A scheduling method, system, device and medium based on source load side dynamic evaluation

Abstract

This invention discloses a scheduling method, system, device, and medium based on dynamic evaluation of the source and load sides, belonging to the field of power grid technology. The method includes: acquiring resource data corresponding to each regulating resource on the source and load sides, as well as the real-time operating status of the power grid, and acquiring at least two demand models; inputting the real-time operating status into each demand model to obtain demand information corresponding to each first stability type, and calculating the resource data for each regulating resource based on each demand information to obtain an evaluation value corresponding to each first stability type; responding to the stability request corresponding to the target stability type, sorting all regulating resources according to the evaluation value corresponding to the target stability type to obtain a resource sequence; determining and executing scheduling instructions based on the resource sequence to realize power grid scheduling. Therefore, by implementing this invention, resource scheduling efficiency and power grid stability can be improved.

Description

Technical Field

[0001] This invention relates to the field of power grid technology, and in particular to a scheduling method, system, device and medium based on dynamic assessment of the source and load sides. Background Technology

[0002] Maintaining grid stability is the core requirement for ensuring the safe operation of the power grid during power system operation. When there is a disturbance or fault in the power grid, the dispatching system needs to call on the regulation resources on the source and load side for emergency response in a short period of time in order to suppress the instability trend and prevent the accident from escalating.

[0003] Currently, common source-load side resource scheduling methods first analyze historical data of various regulating resources, determine their maximum dispatchable capacity, and record it in the scheduling system. When the power grid experiences instability such as power angle instability, voltage over-limit, or frequency deviation, the scheduling system receives the scheduling request and, according to the preset priority rules, sequentially calls the regulating resources in the system that meet the requirements to respond to the request until the scheduling request is met.

[0004] However, different stability problems have different requirements for the response speed, location, and time of adjustment resources. Existing technologies cannot accurately match specific stability problems, resulting in low resource allocation efficiency and potentially inappropriate responses, thereby increasing the risk of power grid instability. Summary of the Invention

[0005] This invention provides a scheduling method, system, device, and medium based on dynamic evaluation of the source and load sides, which can solve the problem of low resource call efficiency caused by the inability of existing technologies to accurately match scheduling resources for different stability requirements.

[0006] This invention provides a scheduling method based on source-load side dynamic evaluation, comprising: Obtain resource data corresponding to each regulating resource on the source-load side, as well as the real-time operating status of the power grid, and obtain at least two demand models, wherein each demand model corresponds one-to-one with the first stability type; The real-time operating status is input into each of the demand models to obtain the demand information corresponding to each of the first stability types. For each of the adjustment resources, the resource data is calculated based on each of the demand information to obtain the evaluation value corresponding to each of the first stability types. In response to a stability request corresponding to a target stability type, all the adjustment resources are sorted according to the evaluation value corresponding to the target stability type to obtain a resource sequence; Based on the resource sequence, scheduling instructions are determined and executed to achieve power grid scheduling.

[0007] This invention, through the construction of multi-type stability demand models, achieves dynamic matching between the real-time operating status of the power grid and the dispatchable capacity of resources on the source and load sides. It transforms traditional static potential labels into time-varying quantitative evaluation values ​​oriented towards specific stability problems, enabling the dispatching system based on dynamic evaluation on the source and load sides to quickly identify and call upon the optimal adjustment resources according to the actual stability needs of the power grid. This significantly improves the timeliness, accuracy, and resource utilization efficiency of emergency response services, effectively supporting the safe operation of large power grids under various stability problems.

[0008] Furthermore, the first stability type includes power angle stability, voltage stability, and frequency stability, and the demand information includes power angle stability demand information, voltage stability demand information, and frequency stability demand information; The step of inputting the real-time operating status into each of the demand models to obtain the demand information corresponding to each first stable type is specifically as follows: The real-time operating status is input into the first demand model corresponding to the power angle stability class. The first demand model is used to perform transient stability analysis on the real-time operating status to obtain the first call priority rule, the first response speed information and the first duration information, which are used as the power angle stability demand information. The real-time operating status is input into the second demand model corresponding to the voltage stability class. The voltage stability is analyzed by the second demand model to obtain the second call priority rule, the second response speed information and the second duration information, which are used as the voltage stability demand information. The real-time operating status is input into the third demand model corresponding to the frequency stability class. The third demand model is used to perform frequency stability analysis on the real-time operating status to obtain the third call priority rule, the third response speed information, and the third duration information, which are used as the frequency stability demand information.

[0009] In this way, specialized demand models were constructed for the three core stability issues of power angle, voltage, and frequency. The complex power grid stability state was transformed into quantifiable demand indicators in three dimensions: call priority, response speed, and duration. This achieved the standardization and structured representation of stability demand, providing a clear, specific, and operable evaluation benchmark for subsequent precise resource matching, and significantly improving the precision and practicality of emergency response demand analysis.

[0010] Furthermore, the acquisition of resource data corresponding to each adjustment resource on the source-load side specifically involves: For multiple regulating resources in the target aggregator, the first power data, first regulating rate data, first grid topology data, and first resource status data of each regulating resource are obtained respectively; The second power data is obtained by summing all the first power data, the second adjustment rate data is obtained by calculating all the first adjustment rate data according to the preset first aggregation rule, and the second resource status data is obtained by calculating the first resource status data according to the preset second aggregation rule. The resource data is obtained by combining the second power data, the second regulation rate data, the second resource status data, and the first power grid topology data.

[0011] By collecting data from multiple dimensions and performing hierarchical aggregation calculations, resource data integration from individual resources to aggregator level is achieved. This not only preserves the overall power capacity and dynamic response characteristics of the resource group, but also ensures the reliability of status information through aggregation rules, forming a structured dynamic description of resources. This significantly improves the efficiency and practicality of large-scale source-load side resource data management and provides a high-quality data foundation for upper-level stability demand assessment.

[0012] Furthermore, the second power data includes active power data, reactive power data, and duration data; the first regulation rate data includes active ramp rate data and reactive response time data; the first grid topology data includes grid connection node location data and voltage sensitivity data; the second resource status data includes response success rate data, response duration data, and equivalent inertia data; and the evaluation value includes a first evaluation value, a second evaluation value, and a third evaluation value. For each of the adjustment resources, the resource data is calculated based on each type of demand information to obtain an evaluation value corresponding to each of the first stability types, specifically as follows: The first power factor is calculated based on the active power data, the active ramp rate data, and the first response speed information. The first location factor is determined based on the grid-connected node location data and the first call priority rule. The stability factor is determined based on the response success rate data and the response duration data. The first power factor, the first position factor, and the stability factor are multiplied together to obtain the first evaluation value of the regulation resource and the power angle stability class; A sensitivity factor is determined based on the voltage sensitivity data and the second call priority rule, a capacity factor is determined based on the active power data and the reactive power data, a speed matching factor is determined based on the reactive response time data and the second response speed information, and a second location factor is determined based on the grid connection node location and the second call priority rule. The sensitivity factor, capacity factor, velocity matching factor, and second position factor are multiplied together to obtain a second evaluation value for the regulation resource and the voltage stability class. The frequency response factor is determined based on the active power data and active ramp rate data, and the frequency modulation factor is determined based on the active power data, the duration data and the third response speed information. The equivalent inertia factor is determined based on the equivalent inertia data, and the third location factor is determined based on the grid connection node location and the third call priority rule. The frequency response factor, frequency modulation factor, and equivalent inertia factor are weighted and summed to obtain a weighting factor. The weighting factor is then multiplied by the third position factor to obtain the third evaluation value of the regulation resource and the frequency stability class.

