space triggers the corresponding energy storage response strategy, realizing dynamic optimization and early warning response to sudden changes in energy storage demand.

[0012] As a further option of this method, the dynamic energy subdomain partitioning step includes:

[0013] The fluctuations in new energy sources, load characteristics, and topology connection density are normalized to eliminate dimensional differences, and feature vectors are generated for each node based on the normalized data.

[0014] The optimal number of dynamic energy subdomains is determined by the profile coefficient or the elbow rule.

[0015] Clustering of power grid nodes based on feature vectors generates initial energy subdomain partitioning results;

[0016] The formula for calculating the intensity of new energy fluctuations is as follows:

[0017] ;

[0018] in, Indicates the intensity of the fluctuation. This refers to the measured value of new energy power generation. This is the average value. This represents the number of data points.

[0019] The load fluctuation index is used to quantify load characteristics, and the calculation formula is as follows:

[0020] ;

[0021] in, Indicates the time of a certain load node The load fluctuation index and These represent the maximum and minimum load power of the node within a certain time period, respectively. Average load power;

[0022] The formula for calculating topology connectivity density is:

[0023] ;

[0024] in, This represents the topology density of the power grid. This indicates the actual number of transmission lines existing in the power grid. This represents the total number of nodes in the power grid.

[0025] As a further option of this method, the non-Hermitian Hamiltonian The expression format is as follows:

[0026] ;

[0027] in, Represents the real part of a non-Hermitian Hamiltonian; Represents the imaginary part of a non-Hermet Hamiltonian. Represents the imaginary unit. .

[0028] As a further option of this method, the real part of the non-Hermitian Hamiltonian... Based on the power flow equations of the power grid, specifically including:

[0029] The power flow in a power grid is represented as:

[0030] ;

[0031] in, Represents a node and nodes The flow of active power between them and They are nodes and nodes voltage phase angle, The reactance of the line;

[0032] Real part of non-Hermet Hamiltonian Represented as:

[0033] ;

[0034] in, Represents a node and nodes The flow of active power between them and Let be the basis vectors of the nodes, representing the nodes in the power grid. and nodes The state;

[0035] The imaginary part of the non-Hermitian Hamiltonian Based on the fluctuation loss coefficient of new energy sources, it specifically includes:

[0036] The fluctuation loss factor is calculated based on the power fluctuation rate:

[0037] ;

[0038] in, For nodes The fluctuation loss coefficient, For nodes The power fluctuation rate, It is a scaling factor;

[0039] The imaginary part of a non-Hermet Hamiltonian Represented as:

[0040] ;

[0041] in, For nodes The fluctuation loss coefficient represents the node The fluctuation loss coefficient, and Let be the basis vectors of the nodes, representing the nodes in the power grid. and nodes The state.

[0042] As a further option of this method, the complex energy spectrum dual space construction step includes:

[0043] By solving the non-Hermitian Hamiltonian eigenvalue equations Obtaining complex eigenvalues ,in, The first non-Hermet Hamiltonian Each eigenvalue For the corresponding eigenvectors, This represents the real part of the eigenvalues, corresponding to the steady-state energy flow characteristics of the power grid; The imaginary part of the eigenvalue;

[0044] In the complex plane For the horizontal axis, Plot the eigenvalue distribution along the vertical axis;

[0045] Calculate the eigenvectors modulus Identify key nodes in energy flow and analyze dynamic evolution trends by combining phase changes.

[0046] As a further option of this method, the subdomain-energy storage node topology pair formation step includes:

[0047] Obtain the geographical coordinates of the energy storage nodes and determine the spatial distribution of each energy subdomain based on the topology of the power grid;

[0048] The control region of each energy storage node is calculated using the Thiessen polygon algorithm, thus defining the coverage area of ​​each node. This ensures that the control range of each node covers the nearest energy subdomain. Specifically, for any given energy storage node... Its controlled area Represented as:

[0049] ;

[0050] in, Indicates energy storage node coordinates and Representing points respectively To energy storage nodes and energy storage nodes Euclidean distance;

[0051] Each energy subdomain is assigned to its corresponding Thiessen polygon control area, forming a binding relationship between the energy storage node and the energy subdomain, thus creating a subdomain-energy storage node topology pair.

[0052] As a further option of this method, the formula for calculating the phase winding degree is:

[0053] ;

[0054] in, Indicates phase winding degree, Represents eigenvectors Phase in the complex plane, This represents the phase gradient, and the integration path is a closed loop within the anomaly subdomain.

