A method and device for evaluating inter-grid space-time mutual aid demand considering multi-source load correlation

Abstract

This invention belongs to the field of smart grid technology, specifically relating to a method and apparatus for assessing inter-grid spatiotemporal mutual assistance demand considering the correlation of multiple source loads. The steps include: acquiring and preprocessing basic data of the distribution network to be analyzed; establishing a power grid operation simulation model to extrapolate the operating status of each power grid at different times throughout the year; calculating the local net imbalance power of each power grid at different times; constructing a multi-grid multi-source load random variable vector to generate a set of typical operating scenarios and scenario probabilities that satisfy spatiotemporal correlation characteristics; establishing and solving a spatiotemporal mutual assistance demand assessment model; and based on the solution results, calculating the expected mutual assistance power, mutual assistance dependence, deficit fulfillment rate, and interconnection capacity demand indicators between grids to form the spatiotemporal mutual assistance demand assessment results between power grids. This invention can accurately quantify inter-grid mutual assistance demand, providing a scientific and practical quantitative basis for distribution network planning and coordinated operation.

Description

Technical Field

[0001] This invention belongs to the field of smart grid technology, specifically relating to a method and apparatus for assessing inter-grid spatiotemporal mutual assistance demand considering the correlation of multiple source loads. Background Technology

[0002] With the continuous advancement of new power system construction, various resources such as distributed power sources, energy storage, flexible loads, electric vehicles, and adjustable loads are increasingly being connected to the distribution network. Traditional distribution systems are gradually evolving towards a deeply integrated operation model encompassing power generation, grid, load, and storage. Against this backdrop, the power grid, as a crucial unit for the refined planning, operation control, and resource coordination of the distribution network, directly impacts the power supply reliability, renewable energy absorption capacity, and operational flexibility of the distribution network, particularly its internal supply-demand balance and its collaborative support relationships with adjacent grids.

[0003] Existing distribution network analysis methods mostly focus on single-moment power flow calculations, static load distribution analysis, or grid partitioning based on empirical rules, typically relying on geographical location, administrative boundaries, power supply radius, feeder affiliation, or load size as primary criteria. While these methods have some applicability under traditional unidirectional power supply models, under conditions of high-proportion distributed resource integration, both power output and load demand within the power grid exhibit significant temporal fluctuations. Energy storage devices also possess cross-time period energy transfer characteristics, and wind power, photovoltaic, and load changes between different power grids often show strong spatial correlations. Analysis based solely on static topology relationships or local empirical rules is insufficient to accurately reflect the actual mutual support needs between power grids.

[0004] In actual operation, a power deficit in one power grid during a certain period may correspond to a power surplus in another power grid during the same or adjacent period. Furthermore, the effectiveness of mutual support between grids is influenced by various factors, including interconnection channel capacity, local regulation resources, and dynamic response constraints. Therefore, establishing an assessment method that quantifies the direction and intensity of mutual support, interconnection capacity requirements, and deficit fulfillment levels on a year-round time-series scale, comprehensively considering the operational characteristics of distributed power generation, load, and storage systems as well as the temporal-spatial correlations between multiple grids, has become a key issue in the collaborative operation analysis of distribution networks. Summary of the Invention

[0005] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method and device for assessing the inter-grid spatiotemporal mutual assistance demand considering the correlation of multiple source loads. By comprehensively considering the spatiotemporal correlation of multiple source loads and the annual time sequence operation characteristics, it can accurately quantify the inter-grid mutual assistance demand, and provide a scientific and practical quantitative basis for distribution network planning and coordinated operation.

[0006] To achieve the above objectives, this invention provides a method for assessing the spatiotemporal mutual assistance demand between grids that considers the correlation of multiple source loads, comprising the following steps: S1. Obtain the basic data of the distribution network to be analyzed, and perform unified time-stamping processing and scenario-based basic data preprocessing on various types of data; S2. Based on the hierarchical architecture of regional power grid, grid, and unit, establish a power grid operation simulation model that considers the time sequence characteristics throughout the year. Through the set constraints, the operation status of each power grid in each time period of the year is simulated hour by hour. S3. Based on the time-series simulation results obtained from the hourly extrapolation, calculate the local net imbalance power of each power grid in each time period, and decompose it into power deficit and power surplus to characterize the degree of local supply and demand deviation within the grid, external support requirements and external support potential. S4. Combining historical time-series samples of load, distributed power sources and adjustable resources, construct a multi-grid multi-source load random variable vector, establish its mean vector and covariance matrix, and generate a set of typical operating scenarios and scenario probabilities that satisfy the spatiotemporal correlation characteristics. S5. In various scenarios and time periods, construct inter-grid mutual assistance power variables and local adjustment variables, and establish a spatiotemporal mutual assistance demand assessment model that meets the constraints of deficit coverage, surplus supply, local adjustment capacity, mutual assistance power boundary and dynamic response. S6. Solve the spatiotemporal mutual assistance demand assessment model to obtain the mutual assistance direction, mutual assistance power and unmet demand between each power supply grid in different scenarios and at different times. S7. Based on the solution results, calculate the expected mutual assistance power, mutual assistance dependence, deficit satisfaction rate and communication capability demand indicators between grids to form the spatiotemporal mutual assistance demand assessment results between power supply grids.