[0013] By employing refined data classification and multi-factor coupled calculations, independent evaluation models were constructed for three types of stability problems: power angle, voltage, and frequency. These models quantify resource potential from multiple dimensions, including power capacity, dynamic response, spatial location, reliability, sensitivity, and inertial support, forming a scientific, comprehensive, and comparable evaluation value system. This significantly improves the accuracy, multidimensionality, and practicality of source-load side resource potential assessment, providing a reliable decision-making basis for the optimal allocation of resources under different types of stability problems.

[0014] Furthermore, the transient stability analysis of the real-time operating state using the first demand model specifically includes: Retrieve a preset list of faults; Based on the real-time operating status, transient stability analysis is performed on each fault in the fault list to obtain the transient stability margin corresponding to each fault, and power angle instability fault is determined among all the faults according to each transient stability margin. The power angle instability fault is simulated and analyzed to obtain the power angle swing characteristics, the power flow change characteristics of each transmission section, the swing period and the support time corresponding to the damping oscillation. Based on the power angle swing characteristics and the power flow change characteristics of the section, a first region for output power and a second region for receiving power are identified. Based on the first region and the second region, and in conjunction with the grid connection node where each of the regulating resources is located, the first call priority rule corresponding to each of the regulating resources is determined; The first response speed information is determined based on the swing period, and the first duration information is determined based on the support time.

[0015] By using fault-driven transient stability analysis and time-domain simulation, the risk of power angle instability can be accurately identified and its dynamic characteristics can be deeply analyzed. The complex power angle stability problem is transformed into three quantifiable requirements: spatial priority, response speed, and duration. This significantly improves the accuracy, timeliness, and operability of power angle emergency response requirement analysis, and provides precise requirement guidance for resource optimization and allocation in power angle stability scenarios.

[0016] Furthermore, the voltage stability analysis of the real-time operating state using the second demand model specifically includes: For each grid-connected node where the regulating resource is located, obtain the voltage value of each grid-connected node, and calculate the voltage stability margin of each grid-connected node based on the real-time operating status. When the voltage stability margin is less than the preset voltage stability threshold, or the voltage value is not within the preset voltage operating range, it is determined that the grid-connected node has a risk of voltage instability, and a voltage instability node is obtained. Simulation analysis was performed on all the voltage instability nodes to identify the target instability nodes and the target reactive power injection nodes, and the voltage instability type and recovery time corresponding to the voltage instability nodes were obtained. Based on the target unstable node and the target reactive power injection node, determine the second call priority rule corresponding to each of the adjustment resources; The voltage instability type is determined, the second response speed information corresponding to the voltage instability node is determined based on the voltage instability type, and the second duration information is determined based on the recovery time.

[0017] By monitoring node voltage and calculating stability margin, this method accurately identifies voltage instability risks and deeply analyzes their types and characteristics. It transforms voltage stability requirements into four quantifiable indicators: target unstable nodes, reactive power injection nodes, response speed, and duration. It establishes resource space priority based on electrical sensitivity, achieving precise deconstruction and quantitative characterization of voltage stability requirements. This significantly improves the accuracy, relevance, and operability of voltage emergency response requirement analysis, providing precise demand guidance for reactive power optimization in voltage stability scenarios.

[0018] Furthermore, the frequency stability analysis of the real-time operating state using the third demand model specifically includes: Based on the grid frequency and frequency change rate in the real-time operating state, when the frequency change rate is greater than the preset disturbance threshold, it is determined that there is a risk of frequency instability, the disturbance location is determined, and the active power deviation is calculated based on the grid frequency. Obtain the network congestion status, determine the target active power injection location based on the disturbance location and the network congestion status, and determine the third call priority rule corresponding to each of the adjustment resources based on the target active power injection node; The third response speed information is determined based on the preset frequency stabilization time, and the third duration information is determined based on the preset transition time from primary frequency modulation to secondary frequency modulation.

[0019] By monitoring the rate of change of frequency and calculating power deviation, frequency instability risks can be quickly identified and support requirements can be quantified. The optimal active power injection location can be determined by comprehensively considering the location of disturbances and network congestion. Frequency stability requirements are transformed into four quantifiable indicators: power deviation, injection location, response speed, and duration. This enables rapid identification, accurate quantification, and optimized positioning of frequency emergency response requirements, significantly improving the timeliness, accuracy, and network adaptability of frequency emergency response requirement analysis. It also provides precise demand guidance for active power optimization in frequency stable scenarios.

[0020] Another embodiment of the present invention provides a scheduling system based on dynamic evaluation of the source and load side, including: an acquisition module, an evaluation module, a matching module and an execution module; The acquisition module is used to acquire resource data corresponding to each regulating resource on the source-load side, as well as the real-time operating status of the power grid, and to acquire at least two demand models, wherein each demand model corresponds one-to-one with the first stability type. The evaluation module is used to input the real-time operating status into each of the demand models to obtain the demand information corresponding to each of the first stability types, and to calculate the resource data according to each of the demand information for each of the adjustment resources to obtain the evaluation value corresponding to each of the first stability types. The matching module is used to respond to the stability request corresponding to the target stability type, and sort all the adjustment resources according to the evaluation value corresponding to the target stability type to obtain a resource sequence; The execution module is used to determine and execute scheduling instructions based on the resource sequence to realize power grid scheduling.

[0021] Another embodiment of the present invention provides a terminal device, including: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the steps of the scheduling method based on source-load side dynamic evaluation of the present invention.

[0022] Another embodiment of the present invention provides a computer-readable storage medium item, including: a stored computer program, which, when the computer program is running, controls the device where the computer-readable storage medium is located to perform the steps of the scheduling method based on source-load side dynamic evaluation of the present invention. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a scheduling method based on dynamic evaluation of the source and load side provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a scheduling system based on dynamic evaluation of the source and load side provided in an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0027] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0030] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0031] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0032] See Figure 1 To address the problem of low resource allocation efficiency caused by the inability of existing technologies to accurately match and schedule resources for different stability requirements, an embodiment of the present invention provides a scheduling method based on dynamic evaluation on the source-load side, comprising: Step 101: Obtain resource data corresponding to each regulating resource on the source-load side, as well as the real-time operating status of the power grid, and obtain at least two demand models, wherein each demand model corresponds one-to-one with the first stability type.

[0033] It should be noted that active regulation resources on the source and load side are the core resources for emergency response of large power grids. The essence of an emergency power grid failure is an instantaneous imbalance between active and reactive power supply and demand. Active regulation resources on the source and load side can directly exert force from both the supply and demand sides, which is more direct and faster than indirect regulation in the transmission and distribution links. It can curb the imbalance trend within a second-level window period after the failure occurs. In addition, it also has advantages such as fast response speed and large regulation capacity.

