A power grid flexibility resource scheduling method, device and equipment and storage medium

Abstract

The application discloses a kind of power grid flexibility resource scheduling method, device, equipment and storage medium, the method includes: according to the error of wind and light power station output prediction data and measured data, error change rate and the power gap value of system are determined, and then the comprehensive unbalance risk assessment value of system is determined;According to the power gap value and the upward adjustment capacity of thermal power generating set, the power compensation amount that hydroelectric generating set needs to undertake is determined, the dynamic response capability of hydroelectric generating set is determined in combination with the available adjustment capacity of hydroelectric generating set;Based on comprehensive unbalance risk assessment value and the dynamic response capability of hydroelectric generating set, the system comprehensive risk index is determined;If the reconfiguration standby resource is needed by the overrun amplitude of system comprehensive risk index, the output adjustment amount and scheduling priority sequence of various resources are determined based on the standby capacity value of thermal power generating set, hydroelectric generating set and energy storage device to carry out power grid scheduling.The application improves the safe and stable operation ability of system.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching technology, and in particular to a method, apparatus, equipment and storage medium for power grid flexibility resource dispatching. Background Technology

[0002] To address fluctuations in renewable energy output, optimized allocation of flexible resources and intelligent dispatching technology are core supports for ensuring the safe and stable operation of the power grid. However, current resource dispatching methods mostly adopt static preset modes, primarily relying on historical data and empirical formulas to determine reserve capacity requirements. They lack deep integration with the output characteristics of new energy sources such as wind and solar resources and the ability to dynamically perceive their real-time operating status. This results in dispatching systems often responding slowly to rapid changes in renewable energy output, failing to effectively cope with complex and ever-changing operating conditions, and leading to insufficient reliability assurance capabilities under extreme scenarios. Summary of the Invention

[0003] To address the above technical problems, this invention provides a method, apparatus, equipment, and storage medium for power grid flexibility resource scheduling, which can detect system risks in real time and realize intelligent resource allocation, effectively improving the reliability of the system under extreme scenarios.

[0004] This invention provides a method for power grid flexibility resource scheduling, comprising:

[0005] Obtain predicted and measured power output data of wind and solar power plants; determine the error change rate and the power gap value of the system based on the error between the predicted and measured data; and determine the comprehensive imbalance risk assessment value of the high-proportion new energy power system based on the error change rate and the power gap value.

[0006] The upward adjustment capacity of the thermal power unit is obtained, and the power compensation amount required by the hydropower unit is determined in combination with the power gap value. The dynamic response capability of the hydropower unit is determined based on the power compensation amount and the available adjustment capacity of the hydropower unit.

[0007] Based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, the comprehensive system risk index is determined, and the extent of exceeding the limit of the comprehensive system risk index is used to determine whether it is necessary to reconfigure the standby resources.

[0008] If it is necessary to reconfigure standby resources, the configuration optimization is carried out based on the standby capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total dispatch cost. The output adjustment amount and dispatch priority sequence of various resources are determined for grid dispatch.

[0009] As an improvement to the above scheme, the step of obtaining predicted and measured power output data from wind and solar power plants, and determining the error change rate and the system power deficit value based on the error between the predicted and measured data, includes:

[0010] Obtain meteorological forecast data for wind and solar power stations, and predict wind and solar power output based on the meteorological forecast data to obtain predicted power output data for wind and solar power stations.

[0011] Obtain measured data of the output of the wind and solar power station, and use a preset sliding time window to calculate the error value between the measured data and the predicted data at the corresponding time.

[0012] The error change rate is determined based on the amount of change in error values ​​within adjacent time windows.

[0013] The time period in which the error change rate is greater than a preset error threshold is designated as a high-risk time period.

[0014] For the high-risk period, the power deficit value of the system is determined based on the error change rate, combined with the total load demand of the power grid and the measured data.

[0015] As an improvement to the above scheme, the step of determining the comprehensive imbalance risk assessment value of a high-proportion renewable energy power system based on the error change rate and the power gap value includes:

[0016] The power gap expansion rate is obtained based on the increase of the system power gap value within a continuous time window.

[0017] Based on the high-risk periods, determine the duration of the high-risk periods;

[0018] The error change rate, the power gap expansion rate, and the duration of the high-risk period are normalized respectively. Then, the normalized error change rate, the power gap expansion rate, and the duration of the high-risk period are weighted and summed to obtain the comprehensive imbalance risk assessment value of the high-proportion new energy power system.

[0019] As an improvement to the above scheme, the step of obtaining the upward adjustment capacity of the thermal power unit, determining the power compensation amount required by the hydropower unit in combination with the power gap value, and determining the dynamic response capability of the hydropower unit based on the power compensation amount and the available adjustment capacity of the hydropower unit includes:

[0020] The output data of the thermal power unit is obtained, and the upward adjustment capacity of the thermal power unit is calculated based on the difference between the rated capacity and the current active power in the output data of the thermal power unit.

[0021] If the power deficit value is greater than the upward adjustment capacity of the thermal power unit, the power compensation amount required by the hydropower unit is calculated based on the difference between the power deficit value and the upward adjustment capacity.

[0022] Based on the power compensation amount and the hydropower unit's ramping speed, determine the response delay time required for the hydropower unit to increase from its current output to the target output;

[0023] The dynamic response capability of the hydropower unit is determined by comparing the response delay time with a preset response time threshold and comparing the power compensation amount with the available adjustment capacity of the hydropower unit; wherein, if the response delay time is less than the response time threshold and the available adjustment capacity is greater than the power compensation amount, the hydropower unit is determined to have dynamic response capability.

[0024] As an improvement to the above scheme, the step of determining the system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and determining whether it is necessary to reconfigure standby resources based on the extent of exceeding the limit of the system comprehensive risk index, includes:

[0025] Meteorological data from wind and solar power plants is acquired, and the meteorological data is input into a preset risk perception model to obtain real-time risk perception values; the risk perception model is trained by a neural network based on historical meteorological data.

[0026] The system comprehensive risk index is obtained by weighting the real-time risk perception value, the comprehensive imbalance risk assessment value, and the dynamic response capability of the hydropower unit.

[0027] The risk exceedance range is calculated based on the difference between the system's comprehensive risk index and the preset risk threshold.

[0028] If the risk exceeds the limit by less than or equal to zero, the system will continue to operate normally; if the risk exceeds the limit by more than zero, the backup resources will need to be reconfigured.

[0029] As an improvement to the above scheme, the configuration optimization based on the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total scheduling cost, determines the output adjustment amount and scheduling priority sequence of various resources, including:

[0030] Using the reserve capacity of thermal power units, hydropower units, and energy storage devices as decision variables, and minimizing the total scheduling cost as the objective, an objective function is constructed based on the unit capacity cost of each type of resource, and constraints are constructed based on the adjustment capabilities of each type of resource.

[0031] Based on the objective function and constraints, iterative optimization is performed to obtain the optimal reserve capacity of thermal power units, hydropower units, and energy storage devices.

[0032] Based on the ratio of the optimal reserve capacity to the total reserve capacity of each type of resource, an optimized reserve capacity allocation ratio is obtained. Based on this reserve capacity allocation ratio, the difference between the current output and the target output of each type of resource is calculated to obtain the output adjustment amount of each type of resource.

[0033] The scheduling priority sequence is determined by ranking the unit capacity cost of various resources from low to high.