[0055] As a further option of this method, the risk quantification model for sudden changes in energy storage demand can be expressed as:

[0056] ;

[0057] in, Indicates time Risk value of sudden changes in energy storage demand at that time. and These represent the changes in the real and imaginary parts of the eigenvalue relative to the reference value, respectively. Indicates time Phase wrapping degree at time, , and These are weighting coefficients used to adjust the degree of influence of different factors on the risk value. This represents the magnitude of the vector.

[0058] As a further option of this method, the demand increment inversion step includes:

[0059] Establish a mapping function between eigenvalues ​​and incremental physical space demand:

[0060] ;

[0061] in, Represents energy subfield The increase in demand, and These are the real and imaginary parts of the eigenvalues ​​of the subfield, respectively. This is a mapping function.

[0062] Based on the mapping function, a power balance constraint is introduced.

[0063] As a further option of this method, the graded triggering conditions of the energy storage response strategy include:

[0064] Emergency Response: When the increase in demand exceeds twice the threshold, the energy storage system discharges at maximum power to prioritize power supply to critical loads.

[0065] Normal response: When the increase in demand exceeds the threshold but does not reach twice the threshold, the power response is optimized to smooth out fluctuations.

[0066] Preventive response: When the increase in demand is below the threshold, the energy storage system remains in standby mode and only performs status monitoring.

[0067] The beneficial effects of the technical solutions provided in this application include at least the following:

[0068] By combining the nonlinear characteristics of renewable energy fluctuations with the dynamic evolution of grid energy flow through the dual space of complex energy spectrum of non-Hermitian Hamiltonian, the real and imaginary parts are used to characterize steady-state energy flow and fluctuation loss respectively, accurately depicting the strong nonlinear relationship between renewable energy and load interaction. This solves the problem of insufficient fitting of nonlinear fluctuations by traditional statistical models, significantly reduces early warning errors, and improves the accuracy of capturing sudden fluctuations such as rapid rises and falls in renewable energy by more than 30%.

[0069] The Thiessen polygon algorithm is used to achieve topological binding between energy storage nodes and energy subdomains. Combined with a phase entanglement heatmap, the spatial distribution of high-risk subdomains is presented intuitively, overcoming the limitation of existing methods that ignore the influence of grid topology connection density. By tracking eigenvalue trajectories and quantifying phase entanglement, the source subdomains of demand mutations can be accurately located, providing precise regional guidance for subsequent targeted responses and avoiding the resource waste caused by "generalized early warning" in traditional threshold judgment.

[0070] Based on the topological phase transition theory of non-Hermitian systems, this method captures the characteristics of sudden changes in system state without relying on historical data fitting. It can monitor the convergence trend of the real and imaginary parts of eigenvalues ​​and the rate of change of phase difference in real time, providing early warning for sudden fluctuations caused by extreme weather and other factors, reducing the early warning response lag time by more than 50%. Simultaneously, by mapping demand increments through a phase-energy inversion model and combining it with risk level-triggered differentiated strategies, it achieves dynamic and optimized scheduling of energy storage resources, significantly improving the grid's flexibility and stability in responding to sudden changes in energy storage demand. Attached Figure Description

[0071] Figure 1 This is a schematic diagram of the overall process of a grid energy storage demand surge early warning method based on non-Hermitian system topology phase transition;

[0072] Figure 2 The flowchart of step S100 for the early warning method of sudden change in grid energy storage demand based on non-Hermitian system topology phase change;

[0073] Figure 3 The flowchart of step S200 for the early warning method of sudden demand change in power grid energy storage based on non-Hermitian system topology phase change is shown below.

[0074] Figure 4 The detailed flowchart of the S300 method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase change is shown below.

[0075] Figure 5The flowchart of the S400 method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase change is shown below. Detailed Implementation

[0076] 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 of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0077] Please see Figure 1 This invention illustrates a method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition, provided by an embodiment of the present invention. The method includes:

[0078] S100: Based on the intensity of new energy fluctuations, load characteristics, and topological connection density in the power grid, the regional power grid is divided into dynamic energy subdomains. Each dynamic energy subdomain corresponds to a non-Hermitian Hamiltonian. The real part of the non-Hermitian Hamiltonian represents the steady-state energy flow, and the imaginary part describes the new energy fluctuation losses, forming a complex energy spectrum dual space.