[0034] Among them, source-load side resources are all dispatchable and perceptible resources located on the power source side and load side of the power system, excluding resources in the transmission and distribution links; active adjustment resources are resources that have the characteristic of actively changing their own output and load and can respond to dispatch instructions to participate in grid stability control; the source-load side active adjustment resources used in this embodiment are resources that have the characteristics of both source-load side resources and active adjustment resources, and can also be understood as the intersection of the two.

[0035] In the above steps, taking the dispatch server of the dispatch center as the main execution entity as an example, the dispatch server can collect multi-dimensional time-varying data streams in parallel from the Supervisory Control and Data Acquisition (SCADA) system, Wide Area Measurement System (WAMS), resource local controller, and market information platform as resource data, and obtain the voltage amplitude (V) of all nodes in the entire network from the Power Grid Energy Management System (EMS). i Voltage phase angle (θ)i The power flow distribution of all branches serves as the real-time operating status of the power grid.

[0036] Step 102: Input the real-time operating status into each of the demand models to obtain the demand information corresponding to each of the first stability types, and calculate the resource data according to each of the demand information for each of the adjustment resources to obtain the evaluation value corresponding to each of the first stability types.

[0037] In the above steps, at least two independent online analysis engines, each corresponding one-to-one with a stability type, are constructed and run in parallel as demand models to map the current operating state to specific demands for emergency response services. For example, the power angle stability scenario demand model is used to identify the key power support / absorption locations, power magnitudes, and time scales required to maintain synchronous operation stability based on the real-time operating state, generating power angle stability demand information; the voltage stability emergency scenario demand model is used to identify the localized reactive / active power support requirements required to maintain voltage within a safe range based on the real-time operating state, generating voltage stability demand information; and the frequency stability emergency scenario demand model is used to quantify the total active power, regulation rate, and distribution of the entire network required to suppress frequency deviation and restore rated frequency based on the real-time operating state, generating frequency stability demand information.

[0038] It should be noted that the effective potential of the same resource varies at different times and when facing different stability problems of the power grid. Therefore, for each resource, its independent potential index for the three major stability problems is calculated in parallel. This can also be described as the contribution assessment value of the resource in the three dimensions of power angle stability, voltage stability and frequency stability.

[0039] The evaluation values ​​include the transient power support potential index (I) for power angle stability, which serves as the first value. angle The reactive / active power sensitivity regulation potential index (I0) is used as the second value for voltage stabilization. voltage ), and as a third value, the inertia for frequency stabilization and the primary frequency modulation potential index (I). frequency Index I angle This index I measures the ability of resources to provide rapid, robust, and appropriately positioned power support to suppress power angle instability under specific grid conditions. voltage This measure is used to evaluate the effectiveness of resources in maintaining or improving the voltage of specific weak nodes under specific power grid conditions. A high score indicates high efficiency, large quantity, fast speed, and proximity.

[0040] Step 103: In response to the stability request corresponding to the target stability type, sort all the adjustment resources according to the evaluation value corresponding to the target stability type to obtain a resource sequence.

[0041] In the above steps, upon receiving an emergency response request, the request parameters are first parsed, including: an event type identifier to determine whether the stability problem to be addressed is power angle stability, voltage stability, frequency stability, or a combination thereof; demand quantification indicators, for power angle / frequency stability, the total amount of active power adjustment required; for voltage stability, the total amount of voltage increase required; and response time constraints, including the latest start time and the latest time to achieve the required effect. Based on the stability problem type, resources are prioritized. For example, if the target stability type is a single type of stability problem, it is prioritized according to the evaluation value corresponding to that target stability type; if the target stability type is a combination of multiple types of stability problems, the three evaluation values ​​of the resources are weighted and summed to obtain the target evaluation value.

[0042] It should be noted that in the weighted summation of the evaluation values, the power angle stability problem has the highest weight, while the other two have the same weight; weighted summation can also be performed based on the priority specified in the request.

[0043] The adjustable resources in the resource pool are filtered to remove those with communication interruptions or fault alarms; resources with evaluations below the minimum evaluation threshold are removed. This minimum threshold can be determined by demand indicators and the total number of resources, such as minimum threshold = demand indicator / (empirical coefficient * total number of resources). The remaining resources are then screened based on the response speed and duration requirements of the corresponding stability problem.

[0044] Specifically, the selected resources are sorted in descending order based on their evaluation values, and a greedy selection and accumulation process is performed to obtain a resource sequence. For example, for the power angle / frequency stability problem (where the requirement is...), ): 1. Initialize the selected resource list to an empty set, and the accumulated contribution amount is 0.

[0045] 2. Starting from the top of the sorted list, add resources to the Selected List in sequence.

[0046] 3. For each resource added, calculate the Callable Amount of that resource for the current demand. The calculation formula is as follows: ; in, This is the maximum available power of the resource in the adjustment direction. This is the current assessment value. In this way, high-potential resources provide more calls, and low-potential resources provide fewer calls, making it more accurate and efficient.

[0047] 4. Update Accumulated=Accumulated+Callable Amount.

[0048] 5. Check if the requirements are met: If Accumulated is greater than or equal to the target requirement index, stop selecting; otherwise, continue to select the next resource and add it to the Selected List.

[0049] Regarding the voltage stability issue (the requirement is...) ): 1. Initialize the remaining voltage deviation to be compensated = .

[0050] 2. Starting from the top of the sorted list, process each resource in turn and determine its usage volume through physical calculations.

[0051] a. For the current resource, first calculate whether the voltage boost provided by its reactive power is sufficient. The formula for calculating the voltage boost is as follows: ; like Sufficient to meet current remaining voltage requirements If the required reactive power is used, the calculation ends. If it is insufficient, all available reactive power capacity is used.

[0052] b. If reactive power alone cannot meet the remaining demand, and the resource has active power regulation capability, then calculate the active power that needs to be supplemented. Based on the voltage-active power sensitivity of the resource, calculate the active power required to fill the remaining voltage gap, and the amount of active power called up shall not exceed the upper limit of active power that can currently be used for voltage regulation.

[0053] c. Add the voltage boost contributed by this resource (from reactive power + active power) to the total contribution, and determine whether the total contribution has reached or exceeded the target. If the requirement has been met, the selection process stops; otherwise, the next resource in the list is selected.

[0054] For complex problems, the priority is to meet the power gap, and resources are selected according to the process for solving the power angle / frequency stability problem described above. If the voltage demand is still not met after the power gap is met, resources are selected according to the process for solving the voltage stability problem described above.

[0055] After completing the above process, a structured resource matching result object is output. This result object includes the matching event type and the selected resource sequence. The resource sequence is a list of resource IDs arranged by call priority. Each resource contains a resource identifier, a suggested call amount, and a corresponding potential value.

[0056] Step 104: Determine and execute scheduling instructions based on the resource sequence to realize power grid scheduling.

