A power system resource scheduling method and system considering flexibility scarcity

Abstract

The application relates to a power system resource scheduling method and system considering flexibility scarcity, which comprises the following steps: acquiring power grid operation time sequence data and flexibility resource parameters, and calculating node flexibility supply and node flexibility demand; subtracting the node flexibility supply from the node flexibility demand to obtain node flexibility margin; based on the node flexibility margin, calculating line flexibility power flow and line flexibility margin; further calculating a node dynamic flexibility scarcity index and a line dynamic transmission scarcity index; taking the minimization of a system flexibility calling index as a target, calculating a system balance adjustment component, a line congestion adjustment component and a node scarcity adjustment component, and linearly superimposing the three components to obtain a node comprehensive adjustment weight. According to the node comprehensive adjustment weight, scheduling control instructions for various flexibility resources are generated. Compared with the prior art, the application has the advantages that the marginal value of flexibility resources at different space-time nodes can be quantified in detail.

Description

Technical Field

[0001] This invention relates to the fields of power system operation and power market technology, and in particular to a power system resource scheduling method and system that takes into account the scarcity of flexibility. Background Technology

[0002] With the continuous increase in the proportion of renewable energy generation, the uncertainty and regulation pressure faced by the power system have significantly increased. As an important means of coping with net load fluctuations, the effective allocation and dispatch of flexibility resources has become crucial to ensuring the safe and economical operation of the power grid.

[0003] Existing flexible resource regulation mechanisms have limitations in addressing actual grid operation constraints. Constrained by grid structure and power flow distribution, sufficient capacity at the system level does not equate to actual availability at the node level. Ignoring network constraints leads to a large amount of ineffective supply that is "nominally available but actually undeliverable," resulting in extreme scarcity of flexibility at local nodes and consequently triggering wind and solar power curtailment. Simultaneously, existing mechanisms struggle to accurately characterize the marginal scarcity value of flexible resources within the network, resulting in insufficient adjustment weight signals and consequently, market incentive failure and resource scheduling difficulties. To address this, this invention introduces a graph neural network to predict flexibility margins in spatiotemporally coupled scenarios and dynamically characterizes the regulation capacity and transmission pressure of nodes and lines using scarcity indices. This effectively solves the problem of neglecting network constraints and spatiotemporal factors in traditional regulation mechanisms. By embedding scarcity assessment results into the regulation model, it achieves precise quantification of the marginal value of flexible resources, forming node regulation signals that reflect local scarcity and network congestion levels. This incentivizes efficient spatiotemporal allocation and scheduling of flexible resources, improving system efficiency and security.

[0004] The invention disclosed in CN114925962A presents a quantitative analysis method for the operational flexibility of active distribution networks based on nodal marginal electricity prices. The method involves: inputting parameter information of the selected active distribution network and the intraday operating status of the distribution network obtained from the day-ahead power flow calculation; establishing quantitative constraints for the operational flexibility of the active distribution network based on the provided information; establishing a transmission model for operational flexibility based on nodal net power and calculating the sensitivity factor of flexible node net power; establishing a Lagrange dual function for pricing the operational flexibility of the distribution network, and solving for the flexibility price results of nodal net active power and nodal net reactive power at different nodes in different time periods; and outputting the results to power users with flexible resources to guide them in adjusting their dispatchable resource operation strategies. This invention achieves flexibility quantification within a unified framework, providing reference information for the optimized scheduling of various types of flexible equipment on the source, grid, load, and storage sides of active distribution networks, thereby improving system operational flexibility. However, this scheme neglects the spatiotemporal constraints and struggles to accurately characterize the marginal scarcity value of flexible resources in the network. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the existing technology by providing a power system resource scheduling method and system that considers flexibility scarcity. Addressing the problem that existing flexibility resource regulation mechanisms neglect grid topology constraints and spatiotemporal coupling characteristics, and fail to accurately reflect the local scarcity value of nodes, this invention proposes a node- and line-oriented flexibility scarcity index by implementing multi-period flexibility margin prediction based on MRA-BGCN. The flexibility scarcity assessment results are then incorporated into a flexibility resource regulation mechanism driven by flexibility scarcity signals, thereby simultaneously reflecting both the spatiotemporal scarcity of flexibility and network congestion effects, providing a scientific regulation signal to incentivize the efficient utilization of flexibility resources.

[0006] The objective of this invention can be achieved through the following technical solutions: A power system resource scheduling method that considers the scarcity of flexibility includes: Obtain power grid operation sequence data and flexibility resource parameters, and calculate node flexibility supply and node flexibility demand; calculate the difference between the node flexibility supply and node flexibility demand to obtain the node flexibility margin; Based on the node flexibility margin, calculate the line flexibility power flow; based on the line flexibility power flow, calculate the line flexibility margin; based on the node flexibility margin, calculate the node dynamic flexibility scarcity index; based on the line flexibility margin, calculate the line dynamic transmission scarcity index. Based on the node dynamic flexibility scarcity index and the line dynamic transmission scarcity index, with the goal of minimizing the system flexibility call index, the system balance adjustment component, the line congestion adjustment component, and the node scarcity adjustment component are calculated; the system balance adjustment component, the line congestion adjustment component, and the node scarcity adjustment component are linearly superimposed to obtain the node comprehensive adjustment weight; according to the node comprehensive adjustment weight, scheduling control instructions for each flexibility resource are generated.

