A power grid frequency stability emergency resource optimization method and device

Abstract

The application discloses a power grid frequency stability emergency resource optimization method and device, the method comprises the following steps: acquiring resource operation data of a power grid; if the power grid frequency exceeds a preset threshold, then based on the resource response reliability coefficient in the resource operation data, a distribution decision process is executed on the power grid; wherein, the distribution decision process is realized by sequentially distributing a preset amount of power to the resources according to the resource priority value; and the resource response reliability coefficient is updated according to the power regulation result of the power grid. The application proposes a power grid frequency stability emergency resource optimization method and device, which effectively constructs a technical collaborative closed loop through a resource priority value distribution mechanism and a data updating mode, overcomes the defects of the existing power grid emergency control lacking a technical collaborative closed loop, and can solve the problems of unreasonable emergency resource distribution of the existing power grid emergency control, the inability to form a technical collaborative closed loop, and the difficulty in maintaining the stability of the power grid operation.

Description

Technical Field

[0001] This invention relates to the field of power distribution network control technology, and in particular to a method and apparatus for optimizing emergency resources for power grid frequency stability. Background Technology

[0002] The current power system is transforming towards a high proportion of renewable energy grid integration and diversified interaction among power sources, grids, loads, and storage. This significantly increases the randomness and volatility of grid operation, posing greater challenges to grid control in emergency situations. Distributed resources, with their rapid adjustment advantages, have become the core force for supplementing reserve capacity in grid emergency control. However, the existing grid emergency control model, adapted to traditional operating scenarios, is no longer sufficient to meet the demands of safe and efficient grid operation under the new circumstances of large-scale distributed resource integration. Current grid emergency control employs a fixed reserve capacity call mechanism, coupled with a simple on-demand compensation model for regulation. It fails to specifically characterize and differentiate the response performance of distributed resources, nor does it establish corresponding resource priority call designs. It only conducts passive response operations after a grid emergency occurs, and lacks proactive perception and pre-emptive planning for resource availability risks.

[0003] Existing power grid emergency control mostly adopts a passive control mode, which not only lacks the technical support for flexible dispatch, making it impossible to achieve reasonable allocation of emergency resources and dynamic multi-path dispatch, but also fails to build the connection and coordination logic of each control link. It is difficult to form a systematic and integrated linkage between resource assessment, dispatch execution, risk management and incentive feedback, and ultimately fails to form a technical coordination closed loop with the stability of power grid operation as the core. Due to the unreasonable allocation of emergency resources and insufficient control coordination, it is difficult to fundamentally improve the overall operational stability of the power grid and the control capability in emergency scenarios. Summary of the Invention

[0004] This invention provides a method and apparatus for optimizing emergency resources for power grid frequency stability, in order to solve the problem that the allocation of emergency control resources in the existing power grid is unreasonable and cannot form a technical synergy closed loop, which makes it difficult to maintain the stability of power grid operation.

[0005] To achieve the above objectives, the present invention provides a method for optimizing emergency resources for power grid frequency stability, comprising: Obtain power grid resource operation data; If the grid frequency of the power grid exceeds a preset threshold, an allocation decision process is executed on the power grid based on the resource response reliability coefficient in the resource operation data; wherein, the allocation decision process is implemented by allocating preset power to resources in sequence according to resource priority values; The resource response reliability coefficient is updated based on the power regulation results of the power grid.

[0006] The allocation decision-making process of this invention is based on resource priority, which is set according to the resource response reliability coefficient in the resource operation data. This ensures that the priority ranking matches the actual operating performance of the resources, avoiding the drawbacks of blind allocation and prioritizing quantity over quality in existing allocation models. It prioritizes emergency resources with high response efficiency and strong reliability, allocating preset power quotas according to priority. This not only enables rapid response to emergency needs caused by grid frequency anomalies, minimizing dispatch response time and ensuring high efficiency in emergency control, but also achieves precise matching and rational allocation of resources, avoiding resource waste or delays in the dispatch of critical resources. This ensures that the grid frequency quickly stabilizes, addressing the instability problem caused by unreasonable allocation at its source. Simultaneously, this invention includes a dynamic parameter update mechanism. Using grid power regulation results as real-time feedback, the coefficients are dynamically adjusted, breaking the limitations of fixed dispatch parameters and lack of feedback optimization in existing control models. This continuously optimizes the dispatch decision-making method, improves the grid's adaptability to emergency situations, and ultimately effectively ensures the stability of grid operation.

[0007] Compared with existing technologies, this invention effectively constructs a technical collaboration closed loop by following the resource priority allocation mechanism and data update method, overcoming the deficiency of the lack of technical collaboration closed loop in the existing power grid emergency control. Therefore, it can solve the problem that the unreasonable allocation of emergency resources in the existing power grid emergency control and the inability to form a technical collaboration closed loop make it difficult to maintain the stability of power grid operation.

[0008] As a preferred embodiment, based on the resource response reliability coefficient in the resource operation data, the power grid is subjected to an allocation decision process, specifically as follows: Obtain all resources in the power grid; Remove resources that do not meet the preset availability requirements, as well as those with zero schedulable capacity limit and zero real-time power difference, to obtain the remaining resources; Based on the resource response reliability coefficient and the distance parameter between the resource and the weak node of the power grid, the comprehensive priority value of each resource in the remaining resources is calculated. The remaining resources are sorted in descending order according to the comprehensive priority value, and each of the remaining resources is allocated a preset amount of power according to the descending order result, until the grid frequency of the power grid is within the preset threshold.

[0009] This optimal solution eliminates resources that do not meet availability requirements or whose dispatchable capacity is saturated, avoiding the waste of dispatching computing power by ineffective resources, reducing redundant steps in dispatching decisions, and improving decision-making efficiency. Secondly, it calculates a comprehensive priority value based on the overall resource response reliability coefficient and the distance parameters between the resource and the target weak nodes in the power grid. This ensures resource reliability while also considering dispatching efficiency and targeted control, making resource allocation more aligned with the actual weak links in the power grid. Finally, power is allocated in descending order according to the comprehensive priority value until the frequency regression threshold is reached, achieving optimal resource allocation, ensuring rapid restoration of grid frequency stability, and providing a clear and traceable allocation basis for subsequent dynamic credit assessment and incentive settlement. This ensures the fairness of incentive settlement and encourages resources to actively participate in emergency response services.

[0010] As a preferred embodiment, if the power grid has several target weak nodes, then each target weak node is taken as the target in turn, and the comprehensive priority value for the corresponding round is calculated respectively, and power allocation is performed in turn according to the comprehensive priority value of each weak node for the corresponding round.

