Power grid flexibility resource scheduling method, device and equipment and storage medium

Abstract

The invention discloses a power grid flexibility resource scheduling method, device and equipment and a storage medium, and the method comprises the steps: determining an error change rate and a power gap value of a system according to an error between prediction data and actual measurement data of the output of a wind and light power station, and further determining a comprehensive imbalance risk assessment value of the system; determining the power compensation amount borne by the hydroelectric generating set according to the power gap value and the upward adjustment capacity of the thermal power generating set, and determining the dynamic response capability of the hydroelectric generating set by combining the available adjustment capacity of the hydroelectric generating set; determining a comprehensive risk index of the system based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydroelectric generating set; if it is determined that standby resources need to be reconfigured according to the over-limit amplitude of the comprehensive risk index of the system, the output adjustment amount and the scheduling priority sequence of all kinds of resources are determined based on the standby capacity values of the thermal power generating unit, the hydroelectric generating unit and the energy storage device so that power grid scheduling can be conducted. The safe and stable operation capability of the system is improved.

Description

Technical Field

[0001] The present invention relates to the field of power grid dispatching technology, and in particular to a power grid flexibility resource dispatching method, device, equipment and storage medium. Background Art

[0002] To address fluctuations in renewable energy output, the optimal allocation of flexible resources and intelligent dispatching technologies are key to ensuring the safe and stable operation of the power grid. However, current resource dispatching methods often use static preset modes, primarily determining reserve capacity requirements based on historical data and empirical formulas. These methods lack the ability to deeply integrate the output characteristics of renewable energy sources like wind and solar power, nor the ability to dynamically perceive their real-time operating status. This results in a delayed response to rapid changes in renewable energy output, making it ineffective in coping with complex and changing operating conditions, and ultimately, insufficient system reliability assurance in extreme scenarios. Summary of the Invention

[0003] To solve the above technical problems, the present invention provides a power grid flexibility resource scheduling method, device, equipment and storage medium, which can perceive system risks in real time and realize intelligent allocation of resources, effectively improving the reliability of the system in extreme scenarios.

[0004] The present invention provides a method for scheduling power grid flexibility resources, comprising: Obtaining predicted data and measured data on the output of a wind and solar power station, determining an error change rate and a power gap value of the system based on an error between the predicted data and the measured data, and determining a comprehensive imbalance risk assessment value of the high-proportion new energy power system based on the error change rate and the power gap value; Obtaining the upward regulation 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 regulation capacity of the hydropower unit; Determining a system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and judging whether it is necessary to reconfigure backup resources according to the excess range of the system comprehensive risk index; If reserve resources need to be reconfigured, configuration optimization is performed 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 each type of resource are determined to carry out grid dispatch.

[0005] As an improvement to the above solution, the step of obtaining predicted data and measured data of wind and solar power station output, and determining an error change rate and a power gap value of the system based on an error between the predicted data and the measured data, includes: Obtaining weather forecast data for the wind and solar power station, and performing wind and solar output forecasting based on the weather forecast data to obtain forecast data for the wind and solar power station output; Obtaining measured data of the output of the wind and solar power station, and calculating the error value between the measured data and the predicted data at the corresponding time using a preset sliding time window; Determine the error change rate based on the change in error values ​​within adjacent time windows; The period when the error change rate is greater than the preset error threshold is regarded as a high-risk period; For the high-risk period, the power gap value of the system is determined according to the error change rate, combined with the total load demand of the power grid and the measured data.

[0006] As an improvement to the above solution, the method of determining 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 includes: Obtaining a power gap expansion rate according to the growth rate of the system power gap value within a continuous time window; Based on the high-risk period, determining a duration of the high-risk period; The error change rate, the power gap expansion rate and the duration of the high-risk period are normalized respectively, and the normalized error change rate, power gap expansion rate and the duration of the high-risk period are weighted and summed to obtain a comprehensive imbalance risk assessment value of a high-proportion new energy power system.

[0007] As an improvement to the above solution, the upward regulation capacity of the thermal power unit is obtained, and the power compensation amount required to be borne 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 regulation capacity of the hydropower unit, including: Obtaining output data of the thermal power unit, and calculating the upward adjustment capacity of the thermal power unit 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 gap value is greater than the upward adjustment capacity of the thermal power unit, the power compensation amount required to be borne by the hydropower unit is calculated according to the difference between the power gap value and the upward adjustment capacity; Determining the response delay time required for the hydropower unit to increase from a current output to a target output based on the power compensation amount and the ramp speed of the hydropower unit; 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 regulation capacity of the hydropower unit; wherein, if the response delay time is less than the response time threshold and the available regulation capacity is greater than the power compensation amount, it is determined that the hydropower unit has dynamic response capability.