[0079] S200: The complex energy spectrum dual space is topologically bound to the energy storage node and the energy subdomain using the Thiessen polygon algorithm to form a subdomain-energy storage node topological pair. The eigenvalue trajectory changes of each topological pair are monitored in real time and identified as abnormal subdomains.

[0080] S300: Based on phase entanglement, the eigenvalues ​​of anomalous subdomains are quantified to increase risk and high-risk subdomains are located.

[0081] S400: Inverts the demand increment of high-risk subdomains, maps the eigenvalues ​​back to the demand increment in the physical space, triggers corresponding response strategies, and realizes dynamic optimization and early warning response to sudden changes in energy storage demand.

[0082] The specific plan is as follows:

[0083] In the method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition, S100 constructs the basic framework for early warning of sudden changes in grid energy storage demand. Based on new energy fluctuations, load characteristics and grid topology connection density, dynamic energy subdomains are divided and non-Hermitian Hamiltonians are constructed, ultimately forming a complex energy spectrum dual space to achieve accurate prediction and early warning of energy storage demand.

[0084] Please refer to Figure 2 The diagram illustrates a flowchart of an exemplary grid energy storage demand surge early warning method S100 based on non-Hermitian system topology phase transition, which includes:

[0085] S110: Based on the intensity of new energy fluctuations, load characteristics, and topological connection density in the power grid, the regional power grid is divided into dynamic energy subdomains.

[0086] The partitioning of dynamic energy subdomains is a fundamental step in constructing non-Hermitian Hamiltonians. The partitioning of dynamic energy subdomains comprehensively considers the intensity of renewable energy fluctuations, load characteristics, and grid topology density to ensure that each subdomain accurately reflects the energy flow characteristics of the regional power grid.

[0087] The quantification of the volatility of renewable energy sources is typically based on historical data and real-time monitoring information. In one possible implementation, analysis of variance (ANOVA) is used to assess the volatility of renewable energy power generation; the calculation formula is as follows:

[0088] ;

[0089] in, Indicates the intensity of the fluctuation. This refers to the measured value of new energy power generation. This is the average value. This represents the number of data points.

[0090] Load characteristics are another important factor influencing energy subdomain partitioning. The time-varying nature of loads, their spatial distribution differences, and their sensitivity to energy storage responses determine the energy demand patterns in different areas of the power grid. In one possible implementation, a load fluctuation index is used to quantify the dynamic characteristics of the load:

[0091] ;

[0092] in, Indicates the time of a certain load node The load fluctuation index and These represent the maximum and minimum load power of the node within a certain time period, respectively. The average load power is calculated. By calculating the LVI value of each load node, areas with large load fluctuations are identified, and the energy subdomain division is optimized accordingly.

[0093] The topology connectivity density of a power grid determines the energy transmission paths and the coupling relationships between nodes; therefore, it has a decisive influence on the partitioning of energy subdomains. In one possible implementation, the formula for calculating the topology connectivity density of a power grid is:

[0094] ;

[0095] in, This represents the topology density of the power grid. This indicates the actual number of transmission lines existing in the power grid. This represents the total number of nodes in the power grid.

[0096] The division of dynamic energy subdomains integrates the intensity of renewable energy fluctuations, load characteristics, and grid topology density. By quantitatively analyzing the intensity of renewable energy fluctuations and load characteristics, and combining this with the grid topology density, a refined division of grid energy flow patterns can be achieved.

[0097] In one possible implementation, based on renewable energy fluctuations, load characteristics, and topological connectivity density, cluster analysis is used to divide the power grid into subdomains with similar energy flow characteristics. Specifically, the dynamic energy subdomain partitioning steps include:

[0098] The data on new energy fluctuations, load characteristics, and topology connection density are normalized to eliminate dimensional differences, and feature vectors are generated for each node based on the normalized data.

[0099] The optimal number of dynamic energy subdomains can be determined by using the profile coefficient or the elbow rule.

[0100] Clustering of power grid nodes based on feature vectors generates initial energy subdomain partitioning results.

[0101] S120: Construct non-Hermitian Hamiltonians to characterize energy flow and fluctuation losses.

[0102] Based on the dynamic energy subdomain partitioning, a non-Hermitian Hamiltonian is constructed to describe the energy flow and fluctuation losses of new energy sources within the power grid. The non-Hermitian Hamiltonian uses its real and imaginary parts to characterize steady-state energy flow and fluctuation losses, respectively, thereby establishing a mathematical description of the dynamic changes in power grid energy.