[0007] Furthermore, based on the node flexibility margin, the node flexibility scarcity depth, node flexibility exhaustion rate, and node flexibility prediction confidence margin are calculated; based on the node flexibility scarcity depth, node flexibility exhaustion rate, and node flexibility prediction confidence margin, the node dynamic flexibility scarcity index is calculated, and the corresponding calculation formula is as follows: in, This is a dynamic scarcity index for nodes. This represents the normalized node flexibility exhaustion rate. The normalized node flexibility is scarce in depth. Confidence margin for predicting node flexibility The rate of exhaustion for node flexibility. For nodes with scarce flexibility, depth is crucial. For the purpose of penalizing the multiplier for the substantial gap, This represents the minimum rate at which node flexibility is exhausted. This represents the maximum rate at which node flexibility is exhausted. This represents the maximum value for depth where node flexibility is scarce. The standard deviation of the prediction error is represented by the standard deviation of the prediction error. It represents the inverse function of the cumulative distribution function of the standard normal distribution. This represents the total amount of flexible resources that node i is predicted to have available at time t. For safety margin warning coefficient, This indicates the predicted flexibility margin of node i at time t. Indicates time interval, This represents the maximum theoretical flexibility margin of the node.

[0008] Furthermore, based on the aforementioned line flexibility margin, the line congestion depth, line flexibility exhaustion rate, and line flexibility prediction confidence margin are calculated; based on the aforementioned line congestion depth, line flexibility exhaustion rate, and line flexibility prediction confidence margin, the line dynamic transmission scarcity index is calculated, and the corresponding calculation formula is as follows: in, This is an indicator of the scarcity of dynamic transmission on the line. This is the normalized line flexibility exhaustion rate. This represents the normalized line congestion depth. This indicates the confidence margin for line flexibility prediction. For line flexibility depletion rate, The depth of line congestion. This represents the maximum rate at which line flexibility is exhausted. This represents the minimum rate at which line flexibility is exhausted. This indicates the maximum depth of line congestion. It represents the inverse function of the cumulative distribution function of the standard normal distribution. The standard deviation of the prediction error is represented by the standard deviation of the prediction error. Indicates the predicted value. This indicates the upper limit of the thermal stability capacity of the line.

[0009] Furthermore, with the goal of minimizing the system flexibility call index, the system balance adjustment component, line congestion adjustment component, and node scarcity adjustment component are calculated, and the corresponding objective function is: in, Let be the objective function. The pricing cost function represents the cost of the flexibility provided by various flexibility resources at node i. This represents the flexibility of various flexible resources actually being used by node i at time t. The price conversion coefficient represents the scarcity of nodes. This represents the scarcity index of the dynamic flexibility of node i in time period t. Inject incremental improvements into the flexibility of node i at time t. The price conversion factor represents the scarcity of the route. This represents the dynamic transmission scarcity index of the line during time period t. This indicates the flexible power flow of the line during time period t.

[0010] Furthermore, node flexibility supply includes flexibility supply provided by the upstream power grid, thermal power units, demand response, energy storage, and between nodes; the node flexibility demand includes upstream flexibility demand and downstream flexibility demand.

[0011] Furthermore, the node flexibility margin is obtained by subtracting the node flexibility supply from the node flexibility demand, and the corresponding calculation formula is as follows: in, This represents the flexibility margin that node i can be adjusted upwards. Provides upward flexibility for node i at time t. For node i's upward flexibility requirement at time t. Provides downward flexibility for node i at time t. The downward flexibility requirement of node i at time t.

[0012] Furthermore, based on the node flexibility margin, the line flexibility power flow is calculated, and the corresponding calculation formula is as follows: in, Indicates the injection of incremental flexibility. This indicates the actual upscaling flexibility of node i at time t. This represents the downscaling flexibility of node i at time t, where N represents the total number of nodes. This represents the incremental power flow on the network caused by the flexibility adjustment at time t. Represents the power transfer distribution factor. The adjustable flexibility margin for node i. This represents the flexibility margin that node i can be adjusted down.

[0013] Furthermore, based on the aforementioned line flexibility power flow, the line flexibility margin is calculated, and the corresponding calculation formula is as follows: in, Indicates the line flexibility margin, This represents the upper limit of the baseline power flow for the line at time t. This represents the baseline power flow of the line at time t.

[0014] Furthermore, the system balance price component, line congestion price component, and node scarcity price component are constrained by power balance constraints, resource allocation upper and lower limit constraints, and line transmission capacity constraints. The constraint expression for the line transmission capacity constraint is as follows: in, This represents the ground-state power flow of the line. Represents the power transfer distribution factor. Indicates the injection of incremental flexibility. This represents the upper limit of the baseline power flow for the line at time t.

[0015] The present invention also provides a system for a power system resource scheduling method that takes into account the scarcity of flexibility, comprising a memory and a processor, wherein the memory stores a computer program, and the processor invokes the computer program to execute the steps of any of the methods described above.

[0016] Compared with the prior art, the present invention has the following advantages: (1) This invention overcomes the problem that traditional methods only focus on the power balance of the node itself and ignore the physical reality that flexibility resources are inevitably constrained by the line transmission capacity when called across nodes by simultaneously calculating the node flexibility margin and the line dynamic transmission scarcity index. On the one hand, the node scarcity index can accurately identify which nodes have insufficient self-regulation capacity; on the other hand, the line scarcity index can provide early warnings of which transmission channels will become bottlenecks hindering the flow of flexibility resources. The combination of these two factors enables subsequent regulation models to not only optimize the spatiotemporal distribution of flexibility resources but also proactively avoid the risk of local regulation failures caused by insufficient line transmission capacity. This dual-dimensional scarcity assessment mechanism represents a systematic leap from single-point supply and demand balance to network-wide transmission coupling, providing a more complete and realistic physical foundation for calculating the comprehensive regulation weights of nodes and generating various scheduling and control commands. This fundamentally improves the adaptability and precision of responses to complex power grid constraints.