[0011] This preferred solution addresses scenarios where the power grid has multiple vulnerable nodes. It proposes a targeted power allocation strategy that effectively solves the coordination challenge of simultaneously controlling multiple vulnerable nodes, thereby enhancing the overall emergency control capability of the power grid. When multiple vulnerable nodes exist in the power grid, power allocation to a single node may exacerbate the risks to other nodes. By calculating a comprehensive priority value targeting each vulnerable node in turn and allocating power sequentially, a balanced coverage of the control needs of each vulnerable node can be achieved, avoiding problems of excessive or insufficient local control. This approach retains the rationality of resource allocation brought by the comprehensive priority value while also considering the coordinated stability of multiple nodes in the power grid, effectively reducing the risk of power grid failures caused by multiple vulnerable nodes simultaneously. Furthermore, this strategy can fully utilize limited resources, achieving optimal resource allocation among multiple vulnerable nodes, improving resource utilization efficiency, ensuring overall frequency stability of the power grid, and further enhancing the adaptability and comprehensiveness of emergency response services.

[0012] As a preferred option, the sum of the power allocated to each resource and the real-time power of the corresponding resource shall not exceed the upper limit of the schedulable capacity of the corresponding resource.

[0013] This preferred solution effectively mitigates the risk of resource overload by clearly defining the boundary limits of power allocation, ensuring the safety and stability of response equipment and grid operation. During emergency response, excessive power allocation may cause the actual operating power of resources to exceed their dispatchable capacity limit, leading to equipment damage, downtime, and other problems, thus negatively impacting the effectiveness of grid emergency control. By limiting the sum of allocated power and real-time resource power to not exceed the dispatchable capacity limit, it ensures that resources always operate within a safe range, extending equipment lifespan and reducing the risk of emergency interruptions due to equipment failure. Simultaneously, this constraint makes power allocation more scientific and standardized, avoiding resource waste and improper control, ensuring that each power allocation effectively serves grid frequency restoration. This not only guarantees the effectiveness of emergency response but also provides a clear compliance basis for subsequent incentive settlement, improving the standardization and reliability of the entire service process.

[0014] As a preferred embodiment, if the grid frequency exceeds a preset threshold, the grid is regulated based on the resources locked by the Virtual Response Capacity Guarantee Protocol. The method for obtaining the Virtual Response Capacity Guarantee Protocol includes: The future target period of the power grid is divided into several time periods. Based on the resource operation data, the predicted probability of power shortage events and the expected power shortage amount are calculated according to the probability statistics method and the scenario analysis method. The physical deficit risk value is calculated based on the predicted probability of the power deficit event and the expected power deficit amount. The ratio of the real-time energy market price forecast to the preset market price psychological threshold is calculated to obtain the relative incentive level value. Based on the relative incentive level value, the degree of insufficient price incentive is linearly mapped to a risk value to obtain the economic incentive risk value. The period in which both the physical deficit risk value and the economic incentive risk value exceed a preset threshold is defined as a critical risk period. A preset safety margin is superimposed on the physical deficit risk value corresponding to the critical risk period to obtain the total virtual response capacity for the critical risk period, and the virtual response capacity guarantee protocol is generated based on the total virtual response capacity.

[0015] This preferred scheme, by dividing time periods and combining probabilistic statistical methods and scenario analysis to predict relevant data, can accurately identify high-risk periods in the future operation of the power grid, enabling early risk prediction. Furthermore, by calculating physical deficit risk values ​​and economic incentive risk values, it comprehensively considers the grid security risks and the rationality of resource incentives, accurately defining critical risk periods and making the configuration of virtual response capacity more targeted. Finally, by overlaying a safety margin generation protocol during critical risk periods, resources are locked in advance to ensure rapid resource allocation in emergencies, achieving timely compensation for power deficits. Simultaneously, by guiding resource participation in the protocol through economic incentives, the interests of resource owners and grid dispatchers are balanced, reducing the cost and risk of grid emergency response.

[0016] As a preferred option, the allocation decision process is replaced by a power allocation scheme, which is obtained by solving a power command optimization model. The power command optimization model aims to achieve full compensation and absorption balance of the power grid deficit, and is established by restricting the range of values ​​of the power decision variables.

[0017] This optimized solution replaces the original allocation decision process, which sequentially allocates preset power quotas based on resource priority, with a power allocation scheme obtained by solving a power command optimization model. By limiting the value range of power decision variables, it achieves global optimized scheduling, accurately filling power shortages and avoiding frequency fluctuations, while rationally allocating power to various resources, fully exploring control potential, avoiding resource idleness and overload, and reducing control costs. Simultaneously, the model can be dynamically adjusted based on real-time grid data to adapt to different frequency anomaly scenarios and various resource characteristics, significantly improving the accuracy, reliability, and flexibility of grid frequency emergency control.

[0018] As a preferred embodiment, the power command optimization model aims to achieve sufficient compensation and absorption balance for the power deficit in the power grid. It is established by restricting the range of values ​​for the power decision variables, specifically: Based on the power deficit data to be compensated and the sum of all resource power command values, the power deficit compensation deviation data is calculated. The power deficit compensation deviation data is then mapped using a binding penalty coefficient to obtain penalty data for insufficient power deficit compensation. With the goal of achieving full compensation and absorption balance for the power deficit of the power grid, the total virtual cost data of the system and the penalty term data are combined and calculated, and a comprehensive optimization objective function is established by combining the resource response reliability coefficient. By restricting the range of values ​​for the power decision variables, a constraint formula is established. Based on the comprehensive optimization objective function and the constraints, the power command optimization model is established.

[0019] This optimized scheme, by calculating power deficit compensation deviation data and incorporating binding penalty terms, forces the precision of power commands, reducing instances of insufficient or excessive compensation. This ensures rapid restoration of grid frequency stability while avoiding power consumption problems caused by excess power. The comprehensive optimization objective function balances the total virtual cost of the system with penalty terms, achieving a balance between control effectiveness and economy, ensuring grid security while reducing emergency response costs. By restricting the value range of power decision variables, the feasibility and security of the model solution are ensured, preventing damage to the grid and resources caused by unreasonable power commands. The establishment of this model provides scientific decision support for power allocation schemes, making power allocation more accurate and efficient, and further improving the technical system of emergency response services.

[0020] As a preferred embodiment, the method for obtaining the resource response reliability coefficient includes: Based on the resource operation data, the action delay time, power tracking deviation, and command completion rate are weighted and calculated to obtain the resource response reliability coefficient; wherein, the power tracking deviation is obtained by calculating the root mean square error between the actual power curve and the command power curve and then normalizing it.

[0021] This preferred scheme comprehensively covers the key performance dimensions of resources in emergency control by weighting the action delay time, power tracking deviation, and command completion rate, avoiding the one-sidedness of single-indicator evaluation. Specifically, using root mean square error to calculate power tracking deviation accurately quantifies the degree of deviation between actual and commanded power, intuitively reflecting the resource's response accuracy and providing a quantifiable standard for differentiating the service quality of different resources. The reasonable acquisition of this coefficient not only provides reliable support for the calculation of comprehensive priority values, ensuring the rationality of resource allocation, but also provides a clear quality assessment basis for subsequent incentive settlement, guiding resources to improve their response performance, thereby enhancing the overall reliability and stability of the power grid's emergency response.

[0022] As a preferred embodiment, the resource is a dispatchable distributed response device in the power grid.

