A method, apparatus, equipment and medium for flexible resource collaborative planning of regional power grids

Abstract

This invention discloses a method, apparatus, equipment, and medium for regional power grid flexible resource collaborative planning. The method includes: acquiring resource availability and flexible resource response speed of each region at different time periods to obtain regional resource distribution data; analyzing the capacity limitations of inter-regional power transmission channels to determine restricted paths; determining a preliminary allocation plan based on resource availability, flexible resource response speed, and resource demand at both ends of the restricted path; acquiring flow data of inter-regional power transmission channels after implementing the preliminary allocation plan, conducting path risk assessment, and identifying high-risk paths; acquiring local resource composition data of the regions at both ends of the high-risk path, identifying local resources with substitution potential, and obtaining alternative resources; determining the comprehensive matching degree between the alternative resources and the current allocation plan based on the available capacity and response time of the alternative resources, and generating an optimized resource allocation plan. This invention effectively improves system stability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid resource planning, in particular to a regional power grid flexible resource collaborative planning method, device, equipment and medium. BACKGROUND

[0002] With the large-scale access of new energy and the intensification of load fluctuations, the flexible resource planning of regional power grids has become an important pillar for ensuring the stability of power supply and promoting green development. However, the current flexible resource planning method has limitations in the face of complex scenarios, and many schemes ignore the influence of resources in time and space, resulting in that the planning results are difficult to adapt to the dynamic changes in demand in actual operation, especially in the case of limited regional power transmission, the reliability of resource allocation cannot meet the demand, and the overall robustness of the system needs to be improved. SUMMARY

[0003] To solve the above technical problems, the present application provides a regional power grid flexible resource collaborative planning method, device, equipment and medium, which can realize the coordinated optimization configuration of cross-regional power resources and effectively improve the system stability.

[0004] The present application provides a regional power grid flexible resource collaborative planning method, comprising:

[0005] Obtain the resource available amount and flexible resource response speed of each region in different time periods, and obtain regional resource distribution data according to the resource available amount and the flexible resource response speed;

[0006] Based on the regional resource distribution data, analyze the capacity limit of the inter-regional power transmission channel, and determine the limited path;

[0007] According to the resource available amount, flexible resource response speed and resource demand amount of the two end regions of the limited path, determine the resource allocation priority, and obtain a preliminary allocation scheme based on the resource allocation priority, and execute the preliminary allocation scheme;

[0008] Obtain the flow data of the inter-regional power transmission channel after executing the preliminary allocation scheme, and perform path risk assessment based on the change trend of the flow data to determine the high-risk path;

[0009] Obtain the local resource composition data in the two end regions of the high-risk path, identify the local resources with substitution potential according to the local resource composition data, and obtain the substitution resources;

[0010] According to the available capacity and response time of the substitution resources, determine the comprehensive matching degree of the substitution resources and the current allocation scheme, and generate an optimized resource configuration scheme based on the comprehensive matching degree.

[0011] As an improvement of the above scheme, the resource available amount and flexible resource response speed of each region in different time periods are obtained, and region resource distribution data is obtained according to the resource available amount and the flexible resource response speed, comprising:

[0012] The power load and the maximum output power of the energy storage device of each region in different time periods are obtained;

[0013] The resource available amount is obtained according to the difference between the maximum output power of the energy storage device and the power load;

[0014] For each region, a clustering algorithm is used to classify the response time of the flexible resources in the region, and the resources in the fast response class are determined; if the proportion of the resources in the fast response class in the total resource capacity of the region is greater than a preset threshold, the response weight of the region is determined as a first weight, otherwise the response weight of the region is determined as a second weight, and the first weight is greater than the second weight;

[0015] The product of the response weight of each region and the resource available amount is taken as a matrix element, and the region and the time period are taken as the row and column of the matrix respectively to construct a resource distribution matrix;

[0016] Based on the resource distribution matrix, the difference between the corresponding time period matrix elements of adjacent regions is calculated to obtain a resource difference degree;

[0017] Adjacent regions with the same response weight and a resource difference degree less than a preset threshold are merged into the same resource cluster to generate region resource distribution data.

[0018] As an improvement of the above scheme, the capacity limit of the power transmission channel between regions is analyzed based on the region resource distribution data to determine a limited path, comprising:

[0019] Based on the region resource distribution data, the rated capacity and the current transmission power of the power transmission line between adjacent regions are obtained, and a high-load line is determined according to the rated capacity and the current transmission power;

[0020] According to the high-load line and the connection node information thereof, the power transmission path between each resource cluster is determined;

[0021] According to the capacity utilization rate of the power transmission path and the historical transmission power data, a linear regression method is used to calculate the predicted transmission power of each path in the next scheduling period;

[0022] For each of the power transmission paths, if the difference between the predicted transmission power of the path and the transmission upper limit value of the path is less than a preset safety threshold, or the capacity utilization rate of the path is greater than a preset limit threshold, the power transmission path is marked as a limited path.

[0023] As an improvement of the above scheme, the method further comprises:

[0024] Based on the regional resource distribution data, the resource available amount, the flexible resource response speed and the resource demand amount in the regions at both ends of the restricted path are obtained; the regions at both ends of the restricted path are divided into a supply region and a demand region;

[0025] For the supply region, a unit-time schedulable resource amount is calculated according to a ratio of the resource available amount to the flexible resource response speed;

[0026] A resource matching coefficient is obtained according to a ratio of the resource available amount of the supply region to the resource demand amount of the demand region;

[0027] For each restricted path, a feasibility coefficient is obtained by weighted summation of the unit-time schedulable resource amount and the resource matching coefficient;

[0028] The resource allocation priority of the restricted path is determined according to an order of the feasibility coefficient from large to small.

[0029] As an improvement of the above scheme, the method further comprises:

[0030] Flow data of the power transmission channel between regions after the preliminary allocation scheme is executed is obtained, and a residual transmission capacity of the restricted path is obtained according to the flow data;

[0031] A safety margin ratio is obtained according to a ratio of the residual transmission capacity to a path rated capacity;

[0032] For each restricted path, if the safety margin ratio is less than a preset stability threshold, a safety margin decline rate is calculated according to a safety margin ratio of an adjacent time, and a path with a safety margin decline rate greater than a preset speed threshold is marked as a risk path;

[0033] Based on the risk path, a direct current flow algorithm is used to calculate active power distribution of each line after allocation, power prediction is performed on the path to obtain a predicted power value, and a path with a predicted power value greater than a preset power threshold is marked as a high-risk path.

[0034] As an improvement of the above scheme, the method further comprises:

[0035] Obtain local resource composition data and predicted overload power in the area on both ends of the high-risk path, and obtain adjustable power of local resources in different periods based on the local resource composition data;

[0036] Calculate the ratio of the adjustable power of the local resource to the predicted overload power of the corresponding high-risk path, and if the ratio is greater than a preset replacement threshold, determine that the local resource has replacement potential;

[0037] For local resources with replacement potential, obtain the resource response time of each local resource, and filter out resources with resource response time less than a preset time limit requirement to obtain replacement resources.

[0038] As an improvement of the above scheme, the comprehensive matching degree of the replacement resource and the current deployment scheme is determined according to the available capacity and response time of the replacement resource, and an optimized resource configuration scheme is generated based on the comprehensive matching degree, comprising:

[0039] Based on the replacement resource, a target function is constructed to maximize the resource output power adjustment amount, and the available capacity and response time of the replacement resource are obtained by linear programming method according to the output power limit constraint, the climbing rate constraint and the power maintenance time constraint.