[0008] As an improvement to the above solution, the method of determining a system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and judging whether to reconfigure backup resources according to the excess range of the system comprehensive risk index, includes: Obtaining meteorological data of the wind and solar power stations, inputting the meteorological data into a preset risk perception model to obtain a real-time risk perception value; the risk perception model is obtained by training a neural network based on historical meteorological data; Obtaining a system comprehensive risk index based on a weighted calculation of the real-time risk perception value, the comprehensive imbalance risk assessment value, and the dynamic response capability of the hydropower unit; Calculate the risk excess margin based on the difference between the system comprehensive risk index and the preset risk threshold; If the risk exceeds the limit by less than or equal to zero, the normal operation of the system is maintained; if the risk exceeds the limit by greater than zero, it is determined that the backup resources need to be reconfigured.

[0009] As an improvement to the above solution, the configuration optimization is performed 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, and the output adjustment amount and dispatch priority sequence of each resource are determined, including: Taking the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices as decision variables, minimizing the total dispatch cost as the goal, constructing an objective function based on the unit capacity cost of each resource, and constructing constraints based on the regulation capacity of each resource; Perform iterative optimization based on the objective function and constraints to obtain the optimal backup capacity of the thermal power unit, the hydropower unit, and the energy storage device; Obtaining an optimized spare capacity allocation ratio based on the ratio of the optimal spare capacity to the total spare capacity of each resource, and calculating the difference between the current output and the target output of each resource based on the spare capacity allocation ratio to obtain the output adjustment of each resource; The scheduling priority sequence is determined based on the order of unit capacity cost of each resource from low to high.

[0010] As an improvement to the above solution, after performing power grid dispatching, the method further includes: Obtaining the total power generated and the total load power during the system power balance recovery process during grid dispatch, and determining the power balance recovery time according to the changes in the total power generated and the total load power; Calculating a time deviation value based on the difference between the power balance recovery time and a preset target time; If the time deviation value is greater than the preset time threshold, the current allocation ratio of the spare capacity of the thermal power unit, the hydropower unit, and the energy storage device, as well as the actual ramp rate of each type of resource during the scheduling process, are obtained; Calculating a deviation rate based on a ratio of the time deviation value to the target time; Calculating a capacity ratio correction amount according to the product of the deviation rate and the current allocation ratio, and obtaining a new spare capacity allocation ratio according to the current allocation ratio and the capacity ratio correction amount; The actual ramp rate is used as a new adjustment rate limit value, a configuration parameter group is generated according to the new spare capacity allocation ratio and the adjustment rate limit value, and the configuration parameter group is used as a resource configuration optimization benchmark for the next scheduling period.

[0011] The present invention also provides a power grid flexibility resource scheduling device, comprising: a system imbalance assessment module, configured to obtain predicted data and measured data on the output of wind and solar power stations, determine an error change rate and a power gap value of the system based on an error between the predicted data and the measured data, and determine a comprehensive imbalance risk assessment value of a high-proportion new energy power system based on the error change rate and the power gap value; a hydropower response assessment module, configured to obtain the upward regulation 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 regulation capacity of the hydropower unit; a comprehensive risk assessment module, configured to determine a system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and determine whether it is necessary to reconfigure backup resources based on an over-limit range of the system comprehensive risk index; The grid resource scheduling module is used to optimize the configuration of backup resources if they need to be reconfigured. This module is based on the backup capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total scheduling cost. It also determines the output adjustment amount and scheduling priority sequence of each type of resource to carry out grid scheduling.

[0012] The present invention also provides a computer device comprising a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor, and when the processor executes the computer program, it implements any of the above-mentioned methods for scheduling power grid flexibility resources.

[0013] The present invention also provides a computer-readable storage medium, which stores a computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any of the above-mentioned power grid flexibility resource scheduling methods.