[0034] As an improvement to the above scheme, after power grid dispatching, the method further includes:

[0035] The system obtains the total power generation and total load power during the power balance recovery process of the power grid dispatch, and determines the power balance recovery time based on the changes in the total power generation and total load power.

[0036] The time deviation value is calculated based on the difference between the power balance recovery time and the preset target time;

[0037] If the time deviation value is greater than the preset time threshold, the current allocation ratio of the standby capacity of thermal power units, hydropower units, and energy storage devices, as well as the actual ramp-up rate of various resources during the scheduling process, are obtained.

[0038] The deviation rate is calculated based on the ratio of the time deviation value to the target time.

[0039] The capacity ratio correction amount is calculated based on the product of the deviation rate and the current allocation ratio, and a new reserve capacity allocation ratio is obtained based on the current allocation ratio and the capacity ratio correction amount.

[0040] The actual ramp rate is used as the new adjustment rate limit. A configuration parameter set is generated based on the new reserve capacity allocation ratio and the adjustment rate limit. The configuration parameter set is used as the resource configuration optimization benchmark for the next scheduling cycle.

[0041] The present invention also provides a power grid flexibility resource scheduling device, comprising:

[0042] The system imbalance assessment module is used to acquire the predicted and measured data of the power output of wind and solar power plants, determine the error change rate and the power gap value of the system based on the error between the predicted and measured data, and determine the comprehensive imbalance risk assessment value of the high-proportion new energy power system based on the error change rate and the power gap value.

[0043] The hydropower response assessment module is used to obtain the upward adjustment capacity of the thermal power unit, determine the power compensation amount required by the hydropower unit in combination with the power gap value, and determine the dynamic response capability of the hydropower unit based on the power compensation amount and the available adjustment capacity of the hydropower unit.

[0044] The comprehensive risk assessment module is used to determine the comprehensive system risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and to determine whether it is necessary to reconfigure the backup resources based on the extent of the excess of the comprehensive system risk index.

[0045] The power grid resource scheduling module is used to optimize the configuration of standby resources based on the standby capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total scheduling cost, and to determine the output adjustment amount and scheduling priority sequence of various resources for power grid scheduling if it is necessary to reconfigure standby resources.

[0046] The present invention also provides a computer device, including a processor and a memory, wherein the memory stores a computer program and the computer program is configured to be executed by the processor, wherein the processor, when executing the computer program, implements the power grid flexibility resource scheduling method described in any of the preceding claims.

[0047] The present invention also provides a computer-readable storage medium storing a computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the power grid flexibility resource scheduling method described above.

[0048] Compared with existing technologies, the beneficial effects of the power grid flexibility resource scheduling method, apparatus, equipment, and storage medium provided by this invention are as follows:

[0049] By acquiring predicted and measured power output data from wind and solar power plants, and determining the error rate of change and the system's power deficit value based on the error between the predicted and measured data, a comprehensive imbalance risk assessment value for a high-proportion renewable energy power system is determined based on the error rate of change and the power deficit value. This approach can reflect the stability of the power system under high-proportion renewable energy conditions in real time, accurately quantify wind and solar power output fluctuations, reduce dispatching errors caused by prediction deviations, and improve system operational reliability. Furthermore, by acquiring the upward regulation capacity of thermal power units and combining it with the power deficit value, the required power compensation amount for hydropower units is determined. Based on the power compensation amount and the available regulation capacity of hydropower units, the hydropower... The dynamic response capability of the generating units facilitates rapid response and enhances system flexibility. Based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower units, a comprehensive system risk index is determined, and the extent to which the comprehensive system risk index exceeds the limit is used to determine whether reserve resources need to be reconfigured. If reserve resources need to be reconfigured, the configuration is optimized based on the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total dispatch cost. The output adjustment amount and dispatch priority sequence of various resources are determined for grid dispatch, providing comprehensive risk management and dispatch support. This enables rapid response in the event of sudden fluctuations in new energy sources, improving the safe and stable operation capability of the system. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a power grid flexibility resource scheduling method provided in an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the structure of a power grid flexibility resource scheduling device provided in an embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0053] 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.

[0054] Please see Figure 1 , Figure 1 This is a flowchart illustrating a power grid flexibility resource scheduling method provided in an embodiment of the present invention. The power grid flexibility resource scheduling method includes:

[0055] S1: Obtain the predicted and measured power output data of the wind and solar power station, determine the error change rate and the power gap value of the system based on the error between the predicted and measured data, and determine the comprehensive imbalance risk assessment value of the high-proportion new energy power system based on the error change rate and the power gap value.

[0056] S2: Obtain the upward adjustment capacity of the thermal power unit, determine the power compensation amount required by the hydropower unit in combination with the power gap value, and determine the dynamic response capability of the hydropower unit based on the power compensation amount and the available adjustment capacity of the hydropower unit.

[0057] S3: Based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, determine the comprehensive system risk index, and determine whether it is necessary to reconfigure the standby resources based on the extent of the exceedance of the comprehensive system risk index.

[0058] S4: If it is necessary to reconfigure the standby resources, the configuration optimization is carried out based on the standby capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total scheduling cost. The output adjustment amount and scheduling priority sequence of various resources are determined for grid scheduling.

[0059] Specifically, in step S1, meteorological data from wind and solar power plants is acquired to predict power output, resulting in predicted power output data. The error between the predicted and measured power output data is then calculated to determine the error rate of change. Combined with the difference between the total grid load demand and the actual wind and solar power output, the system power deficit value is determined. Based on this error rate of change and the expanding trend of the total system power deficit, a comprehensive imbalance risk assessment value for the high-proportion renewable energy power system is calculated. This assessment value can be categorized into low, medium, and high risk levels. In step S2, the capacity adjustment range of thermal power units is acquired, and the system's ability to cope with the power deficit is assessed based on the power deficit value. If thermal power units cannot compensate for the deficit, the remaining deficit needs to be covered by other power sources. The remaining power deficit is determined as the power compensation amount required by hydropower units. By comparing this power compensation amount with the available capacity of hydropower units, the dynamic response capability of the hydropower units is evaluated, quantifying the dynamic response capability and determining whether the hydropower units meet the power deficit requirements. Step S3: Combining the comprehensive imbalance risk assessment value from Step S1 with the dynamic response capability of the hydropower units from Step S2, a comprehensive system risk index is determined. When the comprehensive system risk index exceeds the safety boundary, it indicates that the current resource configuration of the system cannot meet the power demand, and it is necessary to reconfigure reserve resources, including the reserve capacity of thermal power units, hydropower units, and energy storage devices. Step S4: When reconfiguration of reserve resources is required, based on the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices, an optimization function is constructed with the objective of minimizing the total scheduling cost for iterative optimization. The optimized configuration result is then obtained, and the output adjustment amount and scheduling priority sequence of various resources are determined. Adjustment instructions for the corresponding output adjustment amount are then issued to each power generation unit according to the scheduling priority sequence to perform grid scheduling and restore system power balance.

[0060] As one optional embodiment, the step of acquiring predicted power output data and measured data of the wind and solar power station, and determining the error change rate and the system power deficit value based on the error between the predicted data and the measured data, includes:

[0061] Obtain meteorological forecast data for wind and solar power stations, and predict wind and solar power output based on the meteorological forecast data to obtain predicted power output data for wind and solar power stations.

[0062] Obtain measured data of the output of the wind and solar power station, and use a preset sliding time window to calculate the error value between the measured data and the predicted data at the corresponding time.