[0007] As a preferred embodiment of the present invention, in S1, the basic data of the distribution network to be analyzed includes network topology, power grid boundary, tie line parameters, distributed generation installed capacity parameters, energy storage device parameters, load time series data, and typical output curves of distributed generation, wherein the typical output curves of distributed generation include typical output curves of wind power and photovoltaic power. The specific steps for unified time-stamping and scenario-based data preprocessing for various data types are as follows: Using a yearly timeline of 8760 hours as the baseline and hourly intervals as the unified time step, mean aggregation is employed to downsample and align data at different resolutions for load time-series data and typical output curves of distributed power sources. Missing data is supplemented using interpolation of the same time period and time labels are unified. Static data, including network topology, power grid boundaries, tie-line parameters, distributed power source installed parameters, and energy storage device parameters, are directly mapped to the unified yearly timeline while maintaining parameter constancy. Based on this, invalid values ​​are removed through validity screening, and... The criteria correct abnormal fluctuation data by unifying the structured data, which includes network topology and various parameters, into a matrix / vector format and creating a three-dimensional index according to "grid-device type-time period". At the same time, the scene characteristics of each time period are labeled to form a standardized scene basic dataset with consistent time sequence and standardized format.

[0008] As a preferred embodiment of the present invention, in the power grid operation simulation model considering the time-series characteristics throughout the year in S2, the regional power grid layer is used to describe the connection relationship between the main grid support boundary and the power grid, the power grid layer is used to describe the coordinated operation process of source, load and storage within the grid, and the grid unit layer is used to reflect the time-series characteristics of distributed power sources, energy storage devices and load objects. By applying power balance constraints, energy storage state evolution constraints, distributed power output constraints, and exchange boundary constraints, with a discrete time step... The system performs hourly simulations of the operating status of each power grid throughout the year, obtaining data on the distributed power output, load demand, energy storage charging and discharging status, external exchange power, and net imbalance power of each power grid at each time period.

[0009] As a preferred embodiment of the present invention, in S2, the power balance constraint is: Let the power supply grid set be G, and the grid... Internal distributed power set is The set of adjacent grids connected to grid g is Then the power balance relationship of grid g in time period t can be expressed as: ; In the formula, Let m be the output of the m-th type of distributed power source within grid g during time period t; Let g be the power purchased by the grid at time t compared to the power purchased by the upstream grid; , These represent the discharge power and charging power of the energy storage device during time period t, respectively. The power input from adjacent grid j to grid g in time period t; Let g be the load power of grid g in time period t; Let g be the power loss of grid g in time period t; The power input from adjacent grid g to grid j in time period t; The energy storage state evolution constraints are: Considering the significant cross-time period state coupling characteristics of energy storage devices, their state of charge (SPC) evolves between adjacent time periods according to the following relationship: ; In the formula, , These represent the energy storage devices within grid g at time periods t+1 and t, respectively. , These are the charging and discharging efficiencies of the energy storage device; The output constraints of distributed power sources are: For various distributed power sources within the grid, their output is limited by resource conditions, installed capacity, and operating status, as follows: ; In the formula, Let m be the maximum available output of the m-th type of distributed power source within grid g during time period t. The output of distributed power sources can be further expressed as the product of installed capacity and the normalized output curve per unit capacity, i.e.: ; In the formula, Let m be the installed capacity of the m-th type of distributed power source within grid g; The unit normalized output of the m-th type of distributed power source within grid g during time period t; The exchange boundary constraints are: Considering that power exchange between the power supply grid and the main grid, as well as adjacent grids, is constrained by interface capacity and tie-line capacity, its exchange boundary is represented as: ; ; In the formula, This represents the maximum power purchasing capacity of grid g at its interface with the upstream power grid. Let g be the net exchange power between grid g and grid j in time period t; This represents the maximum allowed exchange power for the tie line between grid g and grid j.

[0010] As a preferred embodiment of the present invention, in S3, the local net imbalance power of each power supply grid in each time period is calculated and decomposed into two parts: power deficit and power surplus. The process is as follows: Define the net imbalance power of the power grid in time period t to characterize the degree of local supply-demand deviation without considering external support: ; In the formula, This represents the net imbalance power of grid g in time period t. This indicates that grid g has a power deficit during time period t. This indicates that grid g has a power surplus during time period t; The year-round time-series balancing capability of the power grid is measured by the net imbalance power sequence. The mean, peak value, fluctuation range, and duration are used to characterize it, where T is the set of time periods throughout the year; The net imbalance power is decomposed into two parts: power deficit and power surplus, which are expressed as follows: ; ; In the formula, This indicates the power deficit of grid g in time period t; This indicates that grid g has excess power during time period t.

[0011] As a preferred embodiment of the present invention, the fluctuation of net imbalance power is described by variance: ; In the formula, Let g be the variance of the net imbalance power sequence of the grid. This represents the variance of load fluctuations. The variance of total output fluctuation for various distributed power sources; The covariance between the load and the total power output; Let be the variance of the net charge / discharge power sequence of the energy storage devices within grid g; This represents the net imbalance power of grid g; Let g be the load power of the grid. The unit normalized output of the m-th type of distributed power source within grid g; , These are the discharge power and charging power of the energy storage device, respectively.

[0012] As a preferred embodiment of the present invention, the implementation process of step S4 is as follows: For typical operating scenarios, let the scenario set be... for: ; In the formula, the number of scenes is N. That is, the Nth scene; Let s be the probability of scenario s occurring. The net imbalance power of grid g in scene s and time period t is expressed as: ; In the formula, , , , , , respectively under scene s , , , , , ; Define the power deficit of mesh g in scene s during time period t. and power surplus They are respectively: ; ; Considering the correlation between wind power, photovoltaics, loads, and adjustable resources across different power grids, to characterize the joint fluctuation characteristics of multi-grid, multi-source-load random variables in the spatiotemporal dimension, a full-grid random variable vector is constructed for each time period t and scenario s: ; In the formula, Let G represent a vector of random variables composed of the loads of each power grid, the output of various distributed power sources, and adjustable resources under scenario s in time period t; that is, a multi-grid, multi-source load random variable vector. This represents the load power of the Gth grid in scene s during time period t; This represents the adjustable resource perturbation amount of mesh g during time period t in scene s; The statistical properties are described by the mean vector and the covariance matrix: ; ; In the formula, Let be a vector of random variables for time period t; For time period t The mean vector; For time period t The covariance matrix; This represents the mathematical expectation operator, used to take the statistical average of random variables.