[0023] This preferred solution explicitly defines the resources as dispatchable distributed response devices, effectively expanding the sources of emergency resources and improving the flexibility, redundancy, and economy of power grid emergency response. Distributed response devices are characterized by wide distribution, flexible dispatch, and fast response speed, which can compensate for the shortcomings of centralized resources. They are particularly suitable for rapid regulation of local power grid frequency anomalies, reducing transmission losses and improving regulation efficiency. Simultaneously, the dispatchability of distributed response devices enables decentralized and coordinated regulation of resources, avoiding the risk of emergency interruptions caused by the failure of a single centralized resource, and improving the reliability and anti-interference capability of power grid emergency response. Furthermore, the widespread application of distributed response devices can promote the efficient utilization of distributed energy resources, aligning with the trend of smart and distributed power grid development, reducing the power grid's dependence on traditional centralized resources, reducing emergency response costs, and providing more distributed resource owners with opportunities to participate in emergency response services, thus improving the coverage and rationality of the incentive settlement system.

[0024] The present invention also provides a power grid frequency stability emergency resource optimization device, including a data acquisition module, a power regulation module and a positive feedback module; The data acquisition module is used to acquire power grid resource operation data. The power regulation module is used to perform an allocation decision process on the power grid based on the resource response reliability coefficient in the resource operation data if the power grid frequency exceeds a preset threshold; wherein the allocation decision process is implemented by allocating a preset amount of power to resources in sequence according to resource priority values; The positive feedback module is used to update the resource response reliability coefficient based on the power regulation results of the power grid.

[0025] The present invention also provides a storage medium storing a computer program, which is called and executed by a computer to implement the power grid frequency stability emergency resource optimization method described above.

[0026] The present invention also provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implement the above-described method for optimizing emergency resources for power grid frequency stability. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating an emergency resource optimization method for power grid frequency stability provided in an embodiment of the present invention. Figure 2 This is a resource collaborative control diagram provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a power grid frequency stability emergency resource optimization device provided in an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] In the description of this invention, unless otherwise stated, "a number" means two or more.

[0030] The present invention provides a method for optimizing emergency resources for power grid frequency stability, which aims to solve the technical problems in traditional power grid emergency control, such as insufficient control reliability, difficulty in quickly and stably restoring frequency, passive management of reserve resources resulting in insufficient capacity utilization efficiency and system resilience in response to uncertain events, as well as the inability to quantify the economic behavior of resource response, passive risk response methods, and lack of a technical collaborative closed loop centered on power grid operation stability.

[0031] Example 1: Please see Figure 1 The present invention provides a method for optimizing emergency resources for power grid frequency stability, including steps S1 to S3, and the specific implementation steps are as follows: S1. Obtain power grid resource operation data.

[0032] Step S1 in this embodiment of the invention specifically includes: Local control terminals are deployed on each distributed resource side of the power grid. These terminals collect operational data of the corresponding distributed resources at fixed intervals, specifically covering instantaneous power, switch status, and percentage of available capacity.

[0033] After the data collection is completed, the resource operation data is packaged and a standard time stamp generated by the high-precision synchronous clock built into the terminal is attached. Then, the tagged resource operation data is sent in real time to the resource status synchronization monitoring unit of the dispatch master station through the power dedicated communication network.

[0034] The resource status synchronization monitoring unit is used to receive periodic or triggered messages uploaded from each local control terminal. The messages contain resource operation data. Then, based on the data-timestamp data pairs in the messages, it performs data anomaly detection, cleaning, and interpolation. Then, it calculates the call potential of each callable resource in real time using the preprocessed data. Finally, it continuously pushes the calculated resource call potential and resource status to the response performance evaluation unit and the adaptive collaborative control unit in real time through the message bus or shared memory. It should be noted that upon receiving an emergency event trigger signal, the resource status synchronization monitoring unit slices and packages the complete data sequence within a specific time window before and after the event (e.g., from 15 seconds before the event trigger to 30 seconds after the control command is issued), generates a unique event record, and stores it in the historical database for post-event evaluation and auditing. This complete data sequence includes the actual power curve. and scheduling instruction curve Among them, the scheduling instruction curve It is generated based on power deficit and resource reporting performance parameters. For example, when the power deficit of an emergency event is 50MW and the response time requirement is 1s, the resource that can meet the requirement will be selected with historical reliability as the weight, and then the curve of the increased capacity of the resource providing 50MW of power within 1s will be simulated and calculated according to the reported performance parameters (ramp rate, current power) of the resource.

[0035] It should be noted that the resources referred to in this invention specifically refer to distributed response devices with dispatchable attributes, and typical types include distributed photovoltaics, energy storage power stations, adjustable loads, small gas turbines, and other distributed facilities that can participate in emergency grid regulation.

[0036] In this embodiment S1, the resource is explicitly defined as a dispatchable distributed response device, effectively expanding the sources of emergency resources and improving the flexibility, redundancy, and economy of power grid emergency response. Distributed response devices are characterized by wide distribution, flexible scheduling, and fast response speed, which can compensate for the shortcomings of centralized resources. They are particularly suitable for rapid regulation of local power grid frequency anomalies, reducing transmission losses and improving regulation efficiency. Simultaneously, the dispatchability of distributed response devices enables decentralized and coordinated regulation of resources, avoiding the risk of emergency interruptions caused by the failure of a single centralized resource, and improving the reliability and anti-interference capability of power grid emergency response. Furthermore, the widespread application of distributed response devices can promote the efficient utilization of distributed energy resources, aligning with the trend of intelligent and distributed power grid development, reducing the power grid's dependence on traditional centralized resources, reducing emergency response costs, and providing more distributed resource owners with opportunities to participate in emergency response services, thus improving the coverage and rationality of the incentive settlement system.

[0037] S2. If the grid frequency exceeds the preset threshold, the grid will be allocated according to the resource response reliability coefficient in the resource operation data. The allocation decision process is implemented by allocating the preset amount of power to the resources in sequence according to the resource priority value.

[0038] In this embodiment of the invention, step S2 includes S2.1 to S2.4; wherein, S2.1 is the process of obtaining resource response reliability coefficients, S2.2 is the process of generating a virtual response capacity guarantee protocol based on the resource response reliability coefficients, S2.3 is the process of executing a power grid allocation decision process based on the virtual response capacity guarantee protocol, and S2.4 is the process of executing a power allocation scheme for the power grid based on a power command optimization model, specifically as follows: S2.1. Based on resource operation data, a weighted calculation is performed on action delay time, power tracking deviation, and command completion rate to obtain the Resource Response Reliability Coefficient (RRC). The "power tracking deviation" is obtained by calculating the root mean square error between the actual power curve and the commanded power curve, and then normalizing it. The specific calculation process of RRC is completed by the response performance evaluation unit. The response performance evaluation unit continuously monitors power grid system frequency exceedance events and corresponding dispatch command issuance events. When an emergency frequency control event occurs in the power grid and distributed resources are invoked to respond, and the system frequency recovers to the normal range of 49.8 Hz to 50.2 Hz, a dynamic response performance evaluation is immediately triggered. An "emergency frequency control event" refers to an emergency control event where the power grid system frequency deviates from the normal range of 49.8 Hz to 50.2 Hz, requiring the invocation of distributed resources to restore frequency stability. The dynamic response performance evaluation process is as follows: (1) Data acquisition: Retrieve relevant data for this event from the resource status synchronization monitoring unit, including the "scheduling instruction curve" issued by the scheduler. "Resource's "actual power curve" ", the main station timestamp of the instruction issuance and according to The initial timescale at which the actual power change is identified and exceeds a certain percentage of its rated power. The empirical value for "a certain proportion" is 5%.