[0063] The error change rate is determined based on the amount of change in error values ​​within adjacent time windows.

[0064] The time period in which the error change rate is greater than a preset error threshold is designated as a high-risk time period.

[0065] For the high-risk period, the power deficit value of the system is determined based on the error change rate, combined with the total load demand of the power grid and the measured data.

[0066] Specifically, wind and solar power generation systems are significantly affected by weather conditions, exhibiting marked randomness and volatility. In actual operation, sudden changes in wind speed or cloud cover can lead to drastic fluctuations in power generation, posing a challenge to the stable operation of the power grid. This embodiment first acquires real-time monitoring data of wind and solar power output and corresponding weather forecast data through a data interface. For example, the data acquisition system obtains measured power output data from the SCADA systems of the wind farm and photovoltaic power station every 5 minutes. This measured data includes the active power of each wind turbine and the output power of each photovoltaic inverter. Simultaneously, it obtains meteorological parameters such as wind speed, wind direction, irradiance, cloud cover, temperature, and humidity from the weather forecast system. By monitoring wind and solar power output data in real time and combining it with weather forecast information, this embodiment can capture the patterns of power output changes, providing a data foundation for assessing system imbalance risks.

[0067] Furthermore, wind and solar power output is predicted based on meteorological forecast data to obtain predicted output data. Predicting wind and solar power output based on meteorological data is an existing technology in power grid dispatching. Specifically, short-term forecast meteorological elements are first obtained, and then physical models, statistical models, or machine learning models are used to map these meteorological elements into power output. For example, the predicted wind and solar power output values ​​are obtained through a power curve model, where wind power has a cubic relationship with wind speed, and photovoltaic power has a linear relationship with irradiance.

[0068] Since the two types of data come from different sources, the timestamps may be inaccurate. Therefore, after acquiring the data, the data is time-aligned according to a preset time interval. Specifically, an interpolation algorithm is used to unify data with different sampling frequencies to the same time scale.

[0069] Furthermore, a sliding time window is used to calculate the difference between the measured data and the predicted data based on meteorological forecast data, obtaining the error value between the predicted and measured values ​​of wind and solar power output. Specifically, the sliding time window method can dynamically track the evolution of the prediction error, setting the time window length to 30 minutes and sliding forward by 5 minutes each time. Within each time window, the error value between the measured wind and solar power output and the predicted value based on meteorological forecast data is calculated. When the error value of multiple consecutive time windows shows an increasing trend and the rate of change exceeds a preset threshold, it indicates that the prediction accuracy is rapidly declining, and the system faces a high risk of imbalance. Therefore, after obtaining the error value, the error change rate is obtained by dividing the change in error value within adjacent time windows by the time interval. If the error change rate is greater than the preset threshold, the period is marked as a high-risk period.

[0070] Furthermore, the system's power deficit reflects the supply-demand balance. During high-risk periods, actual wind and solar power output may be far lower than expected, resulting in the system's total power generation failing to meet load demand. This embodiment calculates the difference between the total grid load demand and the actual output of all power sources, including wind and solar, to obtain the real-time power deficit value, which helps to identify potential power supply shortage risks in advance.

[0071] As one optional embodiment, determining the comprehensive imbalance risk assessment value of the high-proportion renewable energy power system based on the error change rate and the power gap value includes:

[0072] The power gap expansion rate is obtained based on the increase of the system power gap value within a continuous time window.

[0073] Based on the high-risk periods, determine the duration of the high-risk periods;

[0074] The error change rate, the power gap expansion rate, and the duration of the high-risk period are normalized respectively. Then, the normalized error change rate, the power gap expansion rate, and the duration of the high-risk period are weighted and summed to obtain the comprehensive imbalance risk assessment value of the high-proportion new energy power system.

[0075] Specifically, the widening power gap trend reflects the dynamic evolution of power system supply-demand imbalance. When renewable energy output declines or load surges, the difference between total system power generation and total load demand gradually increases. This trend is quantified by the rate of change of the power gap value at continuous time points, providing an important basis for subsequent dispatch decisions. When the power gap continues to widen within a continuous time window, and the rate of widening exceeds the system's regulation capacity, it indicates that the system is developing towards an imbalance state. In this embodiment, the rate of widening of the power gap is obtained by measuring the increase in the power gap value within a continuous time window; then, the duration of high-risk periods is statistically analyzed as a key indicator for comprehensive imbalance risk assessment.

[0076] For the comprehensive imbalance risk assessment of a high-proportion renewable energy power system, three key indicators are used: error change rate, gap widening rate, and duration of high-risk periods. Based on grid operation experience and historical data analysis, the error change rate and gap widening rate have a significant impact on system stability, so their corresponding weights are set to 0.4. The weight of the duration of high-risk periods is set to 0.2. The comprehensive imbalance risk assessment value is obtained by weighted summation of the three indicators. A value below 0.3 indicates low risk, between 0.3 and 0.7 indicates medium risk, and above 0.7 indicates high risk. This embodiment quantifies the comprehensive imbalance risk of the system, which is beneficial for taking corresponding prevention and response measures according to different risk levels, thereby improving the safe and stable operation level of the renewable energy power system.

[0077] As one optional embodiment, the step of obtaining the upward adjustment capacity of the thermal power unit, determining the power compensation amount required by the hydropower unit in combination with the power gap value, and determining the dynamic response capability of the hydropower unit based on the power compensation amount and the available adjustment capacity of the hydropower unit includes:

[0078] The output data of the thermal power unit is obtained, and the upward adjustment capacity of the thermal power unit is calculated based on the difference between the rated capacity and the current active power in the output data of the thermal power unit.

[0079] If the power deficit value is greater than the upward adjustment capacity of the thermal power unit, the power compensation amount required by the hydropower unit is calculated based on the difference between the power deficit value and the upward adjustment capacity.

[0080] Based on the power compensation amount and the hydropower unit's ramping speed, determine the response delay time required for the hydropower unit to increase from its current output to the target output;

[0081] The dynamic response capability of the hydropower unit is determined by comparing the response delay time with a preset response time threshold and comparing the power compensation amount with the available adjustment capacity of the hydropower unit; wherein, if the response delay time is less than the response time threshold and the available adjustment capacity is greater than the power compensation amount, the hydropower unit is determined to have dynamic response capability.

[0082] Specifically, the assessment of the regulating capacity of thermal power units is based on the current operating status and technical constraints of the units. The rated capacity of a thermal power unit represents its maximum power generation capacity, while the minimum technical output is the lowest output level to ensure stable combustion and equipment safety. First, the output data of the thermal power unit is obtained, including the rated capacity, minimum technical output, and current active power. The upward regulating capacity is calculated based on the difference between the rated capacity and the current active power. The downward regulating capacity is calculated based on the difference between the current active power and the minimum technical output. The remaining regulating capacity range of the thermal power unit is determined based on the upward and downward regulating capacities.

[0083] For example, when a thermal power unit operates at 600MW with a rated capacity of 1000MW and a minimum technical output of 300MW, its upward regulation capacity is 400MW and its downward regulation capacity is 300MW. This bidirectional regulation capability makes thermal power units an important resource for grid frequency regulation and peak shaving. The determination of the remaining regulation capacity range of thermal power units directly affects the system's ability to cope with power shortages. When the power shortage is 500MW and the upward regulation capacity of the thermal power units is only 400MW, even if the thermal power units operate at full capacity, they cannot completely make up for the shortage. In this case, the remaining 100MW shortage needs to be covered by other power sources, and hydropower units, due to their rapid regulation characteristics, become the preferred supplementary power source.