[0013] As a preferred embodiment of the present invention, in S5, let the mutual assistance power transmitted from grid g to grid j in time period t under scene s be... With the goal of minimizing the total cost of mutual assistance, the objective function of the spatiotemporal mutual assistance demand assessment model is: ; In the formula, F represents the total cost of mutual assistance; The unit mutual aid cost of power transferred from grid g to grid j; This is the local adjustment cost coefficient; The local adjustment power of mesh g during scene s time period t; The penalty coefficient for unmet shortfall; The remaining power deficit of grid g that is not satisfied after mutual assistance and local adjustment in scene s at time t; The constraints of the spatiotemporal mutual assistance demand assessment model include deficit coverage and energy conservation constraints, surplus power transfer constraints, local regulation capacity constraints, local regulation ramping constraints, mutual assistance ramping constraints, mutual assistance power boundary constraints, and tie-line capacity constraints.

[0014] As a preferred embodiment of the present invention, in S7, after solving, the mutual power timing matrix between each power supply grid under scenario s is obtained. This further yields the direction of mutual assistance demand, peak mutual assistance power, communication capacity requirements, and the degree of shortfall fulfillment under network constraints: Define the expected mutual charge of grid g to grid j as: ; In the formula, The expected mutual power exchange between grid g and grid j under various time series and multi-scenario conditions throughout the year; Define the mutual dependence of mesh j on external meshes as: ; In the formula, The proportion of the total load power that grid j receives through mutual assistance with other grids during the year's operation; Define the deficit satisfaction rate of grid g. for: ; Define the communication capability requirements between mesh g and mesh j. for: .

[0015] A grid-based spatiotemporal mutual assistance demand assessment device considering the correlation of multiple source loads includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The method described above is implemented by executing the program through the processor.

[0016] The beneficial effects of this invention are: This invention first establishes a power grid operation simulation model considering the year-round temporal characteristics to obtain the distributed power output, load demand, energy storage status, and external exchange power of each power grid at different time periods. It then constructs characterization indicators such as net imbalance power, power deficit, and power surplus to analyze the local balance characteristics of each power grid throughout the year. Based on this, a joint scenario model considering the spatiotemporal correlation of multiple grids and multiple source-load relationships is established. Furthermore, a spatiotemporal mutual assistance demand assessment model for inter-grids is constructed, satisfying network connectivity, local regulation capabilities, and dynamic response constraints. This quantifies the mutual assistance direction, mutual assistance intensity, connectivity requirements, mutual assistance dependence, and deficit satisfaction degree among different power grids. The assessment results can provide a basis for subsequent power grid division, interconnection channel configuration, and regulation resource planning. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the steps of the present invention; Figure 2 This is the evaluation flowchart of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will be further described below with reference to the accompanying drawings: Example 1: During operation, the power distribution network is simultaneously affected by multiple factors, including fluctuations in distributed power generation output, load time-series changes, energy storage state-of-charge evolution, and equipment operating status changes, exhibiting significant time-coupled characteristics. Traditional analysis methods based on single-moment snapshots are insufficient to reflect the energy transfer process of energy storage across time periods, and also fail to characterize the dynamic changes in the power grid's balancing capacity under scenarios such as continuous high load and continuous low output. Therefore, to accurately assess the supply-demand matching level of the power grid throughout the year, it is necessary to establish a balancing capacity simulation model that considers the time-series characteristics of the entire year, and to extrapolate the operating status of each power grid under full-time-series conditions hourly.

[0019] like Figure 1 and Figure 2 As shown, a method for assessing inter-grid spatiotemporal mutual assistance demand considering the correlation of multiple source loads includes the following steps: S1. Obtain the basic data of the distribution network to be analyzed, and perform unified time-stamping processing and scenario-based basic data preprocessing on various types of data; S2. Based on the hierarchical architecture of regional power grid, grid, and unit, a power grid operation simulation model considering the time sequence characteristics throughout the year is established. Through the set constraints, the operation status of each power grid in each time period of the year is simulated hour by hour. S3. Based on the time-series simulation results obtained from the hourly extrapolation, calculate the local net imbalance power of each power grid in each time period, and decompose it into power deficit and power surplus to characterize the degree of local supply and demand deviation within the grid, external support requirements and external support potential. S4. Combining historical time-series samples of load, distributed power sources and adjustable resources, construct a multi-grid multi-source load random variable vector, establish its mean vector and covariance matrix, and generate a set of typical operating scenarios and scenario probabilities that satisfy the spatiotemporal correlation characteristics. S5. In various scenarios and time periods, construct inter-grid mutual assistance power variables and local adjustment variables, and establish a spatiotemporal mutual assistance demand assessment model that meets the constraints of deficit coverage, surplus supply, local adjustment capacity, mutual assistance power boundary and dynamic response. S6. Solve the spatiotemporal mutual assistance demand assessment model to obtain the mutual assistance direction, mutual assistance power and unmet demand between each power supply grid in different scenarios and at different times. S7. Based on the solution results, calculate the expected mutual assistance power, mutual assistance dependence, deficit satisfaction rate and communication capability demand indicators between grids to form the spatiotemporal mutual assistance demand assessment results between power supply grids.