[0057] In the above steps, based on the resource identifier and suggested call amount in the resource matching result object, a power setting value instruction is generated and sent to the corresponding adjustable resource to execute an emergency response.

[0058] As an example of an embodiment of the present invention, the first stability type includes power angle stability type, voltage stability type and frequency stability type, and the demand information includes power angle stability demand information, voltage stability demand information and frequency stability demand information; The step of inputting the real-time operating status into each of the demand models to obtain the demand information corresponding to each first stable type is specifically as follows: The real-time operating status is input into the first demand model corresponding to the power angle stability class. The first demand model is used to perform transient stability analysis on the real-time operating status to obtain the first call priority rule, the first response speed information and the first duration information, which are used as the power angle stability demand information. The real-time operating status is input into the second demand model corresponding to the voltage stability class. The voltage stability is analyzed by the second demand model to obtain the second call priority rule, the second response speed information and the second duration information, which are used as the voltage stability demand information. The real-time operating status is input into the third demand model corresponding to the frequency stability class. The third demand model is used to perform frequency stability analysis on the real-time operating status to obtain the third call priority rule, the third response speed information, and the third duration information, which are used as the frequency stability demand information.

[0059] Among them, power angle stability refers to the stability problem type corresponding to the risk of power angle instability in the power grid. Its core characteristic is that the generator rotor angle swings, requiring rapid power support to maintain synchronous operation. Voltage stability refers to the stability problem type corresponding to the risk of voltage instability in the power grid. Its core characteristic is that the node voltage deviates from the safe range, requiring reactive or active power support to maintain the voltage level. Frequency stability refers to the stability problem type corresponding to the risk of frequency instability in the power grid. Its core characteristic is that the system frequency deviates from the rated value, requiring active power regulation to restore frequency balance. Transient stability analysis is one of the core technologies for the safe operation of power systems. It mainly studies whether the system can recover to a stable state through a transient process after suffering large-scale load changes, line faults, and other large disturbances.

[0060] In this embodiment, firstly, for the demand analysis of power angle stability, the scheduling system based on dynamic assessment of the source and load sides inputs the collected real-time operating status into a specially constructed first demand model. This model internally uses a transient stability analysis algorithm to simulate the power angle swing process of the system after experiencing a large disturbance, thereby determining whether the generator units will lose synchronization. Based on this analysis, the model can generate specific power angle stability demand information, including a first priority rule to guide the sequence of emergency control measures, first response speed information to ensure control is completed before the first power angle swing step, and first duration information describing how long the control measures need to be maintained.

[0061] Secondly, for voltage stability-related demand analysis, the system inputs the same real-time operating status into the second demand model. This model uses voltage stability analysis technology to focus on assessing the voltage drop and recovery characteristics of key nodes, as well as the reactive power reserve margin of the entire network. By quantifying the risk of voltage collapse, the model can output voltage stability demand information, specifically: a second priority rule to guide the deployment sequence of reactive power compensation equipment, second response speed information to distinguish between transient voltage support and static voltage recovery, and second duration information to ensure that the voltage is maintained within a safe threshold.

[0062] Finally, for frequency stability-related demand analysis, the system inputs the real-time operating status into the third demand model. This model, through a frequency stability analysis algorithm, predicts the dynamic trajectory, minimum point, and rate of change of the system frequency when power deficits or surpluses occur. Based on this analysis result, the model outputs frequency stability demand information, including a third call priority rule guiding the sequence of primary frequency regulation and low-frequency load shedding actions, a third response speed information matching the response capabilities of different frequency regulation resources, and a third duration information to ensure that the frequency recovers to its rated value.

[0063] As an example of an embodiment of the present invention, the acquisition of resource data corresponding to each adjustment resource in the source-load side specifically includes: For multiple regulating resources in the target aggregator, the first power data, first regulating rate data, first grid topology data, and first resource status data of each regulating resource are obtained respectively; The second power data is obtained by summing all the first power data, the second adjustment rate data is obtained by calculating all the first adjustment rate data according to the preset first aggregation rule, and the second resource status data is obtained by calculating the first resource status data according to the preset second aggregation rule. The resource data is obtained by combining the second power data, the second regulation rate data, the second resource status data, and the first power grid topology data.

[0064] In this embodiment, the scheduling server collects multi-dimensional time-varying data streams in parallel, including real-time available power / capacity data streams, dynamic adjustment rates, grid connection point electrical topology data, and resource status reliability data. For each registered resource, its current operating data is periodically acquired, and its time-varying capacity boundary is calculated to obtain the real-time maximum available power, i.e., the first power data.

[0065] Specifically, for power generation resources, the current actual output and the current maximum theoretical power generation can be obtained. For photovoltaic and wind power resources, the current maximum theoretical power generation can be obtained from their control systems based on real-time meteorological data (irradiance, wind speed).

[0066] For energy storage resources, their current active power and state of charge (SOC) are obtained, and combined with their fixed parameters, the sustainable power ceiling and duration in the current charge / discharge direction are calculated. For example, the SOC-based power limitation curve and power-duration curve of the energy storage resource are obtained from the battery management system (BMS). In the calculation of the sustainable power ceiling, the maximum continuous discharge power is determined based on the SOC-based power limitation curve. and maximum continuous charging power Combined with temperature / aging-based derating factor The sustainable power limit can be calculated, and the formula for calculating the discharge direction is expressed as follows: ; in, It is the maximum instantaneous power of the converter; The formula for calculating the charging direction is expressed as follows: ; Another simple method is to directly multiply the rated power by the derating factor as the upper limit of sustainable power.

[0067] A standard power value is preset, and the duration under this standard power value is obtained. The duration refers to the duration under the preset standard power value, which is the preset power required for dispatching, used to standardize the assessment of energy storage resource potential. The duration can be directly obtained from the power-duration curve. For example, in the corresponding discharge curve, when SOC=80%, 100% rated power can last for 2 hours, 50% power for 4 hours, and 25% power for 8 hours; in the corresponding charging curve, when SOC=50%, 100% power can last for 1 hour, 50% power for 2 hours, and 25% power for 4 hours. Another method requires using the maximum and minimum allowable SOC of the energy storage. During discharge, the difference between the current SOC and the minimum SOC is calculated and multiplied by the rated capacity to obtain the callable capacity; then the callable capacity is divided by the standard power value to obtain the duration. During charging, the difference between the maximum SOC and the current SOC is multiplied by the rated capacity to obtain the callable capacity.

[0068] For adjustable loads, obtain their current load baseline, i.e., the natural power consumption without incentives; and obtain the current maximum power that can be reduced or increased based on user contracts, real-time preferences, or equipment status from their demand response aggregator.

[0069] It's important to note that for power generation / energy storage discharge, the upward adjustment capacity represents the increase in output, which is the difference between the theoretical maximum generating power and the real-time output power, but cannot exceed the hardware limit of response time multiplied by the upward ramp rate. The downward adjustment capacity represents the decrease in output, which is the difference between the real-time output power and the theoretical minimum generating power, with similar limitations. For energy storage charging, the upward adjustment capacity represents the decrease in charging power, while the downward adjustment represents the increase in charging power. Upward adjustment increases power generation or decreases power consumption on the grid, and downward adjustment is the opposite. Therefore, for adjustable loads, the upward adjustment represents the maximum power that can be reduced, and the downward adjustment represents the maximum power that can be increased, both also limited by the maximum change within the response time. This can be directly obtained.