[0084] Furthermore, by comparing the remaining adjustment capacity range of the thermal power unit with the power deficit value of the system, if the power deficit value exceeds the upward adjustment capacity of the thermal power unit, the maximum power that the thermal power unit can provide is the sum of the current output and the upward adjustment capacity. The power compensation amount required by the hydropower unit can be obtained by the difference between the power deficit value and the upward adjustment capacity of the thermal power unit.

[0085] Furthermore, calculating the response delay time of a hydropower unit requires considering the entire process from receiving the dispatch command to the actual change in output. This includes the governor receiving the signal, adjusting the guide vane opening, accelerating the water flow, changing the speed, and finally, outputting power. Each stage has a certain time delay, which, when accumulated, forms the total response delay time. For conventional hydropower units, this process typically takes between several seconds and tens of seconds. Specifically, based on the required power compensation amount, the hydropower unit's ramp speed is obtained from historical operating data, the current output level is read, and the time required to increase from the current output to the target output is obtained by dividing the power compensation amount by the ramp speed; this is the hydropower unit's response delay time.

[0086] The climbing speed of a hydropower unit refers to the rate of change of the unit's output per unit time, which is affected by factors such as turbine type, governor performance, and water intake system characteristics. Historical operating data records the actual climbing speed under different operating conditions. For example, a certain hydropower unit can reach a climbing speed of 50MW / min under normal operating conditions. When an increase of 100MW in output is required, it takes 2 minutes to complete the power increase.

[0087] Furthermore, the dynamic response capability assessment of hydropower units needs to comprehensively consider both time and capacity dimensions. The response time threshold is set according to the system frequency stability requirements, typically requiring effective power support within 30 seconds of a disturbance occurring, while also verifying whether the available capacity of the hydropower unit meets the demand. Specifically, the response delay time of the hydropower unit is compared with the preset response time threshold. If the response delay time is less than the threshold, and the difference between the rated capacity of the hydropower unit and the current output level is greater than the required power compensation, then the hydropower unit is determined to have dynamic response capability. The output includes the response delay time, adjustable capacity value, and response capability assessment result. For example, when a hydropower unit has a rated capacity of 800MW, a current output of 500MW, an available adjustable capacity of 300MW that is greater than the required 100MW compensation, and a response time less than the 30-second threshold, the unit is assessed as having good dynamic response capability. This assessment method provides a quantitative basis for the dispatch system to quickly select appropriate adjustment resources in emergency situations, helping to maintain the safe and stable operation of the power grid.

[0088] As one optional embodiment, the step of determining the system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and determining whether it is necessary to reconfigure standby resources based on the extent of exceeding the limit of the system comprehensive risk index, includes:

[0089] Meteorological data from wind and solar power plants is acquired, and the meteorological data is input into a preset risk perception model to obtain real-time risk perception values; the risk perception model is trained by a neural network based on historical meteorological data.

[0090] The system comprehensive risk index is obtained by weighting the real-time risk perception value, the comprehensive imbalance risk assessment value, and the dynamic response capability of the hydropower unit.

[0091] The risk exceedance range is calculated based on the difference between the system's comprehensive risk index and the preset risk threshold.

[0092] If the risk exceeds the limit by less than or equal to zero, the system will continue to operate normally; if the risk exceeds the limit by more than zero, the backup resources will need to be reconfigured.

[0093] Specifically, a risk perception model based on a backpropagation (BP) neural network is constructed using meteorological forecast data of wind and solar power output. The input layer of the model receives data on wind speed, light intensity, temperature, and humidity, which are then nonlinearly mapped through a three-layer fully connected network. The output layer generates a normalized real-time risk perception value. The BP neural network, a classic feedforward neural network, effectively handles nonlinear mapping relationships by training network weights using the backpropagation algorithm. In the risk perception model, the number of neurons in the input layer corresponds to the dimensions of the wind and solar meteorological parameters; for example, wind speed, light intensity, temperature, and humidity each occupy one input node. The hidden layer uses a sigmoid activation function to perform a nonlinear transformation, mapping multidimensional meteorological data to a risk feature space. The output layer generates a normalized value between 0 and 1 through a linear activation function, directly reflecting the current risk level of the system. The calculation process of real-time risk perception value integrates multi-source data. For example, when the wind speed suddenly drops from 15m / s to 5m / s, and at the same time the cloud cover causes the light intensity to drop from 800W / m² to 200W / m², the neural network will capture this drastic change pattern. After feature extraction and transformation in the hidden layer, the risk perception value generated by the output layer jumps from 0.2 to 0.8, indicating that the system risk has increased sharply.

[0094] Furthermore, the system comprehensive risk index is obtained by weighting the real-time risk perception value, the system comprehensive imbalance risk assessment value, and the judgment value of the dynamic response capability of hydropower units. For example, the weights corresponding to the real-time risk perception value, the system comprehensive imbalance risk assessment value, and the dynamic response capability of hydropower units are set to 0.3, 0.4, and 0.3, respectively, reflecting the relative importance of different risk factors. The imbalance risk assessment value is weighted at 0.4, reflecting the core position of supply and demand balance in grid stability. When the system power gap continues to widen and the imbalance risk assessment value increases, the comprehensive risk index will rise significantly even if other indicators are normal. The real-time risk perception value and the dynamic response capability judgment value each account for 0.3 weights, supplementing the assessment of system risk from the two dimensions of prediction accuracy and the availability of regulating resources, respectively.

[0095] Furthermore, the risk exceedance range is calculated based on the difference between the system's comprehensive risk index and the preset risk threshold. The risk status is determined by the sign of the risk exceedance range. If the risk exceedance range is less than or equal to zero, the system maintains normal operation. If the risk exceedance range is greater than zero, it is determined that backup resources need to be reconfigured, triggering corresponding prevention and control measures. Specifically, the preset risk threshold is set based on historical operating experience and system capacity. For example, analysis of historical accident cases reveals that when the comprehensive risk index exceeds 0.7, the probability of a cascading failure in the system increases sharply; therefore, 0.7 is set as the risk threshold. When the real-time calculated comprehensive risk index is 0.85, it exceeds the risk threshold by 0.15; this difference is the risk exceedance range, indicating that the system is in a high-risk state. This embodiment uses the risk exceedance range as a triggering mechanism, reflecting the shift in power system management from passive response to proactive control, avoiding frequent and unnecessary adjustments, while ensuring timely response at critical moments.

[0096] In one optional embodiment, when the backup resource reconfiguration mechanism is triggered, the demand gap and priority of backup resources are dynamically identified based on the risk exceeding the limit, combined with real-time monitoring data and historical operating data. At the same time, the available backup resource capacity in the region is obtained, the target backup resource combination is matched, a backup resource scheduling instruction is generated, and a backup scheduling instruction is issued to the backup resource unit. This process involves retrieving the startup time, unit adjustment cost, and electrical distance parameters of each resource unit from the backup resource information database based on the backup resource demand gap. Resources with startup times less than a preset threshold are assigned a priority weight of 0.5, those with unit adjustment costs lower than a preset threshold are assigned a weight of 0.3, and those with electrical distances close to a preset threshold are assigned a weight of 0.2. A weighted summation is then performed to obtain a comprehensive score for each backup resource. These scores are sorted from highest to lowest, and the current schedulable capacity of each backup resource is sequentially queried. The schedulable capacity of the selected resources is accumulated. If the accumulated value is less than the backup resource demand gap, the next resource is selected until the accumulated value is greater than or equal to the demand gap, resulting in a target backup resource combination. Using the unit number, allocated scheduling capacity, and expected response time of each unit in the target backup resource combination, a scheduling instruction data packet containing the resource number, scheduling capacity value, and startup time is generated. After encryption, this packet is sent to the corresponding backup resource unit via the power communication network. This embodiment ensures resource utilization efficiency while avoiding over-utilization, enabling rapid and accurate allocation of backup resources.