[0039] (2) Calculate the three types of indicators: Based on the retrieved scheduling instruction curve Actual power curve Based on the data obtained in the above steps, three core indicators are calculated for each resource: action latency time (D), power tracking deviation (A), and command completion rate (E). The specific calculation method is as follows: ① Action Delay Time: This measures the response speed of a resource from receiving a scheduling command to initiating a power adjustment action. This parameter is the time from when the command is issued to when the resource's power change exceeds a certain percentage of its rated power. The calculation formula is: D = - ② Power Tracking Deviation: Used to quantify the tracking control accuracy between the actual power of the resource and the commanded power curve. This parameter is the root mean square error (RMSE) between the actual power curve and the commanded power curve, after normalization. The calculation formula is: Where RMSE() is the root mean square error function. ; "" represents the peak-to-valley difference of the instruction curve, used to normalize the error.

[0040] ③ Instruction Completion Rate: This parameter assesses the degree to which the actual total regulated power output of resources fulfills the scheduling instructions. It is the ratio of the total regulated power actually provided by the resources to the total regulated power required by the instructions, using the total task amount (power) as the evaluation basis, rather than the power at a specific moment. The calculation formula is: Here, the electricity consumption is the power integral. In response to power consumption, = ; The command requires power. = .

[0041] (3) Calculate RRC based on three types of indicators: Three first-in-first-out (FIFO) queues are maintained for each resource, storing the D, A, and E index values ​​of its most recent N events respectively. The queue length N is the defined sliding time window. After each new power grid emergency control event occurs, the corresponding queue is updated with the new index values; where N is preferably 20.

[0042] First, an exponentially weighted average is calculated for each type of indicator in the queue, following the principle that newer indicators have greater weights. Taking the delay indicator D as an example, the calculation formula is as follows: Among them, for parameters have: in, It is a weighted average delay. This represents the delay value of the k-th historical event in the queue, where k=1 represents the latest event; Event weight, This is the attenuation factor. Similarly, the weighted average tracking accuracy can be calculated. Consistency with weighted average power consumption .

[0043] Secondly, the RRC is obtained through weighted calculation, and the specific calculation formula is as follows: RRC= Among them, the RRC range is, for example, 0-1, which is a dynamically updated technical parameter that reflects the resource's ability to follow power grid control commands; It is A monotonically decreasing function mapped to the interval 0 to 1. For example: = exp( - / τ), where τ is the time constant. The greater the delay, the lower the score for this item. The three weights in the formula can be flexibly adjusted according to the different emphases of the power grid on control speed or accuracy.

[0044] Furthermore, after the assessment process for each emergency event is completed, the RRC values ​​of each resource will be updated immediately, and the updated parameters will be stored and pushed to the adaptive collaborative control unit through internal system services.

[0045] This embodiment, S2.1, comprehensively covers the key performance dimensions of resources in emergency control by weighting the action delay time, power tracking deviation, and command completion rate, avoiding the one-sidedness of single-indicator evaluation. Specifically, using root mean square error to calculate power tracking deviation accurately quantifies the degree of deviation between actual power and commanded power, intuitively reflecting the resource's response accuracy and providing a quantifiable standard for distinguishing the service quality of different resources. The reasonable acquisition of this coefficient not only provides reliable support for the calculation of comprehensive priority values, ensuring the rationality of resource allocation, but also provides a clear quality assessment basis for subsequent incentive settlement, guiding resources to improve their response performance, thereby enhancing the overall reliability and stability of the power grid's emergency response.

[0046] S2.2. Establish a virtual response capacity assurance protocol based on the risk contingency plan generation unit, specifically as follows: (1) Calculate the predicted probability of power deficit events, the expected power deficit amount, and the real-time energy market price forecast: Data on grid operation and market conditions are obtained from the Energy Management System (EMS) and Advanced Measurement System (AMI), including weather forecasting systems (future wind speed, sunshine, temperature), load forecasting systems (future load), generation planning systems (future generation), market clearing price forecasting models (future prices), and equipment maintenance plans in the management system. The future target cycle of the power grid is divided into several time periods. Based on resource operation data, power grid operation data, and market data, physical risk and behavioral risk predictions are calculated for each time period using probabilistic statistics and scenario analysis methods. "Physical risk" refers to the probability distribution and expected amount of power deficits of varying severity occurring in a specific future time period. "Behavioral risk" refers to analyzing the time periods and risk levels where economic factors may lead to a significant decrease in the willingness to respond to resources. Through this calculation, the predicted probability of power deficit events can be obtained. and expected power deficit ; in, The predicted probability of an emergency power deficit event occurring in time period t is calculated based on historical statistical models such as renewable energy output fluctuations, load forecasting errors, and unit failure rates. The predicted value of the expected power deficit (kW) when a deficit event occurs in time period t is obtained as follows: For the time period t to be calculated, the basic data for scenario analysis of power output and load are first established to provide a basis for subsequent simulation calculations. The predicted power output of renewable energy in time period t is superimposed with the power output fluctuation error distribution of renewable energy obtained based on historical statistical models to obtain the calculated power output value of renewable energy in that time period. At the same time, the planned power output of traditional units in time period t is obtained, and the planned power output is discounted and corrected by combining the historical statistical model of unit failure rate to obtain the effective power output of traditional units. The calculated power output value of renewable energy and the effective power output of traditional units are added together to form the basis for calculating the total predicted power output of the power grid in time period t. For the power grid load, the predicted power grid load in time period t is also superimposed with the historical statistical model data of load prediction error distribution to obtain the basis for calculating the load of the power grid in time period t. After completing the basic data setup, N independent Monte Carlo stochastic simulations are performed on the calculation basis of the total predicted power output and grid load to form a complete simulation result set. Each simulation randomly extracts error values ​​from the renewable energy power output fluctuation error distribution and the load prediction error distribution, and combines these with the corresponding calculation basis to generate the total grid output and grid load for that simulation. Then, it is determined whether a power deficit event has occurred: if the grid load in this simulation is greater than the total output, a deficit event is determined to have occurred, and the amount of the power deficit is calculated and recorded; if no deficit event has occurred, the deficit amount is recorded as 0. After completing N simulations, a dataset containing the core information of "whether there is a deficit" and "the amount of the deficit" for each simulation is finally formed. Based on the result set of N Monte Carlo simulations, statistical calculations were performed respectively. and Two indicators. One is the predicted probability of power deficit events. The calculation method is as follows: The number of simulations identified as power deficit events is statistically analyzed from the result set. This number is then compared to the total number of simulations N. The resulting ratio is the percentage of power deficit events in time period t. Expected power deficit Then, valid scenarios with a deficit greater than 0 are selected from the simulation results set. The deficit amounts of these scenarios that actually have deficit events are arithmetically averaged, and the result is the expected power deficit amount when a deficit event occurs in time period t.