[0070] For generators and energy storage, considering that the current actual ramp rate may be lower than the rated ramp rate due to equipment temperature, aging, or operating mode, this embodiment calculates the actual ramp rate as the first adjustment rate data by analyzing the historical sequence of recent (e.g., the past minute) actual power changes and using linear regression or the difference method. For aggregated load resources, considering that their actual aggregated response speed is affected by communication delays and the dispersion of actions of numerous distributed devices, the average delay time from the issuance of the command to the start of aggregated power change, and the average rise time from the start of change to reaching the target value are statistically analyzed using data from multiple historical response events.

[0071] From the topology model of the power grid energy management system (EMS), the static connection relationship of each resource grid connection point is obtained, and the bus node number connected to each resource grid connection point is obtained, thus obtaining the electrical topology data of the grid connection point as the first power grid topology data. Specifically, real-time measurement data of the entire network is received, state estimation is performed, and the real-time operating section of the current power grid is obtained. Based on this section, the sensitivity coefficient of each resource grid connection point i to key system state variables is calculated, including the power transmission distribution factor of the power flow T of the key transmission section due to changes in resource active power. Voltage-active power sensitivity to node j voltage ( ), and voltage-reactive power sensitivity ( Among them, a transmission section is a collection of transmission lines connected in series or in parallel in a power grid, usually used to connect two regional power grids; a critical transmission section refers to a section where the power flow, once exceeding the safety limit, will directly threaten the stable operation of the entire power grid and may even trigger a cascading failure.

[0072] Get the response success rate within the scrolling time window ( The average response deviation (the average absolute error between the actual response and the command) and the time since the most recent response (used to assess whether the resource is in the "post-response recovery period", such as the need to recharge after the energy storage response discharge) are used as the first resource status data.

[0073] Each resource is assigned a unique identifier, and the received data is stored in a real-time database with timestamps. This is a standard data integration operation.

[0074] For data from different systems with different transmission delays and refresh cycles, interpolation or nearest neighbor methods are used to align them to a unified evaluation time base, forming a complete data snapshot of the resources at the same moment.

[0075] For each resource, in each evaluation period, the latest value after aligning the above four types of data streams is automatically encapsulated into a structured "Resource Dynamic Potential Description Object," which is a dynamic collection containing the following fields: First power data: {Current power, available for upward adjustment, available for downward adjustment}; First adjustment rate data: {actual ramp rate, response delay time, rise time constant}; First grid topology data: grid connection nodes, key PTDFs, key voltage sensitivity; First resource status data: {communication status, fault flag, recent success rate, time since last response}.

[0076] For resource clusters accessed through aggregators, the server performs dynamic aggregation at the data layer. That is, it accumulates all the first power data in the dynamic potential description objects of all individual resources in the cluster to obtain second power data. For example, power and capacity-related fields are algebraically summed at the common connection point. According to the preset first aggregation rule, all the first regulation rate data are calculated to obtain second regulation rate data. And according to the preset second aggregation rule, the first resource status data are calculated to obtain second resource status data. The first aggregation rule and the second aggregation rule can be taking the minimum value, taking the average value, or weighted average. For example, all data in the rate field are aggregated according to the weighted average algorithm, and all data in the status field are aggregated according to the average value to obtain a virtual aggregated resource object for subsequent steps to call for calculation.

[0077] As an example of an embodiment of the present invention, the second power data includes active power data, reactive power data and duration data; the first regulation rate data includes active power ramp rate data and reactive power response time data; the first grid topology data includes grid connection node location data and voltage sensitivity data; the second resource status data includes response success rate data, response duration data and equivalent inertia data; and the evaluation value includes a first evaluation value, a second evaluation value and a third evaluation value. For each of the adjustment resources, the resource data is calculated based on each type of demand information to obtain an evaluation value corresponding to each of the first stability types, specifically as follows: The first power factor is calculated based on the active power data, the active ramp rate data, and the first response speed information. The first location factor is determined based on the grid-connected node location data and the first call priority rule. The stability factor is determined based on the response success rate data and the response duration data. The first power factor, the first position factor, and the stability factor are multiplied together to obtain the first evaluation value of the regulation resource and the power angle stability class; A sensitivity factor is determined based on the voltage sensitivity data and the second call priority rule, a capacity factor is determined based on the active power data and the reactive power data, a speed matching factor is determined based on the reactive response time data and the second response speed information, and a second location factor is determined based on the grid connection node location and the second call priority rule. The sensitivity factor, capacity factor, velocity matching factor, and second position factor are multiplied together to obtain a second evaluation value for the regulation resource and the voltage stability class. The frequency response factor is determined based on the active power data and active ramp rate data, and the frequency modulation factor is determined based on the active power data, the duration data and the third response speed information. The equivalent inertia factor is determined based on the equivalent inertia data, and the third location factor is determined based on the grid connection node location and the third call priority rule. The frequency response factor, frequency modulation factor, and equivalent inertia factor are weighted and summed to obtain a weighting factor. The weighting factor is then multiplied by the third position factor to obtain the third evaluation value of the regulation resource and the frequency stability class.

[0078] In this embodiment, the transient power support potential index ( First, calculate the power that the resources can release within the first swing cycle of the first response speed information. For power generation resources, the current maximum theoretical power output can be directly obtained as... For energy storage resources, the calculation is performed using the following formula, expressed as: ; in, The ramp rate in the active ramp rate data. The duration of the first swing cycle. These are the rated power and current power in the active power data; for fast gas turbines, For its rapid start-up power; for load-type resources, It is a power block that can be quickly cut off.

[0079] This Divide by the largest of all resources Normalization is performed to obtain the fast available power factor, i.e., the first power factor. Then, based on the power injection location information in the first call priority rule... and resources The corresponding key section ,Will Divide by the largest of all resources Normalization is performed, and regional weights are determined based on the location data of grid-connected nodes. For example, resources located in the sending-end region are assigned a weight of 1.2, those in the receiving-end region are assigned 0.8, and those elsewhere are assigned 0.5. The normalized value is then multiplied by the regional weight to obtain the geographical location effectiveness factor. This serves as the first position factor. Then, based on the recent response success rate (CR) and the time elapsed since the most recent response in the first resource status data... A reputation factor is defined as a stability factor, and its calculation formula is expressed as: ; in It is the recovery time constant, which characterizes the time scale required for resources to recover from a single response.

[0080] Finally, multiplying the above three factors together yields the transient power support potential index. .