[0040] The adjustment contribution value of the replacement resource is determined according to the available capacity and the response time of the replacement resource, and a resource scheduling sequence is generated according to the order from large to small of the adjustment contribution value;

[0041] The capacity matching coefficient is obtained according to the ratio of the available capacity of the replacement resource to the power demand of the corresponding area;

[0042] The time margin is calculated according to the difference between the time requirement of the corresponding area and the response time of the replacement resource, and the time matching coefficient is obtained according to the ratio of the time margin to the time requirement;

[0043] The capacity matching coefficient and the time matching coefficient are weighted and summed to obtain the comprehensive matching degree of the replacement resource and the current deployment scheme;

[0044] If the comprehensive matching degree is greater than a preset matching threshold, resource scheduling is performed according to the resource scheduling sequence; if the comprehensive matching degree is less than a preset matching threshold, a candidate deployment relationship table is constructed according to the remaining adjustable power of each resource in the current resource allocation scheme and the electrical distance from each resource to each demand point, and the particle swarm optimization algorithm is used to optimize the resource power allocation to obtain the optimized resource configuration result.

[0045] The application also provides a regional power grid flexible resource collaborative planning device, comprising:

[0046] A regional resource analysis module is configured to obtain resource availability and flexible resource response speed of each region in different time periods, and obtain regional resource distribution data according to the resource availability and the flexible resource response speed.

[0047] A limited path determination module is configured to analyze capacity limitation of power transmission channels between regions based on the regional resource distribution data, and determine a limited path.

[0048] A preliminary resource allocation module is configured to determine resource allocation priority according to resource availability, flexible resource response speed and resource demand of regions at both ends of the limited path, and obtain a preliminary allocation scheme based on the resource allocation priority, so as to execute the preliminary allocation scheme.

[0049] A risk path determination module is configured to obtain flow data of power transmission channels between regions after the preliminary allocation scheme is executed, perform path risk assessment based on a change trend of the flow data, and determine a high-risk path.

[0050] A local resource analysis module is configured to obtain local resource composition data of regions at both ends of the high-risk path, identify local resources with substitution potential according to the local resource composition data, and obtain substitution resources.

[0051] A resource configuration optimization module is configured to determine comprehensive matching degree of substitution resources and a current allocation scheme according to available capacity and response time of the substitution resources, and generate an optimized resource configuration scheme based on the comprehensive matching degree.

[0052] The application further provides a computer device comprising a processor and a memory, the memory storing a computer program, and the computer program being configured to be executed by the processor, and the processor implements the regional power grid flexible resource collaborative planning method when executing the computer program.

[0053] The application further provides a computer readable storage medium storing a computer program, wherein the computer program controls a device where the computer readable storage medium is located to execute the regional power grid flexible resource collaborative planning method when running.

[0054] Compared with the prior art, the regional power grid flexible resource collaborative planning method, device, equipment and medium provided by the application have the following advantages:

[0055] By acquiring the resource available amount and flexible resource response speed of each region in different time periods, the regional resource distribution data is obtained, which can intuitively reflect the real-time supply capacity of each region, also takes into account the influence of response speed on scheduling, and provides an accurate basis for subsequent power transmission channel capacity evaluation and risk decision-making; based on the regional resource distribution data, the capacity limit of the power transmission channel between regions is analyzed, the limited path is determined, the bottleneck path can be quickly located, blind scheduling in the whole network is avoided, thereby saving the calculation and communication cost; according to the resource available amount, flexible resource response speed and resource demand amount of the regions at both ends of the limited path, the resource allocation priority is determined, and then the preliminary allocation scheme is obtained, which effectively improves the power grid response efficiency and resource utilization rate; the flow data of the power transmission channel between regions after the preliminary allocation scheme is executed is acquired, the high-risk path is obtained based on the change trend of the flow data, which can timely find the overload or imbalance problem after scheduling, forms a closed-loop feedback in the scheduling process, and enhances the robustness and safety of the system; the local resource composition data in the regions at both ends of the high-risk path is acquired, and the local resources with substitution potential are identified to obtain substitution resources, which effectively supplement the external support and enhance the self-healing ability of the local region; according to the available capacity and response time of the substitution resources, the comprehensive matching degree of the substitution resources and the current allocation scheme is determined, and then the optimized resource allocation scheme is generated, which improves the system stability. The present application realizes the coordinated optimization of cross-regional power resources, effectively improves the safety and economy of power grid operation, and provides technical support for large-scale new energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 is a flow diagram of a regional power grid flexible resource collaborative planning method provided by an embodiment of the present application;

[0057] Figure 2 is a structural diagram of a regional power grid flexible resource collaborative planning device provided by an embodiment of the present application;

[0058] Figure 3 is a structural diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0059] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0060] Please refer to Figure 1 , Figure 1is a flowchart of a regional power grid flexible resource collaborative planning method provided by an embodiment of the present application. The regional power grid flexible resource collaborative planning method comprises:

[0061] S1: obtaining resource available amount and flexible resource response speed of each region in different time periods, and obtaining regional resource distribution data according to the resource available amount and the flexible resource response speed;

[0062] As one of the optional embodiments, the obtaining resource available amount and flexible resource response speed of each region in different time periods, and obtaining regional resource distribution data according to the resource available amount and the flexible resource response speed comprises:

[0063] obtaining power load and maximum output power of energy storage equipment of each region in different time periods;

[0064] obtaining resource available amount according to the difference between the maximum output power of the energy storage equipment and the power load;

[0065] For each region, a clustering algorithm is used to classify the response time of the flexible resource in the region to determine the resource of the fast response class; if the proportion of the resource of the fast response class in the total resource capacity of the region is greater than a preset threshold, the response weight of the region is determined as a first weight, otherwise the response weight of the region is determined as a second weight, the first weight being greater than the second weight;

[0066] multiplying the response weight of each region and the resource available amount to obtain a matrix element, and taking region and time period as row and column of the matrix respectively to construct a resource distribution matrix;

[0067] based on the resource distribution matrix, calculating the difference of the matrix elements of the corresponding time periods of adjacent regions to obtain resource difference degree;

[0068] merging adjacent regions with the same response weight and resource difference degree less than a preset threshold into the same resource cluster to generate regional resource distribution data.

[0069] Specifically, in the regional resource scheduling of the power system, obtaining accurate resource availability data is the basis for efficient scheduling. The embodiment first obtains the power load data and energy storage device operating parameters of each region within a preset time period. The power load data is obtained from the real-time monitoring system of the substation in each region and records the total power consumption of the region. The energy storage device operating parameters include the current power of the energy storage battery, the maximum charging and discharging power, and the charging and discharging efficiency, and other key indicators. Further, after obtaining the original data, the data points of the missing period are completed by interpolation method, and then the resource availability is obtained by subtracting the current period load value from the maximum output power of the energy storage device. The resource availability reflects the power supply capacity of the region, indicating the power support capability that the region can provide externally after meeting its own load demand. At the same time, the power regulation demand of each region is read from the dispatch center database, and the ratio of resource availability to regulation demand is calculated as the supply-demand ratio, which further quantifies the degree of resource tension. When the supply-demand ratio is less than 1, it indicates that the resource supply of the region is tight, and power needs to be imported from other regions.

[0070] Further, the K-means clustering algorithm is used to classify the flexible resources in the region according to the response time. Specifically, the response time less than 1 minute is classified as fast response class, the response time between 1 to 5 minutes is classified as medium response class, and the response time greater than 5 minutes is classified as slow response class. The fast response class resources mainly include flywheel energy storage and super capacitor, which can complete power regulation within a few seconds; the medium response class resources mainly include lithium battery energy storage system, with response time in the minute level; and the slow response class resources mainly include large-scale energy storage facilities such as pumped storage power station. Through resource response time classification, the dispatch center can select the appropriate resource type according to different regulation demands. If the proportion of fast response class resources in the total resource capacity of the region is greater than a preset threshold, the response weight of the region is determined as a first weight, otherwise the response weight of the region is determined as a second weight. The first weight is greater than the second weight, for example, the first weight is set to 1 and the second weight is set to 0.5.