[0017] (2) After completing the assessment of flexibility scarcity, this invention embeds the node dynamic flexibility scarcity index and the line dynamic transmission scarcity index into the objective function and constraints of the node flexibility adjustment model. This makes the objective function not only include adjustment weight terms of various flexibility resources, but also innovatively adds system risk terms represented by node scarcity and line scarcity. This achieves a qualitative leap from a single adjustment signal to multi-dimensional adjustment components. Each component has a clear physical meaning and corresponds to the adjustment index in the adjustment model. This enables precise quantification of the marginal value of flexibility resources at different spatiotemporal nodes, providing a transparent and interpretable theoretical basis for the subsequent calculation of superimposed adjustment weights.

[0018] (3) Based on the three regulation components obtained by solving the KKT conditions, this invention linearly superimposes the system balance regulation component, the line congestion regulation component, and the node scarcity regulation component to form a complete node integrated regulation weight with spatiotemporal differences. This fusion mechanism also incorporates the global supply and demand balance relationship, the congestion factors caused by line transmission capacity constraints, and the scarcity of the node's own flexibility supply relative to demand. It fully reflects the real value difference of flexibility resources in different spatial locations and different time sections, so that the final generated scheduling and control instructions for each flexibility resource can accurately depict the real operating status of each node in each time period, and significantly improve the resource allocation efficiency of flexibility resources in the power grid system. Attached Figure Description

[0019] Figure 1 This is a flowchart of a power system resource scheduling method that considers the scarcity of flexibility, provided in an embodiment of the present invention. Figure 2 This is a model architecture diagram of a node-line flexibility margin prediction model for a power system resource scheduling method that considers flexibility scarcity, provided in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0023] Definitions: The KKT conditions are a set of necessary conditions used in mathematical optimization to determine the optimal solution to a nonlinear programming problem with inequality constraints. It extends the Lagrange multiplier method from handling only equality constraints to handling both equality and inequality constraints simultaneously. By introducing Lagrange multipliers and complementary relaxation conditions, it transforms the original constrained optimization problem into a system of equations satisfying the condition that the gradient of the Lagrange function is zero, the original constraints are feasible, the dual constraints are feasible, and the complementary relaxation conditions are met.

[0024] MRA-BGCN is the core deep learning model in this invention for predicting node-line flexibility margin. This model, tailored to the characteristics of the power grid topology, abstracts the power grid into a graph structure consisting of node feature matrices reflecting the time-series characteristics of each node's flexibility supply, demand, and net load, and edge feature matrices reflecting the physical parameters of lines, such as transmission capacity, reactance, and PTDF. Then, a two-component graph convolutional network is used to process the information interaction between nodes and edges, while a multi-range attention mechanism is introduced to capture dependencies at different spatial scales (local neighborhood and global topology) and different temporal scales (nearby and distant time periods).

[0025] Example 1 like Figure 1 As shown, this embodiment provides a power system resource scheduling method that considers the scarcity of flexibility. The method includes the following steps: S1: Obtain power grid operation sequence data and flexibility resource parameters, and calculate node flexibility supply and node flexibility demand; calculate the difference between node flexibility supply and node flexibility demand to obtain node flexibility margin; like Figure 2 As shown, a node-line flexibility margin prediction model based on the time and space characteristics of MRA-BGCN is constructed. Based on the node flexibility supply model and node flexibility demand model of the power grid, the node flexibility margin is calculated. Based on the power transmission distribution factor and the node flexibility call amount, the line flexibility power flow caused by flexibility adjustment is calculated, and a line flexibility margin model is constructed accordingly. The power grid topology is abstracted into a graph structure. Using the node feature matrix and edge feature matrix as input features, a multi-range attention bicomponent graph convolutional network (MRA-BGCN) is used to mine the evolution law of node flexibility margin in time and space dimensions, outputting the predicted values ​​of node flexibility margin and the predicted values ​​of actual node flexibility call amount for future multi-period periods, and further deriving the time and space prediction results of line flexibility margin.

[0026] Preferred, The node flexibility supply model includes a flexibility supply model for the upper-level power grid, reflecting the adjustment margin of tie-line power; a ramp-up capability model for thermal / gas-fired power units, characterizing the range of power output increase and decrease within a given time scale; a maximum interruptible capacity model for demand-side response, quantifying the contribution of adjustable loads on the user side to flexibility supply; and a charge / discharge state model for energy storage devices, determining their available charge / discharge power and duration. The node flexibility demand model is constructed based on the changing trend of the system net load and the prediction errors of new energy sources and loads to quantify the node's demand for upward and downward flexibility adjustments. The node flexibility margin is defined as the difference between node flexibility supply and node flexibility demand in the same direction. The line flexibility margin model, based on the power transmission distribution factor (PTDF), maps the flexibility call amount of each node to the flexibility power flow increment on the line. This increment is then superimposed on the line's baseline power flow, and the difference between this increment and the line's transmission capacity upper limit is calculated to obtain the remaining available transmission capacity of the line to meet flexibility demands. The prediction model based on MRA-BGCN describes the topological connectivity of grid nodes using an adjacency matrix. It takes multi-dimensional features, including renewable energy output, load level, and energy storage status, as input and constructs samples through a sliding time window to achieve spatiotemporal prediction of the upward / downward flexibility margin of nodes and the actual amount of node flexibility utilization in future periods.