[0097] As one optional embodiment, the configuration optimization based on the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total scheduling cost, determines the output adjustment amount and scheduling priority sequence of various resources, including:

[0098] Using the reserve capacity of thermal power units, hydropower units, and energy storage devices as decision variables, and minimizing the total scheduling cost as the objective, an objective function is constructed based on the unit capacity cost of each type of resource, and constraints are constructed based on the adjustment capabilities of each type of resource.

[0099] Based on the objective function and constraints, iterative optimization is performed to obtain the optimal reserve capacity of thermal power units, hydropower units, and energy storage devices.

[0100] Based on the ratio of the optimal reserve capacity to the total reserve capacity of each type of resource, an optimized reserve capacity allocation ratio is obtained. Based on this reserve capacity allocation ratio, the difference between the current output and the target output of each type of resource is calculated to obtain the output adjustment amount of each type of resource.

[0101] The scheduling priority sequence is determined by ranking the unit capacity cost of various resources from low to high.

[0102] Specifically, when the risk exceeds the limit by more than zero, reserve resources need to be reconfigured. First, the objective function is set as minimizing the total scheduling cost, with the decision variables being the reserve capacity values ​​of thermal power, hydropower, and energy storage devices. A linear objective function is constructed by reading the unit capacity cost of each resource from the cost database. Real-time operational data is used to obtain the current output and ramp-up rate of thermal power units, the relationship between reservoir water level and power generation flow of hydropower units, and the state of charge and maximum charging / discharging power of energy storage devices. Combined with the upper and lower capacity limits in the rated equipment parameters, a set of linear inequality constraints for the adjustment range of each resource is formed. Using the set of linear inequality constraints and the linear objective function, the optimal reserve capacity values ​​for thermal power, hydropower, and energy storage devices are obtained through iterative solution using the simplex method.

[0103] The unit capacity cost of thermal power units typically includes fuel costs and start-up and shutdown losses; hydropower units mainly consider the opportunity cost of water resources, which is usually lower; energy storage devices require calculation of the loss costs of charge-discharge cycles. Multiplying these cost coefficients by the corresponding reserve capacity decision variables and summing them forms the total cost function that needs to be minimized. For example, the formula for the objective function is as follows:

[0104]

[0105] in, This represents the cost per unit of standby capacity for thermal power plants. Indicates the reserve capacity of thermal power plants. Indicates the number of thermal power units; This represents the cost per unit of standby capacity for hydropower. Indicates the reserve capacity of hydropower. Indicates the number of hydropower units; This indicates the cost per unit of standby capacity of an energy storage device. Indicates energy storage reserve capacity. Indicates the number of energy storage devices.

[0106] The simplex method is an algorithm for solving linear programming problems. It finds the optimal solution by iteratively searching among the vertices of the feasible region. The algorithm starts with an initial feasible solution, checks whether it has reached the optimum using a check number, and if not, moves to an adjacent vertex along the direction that causes the objective function value to decrease the fastest. After a finite number of iterations, it can obtain the spare capacity configuration scheme that minimizes the total cost.

[0107] Furthermore, the optimal reserve capacity of each type of resource is divided by the total reserve capacity to obtain the reserve capacity allocation ratio; the scheduling priority sequence is determined by sorting the unit capacity costs from low to high; based on the scheduling priority sequence and the reserve capacity allocation ratio, the difference between the current output and the target output of each type of resource is calculated as the output adjustment amount, and an output adjustment instruction containing the resource type, the positive or negative sign of the output adjustment amount, and the absolute value of the adjustment amount is generated.

[0108] For example, the calculation of the reserve capacity allocation ratio intuitively reflects the resource structure. If the calculated optimal reserve capacities for thermal power units, hydropower units, and energy storage devices are 300MW, 200MW, and 100MW respectively, the corresponding allocation ratios are 50% for thermal power, 33.3% for hydropower, and 16.7% for energy storage, providing a clear quantitative basis for subsequent coordinated control. Simultaneously, based on unit cost ranking, hydropower, with the lowest cost, is prioritized in the scheduling sequence. If the current thermal power output is 400MW and the target output is 700MW, the adjustment is +300MW; if the current hydropower output is 150MW and the target is 350MW, the adjustment is +200MW. A positive sign indicates an increase in output, and a negative sign indicates a decrease. Furthermore, each power plant can accurately understand and execute scheduling requirements, ensuring rapid response and stable operation of the system under risk conditions. By optimizing the reserve capacity allocation of different types of resources, the technical and economic characteristics of various power sources are fully utilized, improving the grid's ability to cope with new energy fluctuations.

[0109] Furthermore, power adjustment commands are issued to each power generation unit according to the scheduling priority sequence to ensure that the actual power output adjustment of each power generation unit can meet the capacity allocation and scheduling priority requirements in the dynamic configuration scheme. If the energy storage device's response speed reaches the target speed, the energy storage backup capacity is called first to obtain the real-time control effect of the system power balance recovery process. Specifically, the resource list is read according to the scheduling priority sequence. Starting from the first position of the list, the identifier of each power generation unit and the output adjustment command value generated by multivariate linear programming are extracted sequentially. The command data packet containing the unit number, target output value, and execution time is sent to each power generation unit through the scheduling communication channel. Based on the response time parameters of each power generation unit obtained after the command data packet is sent, the actual response time of the energy storage device is detected. If the response time of the energy storage device is less than a preset threshold, the output adjustment command of the energy storage device is executed first, and the energy storage reserve capacity is called to compensate for the system power gap. After calling the energy storage reserve capacity, the output adjustment of the remaining power generation units continues to be executed according to the scheduling priority sequence. The actual output value of each unit is collected in real time to verify the consistency between the actual output value and the target output value, ensuring that the capacity allocation requirements are met. The difference between the total power generation of the system and the total load demand is calculated by real-time monitoring. When the absolute value of the power difference is continuously less than the preset balance threshold, the system power balance recovery time is recorded to obtain the real-time control effect of the system power balance recovery process achieved by prioritizing the use of fast-response resources and coordinating the output adjustment of other resources.

[0110] The system power balance determination employs a continuity principle. A power difference below a threshold at any given moment does not indicate system stability; the difference must remain within acceptable limits for multiple consecutive sampling periods. For example, a system is considered balanced only when the difference between total generating power and total load power remains within 10MW for five consecutive minutes. This method avoids misjudgments caused by transient fluctuations, ensuring the system truly returns to a stable operating state. By recording the time from imbalance to balance restoration, the effectiveness of the entire control process can be evaluated, providing data support for subsequent optimization of scheduling strategies.

[0111] As one optional embodiment, after grid dispatching, the method further includes:

[0112] The system obtains the total power generation and total load power during the power balance recovery process of the power grid dispatch, and determines the power balance recovery time based on the changes in the total power generation and total load power.