[0020] In S1, the basic data of the distribution network to be analyzed includes network topology, power grid boundary, tie line parameters, distributed generation capacity parameters, energy storage device parameters, load time series data, and typical output curves of distributed generation, including typical output curves of wind power and photovoltaic power. The specific steps for unified time-stamping and scenario-based data preprocessing for various data types are as follows: Using a yearly timeline of 8760 hours as the baseline and hourly intervals as the unified time step, mean aggregation is employed to downsample and align data at different resolutions for load time-series data and typical output curves of distributed power sources. Missing data is supplemented using interpolation of the same time period and time labels are unified. Static data, including network topology, power grid boundaries, tie-line parameters, distributed power source installed parameters, and energy storage device parameters, are directly mapped to the unified yearly timeline while maintaining parameter constancy. Based on this, invalid values ​​are removed through validity screening, and... The criteria correct abnormal fluctuation data by unifying the structured data, which includes network topology and various parameters, into a matrix / vector format and creating a three-dimensional index according to "grid-device type-time period". At the same time, the scene characteristics of each time period are labeled to form a standardized scene basic dataset with consistent time sequence and standardized format.

[0021] In S2, the power grid operation simulation model considering the time-series characteristics throughout the year consists of a regional power grid layer, which describes the connection between the main grid support boundary and the power grid; a power grid layer, which describes the coordinated operation of sources, loads and storage within the grid; and a grid cell layer, which reflects the time-series characteristics of distributed power sources, energy storage devices and load objects. By applying power balance constraints, energy storage state evolution constraints, distributed power output constraints, and exchange boundary constraints, with a discrete time step... The system performs hourly simulations of the operating status of each power grid throughout the year, obtaining data on the distributed power output, load demand, energy storage charging and discharging status, external exchange power, and net imbalance power of each power grid at each time period.

[0022] The power balance constraint is: Let the power supply grid set be G, and the grid... Internal distributed power set is The set of adjacent grids connected to grid g is Then the power balance relationship of grid g in time period t can be expressed as: ; In the formula, Let m be the output of the m-th type of distributed power source within grid g during time period t; Let g be the power purchased by the grid at time t compared to the power purchased by the upstream grid; , These represent the discharge power and charging power of the energy storage device during time period t, respectively. The power input from adjacent grid j to grid g in time period t; Let g be the load power of grid g in time period t; Let g be the power loss of grid g in time period t; Let g be the power input from adjacent grid g to grid j in time period t; this formula characterizes the instantaneous power conservation relationship of the power supply grid under the combined effects of source, load, storage and inter-grid exchange in each time period.

[0023] The energy storage state evolution constraints are: Considering the significant cross-time period state coupling characteristics of energy storage devices, their state of charge (SPC) evolves between adjacent time periods according to the following relationship: ; In the formula, , These represent the energy storage devices within grid g at time periods t+1 and t, respectively. , These are the charging and discharging efficiencies of the energy storage device; To ensure the physical feasibility of energy storage operation, energy boundary constraints and power boundary constraints must also be met: ; In the formula, , These are the minimum and maximum allowable energy storage capacity, respectively. , These are the maximum charging power and maximum discharging power of the energy storage, respectively. The output constraints of distributed power sources are: For various distributed power sources within the grid, their output is limited by resource conditions, installed capacity, and operating status, as follows: ; In the formula, Let m be the maximum available output of the m-th type of distributed power source within grid g during time period t. To facilitate subsequent analysis of source-load correlation and capacity matching, the output of distributed power sources is further expressed as the product of installed capacity and the normalized output curve per unit capacity, i.e.: ; In the formula, Let m be the installed capacity of the m-th type of distributed power source within grid g; The unit normalized output of the m-th type of distributed power source within grid g during time period t; The exchange boundary constraints are: Considering that power exchange between the power supply grid and the main grid, as well as adjacent grids, is constrained by interface capacity and tie-line capacity, its exchange boundary is represented as: ; ; In the formula, This represents the maximum power purchasing capacity of grid g at its interface with the upstream power grid. Let g be the net exchange power between grid g and grid j in time period t; This represents the maximum allowed exchange power for the tie line between grid g and grid j.

[0024] The power grid operation simulation model can be realized based on existing mature technologies such as power system time-series power flow calculation, energy storage dynamic modeling, and distribution network operation analysis. It can be modeled and simulated using general numerical calculation and optimization programming platforms such as MATLAB, Python, Pyomo, and Gurobi, and can also be integrated into various distribution network planning and analysis software.

[0025] As demonstrated by the aforementioned year-round time-series simulation, the operational status of the power grid's sources, loads, and energy storage at each time period can ultimately be attributed to the local supply-demand imbalance problem. To uniformly characterize the local balance state of the power grid at each time period, a net imbalance power is defined for the grid. This net imbalance power describes the degree of supply-demand imbalance after considering the combined effects of power sources, loads, and energy storage within the grid, without considering external grid mutual support. The sign and magnitude of the net imbalance power simultaneously reflect the grid's external support needs and potential for external support at that time period, serving as a key intermediate variable connecting single-grid operation simulation and multi-grid mutual support demand assessment. The net imbalance power sequence serves as a key intermediate variable connecting single-grid operation simulation and multi-grid mutual support demand assessment.

[0026] In S3, the local net imbalance power of each power grid in each time period is calculated and decomposed into two parts: power deficit and power surplus. The process is as follows: Define the net imbalance power of the power grid in time period t to characterize the degree of local supply-demand deviation without considering external support: ; In the formula, This represents the net imbalance power of grid g in time period t. This indicates that grid g has a power deficit during time period t, requiring support from an external system. This indicates that grid g has a power surplus during time period t, possessing the potential to provide external support. The magnitude reflects the degree of local supply and demand imbalance during that period; The year-round time-series balancing capability of the power grid is measured by the net imbalance power sequence. The characteristics of the average, peak, fluctuation range and duration are used to characterize it, where T is the set of time periods throughout the year; The net imbalance power is decomposed into two parts: power deficit and power surplus, which are expressed as follows: ; ; In the formula, This indicates the power deficit of grid g in time period t; This indicates that grid g has excess power during time period t.