[0027] Preferred, Node flexibility supply: 1. Upper-level power grid The flexibility supply of the upstream power grid is mainly limited by the transmission power of the tie lines. This paper characterizes the upward flexibility supply of the upstream power grid as the difference between the maximum tie line power and the current tie line transmission power; and the downward flexibility supply of the upstream power grid as the difference between the current tie line transmission power and the minimum tie line power. The expression for its flexibility supply is: In the formula: and These represent the upward and downward adjustment flexibility capacity that the upstream power grid can provide to the distribution network through the tie line at time t; Let t be the actual active power transmitted by the tie line at time t; and These represent the maximum and minimum active power that the tie line is allowed to transmit, reflecting constraints such as thermal stability and safe operation of the tie line.

[0028] 2. Thermal power units In traditional power systems, conventional thermal power units are the primary resource for flexibility supply, mainly divided into coal-fired and gas-fired units. Gas-fired units generally have a faster ramp-up rate than coal-fired units. Their flexibility supply expression is: In the formula: and Provides upward and downward flexibility for thermal power unit g at time t; and For the maximum and minimum output of the thermal power unit g, Let g be the output of the thermal power unit at time t; and Let g be the upward and downward gradient rates of the thermal power unit. Given the scheduling time.

[0029] 3. Demand Response The flexibility offered by demand-side response is primarily based on interruptible loads. The mathematical model for interruptible loads on the load side is as follows: In the formula: The load output can be interrupted at time t. and The load can be flexibly supplied upwards and downwards at time t; and These represent the maximum and minimum values ​​of the interruptible load output.

[0030] 4. Energy storage Energy storage has a two-way regulation function, and its flexible supply is as follows: In the formula: and Provides flexible upward and downward supply of energy storage b at time t; and This represents the maximum discharge and charging power of energy storage b. The energy stored at time t is b, which outputs power. and To determine the maximum and minimum output of energy storage; and The discharge and charging efficiency of energy storage b.

[0031] 5. Flexibility provided between nodes Inter-node tie lines include DC and AC tie lines. Their upward and downward flexibility resources are primarily limited by the tie line's maximum and current transmission power. The expression for their flexibility supply is: In the formula: and They are nodes With nodes The up-short transmission flexibility that the interconnection line can provide at time t; Let node i and node t be at time t. The actual active power transmission value of the connecting lines between them; and They are nodes With nodes The maximum and minimum active power allowed for the interconnecting lines; and These are the upper and lower limits of the upward and downward change rates of the power flow in the tie line, respectively. Given the scheduling time.

[0032] This embodiment quantifies flexibility provision by considering the flexibility provided by the upstream power grid, thermal power units, energy storage, interruptible loads, and nodes. Therefore, the node flexibility provision defined in this paper is: In the formula: Provides upward flexibility for node i at time t; Provides downward flexibility for node i at time t.

[0033] Node flexibility requirements: In traditional power systems, load fluctuations are the primary source of power system flexibility requirements. When the system experiences an upward load fluctuation at a certain moment, the system will increase the output of various flexibility resources or reduce load demand to maintain the power system's supply and demand balance. This situation indicates that the system generates upward flexibility requirements. In power grids with renewable energy integration, system flexibility requirements not only stem from load fluctuations but also from the output of renewable energy sources, which exhibits random fluctuation characteristics.

[0034] This embodiment quantifies the node flexibility requirements, specifically as follows: In the formula: The upward flexibility requirement of node i at time t; The downward flexibility requirement of node i at time t. Let be the net load of node i at time t; Let be the actual power value of load i at time t; Let i be the actual output value of the new energy source at time t.

[0035] Node flexibility margin: The nodal flexibility margin of a power system is defined as the difference between the supply and demand for nodal flexibility in the same direction during each time period. Therefore, the nodal flexibility margin is further divided into the upper adjustment point flexibility margin and the lower adjustment point flexibility margin, as follows: In the formula: and This represents the flexibility margin that node i can be adjusted up or down.

[0036] 4. Flexibility Trend Flexibility flow is defined as the additional flow increment on the network caused by flexibility adjustment, representing the transmission of flexibility supply and demand in the power system. The flexibility flow of line l at time t can be defined as: In the formula: and The flexibility of up / down calls actually invoked by node i at time t; Inject incremental improvements into the flexibility of node i at time t; Let N be the incremental power flow caused by the flexibility adjustment at time t, i.e., the flexibility power flow; N is the total number of nodes. The power transfer distribution factor (PTDF) is the power injected at node i onto line L.

[0037] 5. Line flexibility margin While the line itself does not provide or consume flexibility, it supports flexibility requirements and is an indispensable part of flexibility assessment. Line flexibility transmission margin is defined as the line margin after allocating the required number of lines to meet node flexibility needs. It reflects the line's transmission capacity to handle flexibility and is expressed as: In the formula: For line flexibility margin; and These represent the baseline power flow and its upper limit for line l at time t.