[0113] The time deviation value is calculated based on the difference between the power balance recovery time and the preset target time;

[0114] If the time deviation value is greater than the preset time threshold, the current allocation ratio of the standby capacity of thermal power units, hydropower units, and energy storage devices, as well as the actual ramp-up rate of various resources during the scheduling process, are obtained.

[0115] The deviation rate is calculated based on the ratio of the time deviation value to the target time.

[0116] The capacity ratio correction amount is calculated based on the product of the deviation rate and the current allocation ratio, and a new reserve capacity allocation ratio is obtained based on the current allocation ratio and the capacity ratio correction amount.

[0117] The actual ramp rate is used as the new adjustment rate limit. A configuration parameter set is generated based on the new reserve capacity allocation ratio and the adjustment rate limit. The configuration parameter set is used as the resource configuration optimization benchmark for the next scheduling cycle.

[0118] Specifically, the total power generation, total load, and system frequency data during the system power balance recovery process are collected in real time through a closed-loop feedback mechanism. The time from the moment of power imbalance to the recovery of the power difference to the allowable range is recorded as the power balance recovery time, and the time from the recovery of the frequency deviation to the preset range is recorded as the frequency stabilization time. The frequency stabilization time reflects the system's inertial response characteristics. The power balance recovery time and the frequency stabilization time together constitute a quantitative evaluation of the actual response effect.

[0119] For example, during the power balance recovery process, the system collects data on total power generation and total load power every second and calculates the difference between the two. When a sudden drop in renewable energy output leads to a 500MW power deficit, this moment is recorded as the start of the imbalance. Monitoring continues until the power difference recovers to within 10MW and remains stable; this period is the power balance recovery time.

[0120] Furthermore, the time deviation is calculated by comparing the power balance recovery time with the preset target time, which is determined based on the requirements for safe and stable operation of the power grid. According to the power system stability guidelines, power imbalance should be restored within 5 minutes, and frequency deviation should return to the allowable range within 2 minutes. When the actual recovery time is 7 minutes, exceeding the target time by 2 minutes, it indicates that the current reserve resource allocation is insufficient. Then, if the time deviation exceeds the preset time threshold, the reserve capacity allocation ratios of thermal power, hydropower, and energy storage, as well as the actual ramp-up rates of various resources during the dispatch process, are extracted from the current operating data. The deviation rate is obtained by dividing the time deviation by the target time. The deviation rate is multiplied by the current reserve capacity allocation ratio to obtain the capacity ratio correction. The current allocation ratio is added to the correction to obtain the new allocation ratio, and the actual ramp-up rate is set as the new regulation rate limit. Using the new reserve capacity allocation ratio and regulation rate limit, combined with the upper and lower limits of the rated capacity of various resources, a configuration parameter set is formed, including the thermal power capacity ratio, hydropower capacity ratio, energy storage capacity ratio, and corresponding regulation rate. This parameter set is determined as the resource allocation optimization benchmark for the next dispatch cycle.

[0121] The updating of the regulation rate limit reflects the difference between the actual capacity of the equipment and the theoretical parameters. For example, in actual scheduling, due to factors such as equipment aging, the actual ramp rate is lower than the theoretical value. By setting the actual value as the new regulation rate limit, control deviations caused by overestimating the unit's capacity in subsequent scheduling are avoided.

[0122] For example, assuming the current reserve capacity allocation ratio of thermal power, hydropower, and energy storage is 50%, 30%, and 20%, respectively, with a power balance recovery time deviation of 2 minutes and a target time of 5 minutes, the deviation rate is 40%. Considering that energy storage has the fastest response but limited capacity, the energy storage ratio is increased by 40% × 20% = 8%, adjusted to 28%; and the ratio of slower-responding thermal power is correspondingly reduced, thereby making fuller use of fast-response resources. The updated parameter set includes the capacity ratio of 45% for thermal power, 27% for hydropower, and 28% for energy storage, as well as the actual adjustment rates of various resources. This set of parameters serves as the initial configuration in the next scheduling cycle, enabling faster and more effective balance recovery when the system faces similar disturbances again. Through this closed-loop optimization mechanism, the system's dynamic response capability is continuously improved, enhancing the grid's resilience to new energy fluctuations.

[0123] This invention, through obtaining predicted and measured power output data from wind and solar power plants, determines the error rate of change and the system's power deficit value based on the error between the predicted and measured data. Based on the error rate of change and the power deficit value, it determines the comprehensive imbalance risk assessment value of a high-proportion renewable energy power system. This allows for real-time reflection of the power system's stability under high-proportion renewable energy conditions, accurate quantification of wind and solar power output fluctuations, reduced dispatching errors caused by prediction deviations, and improved system operational reliability. Furthermore, it obtains the upward adjustment capacity of thermal power units, determines the required power compensation amount for hydropower units based on the power deficit value, and determines the power compensation amount based on the available adjustment capacity of hydropower units. The dynamic response capability of the hydropower units facilitates rapid response and enhances system flexibility. Based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower units, a comprehensive system risk index is determined, and the extent to which the comprehensive system risk index exceeds its limit is used to determine whether reserve resources need to be reconfigured. If reserve resources need to be reconfigured, the configuration is optimized based on the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total scheduling cost. The output adjustment amount and scheduling priority sequence of various resources are determined for grid scheduling, providing comprehensive risk management and scheduling support. This enables rapid response in the event of sudden fluctuations in new energy sources, improving the system's safe and stable operation capability.

[0124] Accordingly, the present invention also provides a power grid flexibility resource scheduling device, which can implement all the processes of the power grid flexibility resource scheduling method in the above embodiments.

[0125] Please see Figure 2 , Figure 2 This is a schematic diagram of a power grid flexibility resource scheduling device provided in an embodiment of the present invention. The power grid flexibility resource scheduling device includes:

[0126] The system imbalance assessment module 201 is used to acquire the predicted data and the measured data of the wind and solar power station output, determine the error change rate and the power gap value of the system based on the error between the predicted data and the measured data, and determine the comprehensive imbalance risk assessment value of the high proportion of new energy power system based on the error change rate and the power gap value.

[0127] The hydropower response assessment module 202 is used to obtain the upward adjustment capacity of the thermal power unit, determine the power compensation amount required by the hydropower unit in combination with the power gap value, and determine the dynamic response capability of the hydropower unit based on the power compensation amount and the available adjustment capacity of the hydropower unit.

[0128] The comprehensive risk assessment module 203 is used to determine the comprehensive risk index of the system based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and to determine whether it is necessary to reconfigure the backup resources based on the extent of the excess of the comprehensive risk index of the system.

[0129] The power grid resource scheduling module 204 is used to optimize the configuration based on the standby capacity values ​​of thermal power units, hydropower units, and energy storage devices if it is necessary to reconfigure standby resources, with the goal of minimizing the total scheduling cost, and to determine the output adjustment amount and scheduling priority sequence of various resources for power grid scheduling.

[0130] Preferably, the step of acquiring predicted and measured power output data of the wind and solar power station, and determining the error change rate and the system power deficit value based on the error between the predicted and measured data, includes:

[0131] Obtain meteorological forecast data for wind and solar power stations, and predict wind and solar power output based on the meteorological forecast data to obtain predicted power output data for wind and solar power stations.

[0132] Obtain measured data of the output of the wind and solar power station, and use a preset sliding time window to calculate the error value between the measured data and the predicted data at the corresponding time.

[0133] The error change rate is determined based on the amount of change in error values ​​within adjacent time windows.

[0134] The time period in which the error change rate is greater than a preset error threshold is designated as a high-risk time period.