[0071] According to the response weight and resource availability corresponding to the region, a resource distribution matrix is constructed. The number of rows of the matrix is the total number of regions, the number of columns is the total number of time periods, and the matrix elements are the product of resource availability and the response weight of the corresponding region. The matrix elements fully consider the response ability difference of the region. For the region with strong fast response ability, the weight of its resource availability in the matrix is greater, which can more accurately reflect the actual contribution of the region to the grid regulation. Further, the resource difference degree is obtained by calculating the absolute value of the difference between the matrix elements of adjacent regions corresponding to the time period, which can identify the imbalance of resource distribution and provide a basis for subsequent resource optimization configuration.

[0072] Further, based on the resource difference degree, if the resource difference degree of adjacent two regions exceeds a preset threshold, a demarcation mark is set at the region boundary, and adjacent regions with the same response weight and resource difference degree less than the preset threshold are merged into the same resource cluster. The formation process of the region resource cluster is similar to the spatial clustering analysis in the geographic information system. When adjacent regions have similar resource characteristics and response capabilities, they are classified into the same cluster, which can simplify the complexity of the dispatching decision, so that the dispatching center can allocate resources in units of clusters, thereby improving the dispatching efficiency. Furthermore, a unique number is assigned to each resource cluster, and region resource distribution data containing region number, cluster number and resource characteristics are generated. The region resource distribution data contains detailed resource information of each region, which provides important data support for real-time dispatching and long-term planning of the power grid.

[0073] S2: determining a limited path based on the capacity limit of the power transmission channel between regions analyzed based on the region resource distribution data;

[0074] As one of the optional embodiments, the step of determining a limited path based on the capacity limit of the power transmission channel between regions analyzed based on the region resource distribution data comprises:

[0075] Based on the region resource distribution data, the rated capacity and the current transmission power of the power transmission line between adjacent regions are obtained, and the high-load line is determined according to the rated capacity and the current transmission power.

[0076] According to the high-load line and the connection node information thereof, the power transmission path between each resource cluster is determined.

[0077] According to the capacity utilization rate of the power transmission path and the historical transmission power data, the linear regression method is used to calculate the predicted transmission power of each path in the next dispatching period.

[0078] For each of the power transmission paths, if the difference between the predicted transmission power of the path and the transmission upper limit value of the path is less than a preset safety threshold, or the capacity utilization rate of the path is greater than a preset limit threshold, the power transmission path is marked as a limited path.

[0079] Specifically, the rated capacity and the current transmission power of the power transmission line between adjacent regions are obtained according to the region resource distribution data, and the load rate is obtained by dividing the current transmission power by the rated capacity. In the operation of the power system, the load rate of the power transmission line is a key indicator for evaluating the operation state of the line. If the load rate exceeds a preset threshold, the line is marked as a high-load line, and the line number, load rate and connected region node information are recorded.

[0080] Further, based on the high-load line and its connection node information, the power transmission path between each resource cluster is calculated using the Dijkstra algorithm. The application of the Dijkstra algorithm in the calculation of the power transmission path embodies the value of graph theory in power grid analysis. The algorithm abstracts the power grid as a set of nodes and edges, where the nodes represent substations or regional centers, and the edges represent transmission lines. The core of the algorithm is to find the shortest path from the source node to the target node, and the path distance can be defined as the electrical distance or transmission cost. In practical applications, the algorithm will traverse all possible transmission paths and select the path with the smallest total impedance or the lowest transmission loss as the optimal solution. Further, the rated capacity values of all transmission lines on each path are obtained, and the minimum rated capacity in the path is selected as the transmission upper limit value of the path. For example, the transmission path from region A to region C may pass through three lines with capacities of 800 MW, 600 MW, and 900 MW, respectively. Therefore, the transmission upper limit value of this path is 600 MW. Further, the real-time transmission power sum of the path is read from the dispatching data, and the capacity utilization rate is obtained by dividing the real-time transmission power by the transmission upper limit value.

[0081] Further, according to the capacity utilization rate and historical transmission power data, the predicted transmission power of each path in the next dispatching period is calculated through linear regression method. Specifically, a linear relationship model between time and transmission power is established, and the model parameters are determined using the least squares method, wherein the prediction process considers the periodic characteristics of the daily load curve, such as the power growth law during morning and evening peak periods. If the difference between the predicted transmission power and the transmission upper limit value is less than the preset safety threshold, the path is marked as a restricted path. In addition, paths with a capacity utilization rate exceeding the preset limit threshold are also marked as restricted paths.

[0082] Further, for the restricted paths, a restricted level is assigned to each restricted path according to the capacity utilization rate value, and the higher the capacity utilization rate, the higher the restricted level. The restricted path set containing path number, restricted level, and bottleneck line information is determined. By classifying restricted paths with different severity levels, the dispatching center can develop differentiated response measures, such as assigning the highest restricted level to paths with a capacity utilization rate above 0.9. These paths need to be prioritized for power adjustment or the activation of backup transmission channels, thereby effectively preventing the occurrence of power grid congestion.

[0083] S3: determining a resource allocation priority according to the available amount of resources, the response speed of flexible resources, and the resource demand amount of the two regions at both ends of the restricted path, and obtaining a preliminary allocation scheme based on the resource allocation priority to execute the preliminary allocation scheme;

[0084] As one of the optional embodiments, the method further comprises:

[0085] Based on the regional resource distribution data, the resource available amount, the flexible resource response speed and the resource demand amount in the regions at both ends of the restricted path are obtained; the regions at both ends of the restricted path are divided into a supply region and a demand region;

[0086] For the supply region, a unit-time schedulable resource amount is calculated according to a ratio of the resource available amount to the flexible resource response speed;

[0087] A resource matching coefficient is obtained according to a ratio of the resource available amount of the supply region to the resource demand amount of the demand region;

[0088] For each restricted path, a feasibility coefficient is obtained by weighted summation of the unit-time schedulable resource amount and the resource matching coefficient;

[0089] The resource allocation priority of the restricted path is determined according to an order of the feasibility coefficient from large to small.

[0090] Specifically, the resource data of the regions at both ends of each path in the restricted path set is obtained through the regional resource distribution data, the flexible resource capacity value and the resource type identifier of the region are extracted, for the two end regions of the restricted path, i.e. the starting region and the ending region, the difference between the resource available amount of the starting region and the resource available amount of the ending region is calculated, if the difference is greater than a preset threshold, the starting region is marked as a supply region and the ending region is marked as a demand region.

[0091] Further, the current adjustable capacity and the response speed of each type of flexible resource are extracted from the resource distribution data of the supply region, wherein the response speed is the time required for the resource to output rated power from receiving the dispatching instruction, the ratio of the adjustable capacity to the response speed is calculated as the unit-time schedulable resource amount. The unit-time schedulable resource amount can more accurately evaluate the actual contribution ability of different resources in the emergency scheduling scenario by taking the ratio of the adjustable capacity to the response time as the measurement standard. Secondly, the resource demand amount is extracted from the load data of the demand region, the ratio of the adjustable capacity of the supply region to the resource demand amount of the demand region is calculated to obtain the resource matching coefficient, and the resource matching coefficient can reflect the quantitative relationship between supply and demand.

[0092] In one specific example, the adjustable capacity of the supply area is 600 MW, and the resource demand of the demand area is 400 MW, resulting in a resource matching coefficient of 1.5, which is greater than 1, indicating that the supply is sufficient, and the greater the coefficient, the higher the feasibility of the allocation. On the contrary, if the coefficient is less than 1, it means that the resources of the supply area are not enough to fully meet the needs of the demand area, and it may be necessary to consider multi-source supply or partial satisfaction solutions.