[0038] Secondly, the flexibility scarcity assessment takes into account the flexibility margin prediction error. Based on the margin prediction results, a flexibility margin prediction error index is constructed. A node dynamic flexibility scarcity index and a line dynamic transmission scarcity index are established, taking into account the flexibility margin prediction error, to identify flexible scarce nodes and blocking critical lines in the system in advance.

[0039] The flexibility margins of nodes and lines in a power grid directly reflect the physical adjustment space of the system under different spatiotemporal dimensions. However, focusing solely on the absolute value of the margin makes it difficult to accurately capture the urgency of risks when the system approaches its operational boundaries. Therefore, this section comprehensively considers the shortage of adjustment resources on the node side and the obstruction of power flow transmission on the line side, quantifying the deficits of these two types of physical constraint bottlenecks into a flexibility scarcity index. This index directly corresponds to the shadow price in the market clearing model, thus providing a clear adjustment signal for the spatial optimization and efficient scheduling of flexibility resources.

[0040] S2: Calculate line flexibility power flow based on node flexibility margin; calculate line flexibility margin based on line flexibility power flow; calculate node dynamic flexibility scarcity index based on node flexibility margin; calculate line dynamic transmission scarcity index based on line flexibility margin. Flexibility scarcity assessment based on flexibility margin prediction. Based on the flexibility margin prediction results for nodes and lines, a flexibility margin prediction error index is constructed, and dynamic flexibility scarcity indices for nodes and dynamic transmission scarcity indices for lines are established, taking into account the flexibility margin prediction error. For nodes, a node scarcity depth index reflecting the degree of deficiency in node self-adjustment capability, a node flexibility exhaustion rate index reflecting the rate of margin decay, and a node flexibility prediction confidence margin index reflecting the degree of "erosion" of available flexibility space by prediction uncertainty are constructed, and these are combined to obtain the node dynamic flexibility scarcity index. For lines, a line congestion depth index reflecting the degree of line congestion, a line flexibility exhaustion rate index reflecting the dynamic change of margin, and a line flexibility prediction confidence margin index reflecting the degree of occupancy of available transmission margin space by prediction uncertainty are constructed, and these are combined to obtain the line dynamic transmission scarcity index, thereby identifying nodes with scarce flexibility and critical congestion lines in the system in advance.

[0041] Preferred, Based on the node flexibility margin prediction results, three indicators are constructed: node flexibility scarcity depth, node flexibility exhaustion rate, and node flexibility prediction confidence margin. These are then combined using a normalized distance function to form a node dynamic flexibility scarcity index. The node flexibility scarcity depth index is negatively correlated with the prediction margin, increasing sharply when it falls below a safety threshold. The node flexibility exhaustion rate index reflects the slope of margin decay between adjacent time periods. Simultaneously, based on the line flexibility margin prediction results, three indicators are constructed: a line congestion depth index reflecting the severity of exceeding limits under negative margin conditions, a line flexibility exhaustion rate index reflecting the dynamic changes in margin between adjacent time periods, and a line flexibility prediction confidence margin index. These are then combined using a normalized distance function to form a line dynamic transmission scarcity index, achieving comprehensive quantification and early warning of node flexibility scarcity and network congestion risks.

[0042] Specifically, 1. Node flexibility is scarce and deep This indicator characterizes the degree of deficiency in the self-regulation capacity of nodes from a static cross-section. The larger the value, the fewer redundant resources the node currently has available for allocation, and the more severe the scarcity of static flexibility it faces. In the formula: The safety margin warning coefficient (10% is used in this article) is used. For the substance gap penalty multiplier ( >1), used to amplify the severity of scarcity; To predict the flexibility margin of node i at time t, This represents the maximum theoretical flexibility margin for this node.

[0043] 2. Node flexibility exhaustion rate This metric characterizes the urgency of how quickly node flexibility margins decay over time. A value greater than 0 indicates increased flexibility margin and alleviated scarcity. A value less than 0 indicates that flexibility is rapidly depleted, scarcity intensifies, and early warnings are necessary. 3. Confidence margin for node flexibility prediction This indicator quantifies the degree to which forecast uncertainty "erodes" the available flexibility space. The closer its value is to 1, the more reliable the forecast result and the less the uncertainty "erodes" the available flexibility; conversely, the smaller its value, the greater the forecast uncertainty, the less the actual available flexible resources can be safely mobilized, and the higher the risk. In the formula: The confidence level is typically set between 0.95 and 0.99. It is the inverse function of the cumulative distribution function of the standard normal distribution; This represents the standard deviation of the prediction error of the prediction model at node i. The larger this value is, the greater the power fluctuation at that node. Let represent the total amount of available flexible resources for node i at time t.

[0044] 4. Comprehensive Node Dynamic Flexibility Scarcity Index (NDFSI) Normalize NFSD: In the formula: for The maximum value that can be obtained.

[0045] Normalize NFDR: In the formula: To maximize the possible recovery rate, This represents the maximum possible rate of exhaustion.