[0135] For the high-risk period, the power deficit value of the system is determined based on the error change rate, combined with the total load demand of the power grid and the measured data.

[0136] Preferably, determining the comprehensive imbalance risk assessment value of the high-proportion renewable energy power system based on the error change rate and the power deficit value includes:

[0137] The power gap expansion rate is obtained based on the increase of the system power gap value within a continuous time window.

[0138] Based on the high-risk periods, determine the duration of the high-risk periods;

[0139] The error change rate, the power gap expansion rate, and the duration of the high-risk period are normalized respectively. Then, the normalized error change rate, the power gap expansion rate, and the duration of the high-risk period are weighted and summed to obtain the comprehensive imbalance risk assessment value of the high-proportion new energy power system.

[0140] Preferably, the step of obtaining the upward adjustment capacity of the thermal power unit, determining the power compensation amount required by the hydropower unit in conjunction with the power deficit value, and determining the dynamic response capability of the hydropower unit based on the power compensation amount and the available adjustment capacity of the hydropower unit includes:

[0141] The output data of the thermal power unit is obtained, and the upward adjustment capacity of the thermal power unit is calculated based on the difference between the rated capacity and the current active power in the output data of the thermal power unit.

[0142] If the power deficit value is greater than the upward adjustment capacity of the thermal power unit, the power compensation amount required by the hydropower unit is calculated based on the difference between the power deficit value and the upward adjustment capacity.

[0143] Based on the power compensation amount and the hydropower unit's ramping speed, determine the response delay time required for the hydropower unit to increase from its current output to the target output;

[0144] The dynamic response capability of the hydropower unit is determined by comparing the response delay time with a preset response time threshold and comparing the power compensation amount with the available adjustment capacity of the hydropower unit; wherein, if the response delay time is less than the response time threshold and the available adjustment capacity is greater than the power compensation amount, the hydropower unit is determined to have dynamic response capability.

[0145] Preferably, the step of determining the system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and determining whether it is necessary to reconfigure standby resources based on the extent to which the system comprehensive risk index exceeds the limit, includes:

[0146] Meteorological data from wind and solar power plants is acquired, and the meteorological data is input into a preset risk perception model to obtain real-time risk perception values; the risk perception model is trained by a neural network based on historical meteorological data.

[0147] The system comprehensive risk index is obtained by weighting the real-time risk perception value, the comprehensive imbalance risk assessment value, and the dynamic response capability of the hydropower unit.

[0148] The risk exceedance range is calculated based on the difference between the system's comprehensive risk index and the preset risk threshold.

[0149] If the risk exceeds the limit by less than or equal to zero, the system will continue to operate normally; if the risk exceeds the limit by more than zero, the backup resources will need to be reconfigured.

[0150] Preferably, the configuration optimization based on the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total scheduling cost, determines the output adjustment amount and scheduling priority sequence of various resources, including:

[0151] Using the reserve capacity of thermal power units, hydropower units, and energy storage devices as decision variables, and minimizing the total scheduling cost as the objective, an objective function is constructed based on the unit capacity cost of each type of resource, and constraints are constructed based on the adjustment capabilities of each type of resource.

[0152] Based on the objective function and constraints, iterative optimization is performed to obtain the optimal reserve capacity of thermal power units, hydropower units, and energy storage devices.

[0153] Based on the ratio of the optimal reserve capacity to the total reserve capacity of each type of resource, an optimized reserve capacity allocation ratio is obtained. Based on this reserve capacity allocation ratio, the difference between the current output and the target output of each type of resource is calculated to obtain the output adjustment amount of each type of resource.

[0154] The scheduling priority sequence is determined by ranking the unit capacity cost of various resources from low to high.

[0155] Preferably, the power grid flexibility resource dispatching device is further used for:

[0156] The system obtains the total power generation and total load power during the power balance recovery process of the power grid dispatch, and determines the power balance recovery time based on the changes in the total power generation and total load power.

[0157] The time deviation value is calculated based on the difference between the power balance recovery time and the preset target time;

[0158] If the time deviation value is greater than the preset time threshold, the current allocation ratio of the standby capacity of thermal power units, hydropower units, and energy storage devices, as well as the actual ramp-up rate of various resources during the scheduling process, are obtained.

[0159] The deviation rate is calculated based on the ratio of the time deviation value to the target time.

[0160] The capacity ratio correction amount is calculated based on the product of the deviation rate and the current allocation ratio, and a new reserve capacity allocation ratio is obtained based on the current allocation ratio and the capacity ratio correction amount.

[0161] The actual ramp rate is used as the new adjustment rate limit. A configuration parameter set is generated based on the new reserve capacity allocation ratio and the adjustment rate limit. The configuration parameter set is used as the resource configuration optimization benchmark for the next scheduling cycle.

[0162] In specific implementation, the working principle, control process and technical effects of the power grid flexibility resource scheduling device provided in this embodiment of the invention are the same as those of the power grid flexibility resource scheduling method in the above embodiments, and will not be repeated here.

[0163] See Figure 3 , Figure 3 This is a structural block diagram of a computer device provided in an embodiment of the present invention. The computer device includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, it implements the steps in the above-described embodiments of the power grid flexibility resource scheduling method. Alternatively, when the processor 301 executes the computer program, it implements the functions of each module / unit in the above-described device embodiments.

[0164] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to complete the present invention. The one or more modules / units 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 computer device.

[0165] The computer device may include, but is not limited to, processor 301 and memory 302. Those skilled in the art will understand that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0166] The processor 301 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. The general-purpose processor can be a microprocessor or any conventional processor. The processor 301 is the control center of the computer device, connecting various parts of the entire computer device through various interfaces and lines.

[0167] The memory 302 can be used to store the computer programs and / or modules. The processor 301 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302 and calling the data stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0168] Wherein, if the modules / units integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of 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 the computer program is executed by the processor 301, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0169] This invention also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the power grid flexibility resource scheduling method described in any of the above embodiments.

[0170] This invention provides a method, apparatus, equipment, and storage medium for power grid flexibility resource scheduling. Its advantages include: by acquiring predicted and measured power output data from wind and solar power plants, determining the error rate of change and the system's power deficit value based on the error between the predicted and measured data, and determining a comprehensive imbalance risk assessment value for a high-proportion renewable energy power system based on the error rate of change and the power deficit value. This allows for real-time reflection of the power system's stability under high-proportion renewable energy conditions, accurate quantification of wind and solar power output fluctuations, reduction of scheduling errors caused by prediction deviations, and improved system operational reliability; acquiring the upward adjustment capacity of thermal power units, determining the required power compensation amount for hydropower units based on the power deficit value, and determining the power compensation amount required based on the power deficit value. The compensation amount and the available regulating capacity of hydropower units determine the dynamic response capability of the hydropower units, which is conducive to achieving rapid response and improving system flexibility. Based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower units, the comprehensive system risk index is determined, and the need to reconfigure standby resources is judged based on the excess range of the comprehensive system risk index. If standby resources need to be reconfigured, the configuration is optimized based on the standby capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total dispatch cost. The output adjustment amount and dispatch priority sequence of various resources are determined for grid dispatch, providing comprehensive risk management and dispatch support. It can respond quickly in the event of sudden fluctuations in new energy sources, thereby improving the safe and stable operation capability of the system.