[0027] The two indicators mentioned above can not only describe the degree of external support required by a single power grid at different time periods and the potential for external support, but also provide a unified data foundation for subsequent analysis of the power grid's self-balancing capability and assessment of the spatiotemporal mutual assistance needs between grids.

[0028] From the perspective of formation mechanism, the magnitude and fluctuation of net imbalance power are mainly affected by load time-series changes, distributed generation output characteristics, and energy storage regulation behavior. When the load curve and the output curve of a certain type of distributed generation have a high degree of matching in the time dimension, that type of generation can provide more local support during periods of high load, thereby reducing the peak net imbalance power. When there is a peak-shifting and complementary relationship between different types of distributed generation, the output decline of one type of generation can be partially offset by the output decline of another type of generation, which helps to reduce the fluctuation of the total grid output and further reduce the fluctuation amplitude of net imbalance power. The role of energy storage is reflected in the shifting and smoothing in the time dimension, that is, absorbing electrical energy when local power output is abundant and releasing electrical energy when local power supply is insufficient, thereby reducing the peak net imbalance power and improving the local balance level of a single grid.

[0029] When the load curve and the output curve of a certain type of distributed power source show a strong co-current trend over time, this type of power source can provide more support during periods of higher load, thereby reducing net imbalance power. When there is a peak-shifting and complementary relationship between different types of distributed power sources, a decrease in the output of one type of power source may be partially offset by an increase in the output of another type, which also helps to reduce overall output fluctuations and thus reduce net imbalance power. To quantitatively characterize this temporal coupling relationship, the volatility of net imbalance power can be described by variance: ; In the formula, Let g be the variance of the net imbalance power sequence of the grid. This represents the variance of load fluctuations. The variance of total output fluctuation for various distributed power sources; The covariance between the load and the total power output; Let be the variance of the net charge / discharge power sequence of the energy storage devices within grid g; This represents the net imbalance power of grid g; Let g be the load power of the grid. The unit normalized output of the m-th type of distributed power source within grid g; , These are the discharge power and charging power of the energy storage device, respectively.

[0030] This formula shows that the higher the positive correlation between load and total power output over time, the larger the covariance term and the smaller the net imbalance fluctuation. Similarly, if different types of distributed power sources have stronger complementarity, the smaller the variance of total power output fluctuation, which also helps reduce net imbalance fluctuation. This analysis demonstrates that net imbalance power not only characterizes the local balance state of the grid in a single time period but also statistically reflects the impact of source-load time-series matching and local regulation capabilities on the balance level.

[0031] To further quantify the local balancing capability of a single power grid, the following characterization indicators are constructed based on the year-round time-series operation results: Define the self-balancing rate of mesh g. for: ; In the formula, This represents the absolute value of the power exchanged between grid g and the external system during time period t. The larger the value of this index, the lower the dependence of the grid on the main grid and other grids, and the stronger its local self-balancing ability.

[0032] External power support required by the grid g during its year-round operation Represented as: ; In the formula, This represents the power deficit of grid g in time period t; The surplus power that grid g can provide to external systems during its year-round operation. Represented as: ; In the formula, This represents the surplus power of grid g during time period t.

[0033] The above indicators, combined with net imbalance power, power deficit, and power surplus, can jointly characterize the local balancing capability and dependence on external systems of the power grid during the year-round time-series operation, and provide basic input for subsequent multi-grid mutual assistance demand assessment.

[0034] Analysis of the net imbalance power of a single power grid reveals that different power grids may exhibit states of continuous power deficit, continuous power surplus, or alternating deficit and surplus throughout the year. In actual power distribution systems, power grids do not operate independently; wind power, photovoltaic power, loads, and adjustable resources in different regions are typically correlated in both temporal and spatial dimensions. Therefore, relying solely on the net imbalance power sequence of a single grid is insufficient to fully reflect the inter-grid mutual support relationships. To address this, it is necessary to construct a joint scenario model that considers the spatiotemporal correlation of multiple power sources and loads, and based on this, establish a spatiotemporal mutual support demand assessment model between grids to quantify the direction, intensity, interconnection capacity requirements, and the degree to which deficits can be met among the power grids.

[0035] The implementation process of step S4 is as follows: For typical operating scenarios, let the scenario set be... for: ; In the formula, the number of scenes is N. That is, the Nth scene; Let s be the probability of scenario s occurring; scenarios can be obtained by joint sampling of historical time series samples, generation of typical scenarios, or clustering dimensionality reduction methods, and are used to reflect the joint fluctuation characteristics of multi-source load variables in the spatiotemporal dimension under different power grids.

[0036] The net imbalance power of grid g in scene s and time period t is expressed as: ; In the formula, , , , , , respectively under scene s , , , , , ; Define the power deficit of mesh g in scene s during time period t. and power surplus They are respectively: ; ; This refers to the power deficit that mesh g needs to support externally during time period t in scene s. The surplus power that a grid can output to the outside during the same period is the essence of the mutual assistance requirement between grids. Under the conditions of satisfying network constraints and local adjustment constraints, the surplus power of some grids is used to cover the power deficit of other grids.