[0046] The above indicators are normalized and synthesized using a normalized distance function to form the final node dynamic scarcity index. This index can simultaneously reflect the static scarcity of node adjustment capabilities, the dynamic deterioration trend, and the risks brought about by prediction uncertainty. The closer the index value is to 1, the healthier the node's flexibility; the closer it is to 0, the closer the node is to a "completely scarce" state in multiple dimensions, requiring priority scheduling or early warning. Evaluation indicators for the scarcity of line flexibility: 1. Line Congestion Depth (LCD) When the indicator value is greater than 0, it indicates that the line has become overloaded after the flexibility resource optimization and has become a congestion-critical line. The indicator value quantifies the severity of the overload and directly corresponds to the shadow price of the line's transmission constraint in the clearing model. For lines with high LCD values, the congestion cost will be distributed across the flexibility prices of related nodes, resulting in spatially differentiated pricing. In the formula: It is the predicted flexibility margin of line l during time period t. This is the upper limit of the thermal stability capacity of the line. When it is negative, Extract its negative value to accurately reflect the static blockage depth of the physical channel. The severity of the overload was quantified, corresponding to the shadow price of the transmission constraint of that line in the clearing model.

[0047] 2. Line Flexibility Depletion Rate (LFDR) This indicator depicts the dynamic trend of line flexibility margin over time. It quantifies the rate of attenuation or recovery of line transmission capacity per unit time by calculating the difference in line flexibility margin between adjacent moments and dividing by the time interval. A positive LFDR indicates that the line flexibility margin is recovering, and the strain on transmission resources is easing; a negative LFDR means that the line flexibility margin is rapidly decreasing, transmission resources are being consumed quickly, and the scarcity is intensifying. In the formula: and These are predicted values.

[0048] 3. Line Flexibility Confidence Margin (LFCM) This indicator quantifies the extent to which forecast uncertainty "occupies" the available transmission margin of a line. The larger the volatility of the forecast error relative to the predicted line availability margin, the smaller the value, indicating a larger buffer space needs to be reserved due to forecast inaccuracies, and a smaller reliable margin for the line's actual flexibility in dealing with uncertainty. Conversely, a smaller value indicates a more reliable available transmission margin for the line. In the formula: The confidence level is typically set between 0.95 and 0.99. It is the inverse function of the cumulative distribution function of the standard normal distribution; The standard deviation of the prediction error generated when predicting the line flexibility margin; The predicted flexibility margin of line l during time period t.

[0049] Integrated Line Dynamic Transmission Scarcity Index (LDTSI): Normalize NFSD: In the formula: This represents the maximum possible blockage depth of the line.

[0050] Normalize NFDR: In the formula: To maximize the possible recovery rate, This represents the maximum possible rate of exhaustion.

[0051] The above indicators are normalized and synthesized using a normalized distance function to form a dynamic transmission scarcity index for lines. This index quantifies the tension and changing trend of line transmission resources, and includes the risks arising from inaccurate predictions. The closer the index value is to 1, the more abundant the line transmission capacity; the closer it is to 0, the more congested the line is in multiple dimensions, potentially becoming a bottleneck for system flexibility. Then, a node flexibility clearing model is established with the goal of minimizing the total cost of system flexibility calls. The total cost includes the bidding cost of various flexibility resources, as well as the system risk cost represented by the node dynamic flexibility scarcity and the line dynamic transmission scarcity. At the same time, the flexibility scarcity assessment results are mapped to the optimization constraints.

[0052] S3: Based on the node dynamic flexibility scarcity index and the line dynamic transmission scarcity index, with the goal of minimizing the system flexibility call index, calculate the system balance adjustment component, the line congestion adjustment component, and the node scarcity adjustment component; linearly superimpose the system balance adjustment component, the line congestion adjustment component, and the node scarcity adjustment component to obtain the node comprehensive adjustment weight; generate scheduling control instructions for each flexibility resource based on the node comprehensive adjustment weight.

[0053] S301: Establish a node flexibility clearing model that considers flexibility scarcity. The objective is to minimize the total cost of system flexibility allocation, which includes the bidding costs of various flexibility resources and the system risk cost represented by the dynamic flexibility scarcity of nodes and the dynamic transmission scarcity of lines. Constraints include power balance constraints, upper and lower bound constraints on resource allocation, line transmission capacity constraints, and node flexibility supply and demand balance and scarcity response constraints. Construct a node flexibility clearing optimization model.

[0054] Preferred, A node flexibility clearing model considering flexibility scarcity is established. First, an optimization function is constructed with the objective of minimizing the total cost of system flexibility allocation. This total cost includes the bidding costs of various flexibility resources and the system risk cost represented by the dynamic scarcity of node flexibility and the dynamic scarcity of line transmission. Second, multi-dimensional constraints are imposed on the optimization model, including power balance constraints, upper and lower bound constraints on resource allocation, line transmission capacity constraints, and constraints on the supply and demand balance and scarcity response of node flexibility. These dynamic constraints more accurately reflect the actual transmission capacity of the network in future time periods, ensuring that the solved line flexibility congestion shadow price reflects the scarcity value of network congestion.

[0055] Preferred, 1. System flexibility: Minimize total cost using the invocation objective function. The total cost includes the cost of quoting various flexibility resources, as well as the system risk cost represented by the scarcity of node dynamic flexibility and line dynamic transmission. In the formula: The pricing cost function for the flexibility provided by various flexibility resources at node i; and These are the price conversion coefficients for node and line scarcity, respectively. The dynamic flexibility scarcity index of node i in time period t; Inject incremental improvements into the flexibility of node i at time t; This is a dynamic transmission scarcity index for line l during time period t. This refers to the flexible power flow of line l during time period t.

[0056] 2. Constraints The constraints include power balance constraints, upper and lower limits for resource allocation constraints, line transmission capacity constraints, and node flexibility supply and demand balance and scarcity response constraints.