[0047] Among them, real-time energy market price forecast This refers to the real-time energy market price forecast value calculated and output by the market-clearing price forecast model, corresponding to a specific future time period t in the power grid risk forecast. The "market-clearing price forecast model" is a professional quantitative analysis model used to predict the clearing price of the electricity market in various future time periods. It can employ time series models, such as GARCH, to complete short-term forecasts by analyzing the periodicity and trend patterns of historical electricity price data, thus obtaining real-time energy market price forecast values. Alternatively, it can use data-driven or AI models, such as LSTM and random forests, to integrate multi-source data such as historical electricity prices, weather, load, and renewable energy output to explore the nonlinear relationship between electricity prices and various factors, thereby obtaining real-time energy market price forecast values.

[0048] (2) Calculate the physical deficit risk value and the economic incentive risk value: ① Physical deficit risk value: Predicting probability based on power deficit events and expected power deficit The physical deficit risk value was calculated. This value is used to measure the probability and severity of a power deficit in a future period, and its calculation formula is: ② Economic incentive risk value: Calculate real-time energy market price forecasts The ratio of the price level to the preset psychological threshold of the market price yields the relative incentive level value. Based on this relative incentive level value, the degree of insufficient price incentive is linearly mapped to a risk value, resulting in the economic incentive risk value. This value measures the risk of weak resource response due to insufficient market price signals, and its calculation formula is: Among them, economic incentive risk value The valid calculation conditions for the calculation formula are: < . For the predicted real-time energy market price in time period t, The psychological threshold for market prices that triggers a significant decrease in willingness to respond to resources can be obtained through historical behavioral data or market research.

[0049] (3) Generate a virtual response capacity guarantee protocol: Physical deficit risk value in several time periods and economic incentive risk value Periods in which all exceed the preset threshold are defined as critical risk periods, thereby clarifying the core time range covered by the agreement; Physical shortfall risk value corresponding to critical risk periods By superimposing a preset safety margin, the total virtual response capacity required to mitigate risks during that period is obtained. This capacity is the core basis for the advance invitation to purchase the corresponding response capacity for the period, and can ensure that when a power shortage actually occurs during a critical risk period, the shortage can be made up by relying on the invited resources.

[0050] Two independent ranking sequences are formed based on RRC (Resource Capacity Ratio) and distance parameters between resources and target weak nodes in the power grid. "Target weak nodes" refer to specific nodes in the power grid where frequency fluctuations are more likely to exceed normal operating limits (49.8 Hz–50.2 Hz). Each resource corresponds to two ranking ranks. The weighted sum of these two ranks yields a comprehensive score for each resource. Resources are selected from the resource pool based on their comprehensive scores, from highest to lowest, as priority invitation targets. This ensures that the selected resources have both high RRC values ​​and are closer to weak nodes in the power grid. Furthermore, this ranking follows fixed rules: for example, RRC is ranked from smallest to largest, with a lower ranking (higher sequence number) representing a higher RRC; distance is ranked from farthest to closest, with a lower ranking representing a closer proximity to weak nodes. Simultaneously, the weighting values ​​can be flexibly adjusted according to actual conditions. If the impact of a weak node exceeding its limits on the overall power grid is small, the weight corresponding to RRC can be set higher.

[0051] Based on total virtual response capacity For each selected priority invitation target (i.e., the target resource), a virtual response capacity guarantee agreement is generated. This agreement explicitly includes the promised response capacity, execution price, validity period, capacity increase commitment, and scheduling gain coefficient increase commitment. Furthermore, this agreement is essentially a set of pre-programmed control logic that clarifies the specific agreement content through conditional rules. For example, it might state that "if the risk level is 'high' next week afternoon, the system will prioritize connections to resources A, B, and C, promising to increase their future schedulable capacity limit when they are actually invoked and the response meets the requirements." and scheduling gain coefficient "These are specific control rules;" Total virtual response capacity The value is maintained at the sum of the committed response capacities in all effective agreements. The "committed response capacity" is the capacity that the resource promises to guarantee during critical risk periods. This capacity typically does not exceed 70% to 80% of its current schedulable capacity limit. Specifically, the "committed response capacity" can also be selected as the minimum of the following three values: the upper limit of the aforementioned percentage, the 95th percentile of the resource's historical maximum output, and the available capacity provided by the resource provider for the corresponding period. Furthermore, the committed increase amount in the agreement can be set in two ways: either as a fixed amount or proportional to the committed response capacity and the degree of emergency risk. The overall principle is to reward and encourage resource providers who respond proactively. The "execution price" is determined by analyzing historical resource operation data. The core idea is to find the price inflection point where the success rate of resource retrieval significantly decreases when the market price falls below a certain value; this inflection point is the execution price. Specifically, this can be determined by plotting a "market price - response success rate" curve for the resource and selecting the point of abrupt change in the curve's curvature as this price inflection point. "Validity period" and "capacity increase commitment and dispatch gain coefficient increase commitment" are core terms pre-defined in the Virtual Response Capacity Assurance Agreement. The "capacity increase commitment" refers to the specific rules stipulated in the agreement regarding the system increasing the upper limit of dispatchable capacity for resource providers after they meet the response compliance conditions. The "dispatch gain coefficient increase commitment" refers to the specific rules stipulated in the agreement regarding the system increasing the dispatch gain coefficient for resource providers after they meet the response compliance conditions. Both commitments are incorporated into the effective Virtual Response Capacity Assurance Agreement after being negotiated and confirmed by the power grid master station and resource providers during the risk contingency plan generation phase. The agreement's "validity period" matches the granularity of risk identification, corresponding to critical risk periods. If resources are actually used during critical risk periods and the response performance meets the preset standards, the system will subsequently increase the upper limit of dispatchable capacity and the dispatch gain coefficient according to the agreed values ​​in the two increase commitments. The increase in the upper limit of dispatchable capacity is positively correlated with the resource provider's committed response capacity; for example, the increase can be calculated as "0.05 × committed response capacity." Meeting the response performance standards requires simultaneously meeting both response speed and response capacity requirements.

[0052] After the virtual response capacity guarantee protocol is generated, the system confirms the protocol's effectiveness with the resource provider through the resource's local control terminal. If the resource provider confirms the protocol's effectiveness, the system archives the effective virtual response capacity guarantee protocol and sends it to the adaptive collaborative control unit; if the resource provider does not confirm the protocol's effectiveness, the system excludes the resource from the protocol objects, reselects suitable resources, and generates a new protocol.