[0037] Considering the correlation between wind power, photovoltaics, loads, and adjustable resources across different power grids, to characterize the joint fluctuation characteristics of multi-grid, multi-source-load random variables in the spatiotemporal dimension, a full-grid random variable vector is constructed for each time period t and scenario s: ; In the formula, Let G represent a vector of random variables composed of the loads of each power grid, the output of various distributed power sources, and adjustable resources under scenario s in time period t; that is, a multi-grid, multi-source load random variable vector. This represents the load power of the Gth grid in scene s during time period t; This represents the adjustable resource perturbation amount of mesh g during time period t in scene s; The statistical properties are described by the mean vector and the covariance matrix: ; ; In the formula, Let be a vector of random variables for time period t; For time period t The mean vector; For time period t The covariance matrix reflects the source-load correlation within the same grid, the load-load correlation between different grids, the wind-wind correlation, the light-light correlation, and the source-load cross-correlation. This matrix is ​​used to characterize the spatial synchronicity, complementarity, or simultaneous rise and fall of net imbalance power in each power supply grid, thus providing a statistical basis for assessing mutual assistance demand. This represents the mathematical expectation operator, used to take the statistical average of random variables.

[0038] In S5, let the mutual assistance power transmitted from grid g to grid j during time period t in scene s be... The inter-grid mutual assistance demand assessment can be expressed as follows: Under the constraints of given scenario probability, multi-source load correlation and network connectivity, by coordinating power transfer between grids and local adjustment of resource operation, the power deficit of each grid is covered as much as possible, and the overall mutual assistance cost is minimized.

[0039] With the goal of minimizing the total cost of mutual assistance, the objective function of the spatiotemporal mutual assistance demand assessment model is: ; In the formula, F represents the total cost of mutual assistance; The unit mutual aid cost of power transferred from grid g to grid j; This is the local adjustment cost coefficient; The local adjustment power of mesh g in scene s during time period t (the local adjustment compensation power that the mesh itself can provide). The penalty coefficient for unmet shortfall; The remaining power deficit of grid g that is not satisfied after mutual assistance and local adjustment in scene s at time t; The objective function takes into account the costs of cross-grid mutual assistance, local adjustment, and residual deficit penalties, thus reflecting the economy and necessity of inter-grid collaborative support.

[0040] To ensure the feasibility of the mutual assistance demand assessment results, the model must meet the following constraints: Deficit Coverage and Energy Conservation Constraints: ; In the formula, Let be the equivalent power transmission and receiving efficiency of grid j to grid g. This indicates the actual effective support power provided by other grids to grid g via the communication channel; Let t be the mutual power transmitted from grid j to grid g during time period t in scenario s; Surplus power transfer constraint: ; In the formula, The upper limit of the surplus power that grid g can transmit in scene s and time period t; This formula indicates that the total amount of mutual aid power output by grid g in scenario s and time period t shall not exceed the power surplus available for external transmission in this time period, thereby ensuring that the mutual aid supply comes from real and available surplus resources.

[0041] Local adjustment capability constraints: ; In the formula, The maximum regulation capacity of the local regulation resources that can be called upon by grid g includes additional regulation capacity of energy storage, controllable load response capacity, and controllable power supply regulation capacity.

[0042] Local adjustment of climbing constraints: ; In the formula, , These represent the limits on the rise and fall of the local regulation power of the grid g, respectively, and its ramping capability. Let g be the local regulation power of grid g in scenario s during time period t-1; this constraint is used to reflect the dynamic response speed limit of resources such as energy storage, adjustable units and interruptible loads in adjacent time periods.

[0043] Mutual assistance ramping constraint: ; In the formula, , These are the rising and falling ramping ability constraints for the mutual power between grid g and grid j, respectively. Let be the mutual aid power transmitted from grid g to grid j during time period t-1 in scenario s; this constraint is used to reflect the limitation of the response speed of communication equipment and control system on the mutual aid process.

[0044] Mutual power boundary constraints: ; In the formula, , These are the rising and falling ramping ability constraints for the mutual power between grid g and grid j, respectively. Tie line capacity constraints: ; In the formula, The maximum allowable capacity is defined for the connection lines between grids g and j.

[0045] In S6, when solving the spatiotemporal mutual assistance demand assessment model, commercial optimization solvers (such as CPLEX, Gurobi, etc.) or open-source solvers (such as SCIP, GLPK, etc.) can be used, combined with linear programming / mixed integer linear programming algorithms to complete the model solution, and obtain the mutual assistance direction, mutual assistance power and unmet demand between each power supply grid in different scenarios and at different times.

[0046] In S7, after solving, the mutual power timing matrix between each power supply grid in scenario s is obtained. This further yields the direction of mutual assistance demand, peak mutual assistance power, communication capacity requirements, and the degree of shortfall fulfillment under network constraints: To quantitatively characterize the cooperative support relationship between grids, the expected mutual support charge of grid g to grid j is defined as: ; In the formula, The expected mutual power exchange between grid g and grid j under various time series and multi-scenario conditions throughout the year; Define the mutual dependence of mesh j on external meshes as: ; In the formula, This refers to the proportion of electricity supplied to grid j through mutual assistance with other grids during the year's operation, relative to its total load electricity. The larger this indicator is, the higher the dependence of grid j on cross-grid collaborative support, and the more obvious its collaborative and mutual balancing characteristics. Accordingly, combined with the previously defined self-balancing rate indicator, the ability of each power supply grid to form a "self-balancing-dominated" or "mutual assistance-dependent-dominated" characteristic can be judged.

[0047] To further characterize the extent to which grid gaps can be covered under existing connectivity and regulation conditions, the gap satisfaction rate of grid g is defined. for: ; This metric measures the extent to which the power deficit of grid g can be covered by mutual assistance and local regulation under multiple scenarios and time periods. The closer this metric is to 1, the easier it is to meet the grid deficit under existing network and regulation boundary conditions.

[0048] Define the communication capability requirements between mesh g and mesh j. for: ; To meet the spatiotemporal mutual assistance needs across multiple scenarios throughout the year, this metric specifies the minimum communication capability required between grid g and grid j. This metric further transforms the timing results of mutual assistance power into communication capability requirements that can be directly used in engineering.