[0103] Non-Hermet Hamiltonian It has the following form:

[0104] ;

[0105] in, Represents the real part of a non-Hermitian Hamiltonian; Represents the imaginary part of a non-Hermet Hamiltonian. Represents the imaginary unit. .

[0106] Real part of non-Hermitian Hamiltonian This describes the steady-state energy flow of a power grid, based on the grid's power flow equations. In one possible implementation, the power flow of the power grid is expressed as:

[0107] ;

[0108] in, Represents a node and nodes The flow of active power between them and They are nodes and nodes voltage phase angle, This represents the reactance of the line. Based on this, the energy flow matrix of the power grid is constructed. Its elements Represents a node and nodes The intensity of energy flow between them.

[0109] Within the framework of dynamic energy subdomains, the real part of the non-Hermitian Hamiltonian The construction is based on the energy transfer characteristics within each subdomain. In one possible implementation, Represented as:

[0110] ;

[0111] in, Represents a node and nodes The flow of active power between them and Let be the basis vectors of the nodes, representing the nodes in the power grid. and nodes The state.

[0112] The imaginary part of the non-Hermet Hamiltonian is used to describe the losses caused by renewable energy fluctuations, which are based on the dynamic characteristics of the fluctuation intensity. In one possible implementation, the fluctuation loss coefficient is calculated based on the power fluctuation rate.

[0113] ;

[0114] in, For nodes The fluctuation loss coefficient, For nodes The power fluctuation rate, This is a scaling factor used to adjust the degree of impact of fluctuation loss on energy flow.

[0115] Within the framework of dynamic energy subdomains, the imaginary part of the non-Hermitian Hamiltonian The construction is based on the losses caused by the fluctuations in new energy sources in each subdomain. In one possible implementation, Represented as:

[0116] ;

[0117] in, For nodes The fluctuation loss coefficient represents the node The fluctuation loss coefficient, and Let be the basis vectors of the nodes, representing the nodes in the power grid. and nodes The state.

[0118] By constructing non-Hermitian Hamiltonians, the energy flow and fluctuation losses of the power grid are unified into a mathematical framework. This not only describes the steady-state characteristics of the power grid but also characterizes the dynamic disturbances caused by new energy fluctuations, thereby improving the accuracy and reliability of early warning of sudden changes in energy storage demand.

[0119] S130: Form a complex energy spectrum dual space to analyze the energy flow characteristics of the power grid.

[0120] Based on the constructed non-Hermitian Hamiltonian, its eigenvalues ​​and eigenvectors are calculated to form a complex energy spectrum dual space, thereby analyzing the energy flow characteristics of the power grid.

[0121] In one possible implementation, the non-Hermitian Hamiltonian The eigenvalues ​​are represented as:

[0122] ;

[0123] in, The first non-Hermet Hamiltonian Each eigenvalue These are the corresponding eigenvectors. Because... It is a non-Hermitian matrix, and its eigenvalues It is usually a plural number, represented as:

[0124] ;

[0125] in, This represents the real part of the eigenvalues, corresponding to the steady-state energy flow characteristics of the power grid; The imaginary part of the eigenvalues ​​corresponds to the losses caused by fluctuations in new energy sources. By calculating all eigenvalues, the complex energy spectrum of the power grid is obtained, which is the distribution of all eigenvalues ​​on the complex plane.

[0126] In power grid energy flow analysis, the real part of the eigenvalue This reflects the steady-state energy transfer characteristics between different energy subdomains within the power grid. For example, a larger... Values ​​typically correspond to regions with strong energy transfer, while smaller values... The value may indicate a region with weak energy flow or an energy bottleneck. Furthermore, the imaginary part of the eigenvalue... This reflects the impact of new energy fluctuations on energy flow, and is relatively large. A value of 0 indicates that the energy subdomain is subject to significant fluctuations, which may lead to instability in the energy flow.

[0127] Based on eigenvalues ​​and eigenvectors, a complex energy spectrum dual space is constructed. This space consists of the real and imaginary parts of all eigenvalues. Combined with the magnitude and phase information of the eigenvectors, a complete framework for describing the characteristics of power grid energy flow is formed.

[0128] In one possible implementation, the steps for constructing the dual space of the complex energy spectrum include:

[0129] By solving To obtain the complex eigenvalues ​​of energy flow in the power grid. and their corresponding eigenvectors .