[0053] This embodiment, S2.2, by dividing time periods and combining probabilistic statistics and scenario analysis to predict relevant data, can accurately identify high-risk periods in the future operation of the power grid, enabling early risk prediction. Furthermore, by calculating physical deficit risk values ​​and economic incentive risk values, it comprehensively considers the power grid security risks and the rationality of resource incentives, accurately defining critical risk periods and making the configuration of virtual response capacity more targeted. Finally, a safety margin generation protocol is superimposed during critical risk periods to lock resources in advance, ensuring rapid resource allocation in emergencies and timely compensation for power deficits. Simultaneously, by guiding resource participation in the protocol through economic incentives, it balances the interests of resource owners and power grid dispatchers, reducing the cost and risk of power grid emergency response.

[0054] S2.3 If the grid frequency is detected to be out of limit, i.e., exceeding the preset threshold, then a total power deficit is determined. Activate the adaptive collaborative control unit to obtain the real-time status of each resource from the resource status synchronization monitoring unit. and real-time power ; and obtain the latest RRC of each resource from the response performance evaluation unit, and calculate the real-time adjustable potential. ; Obtain from the risk contingency plan generation unit whether the current time period is a critical risk period, the latest risk contingency plan (i.e., virtual response capacity guarantee protocol), and the corresponding time period-resource mapping list; Obtain from the database the schedulable capacity limit of resource i. and scheduling gain coefficient .in, This refers to the net power that needs to be rapidly replenished or reduced through emergency control resources to ensure the grid frequency remains stable within a specified range during an emergency. The "Time Period-Resource Mapping List" is a supporting list developed by the risk contingency plan generation unit based on the division of key risk periods and the virtual response capacity guarantee protocol. It accurately associates and matches each key risk period with the corresponding resources included in the protocol. The list clearly defines the main schedulable protocol resources and their core parameters for each key risk period, facilitating the adaptive collaborative control unit to quickly retrieve available protocol resources for the corresponding period during an emergency, providing a direct resource matching basis for power allocation and command issuance.

[0055] The power grid allocation decision-making process based on the adaptive cooperative control unit is as follows: ① Obtain all resources from the power grid from the resource pool; Remove all resources that do not meet the preset availability requirements, or whose schedulable capacity limit and real-time power difference are zero, to obtain the remaining resources.

[0056] ②Based on RRC and scheduling gain coefficient By combining the distance parameters between the resources and the weakest nodes in the power grid, the comprehensive priority value of each remaining resource is calculated. The specific calculation formula is as follows: Overall Priority Value = × +(1- / ) in, It is the distance between resource i and weak node A. It is the maximum distance among all resources to the weak node A.

[0057] ③ The remaining resources are sorted in descending order based on the comprehensive priority value. According to the descending order, the resources locked by the virtual response capacity guarantee protocol are allocated a preset power quota to each of the remaining resources in sequence. After the allocation is completed, the final call quota of each resource is converted into a control command and sent to the local control terminal corresponding to the resource to regulate the power grid frequency until the power grid frequency is within the preset threshold, thus satisfying the total power deficit. As required, after a resource performs a response operation, the corresponding local control terminal will transmit real-time power data back to the resource status synchronization monitoring unit. Furthermore, the power allocated to each resource and the corresponding resource's real-time power will be recorded. The sum shall not exceed the upper limit of the schedulable capacity of the corresponding resource. .

[0058] In addition, if there are several weak nodes in the power grid, the comprehensive priority value will be determined by round-based calculation. That is, the comprehensive priority is calculated in the first round based on the distance parameter corresponding to weak node A, the comprehensive priority is calculated in the second round based on the distance parameter corresponding to weak node B, and the comprehensive priority value is calculated for all weak nodes in the round according to this rule. Then, the subsequent power allocation work will be carried out according to the comprehensive priority value of each weak node in the round.

[0059] This embodiment, S2.3, proposes a targeted power allocation strategy for scenarios with multiple vulnerable nodes in the power grid. This effectively solves the coordination problem of simultaneous regulation of multiple vulnerable nodes, improving the overall emergency regulation capability of the power grid. When multiple vulnerable nodes exist in the power grid, power allocation to a single node may exacerbate the risks to other nodes. However, by calculating a comprehensive priority value with each vulnerable node as the target in turn and allocating power sequentially, a balanced coverage of the regulation needs of each vulnerable node can be achieved, avoiding problems of excessive or insufficient local regulation. This approach retains the rationality of resource allocation brought by the comprehensive priority value while also considering the coordinated stability of multiple nodes in the power grid, effectively reducing the risk of power grid failures caused by multiple vulnerable nodes simultaneously. Furthermore, this strategy can fully utilize limited resources, achieving optimal resource allocation among multiple vulnerable nodes, improving resource utilization efficiency, ensuring overall frequency stability of the power grid, and further improving the adaptability and comprehensiveness of emergency response services. Furthermore, by clearly defining the boundary limits for power allocation, the risk of resource overload is effectively mitigated, ensuring the safety and stability of response equipment and grid operation. During emergency response, excessive power allocation may cause the actual operating power of resources to exceed their dispatchable capacity limit, leading to equipment damage, downtime, and other problems, thus negatively impacting the effectiveness of grid emergency control. By limiting the sum of allocated power and real-time resource power to not exceed the dispatchable capacity limit, it ensures that resources always operate within a safe range, extending equipment lifespan and reducing the risk of emergency interruptions due to equipment failure. Simultaneously, this constraint makes power allocation more scientific and standardized, avoiding resource waste and improper control, ensuring that each power allocation effectively serves grid frequency restoration. This not only guarantees the effectiveness of emergency response but also provides a clear compliance basis for subsequent incentive settlement, improving the standardization and reliability of the entire service process.

[0060] In this embodiment, steps S2.1 to S2.3 eliminate resources that do not meet availability requirements or whose schedulable capacity is saturated, avoiding the waste of scheduling computing power by invalid resources, reducing redundant steps in scheduling decisions, and improving decision-making efficiency. Secondly, a comprehensive priority value is calculated by integrating the resource response reliability coefficient, scheduling gain coefficient, and distance parameters between the resource and the target weak node in the power grid. This ensures resource reliability while also considering scheduling efficiency and targeted control, making resource allocation more aligned with the actual weak links in the power grid. Finally, power is allocated in descending order according to the comprehensive priority value until the frequency regression threshold is reached, achieving optimal resource allocation, ensuring rapid recovery of the power grid frequency to stability, and providing a clear and traceable allocation basis for subsequent dynamic credit assessment and incentive settlement. This ensures the fairness of incentive settlement and encourages resources to actively participate in emergency response services.

[0061] S2.4 If the grid frequency is detected to be out of limit, i.e., exceeding the preset threshold, then a total power deficit is determined. It can also implement power allocation schemes for the power grid, specifically: Based on the power deficit data to be compensated and the sum of all resource power command values, the power deficit compensation deviation data is calculated. The power deficit compensation deviation data is then mapped using a binding penalty coefficient to obtain the penalty data for insufficient power deficit compensation. With the goal of achieving full compensation and absorption balance for the power deficit of the power grid, the total virtual cost data and penalty data of the system are combined and calculated, and a comprehensive optimization objective function is established by combining the resource response reliability coefficient. By restricting the range of values ​​for the power decision variables, a constraint formula is established. Based on the comprehensive optimization objective function and constraints, a power command optimization model is established. Fast optimization algorithms such as linear programming or quadratic programming are used to solve the power command optimization model, obtaining the optimal power allocation scheme. This power allocation scheme is then converted into control commands and issued to the local control terminals corresponding to the resources to regulate the grid frequency until the grid frequency is within a preset threshold, thus satisfying the total power deficit. As required, after the resource performs the response operation, the corresponding local control terminal will transmit the real-time power data back to the resource status synchronization monitoring unit.