[0093] Further, based on the resource matching coefficient and the amount of schedulable resources per unit time, the resource matching coefficient is multiplied by a first preset weight value, and the amount of schedulable resources per unit time is normalized by maximum and minimum value and then multiplied by a second preset weight value. The sum of the two is the feasibility coefficient. The sum of the first preset weight value and the second preset weight value is 1. The feasibility coefficient is calculated for all regions at both ends of the path set, and then sorted in descending order according to the feasibility coefficient to obtain the resource allocation priority sequence. The comprehensive calculation of the feasibility coefficient embodies the idea of multi-factor decision-making. By setting different weight values, the system can adjust the decision preference according to the actual demand. When the power grid is in an emergency state, the weight of response speed may be increased; while in regular dispatching, the weight of resource matching degree may be higher. The final resource allocation priority sequence provides a clear execution order for dispatching decisions, ensuring the efficiency and rationality of resource allocation. This priority sequence also ensures the maximization of resource allocation efficiency, avoiding resource waste or dispatching failure caused by blind allocation.

[0094] Further, a preliminary allocation scheme is generated according to the resource allocation priority sequence, and power grid resource scheduling is performed according to the preliminary allocation scheme. Specifically, flexible resource data that can be allocated is extracted according to the resource allocation priority sequence, combined with different time scale response speed and regional resource difference data, adjustment demand is analyzed, fast response resources are allocated, and a preliminary allocation scheme is determined. Among them, the resource allocation priority sequence is hierarchically processed according to the response time threshold, the resources with a response time less than a first preset threshold are classified into a second level category, the resources with a response time between the first preset threshold and a second preset threshold are classified into a minute level category, and the resources with a response time greater than the second preset threshold are classified into an hour level category. At the same time, the types and quantities of resources contained in each category are recorded to obtain three-layer time scale energy resource grouping data. For wind power, photovoltaic and conventional units involved in the three-layer time scale energy resource grouping data, their historical operation data are read respectively, the square root of the variance of the output difference sequence of adjacent sampling points of the wind farm is calculated to obtain the fluctuation amplitude value, the number of periods with zero daily output of the photovoltaic power station is divided by the total number of periods to obtain the intermittency ratio, and the peak-valley difference coefficient is obtained by dividing the daily maximum value of the grid load curve by the daily minimum value. The fluctuation amplitude value, the intermittency ratio and the peak-valley difference coefficient are compared with the preset fluctuation threshold, intermittency threshold and peak-valley threshold respectively. If the fluctuation amplitude value exceeds the fluctuation threshold, it is recorded that the wind power needs second-level response resource support. If the intermittency ratio exceeds the intermittency threshold, it is recorded that the photovoltaic needs minute-level response resource supplement. If the peak-valley difference coefficient exceeds the peak-valley threshold, it is recorded that the load needs hour-level response resource balance. A resource demand matching matrix is generated. Based on the resource demand matching matrix, a configuration rule is developed: second-level response resources are configured at nodes where wind power is concentrated and the capacity is not less than the wind power installed capacity multiplied by the fluctuation amplitude value, minute-level response resources are configured in regions where photovoltaic is large-scale interconnected and the capacity is not less than the photovoltaic installed capacity multiplied by the intermittency ratio, and hour-level response resources are configured in load centers and the capacity is not less than the daily peak-valley difference. A multi-time scale coordinated fast response resource configuration system is constructed to generate a preliminary allocation scheme.

[0095] S4: obtaining flow data of the inter-regional power transmission channel after the preliminary allocation scheme is executed, performing path risk assessment based on the change trend of the flow data, and determining a high-risk path;

[0096] As one of the optional embodiments, the obtaining flow data of the inter-regional power transmission channel after the preliminary allocation scheme is executed, performing path risk assessment based on the change trend of the flow data, and determining a high-risk path, comprises:

[0097] Obtaining flow data of the inter-regional power transmission channel after the preliminary allocation scheme is executed, and obtaining the remaining transmission capacity of the restricted path according to the flow data;

[0098] According to the ratio of the remaining transmission capacity to the rated capacity of the path, a safety margin ratio is obtained.

[0099] For each limited path, if the safety margin ratio is less than a preset stability threshold, a safety margin decline rate is calculated according to the safety margin ratios of adjacent time instants, and a path with a safety margin decline rate greater than a preset speed threshold is marked as a risk path.

[0100] Based on the risk path, a direct current power flow algorithm is used to calculate the active power distribution of each line after the dispatch, a power prediction value is obtained by power prediction on the path, and a path with a power prediction value greater than a preset power threshold is marked as a high-risk path.

[0101] Specifically, the real-time power transmission value of each power transmission line after the preliminary dispatch scheme is executed is obtained, and compared with the historical flow data before the dispatch, the increase and decrease of the flow of each line is calculated, for all power transmission lines on the limited path, the remaining transmission capacity is obtained by subtracting the current real-time flow from the line rated capacity, and the safety margin ratio is obtained by dividing the remaining transmission capacity by the rated capacity. The safety margin ratio is a key indicator for evaluating the operating state of the power transmission line.

[0102] Further, according to the safety margin ratio, if the ratio is less than a preset stability threshold, it indicates that the line has approached full load operation and there is an overload risk, then the difference between the safety margin ratios of two adjacent sampling time instants is calculated, and the margin decline rate is obtained by dividing the difference by the time interval. If the margin decline rate is positive and exceeds a preset speed threshold, it is determined that the stability of the corresponding path decreases, which reflects the dynamic change trend of system stability. When the decline rate continues to be positive and exceeds the preset threshold, it indicates that the system is rapidly approaching an unstable boundary, and the corresponding path is marked as a risk path.

[0103] Based on the risk path and the injection power of each node, a direct current power flow algorithm is used to calculate the active power distribution of each line after the dispatch, wherein the node voltage phase angle is obtained by solving the linear equation set of the node admittance matrix and the injection power, and the line power is obtained by multiplying the line inductance by the phase angle difference between the two end nodes. The application of the direct current power flow algorithm in power system analysis is based on the linearization model of the power grid. The core of the algorithm is to establish the node power balance equation set, and to obtain the voltage phase angle of each node by solving the relationship between the node admittance matrix and the injection power. In actual calculation, first, the node admittance matrix of the system is constructed, which reflects the topology structure and line parameters of the power grid; then, the injection power of each node is taken as a known quantity, and the node phase angle is solved by matrix operation; finally, the line power is calculated by using the phase angle difference between two nodes and the line inductance.

[0104] The calculation of line power is based on the basic principle of power transmission. When there is a phase angle difference between two nodes, active power transmission occurs. The size of the transmitted power is proportional to the phase angle difference and the line inductance. This linear relationship enables the DC power flow algorithm to quickly solve the power distribution of large-scale power grids, although it ignores reactive power and network loss, but it has sufficient accuracy for evaluating active power distribution and identifying transmission bottlenecks.

[0105] Further, according to the obtained line power, a linear equation of power change over time is fitted by using the least squares method, the slope of the equation is calculated as the power growth rate, the predicted power value is obtained by adding the current power value to the predicted time length multiplied by the power growth rate, if the predicted power value exceeds a preset proportion of the path capacity or the power growth rate exceeds a preset threshold, the path is marked as a high-risk path, and the risk assessment is completed. Wherein, the least squares fitting collects the power values of the risk path at multiple times to form time-power data pairs, and then finds a straight line that minimizes the sum of the squared perpendicular distances of all data points to the straight line. The slope of the fitted straight line directly reflects the growth trend of the power. The greater the slope, the faster the power grows, and the higher the potential risk. The grading determination of risk assessment fully considers the static and dynamic dimensions. The static dimension focuses on whether the predicted power value exceeds the capacity limit. For example, when the predicted power reaches 0.9 times the path capacity, even if the current operation is normal, it also needs to be marked as high risk. The dynamic dimension focuses on the power growth rate. Even if the current power is low, if the growth rate is too fast, it also needs to be marked as high risk. This double determination mechanism ensures the comprehensiveness and accuracy of risk identification.