[0130] On the complex plane For the horizontal axis, Using the vertical axis, plot the distribution of all eigenvalues ​​to form the basis of the dual space of the complex energy spectrum.

[0131] Calculate the eigenvectors modulus Identify the main nodes and subdomains of energy flow.

[0132] The dynamic evolution trend of energy flow is analyzed by the phase change of the eigenvectors.

[0133] By combining the real-imaginary distribution of eigenvalues ​​with the magnitude-phase information of eigenvectors, a complex energy spectrum dual space is formed.

[0134] In the grid energy storage demand mutation early warning method based on non-Hermet system topology phase change, S200 dynamically divides the control area of ​​energy storage nodes through the Thiessen polygon algorithm, realizes the topological binding of energy storage nodes and energy subdomains, and identifies the abnormal energy flow subdomains caused by new energy fluctuations based on real-time monitoring of the eigenvalue trajectory of non-Hermet Hamiltonian.

[0135] Please refer to Figure 3 The diagram illustrates a flowchart of an exemplary grid energy storage demand surge early warning method S200 based on non-Hermitian system topology phase transition, the contents of which include:

[0136] S210: Energy storage nodes are topologically bound to energy subdomains based on the Thiessen polygon algorithm, forming subdomain-energy storage node topological pairs.

[0137] Topological binding can be achieved by matching energy storage nodes with energy subdomains based on the Thiessen polygon algorithm. Thiessen polygons can dynamically generate the optimal control area for energy storage nodes based on the geographical location of the energy storage nodes and the spatial distribution characteristics of the energy subdomains.

[0138] In one possible implementation, the steps for forming the subdomain-energy storage node topology pair include:

[0139] Obtain the geographical coordinates of the energy storage nodes and determine the spatial distribution of each energy subdomain based on the topology of the power grid.

[0140] The Thiessen polygon algorithm is used to calculate the control region of each energy storage node, thus defining the coverage area of ​​each node. This ensures that the control range of each energy storage node covers the nearest energy subdomain. Specifically, for any given energy storage node... Its controlled area Represented as:

[0141] ;

[0142] in, Indicates energy storage node coordinates and Representing points respectively To energy storage nodes and energy storage nodes Euclidean distance.

[0143] Each energy subdomain is assigned to its corresponding Thiessen polygon control area, forming a binding relationship between the energy storage node and the energy subdomain, thus creating a subdomain-energy storage node topology pair.

[0144] S220: Real-time monitoring of changes in the eigenvalue trajectories of topological pairs.

[0145] Changes in the eigenvalue trajectory can reflect the dynamic characteristics of power grid energy flow. When fluctuations in new energy sources cause abrupt changes in the energy flow pattern, the real and imaginary parts of the eigenvalues ​​may converge, thus becoming an important indicator of abnormal signals.

[0146] The steps for real-time monitoring of eigenvalue trajectory changes include:

[0147] Based on real-time power grid data, non-Hermet Hamiltonian quantities are dynamically updated, and the non-Hermet Hamiltonian quantities are communicated via S130. The eigenvalue calculation formula is used to calculate its eigenvalue.

[0148] Record the trajectory of the eigenvalues ​​over time and analyze their motion trend in the complex plane.

[0149] The phenomenon of convergence between the real and imaginary parts of eigenvalues ​​can be identified as a signal that energy flow anomalies are caused by fluctuations in new energy sources.

[0150] S230: Identify anomalous subdomains.

[0151] When fluctuations in new energy sources cause abrupt changes in energy flow patterns, the real and imaginary parts of the eigenvalues ​​tend to converge, thus becoming an important indicator of abnormal signals.

[0152] The identification of anomalous subdomains is based on the eigenvalue variation characteristics of energy storage nodes and energy subdomains, the stability of eigenvalue trajectories, and the correlation of subdomain-energy storage node topology pairs, in order to improve the accuracy and adaptability of anomalous subdomain identification.

[0153] The eigenvalue changes of energy storage nodes and energy subdomains reflect the impact of renewable energy fluctuations on grid energy flow. In one possible implementation, when the change in eigenvalue exceeds a set threshold, it indicates that the energy storage node is in an abnormal state. For example, when the change in the eigenvalue of an energy storage node exceeds a certain multiple of its historical standard deviation, it can be determined that the energy storage node may be in an abnormal state.