[0062] The constraint formula and the comprehensive optimization objective function are as follows: ① Constraints include physical constraints and network security constraints; The physical constraints are: 0≤ ≤ Network security constraints are: in, The decision variable refers to the power command value allocated to resource i. "Resource i" refers to the schedulable resource in the Virtual Response Capacity Guarantee Protocol. It is the power transfer distribution factor, which represents the impact of changes in the output of resource i on the power flow of line l. It is considered a known quantity or can be calculated using existing technology. This is the power flow limit for line l.

[0063] ②The formula for calculating the comprehensive optimization objective function is: For parameters have: in, The virtual cost function for calling resource i; It is a penalty coefficient, which is a very large positive number, to ensure that power deficits are preferentially filled. It is the benchmark cost coefficient; for The calculation formula consists of two terms: the first term is the basic cost based on performance; the second term is the risk period incentive factor. If the current period is a critical risk period and resource i is in the effective agreement, the calculation of the second term is activated; otherwise, the second term is 0. For the excitation intensity coefficient, Let i be the execution price of resource i in the agreement. This represents the predicted market price for the current period. Furthermore, the incentive factors during risk periods have a clear regulatory guiding significance: when the predicted price... Below the agreement threshold hour, A negative value indicates an economic risk of insufficient resource response willingness in this scenario. In the optimization algorithm, this negative value effectively reduces the virtual cost of calling the corresponding resource, thereby guiding the system to prioritize the use of resources within the protocol for regulation. This offsets the response risk caused by insufficient economic incentives, ensures that power deficits are reliably compensated, and achieves the organic coupling of pre-emptive risk planning and real-time scheduling control.

[0064] It should be noted that the allocation decision-making process and power allocation scheme are applicable to different power grid control scenarios, and the corresponding power grid frequency control method can be selected according to actual operational needs. The allocation decision-making process is geared towards emergency scenarios requiring rapid response and ensuring control feasibility. Resources are allocated in descending order of power based on a comprehensive priority value, and the sum of the allocated power and the real-time power of the resource does not exceed its dispatchable capacity limit. If there are multiple weak nodes in the power grid, the comprehensive priority value needs to be calculated and allocation carried out in turn based on each weak node until the power grid frequency returns to the normal range and the total power deficit requirement is met. The power allocation scheme is suitable for refined control scenarios that pursue dispatch compliance and global optimization. During execution, it must strictly meet physical and network security constraints, with the optimization goal of minimizing the comprehensive virtual cost. It prioritizes power deficit filling through a penalty coefficient, and uses risk incentive factors to guide the invocation of resources within the protocol, achieving deep coupling between risk contingency plans and real-time control.

[0065] In this embodiment, S2.4, the preferred scheme adopts a power allocation scheme obtained by solving a power command optimization model. By limiting the value range of power decision variables, global optimization scheduling can be achieved. This not only accurately fills power deficits and avoids frequency fluctuations, but also rationally allocates power to various resources, fully tapping the control potential, avoiding resource idleness and overload, and reducing control costs. Simultaneously, the model can be dynamically adjusted based on real-time grid data to adapt to different frequency anomaly scenarios and various resource characteristics, significantly improving the accuracy, reliability, and flexibility of grid frequency emergency control. Furthermore, by calculating power deficit compensation deviation data and combining it with binding penalty terms, the accuracy of power commands can be forced, reducing instances of insufficient or excessive compensation. This ensures rapid restoration of grid frequency stability while avoiding the absorption problems caused by power surplus. The comprehensive optimization objective function, taking into account both the total virtual cost of the system and the penalty term, achieves a balance between control effectiveness and economy, ensuring grid security while reducing emergency response costs. By restricting the value range of power decision variables, the feasibility and security of the model solution are ensured, avoiding damage to the grid and resources caused by unreasonable power commands. The establishment of this model provides scientific decision support for power allocation schemes, making power allocation more accurate and efficient, and further improving the technical system of emergency response services.

[0066] S3. Update the resource response reliability coefficient based on the power regulation results of the power grid.

[0067] Step S3 in this embodiment of the invention is specifically as follows: After this control event concludes, the system will archive the scheduling instructions and related response data such as the actual power response curve, thereby triggering the dynamic evaluation unit of response performance to conduct a new round of calculations and complete the update of the RRC values ​​of all participating resources.

[0068] If the resources involved in scheduling are within the validity period of the guarantee agreement and their response performance indicators meet the preset standards, then their schedulable capacity limit will be set. and scheduling gain coefficient Perform an upward adjustment operation, such as increasing the schedulable capacity limit. Increase by 5%, scheduling gain coefficient Increase by 0.1.

[0069] If the resource response data does not meet the standard, the upper limit of the schedulable capacity of the resource will be determined based on the normalized result of the actual response data and the standard deviation of the response. and scheduling gain coefficient Implement a gradual reduction in capacity while ensuring the upper limit of schedulable capacity. Not less than the registered capacity of the resource.

[0070] For an explanation of the embodiments of the present invention, please refer to [link / reference]. Figure 2 , Figure 2 This is a resource coordination control diagram provided in an embodiment of the present invention. The diagram fully presents the overall system architecture constructed by the present invention to achieve resource coordination and control in power grid emergency response scenarios. The system mainly consists of two parts: the power grid master station and local control terminals, with the local control terminals deployed on the respective emergency response service resource sides.

[0071] Overall, this embodiment has the following beneficial effects: The allocation decision-making process of this invention is based on resource priority, which is set based on the resource response reliability coefficient in the resource operation data. This ensures that the priority ranking matches the actual operating performance of the resources, avoiding the drawbacks of blind allocation and prioritizing quantity over quality in existing allocation models. It prioritizes emergency resources with high response efficiency and strong reliability, allocating preset power quotas according to priority. This not only enables rapid response to emergency needs caused by grid frequency anomalies, minimizing dispatch response time and ensuring high efficiency in emergency control, but also achieves precise matching and rational allocation of resources, avoiding resource waste or delays in critical resource dispatch. This ensures that the grid frequency quickly stabilizes, addressing the instability problem caused by unreasonable allocation at its source. Simultaneously, this invention includes a dynamic parameter update mechanism. Using grid power regulation results as real-time feedback, the coefficients are dynamically adjusted, breaking the limitations of fixed dispatch parameters and lack of feedback optimization in existing control models. This continuously optimizes the dispatch decision-making method, improves the grid's adaptability to emergency situations, and ultimately effectively ensures grid operational stability. In summary, this invention significantly improves the execution accuracy and response rate of power grid emergency control through dynamic evaluation of resource response performance and a high-reliability resource priority scheduling mechanism, ensuring rapid and stable recovery of power grid frequency and effectively solving the problem of insufficient reliability under traditional control modes. Based on risk perception and virtual capacity guarantee protocols, it constructs an active backup resource management system, which not only improves the efficiency of backup capacity utilization but also enhances the operational resilience of the power grid in the face of various uncertain events. Simultaneously, it transforms the economic behavior of resource response into quantifiable technical parameters and forms a closed-loop feedback mechanism through the regulation of these parameters, constructing a collaborative control system aimed at the safe and stable operation of the power grid. This achieves a shift in risk response from passive handling to proactive prevention and control, comprehensively improving the power grid's frequency control and safe operation capabilities.