[0081] Regarding the reactive / active sensitivity adjustment potential index ( The calculation formula is expressed as follows: ; in, To obtain the sensitivity score, the voltage-reactive sensitivity and voltage-active sensitivity (the average value is taken when there are multiple node data) of the weak node / effective node j in the voltage sensitivity data are normalized and then added together to obtain the sensitivity score. The capacity factor is obtained by normalizing the sum of the upward / downward reactive power regulation capacity and the active power regulation capacity that the resources can currently sustainably provide. The second location factor is calculated based on the weight assigned by the electrical distance between the resource and the target node. The electrical distance is determined based on the location of the grid-connected node and the second call priority rule, and takes a value of 0-1, with the value being larger the closer the node is. This is a speed matching factor used to quantify the degree of agreement between the actual dynamic response characteristics of a resource and the specific speed requirements of the current voltage stability problem. The response time required to output the response power can be calculated based on the ramp rate; alternatively, the average delay time plus the average rise time from historical data can be calculated as the response time, and then the resource-based response time can be divided by the response time requirement in the scheduling instruction. , The maximum value is 1.

[0082] Regarding the frequency modulation potential index ( The calculation formula is expressed as follows: ; ; ; ; in, , , These are the fast frequency response factor, the continuous frequency modulation factor, and the equivalent inertia factor, respectively. , , These are the weighting coefficients. The effective factor for regional frequency regulation is proportional to the degree of electrical connection between the resource area and the disturbance area, and also proportional to the availability of transmission channels between the two areas. For example, the electrical connectivity score and channel margin score for each area are calculated separately, and then weighted and summed. The electrical connectivity score can be calculated using an electrical distance matrix or a node impedance matrix. The channel margin score represents the margin of the most strained line between the two areas, calculated by dividing the line power flow by the line's maximum power limit. This represents the maximum power change that the resource can achieve within 2 seconds. This represents the amount of power change that the resource can achieve within 30 seconds. For the sustainable frequency adjustment time of resources, The equivalent inertial time constant of the resource; For the rated capacity of the resource, It represents the inertial strength per unit volume × the scale of inertia, characterizing the inertial potential of resources.

[0083] It should be noted that 30 seconds is the end of a typical time window for a primary frequency regulation to complete its main tasks. The amount of power change that a resource can achieve within 30 seconds directly measures its ability to support the primary frequency regulation phase.

[0084] Based on the above calculations, each resource will generate a set of three dynamically updated values ​​in each evaluation period: , , These indices are independent of the static type of the resource, but are strongly correlated with its current state and the current operating conditions of the power grid. These three indices are encapsulated back into the dynamic potential description object of the resource, forming a complete, application-oriented demand-capacity matching dataset, providing direct input for the next step of targeted screening.

[0085] As an example of an embodiment of the present invention, the transient stability analysis of the real-time operating state using the first demand model specifically includes: Retrieve a preset list of faults; Based on the real-time operating status, transient stability analysis is performed on each fault in the fault list to obtain the transient stability margin corresponding to each fault, and power angle instability fault is determined among all the faults according to each transient stability margin. The power angle instability fault is simulated and analyzed to obtain the power angle swing characteristics, the power flow change characteristics of each transmission section, the swing period and the support time corresponding to the damping oscillation. Based on the power angle swing characteristics and the power flow change characteristics of the section, a first region for output power and a second region for receiving power are identified. Based on the first region and the second region, and in conjunction with the grid connection node where each of the regulating resources is located, the first call priority rule corresponding to each of the regulating resources is determined; The first response speed information is determined based on the swing period, and the first duration information is determined based on the support time.

[0086] It should be noted that in a stable power angle scenario, a transmission system fault (such as a line trip) will increase the equivalent interconnecting reactance between the sending-end region (A) and the receiving-end region (B), resulting in a decrease in transmission capacity. During the fault, the sending-end generator accelerates due to a sudden drop in electromagnetic power, causing the power angle difference δ to increase rapidly. After the fault is cleared, although the system reactance increases, because δ has already significantly shifted, the transmission power at critical sections during the transient process may momentarily exceed the pre-fault level, exacerbating the risk of line overload and potentially causing δ to continue increasing until it loses synchronism. Therefore, it is necessary to quickly adjust the power balance between the sending and receiving ends to suppress the continued shift of δ.

[0087] In this embodiment, based on the real-time power grid status, for each fault in the fault list, the transient stability margin of the power grid after the corresponding fault occurs is quickly calculated using the Extended Equal Area Criterion (EEAC). );filter Faults below the threshold are verified through time-domain simulation; if the verification shows... If the value is below a threshold, the power angle instability risk of the power grid under this fault is determined. By analyzing the rotor angle swing curves of each generator after the fault obtained from the time-domain simulation and the key transmission sections in the network, different risk areas are identified: The rotor angle swing curve of the generator refers to the curve of the power angle of each generator changing with time after the fault. Generators with large power angle swings and swinging in the opposite direction to the reference unit belong to the electrical region with high instability risk and need to strengthen power support, i.e., the sending end region (first region); the region where the units with smaller swings and tending to be stable are located often need to quickly reduce power to alleviate the transmission pressure of the section due to excessive power surges, i.e., the receiving end region (second region). The power flow curves of the key transmission sections can verify the power transmission problem between regions: if the power flow rises sharply, it confirms the judgment that the sending end unit A has a large power angle swing and a high risk of instability. The fundamental reason for the large swing in power angle instability lies in the obstruction of power transmission: after a fault, damage to the transmission channel leads to mechanical power exceeding the actual electromagnetic power that can be transmitted; the greater the difference between mechanical and electromagnetic power, the faster the rotor accelerates, the larger the power angle swing, and the higher the risk of instability. Therefore, the sending-end region needs to strengthen electromagnetic power support. Based on the above regional division, differentiated emergency control strategies are formulated: For the sending-end region (A): power support needs to be strengthened by increasing local power output and activating energy storage discharge. Activating energy storage discharge involves injecting active power into the sending-end grid, which can raise the voltage of local nodes and enhance the grid's voltage support capability; increasing local power output is to suppress fluctuations in renewable energy output and prevent a further decrease in electromagnetic power at the sending-end region (A) due to a drop in renewable energy output after a fault. For the receiving-end region (B): transmission pressure on critical sections needs to be reduced by disconnecting interruptible loads and activating energy storage charging. After the power angle stabilizes, the absorption of excess power will be considered during the recovery phase, which will not be discussed here. Through the above process, the electrical regions that most need enhanced power support (corresponding to the sending end region) and the electrical regions that most need rapid power reduction (corresponding to the receiving end region) can be identified. At this point, the requirements can be quantified: Power injection location requirements : In the form of electrical distance weights, the priority of resource allocation at or near the sending end A (which needs to increase output or reduce load) and the receiving end B (which needs to quickly increase load or reduce generation) is explicitly specified; this requirement is associated with the electrical topology of the grid connection point.

[0088] Response speed requirements The requirement is that the response resources must achieve their main support effect within the first swing cycle (usually 0.2-1.0 seconds after the fault), which directly corresponds to the dynamic adjustment rate data of the resources.

[0089] Duration requirement The main power support demand typically lasts for several swing cycles (e.g., 2-3 seconds) to help the system dampen oscillations and return to stability.