[0049] The coefficients in this embodiment can be set based on experience or by using expert methods.

[0050] By constructing the above-mentioned spatiotemporal joint scenario and solving the mutual assistance demand assessment model, we can systematically obtain the results of mutual assistance direction, mutual assistance intensity, expected mutual assistance power, mutual assistance dependence, deficit satisfaction rate and communication capability requirements among different power supply grids under the influence of multi-source load correlation.

[0051] Example 2: A grid-to-grid spatiotemporal mutual assistance demand assessment device considering the correlation of multiple source loads, including a memory, a processor, and a computer program stored in the memory and run on the processor, which implements the method in Example 1 by executing the program through the processor.

Claims

1. A method for assessing inter-grid spatiotemporal mutual assistance demand considering the correlation of multiple source loads, characterized in that... Includes the following steps: S1. Obtain the basic data of the distribution network to be analyzed, and perform unified time-stamping processing and scenario-based basic data preprocessing on various types of data; S2. Based on the hierarchical architecture of regional power grid, grid, and unit, establish a power grid operation simulation model that considers the time sequence characteristics throughout the year. Through the set constraints, the operation status of each power grid in each time period of the year is simulated hour by hour. S3. Based on the time-series simulation results obtained from the hourly extrapolation, calculate the local net imbalance power of each power grid in each time period, and decompose it into power deficit and power surplus to characterize the degree of local supply and demand deviation within the grid, external support requirements and external support potential. S4. Combining historical time-series samples of load, distributed power sources and adjustable resources, construct a multi-grid multi-source load random variable vector, establish its mean vector and covariance matrix, and generate a set of typical operating scenarios and scenario probabilities that satisfy the spatiotemporal correlation characteristics. S5. In various scenarios and time periods, construct inter-grid mutual assistance power variables and local adjustment variables, and establish a spatiotemporal mutual assistance demand assessment model that meets the constraints of deficit coverage, surplus supply, local adjustment capacity, mutual assistance power boundary and dynamic response. S6. Solve the spatiotemporal mutual assistance demand assessment model to obtain the mutual assistance direction, mutual assistance power and unmet demand between each power supply grid in different scenarios and at different times. S7. Based on the solution results, calculate the expected mutual assistance power, mutual assistance dependence, deficit satisfaction rate and communication capability demand indicators between grids to form the spatiotemporal mutual assistance demand assessment results between power supply grids.

2. The inter-grid spatiotemporal mutual assistance demand assessment method considering the correlation of multiple source loads as described in claim 1, characterized in that: In S1, the basic data of the distribution network to be analyzed includes network topology, power grid boundary, tie line parameters, distributed generation capacity parameters, energy storage device parameters, load time series data, and typical output curves of distributed generation, including typical output curves of wind power and photovoltaic power. The specific steps for unified time-stamping and scenario-based data preprocessing for various data types are as follows: Using a yearly timeline of 8760 hours as the baseline and hourly intervals as the unified time step, mean aggregation is employed to downsample and align data at different resolutions for load time-series data and typical output curves of distributed power sources. Missing data is supplemented using interpolation of the same time period and time labels are unified. Static data, including network topology, power grid boundaries, tie-line parameters, distributed power source installed parameters, and energy storage device parameters, are directly mapped to the unified yearly timeline while maintaining parameter constancy. Based on this, invalid values ​​are removed through validity screening, and... The criteria correct abnormal fluctuation data by unifying the structured data, which includes network topology and various parameters, into a matrix / vector format and creating a three-dimensional index according to "grid-device type-time period". At the same time, the scene characteristics of each time period are labeled to form a standardized scene basic dataset with consistent time sequence and standardized format.

3. The inter-grid spatiotemporal mutual assistance demand assessment method considering the correlation of multiple source loads according to claim 1, characterized in that: In the S2 described above, in the power grid operation simulation model that considers the time-series characteristics throughout the year, the regional power grid layer is used to describe the connection relationship between the main grid support boundary and the power grid, the power grid layer is used to describe the coordinated operation process of sources, loads and storage within the grid, and the grid cell layer is used to reflect the time-series characteristics of distributed power sources, energy storage devices and load objects. By applying power balance constraints, energy storage state evolution constraints, distributed power output constraints, and exchange boundary constraints, with a discrete time step... The system performs hourly simulations of the operating status of each power grid throughout the year, obtaining data on the distributed power output, load demand, energy storage charging and discharging status, external exchange power, and net imbalance power of each power grid at each time period.

4. The inter-grid spatiotemporal mutual assistance demand assessment method considering the correlation of multiple source loads according to claim 3, characterized in that, In S2, the power balance constraint is: Let the power supply grid set be G, and the grid... Internal distributed power set is The set of adjacent grids connected to grid g is Then the power balance relationship of grid g in time period t can be expressed as: ； In the formula, Let m be the output of the m-th type of distributed power source within grid g during time period t; Let g be the power purchased by the grid at time t compared to the power purchased by the upstream grid; , These represent the discharge power and charging power of the energy storage device during time period t, respectively. The power input from adjacent grid j to grid g in time period t; Let g be the load power of grid g in time period t; Let g be the power loss of grid g in time period t; The power input from adjacent grid g to grid j in time period t; The energy storage state evolution constraints are: Considering the significant cross-time period state coupling characteristics of energy storage devices, their state of charge (SPC) evolves between adjacent time periods according to the following relationship: ； In the formula, , These represent the energy storage devices within grid g at time periods t+1 and t, respectively. , These are the charging and discharging efficiencies of the energy storage device; The output constraints of distributed power sources are: For various distributed power sources within the grid, their output is limited by resource conditions, installed capacity, and operating status, as follows: ； In the formula, Let m be the maximum available output of the m-th type of distributed power source within grid g during time period t. The output of distributed power sources can be further expressed as the product of installed capacity and the normalized output curve per unit capacity, i.e.: ； In the formula, Let m be the installed capacity of the m-th type of distributed power source within grid g; The unit normalized output of the m-th type of distributed power source within grid g during time period t; The exchange boundary constraints are: Considering that power exchange between the power supply grid and the main grid, as well as adjacent grids, is constrained by interface capacity and tie-line capacity, its exchange boundary is represented as: ； ； In the formula, This represents the maximum power purchasing capacity of grid g at its interface with the upstream power grid. Let g be the net exchange power between grid g and grid j in time period t; This represents the maximum allowed exchange power for the tie line between grid g and grid j.