[0106] S5: Obtain local resource composition data in the region on both ends of the high-risk path, identify local resources with replacement potential according to the local resource composition data, and obtain replacement resources;

[0107] As one of the optional embodiments, the obtaining of the local resource composition data in the region on both ends of the high-risk path, the identification of the local resources with replacement potential according to the local resource composition data, and the obtaining of the replacement resources comprise:

[0108] Obtain the local resource composition data in the region on both ends of the high-risk path and the predicted overload power, and obtain the adjustable power of the local resources at different time periods based on the local resource composition data;

[0109] Calculate the ratio of the adjustable power of the local resources to the predicted overload power of the corresponding high-risk path, and if the ratio is greater than a preset replacement threshold, determine that the local resources have replacement potential;

[0110] For the local resources with replacement potential, the resource response time of each local resource is obtained, and resources with a resource response time less than a preset time limit requirement are screened to obtain replacement resources.

[0111] Specifically, the local resource composition data in the starting area and the ending area of the high-risk path is obtained, including the installed capacity, the current output value and the rated parameter of the distributed power supply, the energy storage device and the interruptible load, the adjustable upper limit and lower limit of each resource in different time periods are read, the difference between the current output value and the upper limit is taken as the up-regulation capacity, and the difference between the current output value and the lower limit is taken as the down-regulation capacity, which can accurately reflect the actual adjustment potential of the resource under the current operating state. Further, the resources are screened according to the adjustment direction demand to obtain a set of direction-matched local resources.

[0112] When the high-risk path is overloaded, the load needs to be reduced at the receiving end or the power generation needs to be reduced at the sending end. Further, the ratio of the adjustable power of each local resource to the predicted overload power of the high-risk path is calculated, and if the ratio is greater than a preset replacement threshold, the resource has replacement potential. Illustratively, the replacement threshold is set to 0.8, which means that the adjustable power of a single resource needs to be more than 80% of the overload power to be considered to have independent replacement ability. This threshold setting not only ensures the reliability of the replacement effect, but also avoids the risk of excessive dependence on a single resource. At the same time, the time required for the resource to receive the instruction to output the target power is extracted as the resource response time, which is compared with the time when the path overload is predicted to occur, and resources with a response time less than the time limit requirement are screened to form a time-matched candidate resource group, and replacement resources are obtained.

[0113] S6: According to the available capacity and response time of the replacement resource, the comprehensive matching degree of the replacement resource and the current deployment scheme is determined, and an optimized resource configuration scheme is generated based on the comprehensive matching degree.

[0114] As one of the optional embodiments, the determination of the comprehensive matching degree of the replacement resource and the current deployment scheme according to the available capacity and response time of the replacement resource, and the generation of the optimized resource configuration scheme based on the comprehensive matching degree, comprises:

[0115] Based on the replacement resource, a target function is constructed to maximize the output power adjustment amount of the resource, and the available capacity and response time of the replacement resource are obtained by solving through a linear programming method according to the output power limit constraint, the climbing rate constraint and the power maintenance time constraint;

[0116] The adjustment contribution value of the replacement resource is determined according to the available capacity and the response time of the replacement resource, and a resource scheduling sequence is generated according to the descending order of the adjustment contribution value;

[0117] a capacity matching coefficient is obtained according to a ratio of the available capacity of the alternative resource to a power demand of the corresponding area;

[0118] a time margin is calculated according to a difference between a time requirement of the corresponding area and the response time of the alternative resource, and a time matching coefficient is obtained according to a ratio of the time margin to the time requirement;

[0119] a comprehensive matching degree of the alternative resource and the current deployment scheme is obtained by weighted summation of the capacity matching coefficient and the time matching coefficient;

[0120] If the comprehensive matching degree is greater than a preset matching threshold, resource scheduling is performed according to the resource scheduling sequence; if the comprehensive matching degree is less than the preset matching threshold, a candidate deployment relationship table is constructed according to a remaining adjustable power of each resource in the current resource allocation scheme and an electrical distance from each resource to each demand point, and a particle swarm optimization algorithm is used to optimize resource power allocation to obtain an optimized resource allocation result.

[0121] Specifically, for each alternative resource, a target function is set as maximizing an output power adjustment amount of the resource, and constraint conditions include that the output power is not greater than an upper and lower limit, a power change rate is not greater than a maximum ramp rate, and a maintenance time length of the adjusted power is not less than a demand time length. A maximum adjustment amount satisfying all constraints is obtained as the available capacity by linear programming, and a corresponding response time value is recorded. The target function is set as maximizing the output power adjustment amount of the resource, which corresponds to the demand of relieving line overload, and the constraint conditions are set to ensure the feasibility of the scheduling scheme: the power upper and lower limit constraint ensures safe operation of the equipment, the ramp rate constraint reflects the physical characteristic limitation of the resource, and the continuous time length constraint ensures that the resource can provide support during the entire overload period.

[0122] Further, according to the available capacity and the response time, the adjustable power is obtained by multiplying the available capacity by the continuous time length, and the unit time adjustment contribution value is obtained by dividing the adjustable power by the response time, and the resource scheduling sequence is generated in descending order of the contribution value. It should be noted that the resource response time includes communication delay, control system processing time and device physical response time. For a storage system, the communication and control delay is usually in seconds, and the ramping process from zero power to rated power may take tens of seconds to several minutes. The adjustable power reflects the overall contribution ability of the resource. The unit time adjustment contribution value balances the capacity size and the response speed of two key factors. Through the comprehensive evaluation of the embodiment, a flywheel energy storage with smaller capacity but extremely fast response may obtain a higher scheduling priority than a pumped storage with larger capacity but slower response, thereby realizing the optimized configuration of the resource.

[0123] Further, according to the available capacity and response time data of the alternative resource, the power demand and time requirement of the corresponding area in the global resource allocation scheme are read, the ratio of the available capacity of the alternative resource to the power demand is calculated as a capacity matching coefficient, the time margin is obtained by subtracting the response time of the alternative resource from the time requirement, the time matching coefficient is obtained by dividing the time margin by the time requirement, and finally the capacity matching coefficient and the time matching coefficient are weighted and superimposed to obtain a comprehensive matching degree. The calculation of the comprehensive matching degree realizes multi-dimensional evaluation, and the rationality of resource allocation is judged by quantifying the matching degree of different dimensions.

[0124] Further, the comprehensive matching degree is compared with a preset matching threshold, if the comprehensive matching degree is lower than the threshold, the allocated power and the remaining adjustable power of each resource in the current resource allocation scheme are extracted, the electrical distance from each resource to each demand point is calculated, and the electrical distance reflects the electrical coupling degree between two nodes in resource allocation. The electrical distance not only considers the length of the physical line, but more importantly, considers the electrical parameters such as line impedance and transformer tap position. The electrical distance of two nodes with similar physical distance but connected by a high impedance line may be larger. This distance measurement method more accurately reflects the difficulty of power transmission, providing a scientific basis for resource allocation. Further, a candidate allocation relationship table is constructed according to the electrical distance and the remaining adjustable power, the resource power allocation value to be adjusted is determined, and a particle swarm optimization algorithm is used for optimization. Each particle is defined as a set of resource allocation schemes, the dimension of the particle is equal to the number of combinations of resources and demand points, the objective function is set as the sum of the squares of the difference between the load rate of all transmission paths and the average load rate, and the particle position is iteratively updated under the conditions of satisfying power balance and capacity limitation to obtain the optimized power allocation value.

[0125] Among them, the application of particle swarm optimization algorithm in power system optimization is due to its good global search ability. Each particle represents a possible resource allocation scheme, and each dimension of the particle corresponds to a specific power value of a resource-demand point pair. When the particle moves in the solution space, it will remember its own historical optimal position and the optimal position of the group, and gradually approach the optimal solution through this information sharing mechanism.