[0090] As an example of an embodiment of the present invention, the voltage stability analysis of the real-time operating state using the second demand model specifically includes: For each grid-connected node where the regulating resource is located, obtain the voltage value of each grid-connected node, and calculate the voltage stability margin of each grid-connected node based on the real-time operating status. When the voltage stability margin is less than the preset voltage stability threshold, or the voltage value is not within the preset voltage operating range, it is determined that the grid-connected node has a risk of voltage instability, and a voltage instability node is obtained. Simulation analysis was performed on all the voltage instability nodes to identify the target instability nodes and the target reactive power injection nodes, and the voltage instability type and recovery time corresponding to the voltage instability nodes were obtained. Based on the target unstable node and the target reactive power injection node, determine the second call priority rule corresponding to each of the adjustment resources; The voltage instability type is determined, the second response speed information corresponding to the voltage instability node is determined based on the voltage instability type, and the second duration information is determined based on the recovery time.

[0091] In this embodiment, based on real-time power flow, the static voltage stability margin of each node is calculated, and the voltage sensitivity matrix of key nodes is monitored. When the regional margin (the mean or weighted average of the N nodes with the lowest margins in the region) falls below a threshold or the voltage exceeds the limit, a risk of voltage instability is identified. For regions with risk, key node clusters leading to a decrease in voltage stability margin are further identified through continuous power flow calculation or modal analysis: the most sensitive load node cluster is the set of nodes whose voltage drops fastest during load growth in continuous power flow calculation, and is the source of voltage instability; the most effective reactive power injection nodes are those nodes with the greatest impact on voltage stability, determined by modal analysis based on the voltage sensitivity matrix, that is, those nodes that can maximize the improvement of regional voltage stability margin after reactive power injection (such as substations and energy storage power station grid connection points near load centers). Based on the identified sensitive load node clusters and effective reactive power injection nodes, the demand under voltage stability scenarios can be quantified: Power / voltage injection location requirements This requirement specifies the weak / effective node and provides the effectiveness coefficient for the unit reactive power injection needed to raise the voltage of that node. This requirement is related to the voltage sensitivity of the resources to a particular node in S1.

[0092] Response speed requirements For static instability caused by slow load growth, the response speed is required to be on the order of seconds to minutes. However, for transient voltage drops caused by faults, rapid voltage support on the order of milliseconds to seconds is required.

[0093] Duration requirement Static voltage support may need to last for several minutes to tens of minutes until the underlying cause is eliminated, such as transmission line repair or power generation schedule adjustments.

[0094] As an example of an embodiment of the present invention, the frequency stability analysis of the real-time operating state through the third demand model specifically includes: Based on the grid frequency and frequency change rate in the real-time operating state, when the frequency change rate is greater than the preset disturbance threshold, it is determined that there is a risk of frequency instability, the disturbance location is determined, and the active power deviation is calculated based on the grid frequency. Obtain the network congestion status, determine the target active power injection location based on the disturbance location and the network congestion status, and determine the third call priority rule corresponding to each of the adjustment resources based on the target active power injection node; The third response speed information is determined based on the preset frequency stabilization time, and the third duration information is determined based on the preset transition time from primary frequency modulation to secondary frequency modulation.

[0095] In this embodiment, based on the system's inertial center frequency ( ) and its rate of change ( ), combined with the known total inertial time constant of the system ( The current active power deficit (ΔPloss) of the system is estimated using a first-order frequency response model. The calculation formula is as follows: ; in, This is the rated frequency.

[0096] Based on the calculation of the total active power deficit, we further assess the spatiotemporal quality requirements needed to address this deficit, specifically quantified in the following three dimensions: Power injection location requirements Considering the geographical location of the disturbance (i.e. frequency stability problem) and network congestion, if the disturbance occurs in a weak network area and a large number of frequency modulation resources are located far away and have limited paths, the frequency modulation effect will be greatly reduced. Therefore, the demand model introduces the concept of "effective frequency modulation capacity" and prioritizes the use of resources that are electrically close to the disturbance point and have unobstructed transmission paths.

[0097] Response speed requirements Based on the timescale of frequency dynamic response, it can be decomposed into two levels: Inertial response requirement: This requires providing power support proportional to the rate of frequency change within hundreds of milliseconds after a disturbance occurs to curb the initial rapid drop in frequency. This corresponds to the instantaneous power release capability of the resource; Primary frequency regulation requirement: This requires providing power proportional to the frequency deviation within seconds to tens of seconds after a disturbance to restore the frequency to a new quasi-steady state. This corresponds to the rapid and continuous adjustment capability of the resource.

[0098] Duration requirement A single frequency modulation operation typically takes tens of seconds to several minutes until a second frequency modulation is performed.

[0099] like Figure 2 As shown, based on the above method embodiments, an embodiment of the present invention provides a scheduling system 200 based on source-load side dynamic evaluation, including: an acquisition module 201, an evaluation module 202, a matching module 203 and an execution module 204; The acquisition module 201 is used to acquire resource data corresponding to each regulating resource on the source-load side, as well as the real-time operating status of the power grid, and to acquire at least two demand models, wherein each demand model corresponds one-to-one with the first stability type. The evaluation module 202 is used to input the real-time operating status into each of the demand models to obtain the demand information corresponding to each of the first stability types, and to calculate the resource data according to each of the demand information for each of the adjustment resources to obtain the evaluation value corresponding to each of the first stability types. The matching module 203 is used to respond to the stability request corresponding to the target stability type, sort all the adjustment resources according to the evaluation value corresponding to the target stability type, and obtain a resource sequence; The execution module 204 is used to determine and execute scheduling instructions based on the resource sequence to realize power grid scheduling.

[0100] It is understood that the above system item embodiments correspond to the method item embodiments of the present invention, and can implement the scheduling method based on source-load side dynamic evaluation provided by any of the above method item embodiments of the present invention.

[0101] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0102] For ease of description and brevity, the system embodiments of the present invention include all the implementation methods described in the above embodiments of the scheduling method based on source-load side dynamic evaluation, and will not be repeated here.

[0103] Based on the above embodiments of the scheduling method based on source-load side dynamic evaluation, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the scheduling method based on source-load side dynamic evaluation of any embodiment of the present invention.

[0104] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0105] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0106] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0107] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the scheduling method based on source-load side dynamic evaluation as described in any of the above-described method embodiments of the present invention.

[0108] Based on the above-described method embodiments, this invention also provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of any of the above-described method embodiments, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0109] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0110] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A scheduling method based on dynamic evaluation of the source and load sides, characterized in that, include: Obtain resource data corresponding to each regulating resource on the source-load side, as well as the real-time operating status of the power grid, and obtain at least two demand models, wherein each demand model corresponds one-to-one with the first stability type; The real-time operating status is input into each of the demand models to obtain the demand information corresponding to each of the first stability types. For each of the adjustment resources, the resource data is calculated based on each of the demand information to obtain the evaluation value corresponding to each of the first stability types. In response to a stability request corresponding to a target stability type, all the adjustment resources are sorted according to the evaluation value corresponding to the target stability type to obtain a resource sequence; Based on the resource sequence, scheduling instructions are determined and executed to achieve power grid scheduling.