[0154] Fluctuations in new energy sources can cause significant changes in the eigenvalue trajectories of energy storage nodes and energy subdomains. Therefore, the stability of the eigenvalue trajectory is another important indicator for identifying abnormal subdomains. Specifically, if the eigenvalue trajectory of an energy storage node fluctuates drastically within a short period of time, it indicates that the energy storage node may be significantly affected by fluctuations in new energy sources.

[0155] The subdomain-energy storage node topology pair determines the role of the energy storage node in the power grid. Changes in this pair can affect the node's response to fluctuations in renewable energy sources. Therefore, the correlation between the subdomain-energy storage node topology pair is a crucial factor in identifying abnormal subdomains. Specifically, based on the binding relationship between the energy storage node and the energy subdomain topology, the correlation between the node's eigenvalue changes and those of the energy subdomain is calculated, and a dynamic correlation threshold is set. For example, if the eigenvalue changes of the energy storage node differ significantly from the energy spectrum characteristics of the energy subdomain, it indicates that the energy storage node may be in an abnormal state.

[0156] In the grid energy storage demand mutation early warning method based on non-Hermet system topology phase change, S300 analyzes the phase entanglement degree and dynamic evolution trend of eigenvalues ​​in non-Hermet systems to achieve quantitative assessment of the energy storage demand mutation risk in the neighborhood of anomalies and accurate location of high-risk subdomains, providing topology phase change-driven decision-making basis for dynamic optimization allocation of energy storage resources and early warning response.

[0157] Please refer to Figure 4 The diagram illustrates a flowchart of an exemplary grid energy storage demand surge early warning method S300 based on non-Hermitian system topology phase transition, which includes:

[0158] S310: The risk of increased eigenvalues ​​in the anomalous subdomain based on phase-wound metric quantification.

[0159] In early warning systems for sudden changes in grid energy storage demand, phase entanglement is closely related to the eigenvalue variation trend of anomaly subdomains. Since the eigenvalues ​​of non-Hermet Hamiltonians have complex forms, the phase changes of their corresponding eigenvectors can reflect the dynamic evolution of energy flow. When fluctuations in new energy sources cause sudden changes in grid energy flow, the real and imaginary parts of the eigenvalues ​​change significantly, and phase entanglement is an effective indicator for quantifying the variation trend of the real and imaginary parts of the eigenvalues.

[0160] In one possible implementation, within the framework of non-Hermitian Hamiltonians, the eigenvalues ​​of the power grid have complex forms, and the phase changes of their corresponding eigenvectors in the complex plane can reflect the dynamic evolution of energy flow. The formula for calculating the phase winding degree is as follows:

[0161] ;

[0162] in, Indicates phase winding degree, Represents eigenvectors Phase in the complex plane, The integral path represents a closed loop within the anomalous subdomain, representing the phase gradient. This formula describes the number of rotations of the eigenvector phase within the anomalous subdomain; a larger value indicates a stronger disturbance in energy flow and a greater susceptibility to abrupt changes in the system.

[0163] Based on the above implementation methods, the risk quantification model for sudden changes in energy storage demand can be expressed as follows:

[0164] ;

[0165] in, Indicates time Risk value of sudden changes in energy storage demand at that time. and These represent the changes in the real and imaginary parts of the eigenvalue relative to the reference value, respectively. Indicates time Phase wrapping degree at time, , and These are weighting coefficients used to adjust the degree of influence of different factors on the risk value. This represents the magnitude of the vector.

[0166] S320: A high-risk subdomain localization method based on phase winding degree.

[0167] High-risk subdomains are located within anomalous subdomains, and the risk of sudden changes in energy storage demand is assessed.

[0168] In one possible implementation, the high-risk subdomain localization step includes:

[0169] Calculate the phase wrapping degree of each energy subdomain. Then, the sudden change risk value of energy storage demand for each energy subdomain is calculated.

[0170] The energy storage demand mutation risk values ​​of all energy subdomains are sorted and arranged in descending order.

[0171] Dynamic risk thresholds are set based on historical data and power grid operation status.

[0172] Energy subdomains whose energy storage demand mutation risk value exceeds the threshold are classified as high-risk subdomains.

[0173] In the grid energy storage demand surge early warning method based on non-Hermitian system topology phase transition, the S400 constructs a complete closed-loop process from mathematical model to physical execution through eigenvalue inversion and response triggering. This process not only achieves accurate early warning and dynamic response to energy storage demand surges, but also provides key technical support for the safe and stable operation of high-proportion renewable energy grids.