5. The inter-grid spatiotemporal mutual assistance demand assessment method considering the correlation of multiple source loads according to claim 4, characterized in that, In S3, the local net imbalance power of each power supply grid in each time period is calculated and decomposed into two parts: power deficit and power surplus. The process is as follows: Define the net imbalance power of the power grid in time period t to characterize the degree of local supply-demand deviation without considering external support: ； In the formula, This represents the net imbalance power of grid g in time period t. This indicates that grid g has a power deficit during time period t. This indicates that grid g has a power surplus during time period t; The year-round time-series balancing capability of the power grid is measured by the net imbalance power sequence. The mean, peak value, fluctuation range, and duration are used to characterize it, where T is the set of time periods throughout the year; The net imbalance power is decomposed into two parts: power deficit and power surplus, which are expressed as follows: ； ； In the formula, This indicates the power deficit of grid g in time period t; This indicates that grid g has excess power during time period t.

6. The inter-grid spatiotemporal mutual assistance demand assessment method considering the correlation of multiple source loads according to claim 5, characterized in that, The volatility of net imbalance power is described using variance: ； In the formula, Let g be the variance of the net imbalance power sequence of the grid. This represents the variance of load fluctuations. The variance of total output fluctuation for various distributed power sources; The covariance between the load and the total power output; Let be the variance of the net charge / discharge power sequence of the energy storage devices within grid g; This represents the net imbalance power of grid g; Let g be the load power of the grid. The unit normalized output of the m-th type of distributed power source within grid g; , These are the discharge power and charging power of the energy storage device, respectively.

7. The inter-grid spatiotemporal mutual assistance demand assessment method considering the correlation of multiple source loads according to claim 5, characterized in that, The implementation process of step S4 is as follows: For typical operating scenarios, let the scenario set be... for: ； In the formula, the number of scenes is N. That is, the Nth scene; Let s be the probability of scenario s occurring. The net imbalance power of grid g in scene s and time period t is expressed as: ； In the formula, , , , , , respectively under scene s , , , , , ; Define the power deficit of mesh g in scene s during time period t. and power surplus They are respectively: ； ； Considering the correlation between wind power, photovoltaics, loads, and adjustable resources across different power grids, to characterize the joint fluctuation characteristics of multi-grid, multi-source-load random variables in the spatiotemporal dimension, a full-grid random variable vector is constructed for each time period t and scenario s: ； In the formula, Let G represent a vector of random variables composed of the loads of each power grid, the output of various distributed power sources, and adjustable resources under scenario s in time period t; that is, a multi-grid, multi-source load random variable vector. This represents the load power of the Gth grid in scene s during time period t; This represents the adjustable resource perturbation amount of mesh g during time period t in scene s; The statistical properties are described by the mean vector and the covariance matrix: ； ； In the formula, Let be a vector of random variables for time period t; For time period t The mean vector; For time period t The covariance matrix; This represents the mathematical expectation operator, used to take the statistical average of random variables.

8. The inter-grid spatiotemporal mutual assistance demand assessment method considering the correlation of multiple source loads according to claim 7, characterized in that, In S5, let the mutual assistance power transmitted from grid g to grid j during time period t in scenario s be... With the goal of minimizing the total cost of mutual assistance, the objective function of the spatiotemporal mutual assistance demand assessment model is: ； In the formula, F represents the total cost of mutual assistance; The unit mutual aid cost of power transferred from grid g to grid j; This is the local adjustment cost coefficient; The local adjustment power of mesh g during scene s time period t; The penalty coefficient for unmet shortfall; The remaining power deficit of grid g that is not satisfied after mutual assistance and local adjustment in scene s at time t; The constraints of the spatiotemporal mutual assistance demand assessment model include deficit coverage and energy conservation constraints, surplus power transfer constraints, local regulation capacity constraints, local regulation ramping constraints, mutual assistance ramping constraints, mutual assistance power boundary constraints, and tie-line capacity constraints.

9. The inter-grid spatiotemporal mutual assistance demand assessment method considering the correlation of multiple source loads according to claim 8, characterized in that, In S7, after solving, the mutual power timing matrix between each power supply grid in scenario s is obtained. This further yields the direction of mutual assistance demand, peak mutual assistance power, communication capacity requirements, and the degree of shortfall fulfillment under network constraints: Define the expected mutual charge of grid g to grid j as: ； In the formula, The expected mutual power exchange between grid g and grid j under various time series and multi-scenario conditions throughout the year; Define the mutual dependence of mesh j on external meshes as follows: ； In the formula, The proportion of the total load power that grid j receives through mutual assistance with other grids during the year's operation; Define the deficit satisfaction rate of grid g. for: ； Define the communication capability requirements between mesh g and mesh j. for: 。 10. A grid-based spatiotemporal mutual assistance demand assessment device considering the correlation of multiple source loads, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in any one of claims 1-9.

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