[0126] It should be noted that the objective of minimizing the variance of the load rate is to achieve load balancing and avoid the imbalance phenomenon that some lines are overloaded while others are lightly loaded. When the load rates of all transmission paths are close to the average value, the variance is minimized, and the system runs most evenly. This balanced state not only improves the safety margin of the system, but also reserves adjustment space for unexpected situations. The power balance constraint ensures the physical feasibility of the optimization process, and at any time, the injected power of each node must be equal to the outgoing power; the capacity limitation constraint ensures that the equipment will not operate beyond its physical capability range. These two types of constraints together constitute the boundary of the feasible region of the optimization problem.

[0127] Further, according to the optimized power distribution value, a new allocation power of each resource to each demand area is calculated, a power adjustment amount is obtained by comparing with the original scheme, and an optimized resource configuration result including a resource number, a target area number, an original configuration power and an adjusted configuration power is generated to perform resource scheduling, so that the regional power grid flexible resource collaborative planning is realized.

[0128] The embodiment of the present application can intuitively reflect the real-time supply capacity of each area by obtaining the resource available amount and the flexible resource response speed of each area in different time periods to obtain the regional resource distribution data, and also takes into account the influence of response speed on scheduling, thereby providing an accurate basis for subsequent power transmission channel capacity evaluation and risk decision-making; based on the capacity limitation of the power transmission channel between regions analyzed based on the regional resource distribution data, the limited path is determined, which can quickly locate the bottleneck path and avoid blind scheduling in the whole network range, thereby saving the calculation and communication cost; according to the resource available amount, the flexible resource response speed and the resource demand amount of the regions at both ends of the limited path, the resource allocation priority is determined, and then the preliminary allocation scheme is obtained, thereby effectively improving the power grid response efficiency and resource utilization rate; the flow data of the power transmission channel between regions after the preliminary allocation scheme is executed is obtained, the high-risk path is obtained based on the change trend of the flow data, which can timely find the overload or imbalance problem after scheduling, so that the scheduling process forms a closed-loop feedback, thereby enhancing the robustness and safety of the system; the local resource composition data in the regions at both ends of the high-risk path is obtained, and the local resource with substitution potential is identified to obtain the substitution resource, thereby effectively supplementing the external support and enhancing the self-healing ability of the local area; according to the available capacity and response time of the substitution resource, the comprehensive matching degree of the substitution resource and the current allocation scheme is determined, and then the optimized resource configuration scheme is generated, thereby improving the system stability. The present application realizes the coordinated optimization configuration of cross-regional power resources, effectively improves the safety and economy of power grid operation, and provides technical support for large-scale new energy consumption.

[0129] Correspondingly, the present application also provides a regional power grid flexible resource collaborative planning device, which can realize all processes of the regional power grid flexible resource collaborative planning method in the above embodiment.

[0130] Please refer to Figure 2 , Figure 2 is a structural schematic diagram of a regional power grid flexible resource collaborative planning device provided by the embodiment of the present application. The regional power grid flexible resource collaborative planning device comprises:

[0131] The regional resource analysis module 201 is configured to obtain the resource available amount and the flexible resource response speed of each area in different time periods, and obtain the regional resource distribution data according to the resource available amount and the flexible resource response speed.

[0132] The restricted path determination module 202 is configured to determine a restricted path based on capacity restriction of the inter-regional power transmission channel analyzed based on the regional resource distribution data.

[0133] The preliminary resource allocation module 203 is configured to determine a resource allocation priority according to the resource available amount, flexible resource response speed and resource demand amount of the regions at both ends of the restricted path, and obtain a preliminary allocation scheme based on the resource allocation priority, so as to execute the preliminary allocation scheme.

[0134] The risk path determination module 204 is configured to obtain flow data of the inter-regional power transmission channel after the preliminary allocation scheme is executed, perform path risk assessment based on the change trend of the flow data, and determine a high-risk path.

[0135] The local resource analysis module 205 is configured to obtain local resource composition data in the regions at both ends of the high-risk path, identify local resources with substitution potential according to the local resource composition data, and obtain substitution resources.

[0136] The resource configuration optimization module 206 is configured to determine a comprehensive matching degree of the substitution resources and the current allocation scheme according to the available capacity and response time of the substitution resources, and generate an optimized resource configuration scheme based on the comprehensive matching degree.

[0137] Preferably, the regional resource analysis module 201 is specifically configured to:

[0138] Obtain power load and maximum output power of energy storage devices of each region in different time periods;

[0139] Obtain resource available amount according to the difference between the maximum output power of the energy storage devices and the power load;

[0140] For each region, a clustering algorithm is used to classify the flexible resources in the region according to response time, and resources in a fast response class are determined; if the proportion of the resources in the fast response class in the total resource capacity of the region is greater than a preset threshold, the response weight of the region is determined as a first weight, otherwise the response weight of the region is determined as a second weight, and the first weight is greater than the second weight;

[0141] The product of the response weight and the resource available amount of each region is taken as a matrix element, and the region and the time period are taken as the row and the column of the matrix respectively, so as to construct a resource distribution matrix;

[0142] Based on the resource distribution matrix, the difference between the matrix elements of adjacent regions corresponding to the time period is calculated to obtain resource difference degree;

[0143] merge the adjacent areas with the resource difference less than the preset threshold and the same response weight into a same resource cluster to generate regional resource distribution data.

[0144] Preferably, the limited path determination module 202 is specifically configured to:

[0145] based on the regional resource distribution data, obtain the rated capacity and the current transmission power of the power transmission line between adjacent areas, and determine a high-load line according to the rated capacity and the current transmission power;

[0146] determine the power transmission path between each resource cluster according to the high-load line and the connection node information thereof;

[0147] based on the capacity utilization rate of the power transmission path and the historical transmission power data, calculate the predicted transmission power of each path in the next scheduling period by using a linear regression method;

[0148] For each of the power transmission paths, if the difference between the predicted transmission power of the path and the transmission upper limit value of the path is less than a preset safety threshold, or the capacity utilization rate of the path is greater than a preset limit threshold, the power transmission path is marked as a limited path.

[0149] Preferably, the preliminary resource allocation module 203 is specifically configured to:

[0150] based on the regional resource distribution data, obtain the resource available amount, the flexible resource response speed and the resource demand amount in the areas at both ends of the limited path; the areas at both ends of the limited path are divided into a supply area and a demand area;

[0151] for the supply area, the unit-time schedulable resource amount is calculated according to the ratio of the resource available amount to the flexible resource response speed;

[0152] a resource matching coefficient is obtained according to the ratio of the resource available amount of the supply area to the resource demand amount of the demand area;

[0153] for each limited path, the unit-time schedulable resource amount and the resource matching coefficient are weighted and summed to obtain a feasibility coefficient;

[0154] determine the resource allocation priority of the limited path according to the order of the feasibility coefficient from large to small.

[0155] Preferably, the risk path determination module 204 is specifically configured to:

[0156] obtain the flow data of the inter-regional power transmission channel after the preliminary allocation scheme is executed, and obtain the residual transmission capacity of the limited path according to the flow data;

[0157] According to the ratio of the residual transmission capability and the path rated capacity, a safety margin ratio is obtained;

[0158] For each limited path, if the safety margin ratio is less than a preset stability threshold, a safety margin drop rate is calculated according to the safety margin ratio of the adjacent time, and the path with the safety margin drop rate greater than a preset speed threshold is marked as a risk path;

[0159] Based on the risk path, a direct current flow algorithm is used to calculate the active power distribution of each line after the dispatch, and a predicted power value is obtained by power prediction of the path, and the path with the predicted power value greater than a preset power threshold is marked as a high-risk path.

[0160] Preferably, the local resource analysis module 205 is specifically configured to:

[0161] Obtain local resource composition data and predicted overload power in the region at both ends of the high-risk path, and obtain the adjustable power of the local resource at different time periods based on the local resource composition data;

[0162] Calculate the ratio of the adjustable power of the local resource to the predicted overload power of the corresponding high-risk path, and if the ratio is greater than a preset replacement threshold, it is determined that the local resource has replacement potential;

[0163] For the local resource with replacement potential, obtain the resource response time of each local resource, and select the resource with the resource response time less than a preset time limit requirement to obtain a replacement resource.