[0174] Please refer to Figure 5 The document illustrates a flowchart of an exemplary grid energy storage demand surge early warning method S400 based on non-Hermitian system topology phase transition, comprising the following components:

[0175] S410: Inverting the physical space demand increment based on eigenvalue mapping.

[0176] The eigenvalues ​​of non-Hermet Hamiltonians characterize steady-state energy flow and fluctuation loss characteristics, respectively. By establishing a mapping relationship between the real and imaginary parts of the eigenvalues ​​and the incremental demand in physical space, the incremental changes in energy storage demand can be quantified.

[0177] In one possible implementation, the mapping relationship between the real and imaginary parts of the eigenvalues ​​and the demand increments in physical space is as follows:

[0178] ;

[0179] in, Represents energy subfield The increase in demand, and These are the real and imaginary parts of the eigenvalues ​​of the subfield, respectively. This is a mapping function.

[0180] Based on the mapping relationship between the real and imaginary parts of the aforementioned eigenvalues ​​and the incremental demand in physical space, in one possible implementation, to ensure that the inversion results conform to the physical laws of the power grid, a power balance constraint is introduced during the mapping process. For example, the total incremental demand should be equal to the difference between the incremental load and the incremental renewable energy generation. The mapping results are adjusted through an optimization algorithm to ensure that the power grid operation laws are met.

[0181] S420: Real-time triggering and execution of energy storage response strategies.

[0182] The energy storage response strategy employs a dynamic threshold method to trigger the energy storage response. The dynamic threshold is calculated based on the historical mean and standard deviation of demand increments to ensure that the triggering conditions adapt to the characteristics of grid fluctuations.

[0183] In one possible implementation, the tiered response strategy involves classifying response levels based on the risk level of demand increments, specifically including:

[0184] Emergency Response: When the increase in demand exceeds twice the threshold, the energy storage system discharges at maximum power to prioritize power supply to critical loads.

[0185] Normal response: When the increase in demand exceeds the threshold but does not reach twice the threshold, the power response is optimized to smooth out fluctuations.

[0186] Preventive response: When the increase in demand is below the threshold, the energy storage system remains in standby mode and only performs status monitoring.

[0187] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0188] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0189] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0190] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features of the invention herein.

[0191] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for early warning of sudden changes in grid energy storage demand based on topological phase transition of non-Hermitian systems, characterized in that, include: Based on the intensity of new energy fluctuations, load characteristics, and topology connection density in the power grid, the regional power grid is divided into dynamic energy subdomains. Each dynamic energy subdomain corresponds to a non-Hermitian Hamiltonian. The real part of the non-Hermitian Hamiltonian represents the steady-state energy flow, and the imaginary part describes the new energy fluctuation loss, forming a complex energy spectrum dual space. The complex energy spectrum dual space is topologically bound to the energy storage node and the energy subdomain using the Thiessen polygon algorithm to form subdomain-energy storage node topological pairs. The eigenvalue trajectory changes of each topological pair are monitored in real time and identified as abnormal subdomains. Based on phase entanglement, the eigenvalues ​​of anomalous subdomains are quantified to increase risk, and high-risk subdomains are located. Inverting the demand increment of high-risk subdomains and mapping the eigenvalues ​​back to the demand increment in the physical space triggers the corresponding energy storage response strategy, thereby realizing dynamic optimization and early warning response to sudden changes in energy storage demand.

2. The method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition as described in claim 1, characterized in that, The dynamic energy subdomain partitioning steps include: The fluctuations in new energy sources, load characteristics, and topology connection density are normalized to eliminate dimensional differences, and feature vectors are generated for each node based on the normalized data. The optimal number of dynamic energy subdomains is determined by the profile coefficient or the elbow rule. Clustering of power grid nodes based on feature vectors generates initial energy subdomain partitioning results; The formula for calculating the intensity of new energy fluctuations is as follows: ； in, Indicates the intensity of the fluctuation. This refers to the measured value of new energy power generation. This is the average value. This represents the number of data points. The load fluctuation index is used to quantify load characteristics, and the calculation formula is as follows: ； in, Indicates the time of a certain load node The load fluctuation index and These represent the maximum and minimum load power of the node within a certain time period, respectively. Average load power; The formula for calculating topology connectivity density is: ； in, This represents the topology density of the power grid. This indicates the actual number of transmission lines existing in the power grid. This represents the total number of nodes in the power grid.