2. The scheduling method based on source-load side dynamic evaluation as described in claim 1, characterized in that, in, The first stability type includes power angle stability, voltage stability and frequency stability, and the demand information includes power angle stability demand information, voltage stability demand information and frequency stability demand information; The step of inputting the real-time operating status into each of the demand models to obtain the demand information corresponding to each first stable type is specifically as follows: The real-time operating status is input into the first demand model corresponding to the power angle stability class. The first demand model is used to perform transient stability analysis on the real-time operating status to obtain the first call priority rule, the first response speed information and the first duration information, which are used as the power angle stability demand information. The real-time operating status is input into the second demand model corresponding to the voltage stability class. The voltage stability is analyzed by the second demand model to obtain the second call priority rule, the second response speed information and the second duration information, which are used as the voltage stability demand information. The real-time operating status is input into the third demand model corresponding to the frequency stability class. The third demand model is used to perform frequency stability analysis on the real-time operating status to obtain the third call priority rule, the third response speed information, and the third duration information, which are used as the frequency stability demand information.

3. The scheduling method based on source-load side dynamic evaluation as described in claim 2, characterized in that, The acquisition of resource data corresponding to each adjustment resource on the source-load side specifically involves: For multiple regulating resources in the target aggregator, the first power data, first regulating rate data, first grid topology data, and first resource status data of each regulating resource are obtained respectively; The second power data is obtained by summing all the first power data, the second adjustment rate data is obtained by calculating all the first adjustment rate data according to the preset first aggregation rule, and the second resource status data is obtained by calculating the first resource status data according to the preset second aggregation rule. The resource data is obtained by combining the second power data, the second regulation rate data, the second resource status data, and the first power grid topology data.

4. The scheduling method based on source-load side dynamic evaluation as described in claim 3, characterized in that, in, The second power data includes active power data, reactive power data, and duration data; the first regulation rate data includes active ramp rate data and reactive response time data; the first grid topology data includes grid connection node location data and voltage sensitivity data; the second resource status data includes response success rate data, response duration data, and equivalent inertia data; and the evaluation value includes a first evaluation value, a second evaluation value, and a third evaluation value. For each of the adjustment resources, the resource data is calculated based on each type of demand information to obtain an evaluation value corresponding to each of the first stability types, specifically as follows: The first power factor is calculated based on the active power data, the active ramp rate data, and the first response speed information. The first location factor is determined based on the grid-connected node location data and the first call priority rule. The stability factor is determined based on the response success rate data and the response duration data. The first power factor, the first position factor, and the stability factor are multiplied together to obtain the first evaluation value of the regulation resource and the power angle stability class; A sensitivity factor is determined based on the voltage sensitivity data and the second call priority rule, a capacity factor is determined based on the active power data and the reactive power data, a speed matching factor is determined based on the reactive response time data and the second response speed information, and a second location factor is determined based on the grid connection node location and the second call priority rule. The sensitivity factor, capacity factor, velocity matching factor, and second position factor are multiplied together to obtain a second evaluation value for the regulation resource and the voltage stability class. The frequency response factor is determined based on the active power data and active ramp rate data, and the frequency modulation factor is determined based on the active power data, the duration data and the third response speed information. The equivalent inertia factor is determined based on the equivalent inertia data, and the third location factor is determined based on the grid connection node location and the third call priority rule. The frequency response factor, frequency modulation factor, and equivalent inertia factor are weighted and summed to obtain a weighting factor. The weighting factor is then multiplied by the third position factor to obtain the third evaluation value of the regulation resource and the frequency stability class.

5. The scheduling method based on source-load side dynamic evaluation as described in claim 2, characterized in that, The transient stability analysis of the real-time operating state using the first demand model specifically includes: Retrieve a preset list of faults; Based on the real-time operating status, transient stability analysis is performed on each fault in the fault list to obtain the transient stability margin corresponding to each fault, and power angle instability fault is determined among all the faults according to each transient stability margin. The power angle instability fault is simulated and analyzed to obtain the power angle swing characteristics, the power flow change characteristics of each transmission section, the swing period and the support time corresponding to the damping oscillation. Based on the power angle swing characteristics and the power flow change characteristics of the section, a first region for output power and a second region for receiving power are identified. Based on the first region and the second region, and in conjunction with the grid connection node where each of the regulating resources is located, the first call priority rule corresponding to each of the regulating resources is determined; The first response speed information is determined based on the swing period, and the first duration information is determined based on the support time.

6. The scheduling method based on source-load side dynamic evaluation as described in claim 2, characterized in that, The voltage stability analysis of the real-time operating state using the second demand model specifically involves: For each grid-connected node where the regulating resource is located, obtain the voltage value of each grid-connected node, and calculate the voltage stability margin of each grid-connected node based on the real-time operating status. When the voltage stability margin is less than the preset voltage stability threshold, or the voltage value is not within the preset voltage operating range, it is determined that the grid-connected node has a risk of voltage instability, and a voltage instability node is obtained. Simulation analysis was performed on all the voltage instability nodes to identify the target instability nodes and the target reactive power injection nodes, and the voltage instability type and recovery time corresponding to the voltage instability nodes were obtained. Based on the target unstable node and the target reactive power injection node, determine the second call priority rule corresponding to each of the adjustment resources; The voltage instability type is determined, the second response speed information corresponding to the voltage instability node is determined based on the voltage instability type, and the second duration information is determined based on the recovery time.

7. The scheduling method based on source-load side dynamic evaluation as described in claim 2, characterized in that, The frequency stability analysis of the real-time operating state using the third demand model is specifically as follows: Based on the grid frequency and frequency change rate in the real-time operating state, when the frequency change rate is greater than the preset disturbance threshold, it is determined that there is a risk of frequency instability, the disturbance location is determined, and the active power deviation is calculated based on the grid frequency. Obtain the network congestion status, determine the target active power injection location based on the disturbance location and the network congestion status, and determine the third call priority rule corresponding to each of the adjustment resources based on the target active power injection node; The third response speed information is determined based on the preset frequency stabilization time, and the third duration information is determined based on the preset transition time from primary frequency modulation to secondary frequency modulation.

8. A scheduling system based on dynamic evaluation of the source and load sides, characterized in that, include: The module includes an acquisition module, an evaluation module, a matching module, and an execution module. The acquisition module is used to acquire resource data corresponding to each regulating resource on the source-load side, as well as the real-time operating status of the power grid, and to acquire at least two demand models, wherein each demand model corresponds one-to-one with the first stability type. The evaluation module is used to input the real-time operating status into each of the demand models to obtain the demand information corresponding to each of the first stability types, and to calculate the resource data according to each of the demand information for each of the adjustment resources to obtain the evaluation value corresponding to each of the first stability types. The matching module is used to respond to the stability request corresponding to the target stability type, and sort all the adjustment resources according to the evaluation value corresponding to the target stability type to obtain a resource sequence; The execution module is used to determine and execute scheduling instructions based on the resource sequence to realize power grid scheduling.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the scheduling method based on source-load side dynamic evaluation as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the scheduling method based on source-load side dynamic evaluation as described in any one of claims 1-7.

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