[0164] Preferably, the resource configuration optimization module 206 is specifically configured to:

[0165] Based on the replacement resource, a target function is constructed to maximize the resource output power adjustment amount, and the available capacity and response time of the replacement resource are obtained by solving through a linear programming method according to the output power limit constraint, the climbing rate constraint and the power maintenance time constraint;

[0166] The adjustment contribution value of the replacement resource is determined according to the available capacity and the response time of the replacement resource, and a resource scheduling sequence is generated according to the adjustment contribution value from large to small;

[0167] According to the ratio of the available capacity of the replacement resource and the power demand amount of the corresponding region, a capacity matching coefficient is obtained;

[0168] According to the difference between the time requirement of the corresponding region and the response time of the replacement resource, a time margin is calculated, and according to the ratio of the time margin and the time requirement, a time matching coefficient is obtained;

[0169] The capacity matching coefficient and the time matching coefficient are weighted and summed to obtain a comprehensive matching degree of the alternative resource and the current allocation scheme.

[0170] If the comprehensive matching degree is greater than a preset matching threshold, resource scheduling is performed according to the resource scheduling sequence; if the comprehensive matching degree is less than the preset matching threshold, a candidate allocation relationship table is constructed according to the residual adjustable power of each resource in the current resource allocation scheme and the electrical distance from each resource to each demand point, and a particle swarm optimization algorithm is used to optimize resource power allocation to obtain an optimized resource allocation result.

[0171] In a specific implementation, the working principle, control flow and technical effects of the regional power grid flexible resource collaborative planning device provided by the embodiments of the present application are the same as those of the regional power grid flexible resource collaborative planning method in the above embodiments, and will not be repeated here.

[0172] Referring to Figure 3 , Figure 3 is a structural block diagram of a computer device provided by an embodiment of the present application, which includes a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. The processor 301 implements the steps in the above-mentioned regional power grid flexible resource collaborative planning method embodiment when executing the computer program. Alternatively, the processor 301 implements the functions of each module / unit in the above-mentioned device embodiments when executing the computer program.

[0173] For example, the computer program can be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the computer device.

[0174] The computer device can include, but is not limited to, the processor 301 and the memory 302. Those skilled in the art can understand that the schematic diagram is only an example of the computer device and does not limit the computer device, which can include more or fewer components than the diagram, or combine certain components, or different components, for example, the computer device can also include an input / output device, a network access device, a bus, etc.

[0175] The processor 301 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor 301 is a control center of the computer device, and connects all parts of the computer device through various interfaces and lines.

[0176] The memory 302 can be used to store computer programs and / or modules, and the processor 301 realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302, and calling data stored in the memory 302. The memory 302 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory 302 can include a high-speed random access memory, and can also include a nonvolatile memory, for example, a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0177] The modules / units integrated in the computer device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. When the computer program is executed by the processor 301, the steps of the above-mentioned various method embodiments can be realized. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0178] The embodiment of the present application also provides a computer readable storage medium, which comprises a stored computer program, wherein the computer program controls a device where the computer readable storage medium is located to perform the regional power grid flexible resource cooperative planning method provided in any of the above-mentioned embodiments when the computer program is running.

[0179] Compared with the prior art, the application provides a regional power grid flexible resource cooperative planning method, device, equipment and medium, which has the beneficial effects that: by obtaining the resource available amount and flexible resource response speed of each region in different time periods, regional resource distribution data is obtained, which can intuitively reflect the real-time supply capacity of each region, and also takes into account the influence of response speed on dispatching, providing an accurate basis for subsequent power transmission channel capacity evaluation and risk decision-making; based on the regional resource distribution data, the capacity limit of the power transmission channel between regions is analyzed, the limited path is determined, the bottleneck path can be quickly located, blind dispatching in the whole network is avoided, and the calculation and communication costs are saved; according to the resource available amount, flexible resource response speed and resource demand amount of the regions at both ends of the limited path, the resource allocation priority is determined, and then the preliminary allocation scheme is obtained, which effectively improves the power grid response efficiency and resource utilization rate; the flow data of the power transmission channel between regions after the preliminary allocation scheme is executed is obtained, the high-risk path is obtained based on the change trend of the flow data, the overload or imbalance problem after dispatching can be found in time, the dispatching process forms a closed loop feedback, and the robustness and safety of the system are enhanced; the local resource composition data in the regions at both ends of the high-risk path is obtained, and the local resources with replacement potential are identified to obtain replacement resources, which realizes effective supplement of external support and enhances the self-healing ability of the local region; according to the available capacity and response time of the replacement resources, the comprehensive matching degree of the replacement resources and the current allocation scheme is determined, and then the optimized resource allocation scheme is generated, which improves the system stability. The application realizes the coordinated and optimized allocation of cross-regional power resources, effectively improves the safety and economy of power grid operation, and provides technical support for large-scale new energy consumption.

[0180] The above is the preferred embodiment of the application, and it should be noted that for ordinary skilled persons in the art, without departing from the principles of the application, several improvements and refinements can be made, which are also considered within the protection scope of the application.

Claims

1. A method for coordinated planning of flexible resources in a regional power grid, characterized in that, The method comprises the following steps: obtaining resource availability and flexible resource response speed of each region in different time periods, and obtaining regional resource distribution data according to the resource availability and the flexible resource response speed; based on the regional resource distribution data, analyzing the capacity limit of the power transmission channel between regions to determine the limited path; determining the resource allocation priority according to the resource availability, the flexible resource response speed and the resource demand of the regions at both ends of the limited path, and obtaining a preliminary allocation scheme based on the resource allocation priority to execute the preliminary allocation scheme; obtaining the flow data of the power transmission channel between regions after executing the preliminary allocation scheme, and performing path risk assessment based on the change trend of the flow data to determine the high-risk path; obtaining local resource composition data in the regions at both ends of the high-risk path, identifying local resources with substitution potential according to the local resource composition data, and obtaining substitution resources; determining the comprehensive matching degree of the substitution resources and the current allocation scheme according to the available capacity and response time of the substitution resources, and generating an optimized resource allocation scheme based on the comprehensive matching degree; wherein, the step of obtaining the flow data of the power transmission channel between regions after executing the preliminary allocation scheme, and performing path risk assessment based on the change trend of the flow data to determine the high-risk path comprises: obtaining the flow data of the power transmission channel between regions after executing the preliminary allocation scheme, and obtaining the residual transmission capacity of the limited path according to the flow data; obtaining the safety margin ratio according to the ratio of the residual transmission capacity to the rated capacity of the path; for each limited path, if the safety margin ratio is less than a preset stability threshold, calculating the safety margin decline rate according to the safety margin ratio of the adjacent time, and marking the path with the safety margin decline rate greater than a preset speed threshold as a risk path; based on the risk path, calculating the active power distribution of each line after allocation by using the direct current flow algorithm, performing power prediction on the path to obtain a predicted power value, and marking the path with the predicted power value greater than a preset power threshold as a high-risk path; wherein, the step of determining the comprehensive matching degree of the substitution resources and the current allocation scheme according to the available capacity and response time of the substitution resources, and generating an optimized resource allocation scheme based on the comprehensive matching degree comprises: based on the substitution resources, constructing an objective function to maximize the resource output power adjustment amount, and solving the available capacity and response time of the substitution resources by linear programming method according to the output power limit constraint, the climbing rate constraint and the power maintenance time constraint; determining the adjustment contribution value of the substitution resources according to the available capacity and the response time of the substitution resources, and generating a resource scheduling sequence according to the descending order of the adjustment contribution value; obtaining the capacity matching coefficient according to the ratio of the available capacity of the substitution resources to the power demand of the corresponding region; calculating the time margin according to the difference between the time requirement of the corresponding region and the response time of the substitution resources, and obtaining the time matching coefficient according to the ratio of the time margin to the time requirement; The capacity matching coefficient and the time matching coefficient are weighted and summed to obtain a comprehensive matching degree of the alternative resource and the current allocation scheme; If the comprehensive matching degree is greater than a preset matching threshold, resource scheduling is performed according to the resource scheduling sequence; if the comprehensive matching degree is less than the preset matching threshold, a candidate allocation relationship table is constructed according to the residual adjustable power of each resource in the current resource allocation scheme and the electrical distance from each resource to each demand point, and a particle swarm optimization algorithm is used to optimize resource power allocation to obtain an optimized resource allocation result.