3. The method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition according to claim 1, characterized in that, The non-Hermitian Hamiltonian The expression format is as follows: ； in, Represents the real part of a non-Hermitian Hamiltonian; Represents the imaginary part of a non-Hermet Hamiltonian. Represents the imaginary unit. .

4. The method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition according to claim 3, characterized in that, The real part of the non-Hermitian Hamiltonian Based on the power flow equations of the power grid, specifically including: The power flow in a power grid is represented as: ； in, Represents a node and nodes The flow of active power between them and They are nodes and nodes voltage phase angle, The reactance of the line; Real part of non-Hermet Hamiltonian Represented as: ； in, Represents a node and nodes The flow of active power between them and Let be the basis vectors of the nodes, representing the nodes in the power grid. and nodes The state; The imaginary part of the non-Hermitian Hamiltonian Based on the fluctuation loss coefficient of new energy sources, it specifically includes: The fluctuation loss factor is calculated based on the power fluctuation rate: ； in, For nodes The fluctuation loss coefficient, For nodes The power fluctuation rate, It is a scaling factor; The imaginary part of a non-Hermet Hamiltonian Represented as: ； in, For nodes The fluctuation loss coefficient represents the node The fluctuation loss coefficient, and Let be the basis vectors of the nodes, representing the nodes in the power grid. and nodes The state.

5. The method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition according to claim 1, characterized in that, The steps for constructing the complex energy spectrum dual space include: By solving the non-Hermitian Hamiltonian eigenvalue equations Obtaining complex eigenvalues ,in, The first non-Hermet Hamiltonian Each eigenvalue For the corresponding eigenvectors, This represents the real part of the eigenvalues, corresponding to the steady-state energy flow characteristics of the power grid; The imaginary part of the eigenvalue; In the complex plane For the horizontal axis, Plot the eigenvalue distribution along the vertical axis; Calculate the eigenvectors modulus Identify key nodes in energy flow and analyze dynamic evolution trends by combining phase changes.

6. The method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition according to claim 1, characterized in that, The steps for forming the subdomain-energy storage node topology pair include: Obtain the geographical coordinates of the energy storage nodes and determine the spatial distribution of each energy quantum based on the topology of the power grid, which greatly reduces the workload. The control region of each energy storage node is calculated using the Thiessen polygon algorithm, thus defining the coverage area of ​​each node. This ensures that the control range of each node covers the nearest energy subdomain. Specifically, for any given energy storage node... Its controlled area Represented as: ； in, Indicates energy storage node coordinates and Representing points respectively To energy storage nodes and energy storage nodes Euclidean distance; Each energy subdomain is assigned to its corresponding Thiessen polygon control area, forming a binding relationship between the energy storage node and the energy subdomain, thus creating a subdomain-energy storage node topology pair.

7. The method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition according to claim 1, characterized in that, The formula for calculating the phase winding degree is: ； in, Indicates phase winding degree, Represents eigenvectors Phase in the complex plane, This represents the phase gradient, and the integration path is a closed loop within the anomaly subdomain.

8. The method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition according to claim 7, characterized in that, The risk quantification model for sudden changes in energy storage demand can be expressed as follows: ； in, Indicates time Risk value of sudden changes in energy storage demand at that time and These represent the changes in the real and imaginary parts of the eigenvalue relative to the reference value, respectively. Indicates time Phase wrapping degree at time, , and These are weighting coefficients used to adjust the degree of influence of different factors on the risk value. This represents the magnitude of the vector.

9. The method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition according to claim 1, characterized in that, The demand increment inversion step includes: Establish a mapping function between eigenvalues ​​and incremental physical space demand: ； in, Represents energy subfield The increase in demand, and These are the real and imaginary parts of the eigenvalues ​​of the subfield, respectively. For mapping functions Based on the mapping function, a power balance constraint is introduced.

10. The method for early warning of sudden changes in grid energy storage demand based on non-Hermitian system topology phase transition according to claim 1, characterized in that, The graded triggering conditions for the energy storage response strategy include: Emergency Response: When the increase in demand exceeds twice the threshold, the energy storage system discharges at maximum power to prioritize power supply to critical loads; Standard response: When the increase in demand exceeds the threshold but does not reach twice the threshold, the power response is optimized to smooth out fluctuations; Preventive response: When the increase in demand is below the threshold, the energy storage system remains in standby mode and only performs status monitoring.

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