[0072] Example 2: Please see Figure 3 The present invention provides a power grid frequency stability emergency resource optimization device, including a data acquisition module 10, a power regulation module 20 and a positive feedback module 30. Among them, the data acquisition module 10 is used to acquire the resource operation data of the power grid; The power regulation module 20 is used to perform an allocation decision process on the power grid based on the resource response reliability coefficient in the resource operation data if the power grid frequency exceeds a preset threshold. The allocation decision process is implemented by allocating preset power to resources in sequence according to resource priority values. The positive feedback module 30 is used to update the resource response reliability coefficient based on the power regulation results of the power grid.

[0073] It should be noted that the technical concept of this second embodiment is completely consistent with that of the first embodiment. The two maintain a high degree of synergy at the technical logic level. The specific technical details can be referred to the relevant description of the first embodiment, which will not be repeated here.

[0074] Example 3: This invention provides a computer-readable storage medium including a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the aforementioned power grid frequency stability emergency resource optimization method. The aforementioned method for optimizing emergency resources for power grid frequency stability, when implemented as a software functional unit and used as an independent product, 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.

[0075] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present invention are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both types of storage media.

[0076] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle 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 method for optimizing emergency resources for power grid frequency stability, characterized in that, include: Obtain power grid resource operation data; If the grid frequency of the power grid exceeds a preset threshold, an allocation decision process is executed on the power grid based on the resource response reliability coefficient in the resource operation data; wherein, the allocation decision process is implemented by allocating preset power to resources in sequence according to resource priority values; The resource response reliability coefficient is updated based on the power regulation results of the power grid.

2. The method for optimizing emergency resources for power grid frequency stability as described in claim 1, characterized in that, Based on the resource response reliability coefficient in the resource operation data, the power grid is subjected to an allocation decision process, specifically as follows: Obtain all resources in the power grid; Remove resources that do not meet the preset availability requirements, as well as those with zero schedulable capacity limit and zero real-time power difference, to obtain the remaining resources; Based on the resource response reliability coefficient and the distance parameter between the resource and the weak node of the power grid, the comprehensive priority value of each resource in the remaining resources is calculated. The remaining resources are sorted in descending order according to the comprehensive priority value, and each of the remaining resources is allocated a preset amount of power according to the descending order result, until the grid frequency of the power grid is within the preset threshold.

3. The method for optimizing emergency resources for power grid frequency stability as described in claim 2, characterized in that, If the power grid has several weak nodes, then each weak node is taken as the target in turn, and the comprehensive priority value for the corresponding round is calculated. Power is then allocated according to the comprehensive priority value of each weak node in the corresponding round.

4. The method for optimizing emergency resources for power grid frequency stability as described in claim 2, characterized in that, The sum of the power allocated to each resource and the real-time power of the corresponding resource shall not exceed the upper limit of the schedulable capacity of the corresponding resource.

5. The method for optimizing emergency resources for power grid frequency stability as described in claim 1, characterized in that, If the grid frequency exceeds a preset threshold, the grid is regulated based on the resources locked by the Virtual Response Capacity Guarantee Protocol. The acquisition method of the Virtual Response Capacity Guarantee Protocol includes: The future target period of the power grid is divided into several time periods. Based on the resource operation data, the predicted probability of power shortage events and the expected power shortage amount are calculated according to the probability statistics method and the scenario analysis method. The physical deficit risk value is calculated based on the predicted probability of the power deficit event and the expected power deficit amount. The ratio of the real-time energy market price forecast to the preset market price psychological threshold is calculated to obtain the relative incentive level value. Based on the relative incentive level value, the degree of insufficient price incentive is linearly mapped to a risk value to obtain the economic incentive risk value. The period in which both the physical deficit risk value and the economic incentive risk value exceed a preset threshold is defined as a critical risk period. A preset safety margin is superimposed on the physical deficit risk value corresponding to the critical risk period to obtain the total virtual response capacity for the critical risk period, and the virtual response capacity guarantee protocol is generated based on the total virtual response capacity.

6. The method for optimizing emergency resources for power grid frequency stability as described in claim 1, characterized in that, The allocation decision process is replaced by a power allocation scheme, which is obtained by solving a power command optimization model. The power command optimization model aims to achieve full compensation and absorption balance of the power grid deficit, and is established by restricting the range of values ​​of the power decision variables.

7. The method for optimizing emergency resources for power grid frequency stability as described in claim 6, characterized in that, The power command optimization model aims to achieve sufficient compensation and absorption balance for the power deficit in the power grid. It is established by restricting the range of values ​​for the power decision variables, specifically: Based on the power deficit data to be compensated and the sum of all resource power command values, the power deficit compensation deviation data is calculated. The power deficit compensation deviation data is then mapped using a binding penalty coefficient to obtain penalty data for insufficient power deficit compensation. With the goal of achieving full compensation and absorption balance for the power deficit of the power grid, the total virtual cost data of the system and the penalty term data are combined and calculated, and a comprehensive optimization objective function is established by combining the resource response reliability coefficient. By restricting the range of values ​​for the power decision variables, a constraint formula is established. Based on the comprehensive optimization objective function and the constraints, the power command optimization model is established.

8. A method for optimizing emergency resources for power grid frequency stability as described in any one of claims 1 to 7, characterized in that, The methods for obtaining the resource response reliability coefficient include: Based on the resource operation data, the action delay time, power tracking deviation, and command completion rate are weighted and calculated to obtain the resource response reliability coefficient; wherein, the power tracking deviation is obtained by calculating the root mean square error between the actual power curve and the command power curve and then normalizing it.

9. A method for optimizing emergency resources for power grid frequency stability as described in any one of claims 1 to 7, characterized in that, The resource refers to the dispatchable distributed response devices in the power grid.

10. A power grid frequency stability emergency resource optimization device, characterized in that, It includes a data acquisition module, a power regulation module, and a positive feedback module; The data acquisition module is used to acquire power grid resource operation data. The power regulation module is used to perform an allocation decision process on the power grid based on the resource response reliability coefficient in the resource operation data if the power grid frequency exceeds a preset threshold; wherein the allocation decision process is implemented by allocating a preset amount of power to resources in sequence according to resource priority values; The positive feedback module is used to update the resource response reliability coefficient based on the power regulation results of the power grid.

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