2. The regional power grid flexible resource coordinated planning method of claim 1, wherein, The resource available amount and the flexible resource response speed of each region in different time periods are obtained, and region resource distribution data is obtained according to the resource available amount and the flexible resource response speed, including: The power load and the maximum output power of the energy storage device of each region in different time periods are obtained; The resource available amount is obtained according to the difference between the maximum output power of the energy storage device and the power load; For each region, a clustering algorithm is used to classify the response time of the flexible resource in the region to determine the resource of the fast response type; if the proportion of the resource of the fast response type in the total resource capacity of the region is greater than a preset threshold, the response weight of the region is determined as a first weight, otherwise the response weight of the region is determined as a second weight, the first weight being greater than the second weight; The product of the response weight and the resource available amount of each region is taken as a matrix element, and the region and the time period are taken as the row and the column of the matrix respectively to construct a resource distribution matrix; Based on the resource distribution matrix, the difference between the matrix elements of adjacent regions corresponding to the time period is calculated to obtain a resource difference degree; Adjacent regions with the same response weight and a resource difference degree less than a preset threshold are merged into the same resource cluster to generate region resource distribution data.

3. The regional power grid flexible resource coordinated planning method of claim 1, wherein, The capacity limit of the power transmission channel between regions is analyzed based on the region resource distribution data to determine a limited path, including: Based on the region resource distribution data, the rated capacity and the current transmission power of the power transmission line between adjacent regions are obtained, and a high-load line is determined according to the rated capacity and the current transmission power; According to the high-load line and the connection node information thereof, the power transmission path between each resource cluster is determined; According to the capacity utilization rate of the power transmission path and the historical transmission power data, a linear regression method is used to calculate the predicted transmission power of each path in the next scheduling period; For each power transmission path, if the difference between the predicted transmission power of the path and the transmission upper limit value of the path is less than a preset safety threshold, or the capacity utilization rate of the path is greater than a preset limit threshold, the power transmission path is marked as a limited path.

4. The regional power grid flexible resource coordinated planning method of claim 1, wherein, The resource allocation priority is determined according to the resource available amount, the flexible resource response speed and the resource demand amount of the regions at both ends of the limited path, including: Based on the region resource distribution data, the resource available amount, the flexible resource response speed and the resource demand amount in the regions at both ends of the limited path are obtained; the regions at both ends of the limited path are divided into supply regions and demand regions; According to the ratio of the resource available amount to the flexible resource response speed, a unit-time schedulable resource amount is calculated for the supply area; According to the ratio of the resource available amount of the supply area to the resource demand amount of the demand area, a resource matching coefficient is obtained; For each restricted path, the unit-time schedulable resource amount and the resource matching coefficient are weighted and summed to obtain a feasibility coefficient; According to the order of the feasibility coefficient from large to small, the resource allocation priority of the restricted path is determined.

5. The method of claim 1, wherein, The local resource composition data in the area at both ends of the high-risk path is obtained, and a local resource with substitution potential is identified according to the local resource composition data to obtain a substitution resource, including: The local resource composition data in the area at both ends of the high-risk path and the predicted overload power are obtained, and the adjustable power of the local resource in different time periods is obtained based on the local resource composition data; The ratio of the adjustable power of the local resource to the predicted overload power of the corresponding high-risk path is calculated, and if the ratio is greater than a preset substitution threshold, it is determined that the local resource has substitution potential; For the local resource with substitution potential, the resource response time of each local resource is obtained, and the resources with resource response time less than a preset time limit requirement are screened out to obtain a substitution resource.

6. A regional power grid flexible resource collaborative planning device, characterized in that, Including: The regional resource analysis module is configured to obtain resource available amount and flexible resource response speed of each region in different time periods, and obtain regional resource distribution data according to the resource available amount and the flexible resource response speed; The restricted path determination module is configured to determine a restricted path based on capacity limitation of power transmission channels between regions analyzed based on the regional resource distribution data; The preliminary resource allocation module is configured to determine a resource allocation priority according to resource available amount, flexible resource response speed and resource demand amount of regions at both ends of the restricted path, and obtain a preliminary allocation scheme based on the resource allocation priority to execute the preliminary allocation scheme; The risk path determination module is configured to obtain flow data of power transmission channels between regions after executing the preliminary allocation scheme, and determine a high-risk path based on a change trend of the flow data; The local resource analysis module is configured to obtain local resource composition data in the area at both ends of the high-risk path, and identify a local resource with substitution potential according to the local resource composition data to obtain a substitution resource; The resource configuration optimization module is configured to determine a comprehensive matching degree of the substitution resource and a current allocation scheme according to available capacity and response time of the substitution resource, and generate an optimized resource configuration scheme based on the comprehensive matching degree; The risk path determination module is configured to obtain flow data of power transmission channels between regions after executing the preliminary allocation scheme, and determine a high-risk path based on a change trend of the flow data, including: The flow data of power transmission channels between regions after executing the preliminary allocation scheme is obtained, and the remaining transmission capacity of the restricted path is obtained according to the flow data; According to the ratio of the remaining transmission capacity to the rated capacity of the path, a safety margin ratio is obtained; For each limited path, if the safety margin ratio is less than a preset stability threshold, a safety margin decline rate is calculated according to the safety margin ratio at an adjacent time, and a path with a safety margin decline rate greater than a preset speed threshold is marked as a risk path; Based on the risk path, a direct current flow algorithm is used to calculate the active power distribution of each line after dispatching, and power prediction is performed on the path to obtain a predicted power value, and a path with a predicted power value greater than a preset power threshold is marked as a high-risk path; The method comprises the following steps: Based on the alternative resource, a target function is constructed to maximize the resource output power adjustment amount, and the available capacity and response time of the alternative resource are obtained by linear programming method according to the output power limit constraint, the climbing rate constraint and the power maintenance time constraint; The adjustment contribution value of the alternative resource is determined according to the available capacity and the response time, and a resource scheduling sequence is generated according to the descending order of the adjustment contribution value; The capacity matching coefficient is obtained according to the ratio of the available capacity of the alternative resource to the power demand of the corresponding area; The time margin is calculated according to the difference between the time requirement of the corresponding area and the response time of the alternative resource, and the time matching coefficient is obtained according to the ratio of the time margin to the time requirement; The capacity matching coefficient and the time matching coefficient are weighted and summed to obtain the comprehensive matching degree of the alternative resource and the current dispatching scheme; If the comprehensive matching degree is greater than a preset matching threshold, resource scheduling is performed according to the resource scheduling sequence; if the comprehensive matching degree is less than a preset matching threshold, a candidate dispatching relationship table is constructed according to the remaining adjustable power of each resource in the current resource allocation scheme and the electrical distance from each resource to each demand point, and the resource power distribution is optimized by using a particle swarm optimization algorithm to obtain an optimized resource allocation result.

7. A computer device, characterized by The computer readable storage medium stores a computer program, wherein the device where the computer readable storage medium is located executes the computer program to realize the regional power grid flexible resource collaborative planning method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, wherein the device where the computer readable storage medium is located executes the computer program to realize the regional power grid flexible resource collaborative planning method of any one of claims 1 to 5.

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