[0171] 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 method for scheduling flexible resources in a power grid, characterized in that, include: Obtain predicted and measured power output data of wind and solar power plants; determine the error change rate and the power gap value of the system based on the error between the predicted and measured data; and determine the comprehensive imbalance risk assessment value of the high-proportion new energy power system based on the error change rate and the power gap value. The upward adjustment capacity of the thermal power unit is obtained, and the power compensation amount required by the hydropower unit is determined in combination with the power gap value. The dynamic response capability of the hydropower unit is determined based on the power compensation amount and the available adjustment capacity of the hydropower unit. Based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, the comprehensive system risk index is determined, and the extent of exceeding the limit of the comprehensive system risk index is used to determine whether it is necessary to reconfigure the standby resources. If it is necessary to reconfigure standby resources, the configuration optimization is carried out based on the standby capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total dispatch cost. The output adjustment amount and dispatch priority sequence of various resources are determined for grid dispatch.

2. The power grid flexibility resource scheduling method as described in claim 1, characterized in that, The process of acquiring predicted and measured power output data from wind and solar power plants, and determining the error rate of change and the system's power deficit value based on the error between the predicted and measured data, includes: Obtain meteorological forecast data for wind and solar power stations, and predict wind and solar power output based on the meteorological forecast data to obtain predicted power output data for wind and solar power stations. Obtain measured data of the output of the wind and solar power station, and use a preset sliding time window to calculate the error value between the measured data and the predicted data at the corresponding time. The error change rate is determined based on the amount of change in error values ​​within adjacent time windows. The time period in which the error change rate is greater than a preset error threshold is designated as a high-risk time period. For the high-risk period, the power deficit value of the system is determined based on the error change rate, combined with the total load demand of the power grid and the measured data.

3. The power grid flexibility resource scheduling method as described in claim 2, characterized in that, The determination of the comprehensive imbalance risk assessment value of the high-proportion renewable energy power system based on the error change rate and the power deficit value includes: The power gap expansion rate is obtained based on the increase of the system power gap value within a continuous time window. Based on the high-risk periods, determine the duration of the high-risk periods; The error change rate, the power gap expansion rate, and the duration of the high-risk period are normalized respectively. Then, the normalized error change rate, the power gap expansion rate, and the duration of the high-risk period are weighted and summed to obtain the comprehensive imbalance risk assessment value of the high-proportion new energy power system.

4. The power grid flexibility resource scheduling method as described in claim 1, characterized in that, The process of obtaining the upward adjustment capacity of the thermal power unit, determining the power compensation amount required by the hydropower unit based on the power deficit value, and determining the dynamic response capability of the hydropower unit based on the power compensation amount and the available adjustment capacity of the hydropower unit includes: The output data of the thermal power unit is obtained, and the upward adjustment capacity of the thermal power unit is calculated based on the difference between the rated capacity and the current active power in the output data of the thermal power unit. If the power deficit value is greater than the upward adjustment capacity of the thermal power unit, the power compensation amount required by the hydropower unit is calculated based on the difference between the power deficit value and the upward adjustment capacity. Based on the power compensation amount and the hydropower unit's ramping speed, determine the response delay time required for the hydropower unit to increase from its current output to the target output; The dynamic response capability of the hydropower unit is determined by comparing the response delay time with a preset response time threshold and comparing the power compensation amount with the available adjustment capacity of the hydropower unit; wherein, if the response delay time is less than the response time threshold and the available adjustment capacity is greater than the power compensation amount, the hydropower unit is determined to have dynamic response capability.

5. The power grid flexibility resource scheduling method as described in claim 1, characterized in that, The process of determining a comprehensive system risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and determining whether to reconfigure standby resources based on the extent to which the comprehensive system risk index exceeds the limit, includes: Meteorological data from wind and solar power plants is acquired, and the meteorological data is input into a preset risk perception model to obtain real-time risk perception values; the risk perception model is trained by a neural network based on historical meteorological data. The system comprehensive risk index is obtained by weighting the real-time risk perception value, the comprehensive imbalance risk assessment value, and the dynamic response capability of the hydropower unit. The risk exceedance range is calculated based on the difference between the system's comprehensive risk index and the preset risk threshold. If the risk exceeds the limit by less than or equal to zero, the system will continue to operate normally; if the risk exceeds the limit by more than zero, the backup resources will need to be reconfigured.

6. The power grid flexibility resource scheduling method as described in claim 1, characterized in that, The configuration optimization, based on the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices, aims to minimize the total scheduling cost and determines the output adjustment and scheduling priority sequence of various resources, including: Using the reserve capacity of thermal power units, hydropower units, and energy storage devices as decision variables, and minimizing the total scheduling cost as the objective, an objective function is constructed based on the unit capacity cost of each type of resource, and constraints are constructed based on the adjustment capabilities of each type of resource. Based on the objective function and constraints, iterative optimization is performed to obtain the optimal reserve capacity of thermal power units, hydropower units, and energy storage devices. Based on the ratio of the optimal reserve capacity to the total reserve capacity of each type of resource, an optimized reserve capacity allocation ratio is obtained. Based on this reserve capacity allocation ratio, the difference between the current output and the target output of each type of resource is calculated to obtain the output adjustment amount of each type of resource. The scheduling priority sequence is determined by ranking the unit capacity cost of various resources from low to high.

7. The power grid flexibility resource scheduling method as described in claim 1, characterized in that, After power grid dispatching, the method further includes: The system obtains the total power generation and total load power during the power balance recovery process of the power grid dispatch, and determines the power balance recovery time based on the changes in the total power generation and total load power. The time deviation value is calculated based on the difference between the power balance recovery time and the preset target time; If the time deviation value is greater than the preset time threshold, the current allocation ratio of the standby capacity of thermal power units, hydropower units, and energy storage devices, as well as the actual ramp-up rate of various resources during the scheduling process, are obtained. The deviation rate is calculated based on the ratio of the time deviation value to the target time. The capacity ratio correction amount is calculated based on the product of the deviation rate and the current allocation ratio, and a new reserve capacity allocation ratio is obtained based on the current allocation ratio and the capacity ratio correction amount. The actual ramp rate is used as the new adjustment rate limit. A configuration parameter set is generated based on the new reserve capacity allocation ratio and the adjustment rate limit. The configuration parameter set is used as the resource configuration optimization benchmark for the next scheduling cycle.

8. A power grid flexibility resource dispatching device, characterized in that, include: The system imbalance assessment module is used to acquire predicted and measured data of wind and solar power plant output, determine the error change rate and the power gap value of the system based on the error between the predicted data and the measured data, and determine the comprehensive imbalance risk assessment value of the high-proportion new energy power system based on the error change rate and the power gap value. The hydropower response assessment module is used to obtain the upward adjustment capacity of the thermal power unit, determine the power compensation amount required by the hydropower unit in combination with the power gap value, and determine the dynamic response capability of the hydropower unit based on the power compensation amount and the available adjustment capacity of the hydropower unit. The comprehensive risk assessment module is used to determine the comprehensive system risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and to determine whether it is necessary to reconfigure the backup resources based on the extent of the excess of the comprehensive system risk index. The power grid resource scheduling module is used to optimize the configuration of standby resources based on the standby capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total scheduling cost, and to determine the output adjustment amount and scheduling priority sequence of various resources for power grid scheduling if it is necessary to reconfigure standby resources.

9. A computer device, characterized in that, It includes a processor and a memory, the memory storing a computer program configured to be executed by the processor, the processor executing the computer program to implement the power grid flexibility resource scheduling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the power grid flexibility resource scheduling method as described in any one of claims 1 to 7.

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