[0057] (1) Power balance constraint The sum of the net injections for flexibility calls at each node is zero, that is: (2) Resource allocation upper and lower limit constraints The total amount of various flexible resources called by each node shall not exceed the node's flexible supply capacity predicted in step 1, and shall also meet the technical constraints such as resource ramping. In the formula: Take 5%.

[0058] (3) Line transmission capacity constraints In the formula: Let be the ground state power flow of line l. When this constraint tightens (i.e. reaches its limit), the corresponding Lagrange multiplier, i.e., the shadow price, will increase. This price increment is the line congestion cost, and its value is positively correlated with the severity of congestion represented by the LCD.

[0059] S302: A node flexibility resource adjustment mechanism considering spatiotemporal scarcity and network congestion effects is proposed. Based on the Karush-Kuhn-Tucker (KKT) conditions, the system equilibrium price component and the line congestion price component are obtained by solving the clearing model. The system equilibrium price component corresponds to the Lagrange multiplier of the system power balance constraint, reflecting the marginal cost of the supply and demand balance of system flexibility; the line congestion price component corresponds to the Lagrange multiplier of the transmission capacity constraint of each line, and its value is positively correlated with the line dynamic transmission scarcity index, reflecting the additional cost brought about by network transmission restrictions. Furthermore, to reflect the risk of scarcity of node self-adjustment resources, the node dynamic flexibility scarcity index is mapped to a node scarcity price component, which is linearly superimposed with the aforementioned two price components to jointly constitute the node flexibility marginal price. The node flexibility marginal price is used as the node price signal for flexibility products. Based on the above node flexibility marginal prices, a node flexibility product pricing mechanism considering spatiotemporal scarcity and network congestion effects is formed, outputting the flexibility product price of each node at different time periods.

[0060] Preferred, The marginal price of node flexibility represents the increase in total system cost caused by a node increasing its flexibility requirement under optimal scheduling. Its price components are obtained by taking the partial derivative of the decision variables using the KKT conditions. The expression simultaneously includes the system equilibrium price component and the line congestion price component, accurately allocating network congestion costs to the critical nodes causing congestion. Furthermore, to reflect the risk of node self-regulation resource scarcity, the node dynamic flexibility scarcity index is mapped to a node scarcity price component using preset coefficients. This component, along with the two price components obtained from the clearing model, constitutes the marginal price of node flexibility, thus providing precise incentive signals for market participants and guiding the optimal planning and scheduling of flexibility resources.

[0061] Example 2 This embodiment provides a system for a power system resource scheduling method that considers the scarcity of flexibility, including a memory and a processor. The memory stores a computer program, and the processor calls the computer program to execute the steps of the method as described in Embodiment 1.

[0062] This invention can be used for node flexibility resource scheduling under renewable energy access. On the one hand, by constructing a node-line flexibility margin prediction model based on MRA-BGCN and introducing node dynamic flexibility scarcity index and line dynamic transmission scarcity index that take into account prediction errors, it solves the problem that traditional pricing mechanisms ignore grid topology constraints and spatiotemporal coupling characteristics, making it difficult to accurately characterize the local scarcity value of nodes. This allows for the early identification of nodes with scarce flexibility and critical congestion lines, improving the ability to perceive network constraints and spatiotemporal evolution. Simultaneously, by embedding the scarcity assessment results into the node flexibility clearing model and solving the KKT conditions to obtain the system equilibrium price component, line congestion price component, and node scarcity price component, it achieves a refined quantification of the marginal value of flexibility resources. On the other hand, by forming a node flexibility product pricing mechanism that considers spatiotemporal scarcity and network congestion effects, it outputs the marginal price signals of each node at different times, coordinating flexibility resources such as thermal power, energy storage, demand-side response, and the upper-level grid, guiding the optimized layout and efficient scheduling of flexibility resources in the spatiotemporal dimension, thereby significantly improving the economic efficiency of system operation and the renewable energy absorption capacity. This invention first constructs a node-line flexibility margin prediction model based on the time-space characteristics of MRA-BGCN. Based on the node flexibility supply model and node flexibility demand model of the power grid, the node flexibility margin is calculated. Based on the power transmission distribution factor and node flexibility call-up, the line flexibility power flow caused by flexibility adjustment is calculated, and a line flexibility margin model is constructed accordingly. The power grid topology is abstracted into a graph structure, using the node feature matrix and edge feature matrix as input features, and a multi-time-period node and line flexibility margin prediction is achieved based on the multi-range attention bipartite graph convolutional network (MRA-BGCN).

[0063] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A method for power system resource scheduling considering flexibility scarcity, characterized in that, include: Acquire power grid operation sequence data and flexibility resource parameters, and calculate node flexibility supply and node flexibility demand; The node flexibility margin is obtained by subtracting the node flexibility supply from the node flexibility demand. Based on the node flexibility margin, calculate the line flexibility power flow; Calculate the line flexibility margin based on the aforementioned line flexibility power flow. Based on the node flexibility margin, calculate the node dynamic flexibility scarcity index; based on the line flexibility margin, calculate the line dynamic transmission scarcity index. Based on the node dynamic flexibility scarcity index and the line dynamic transmission scarcity index, with the goal of minimizing the system flexibility call index, the system balance adjustment component, the line congestion adjustment component, and the node scarcity adjustment component are calculated; the system balance adjustment component, the line congestion adjustment component, and the node scarcity adjustment component are linearly superimposed to obtain the node comprehensive adjustment weight. Based on the node's comprehensive adjustment weight, scheduling and control instructions for each flexible resource are generated. 2.The power system resource scheduling method considering flexibility scarcity according to claim 1, wherein, Based on the node flexibility margin, the node flexibility scarcity depth, node flexibility exhaustion rate, and node flexibility prediction confidence margin are calculated; based on the node flexibility scarcity depth, node flexibility exhaustion rate, and node flexibility prediction confidence margin, the node dynamic flexibility scarcity index is calculated, and the corresponding calculation formula is as follows: in, This is a dynamic scarcity index for nodes. This represents the normalized node flexibility exhaustion rate. The normalized node flexibility is scarce in depth. Confidence margin for predicting node flexibility The rate of exhaustion for node flexibility. For nodes with scarce flexibility, depth is crucial. For the purpose of penalizing the multiplier for the substantial gap, This represents the minimum rate at which node flexibility is exhausted. This represents the maximum rate at which node flexibility is exhausted. This represents the maximum value for depth where node flexibility is scarce. The standard deviation of the prediction error is represented by the standard deviation of the prediction error. It represents the inverse function of the cumulative distribution function of the standard normal distribution. This represents the total amount of flexible resources that node i is predicted to have available at time t. For safety margin warning coefficient, This indicates the predicted flexibility margin of node i at time t. Indicates time interval, This represents the maximum theoretical flexibility margin of the node.

3. A power system resource scheduling method considering flexibility scarcity according to claim 1, characterized in that, Based on the line flexibility margin, the line congestion depth, line flexibility exhaustion rate, and line flexibility prediction confidence margin are calculated; based on the line congestion depth, line flexibility exhaustion rate, and line flexibility prediction confidence margin, the line dynamic transmission scarcity index is calculated, and the corresponding calculation formula is as follows: in, This is an indicator of the scarcity of dynamic transmission on the line. This is the normalized line flexibility exhaustion rate. This represents the normalized line congestion depth. This indicates the confidence margin for line flexibility prediction. For line flexibility depletion rate, The depth of line congestion. This represents the maximum rate at which line flexibility is exhausted. This represents the minimum rate at which line flexibility is exhausted. This indicates the maximum depth of line congestion. It represents the inverse function of the cumulative distribution function of the standard normal distribution. The standard deviation of the prediction error is represented by the standard deviation of the prediction error. Indicates the predicted value. This indicates the upper limit of the thermal stability capacity of the line.

4. A power system resource scheduling method considering flexibility scarcity according to claim 1, characterized in that, The objective function for calculating the system balance adjustment component, line congestion adjustment component, and node scarcity adjustment component with the goal of minimizing the system flexibility call index is as follows: in, Let be the objective function. The pricing cost function represents the cost of the flexibility provided by various flexibility resources at node i. This represents the flexibility of various flexible resources actually being used by node i at time t. The price conversion coefficient represents the scarcity of nodes. This represents the scarcity index of the dynamic flexibility of node i in time period t. Inject incremental improvements into the flexibility of node i at time t. The price conversion factor represents the scarcity of the route. This represents the dynamic transmission scarcity index of the line during time period t. This indicates the flexible power flow of the line during time period t.

5. A power system resource scheduling method considering flexibility scarcity according to claim 1, characterized in that, The node flexibility supply includes the flexibility supply provided by the upstream power grid, thermal power units, demand response, energy storage, and between nodes; the node flexibility demand includes upstream flexibility demand and downstream flexibility demand.

6. A power system resource scheduling method considering flexibility scarcity according to claim 5, characterized in that, The node flexibility margin is obtained by subtracting the node flexibility supply from the node flexibility demand, and the corresponding calculation formula is as follows: in, This represents the flexibility margin that node i can be adjusted upwards. Provides upward flexibility for node i at time t. For node i's upward flexibility requirement at time t. Provides downward flexibility for node i at time t. The downward flexibility requirement of node i at time t.

7. A power system resource scheduling method considering flexibility scarcity according to claim 6, characterized in that, Based on the node flexibility margin, the line flexibility power flow is calculated using the following formula: in, Indicates the injection of incremental flexibility. This indicates the actual upscaling flexibility of node i at time t. This represents the downscaling flexibility of node i at time t, where N represents the total number of nodes. This represents the incremental power flow on the network caused by the flexibility adjustment at time t. Represents the power transfer distribution factor. The adjustable flexibility margin for node i. This represents the flexibility margin that node i can be adjusted down.

8. A power system resource scheduling method considering flexibility scarcity according to claim 7, characterized in that, Based on the aforementioned line flexibility power flow, the line flexibility margin is calculated using the following formula: in, Indicates the line flexibility margin, This represents the upper limit of the baseline power flow for the line at time t. This represents the baseline power flow of the line at time t.

9. A power system resource scheduling method considering flexibility scarcity according to claim 1, characterized in that, The system's balanced price component, line congestion price component, and node scarcity price component are subject to power balance constraints, resource allocation upper and lower limit constraints, and line transmission capacity constraints. The constraint expression for the line transmission capacity constraint is as follows: in, This represents the ground-state power flow of the line. Represents the power transfer distribution factor. Indicates the injection of incremental flexibility. This represents the upper limit of the baseline power flow for the line at time t.

10. A system for power system resource scheduling that considers the scarcity of flexibility, characterized in that, It includes a memory and a processor, the memory storing a computer program, the processor invoking the computer program to perform the steps of the method as described in any one of claims 1 to 9.

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