[0014] Compared with the prior art, the present invention provides a method, device, equipment, and storage medium for scheduling power grid flexibility resources, which have the following advantages: By obtaining the predicted data and measured data of the output of wind and solar power stations, determining the error change rate and the power gap value of the system according to the error between the predicted data and the measured data, and determining 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, it can reflect the stability of the power system under the condition of a high proportion of new energy in real time, accurately quantify the fluctuation of wind and solar power generation output, reduce scheduling errors caused by prediction deviation, and improve the operation reliability of the system; obtain the upward adjustment capacity of the thermal power unit, and determine the power compensation amount that the hydropower unit needs to bear in combination with the power gap value, and determine the hydropower unit according to the power compensation amount and the available adjustment capacity of the hydropower unit. The dynamic response capability of the unit 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 unit, the system comprehensive risk index is determined, and the need to reconfigure backup resources is determined based on the excess limit of the system comprehensive risk index; if backup resources need to be reconfigured, the configuration is optimized based on the backup capacity values ​​of thermal power units, hydropower units, and energy storage devices with the goal of minimizing the total dispatch cost, and the output adjustment amount and dispatch priority sequence of various resources are determined for power grid dispatch, providing complete risk management and dispatch support, capable of responding quickly to sudden fluctuations in new energy, and improving the system's safe and stable operation capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a method for scheduling power grid flexibility resources provided by an embodiment of the present invention; Figure 2 This is a schematic structural diagram of a power grid flexibility resource scheduling device provided by an embodiment of the present invention; Figure 3 It is a structural diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0017] See also Figure 1 , Figure 11 is a flow chart of a method for dispatching power grid flexibility resources provided by an embodiment of the present invention. The method for dispatching power grid flexibility resources includes: S1: Obtaining predicted data and measured data on the output of a wind and solar power station, determining an error change rate and a power gap value of the system based on an error between the predicted data and the measured data, and determining a comprehensive imbalance risk assessment value of a high-proportion new energy power system based on the error change rate and the power gap value; S2: Obtain the upward regulation capacity of the thermal power unit, determine the power compensation amount required by the hydropower unit based on the power gap value, and determine the dynamic response capability of the hydropower unit based on the power compensation amount and the available regulation capacity of the hydropower unit; S3: determining a 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 backup resources based on the excess range of the system comprehensive risk index; S4: If the backup resources need to be reconfigured, the configuration is optimized based on the backup 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 each type of resource are determined to carry out grid dispatch.

[0018] Specifically, step S1 involves obtaining meteorological data from wind and solar power stations to predict output, obtaining predicted output data for the stations, and then calculating the error between the predicted output data and the measured data. The error change rate is then determined, and the system power gap is determined based on the difference between the total grid load demand and the actual wind and solar output. Based on this error change rate and the trend of the system's total power gap expansion, a comprehensive imbalance risk assessment value for a high-proportion renewable energy power system is calculated. The assessment value can be categorized into three risk levels: low, medium, and high. Step S2 involves obtaining the capacity adjustment range of the thermal power units and, based on the power gap value, determining the system's ability to cope with the power gap. If the thermal power units are unable to compensate for the gap, the remaining gap will need to be covered by other power sources. The remaining power gap is then determined as the power compensation required by the hydropower units. This power compensation amount is then compared with the available capacity of the hydropower units to assess the dynamic response capability of the hydropower units, quantifying the dynamic response capability and determining whether the hydropower units meet the power gap requirements. Step S3: Determine the system's comprehensive risk index by combining the comprehensive imbalance risk assessment value from step S1 with the dynamic response capability of the hydropower units from step S2. When the system's comprehensive risk index exceeds the safety margin, it indicates that the system's current resource configuration cannot meet power demand and requires reconfiguration of backup resources, including the backup capacity of thermal power units, hydropower units, and energy storage devices. Step S4: When backup resources need to be reconfigured, an optimization function is constructed based on the backup capacity values ​​of the thermal power units, hydropower units, and energy storage devices, and iterative optimization is performed with the goal of minimizing the total dispatch cost. Ultimately, the optimized configuration result is solved, and the output adjustment amount and dispatch priority sequence for each resource type are determined. Adjustment instructions for the corresponding output adjustment amount are issued to each power generation unit according to the dispatch priority sequence, and grid dispatch is performed to restore system power balance.

[0019] As one of the optional embodiments, obtaining the predicted data of the wind and solar power station output and the measured data, and determining the error change rate and the power gap value of the system according to the error between the predicted data and the measured data, includes: Obtaining weather forecast data for the wind and solar power station, and performing wind and solar output forecasting based on the weather forecast data to obtain forecast data for the wind and solar power station output; Obtaining measured data of the output of the wind and solar power station, and calculating the error value between the measured data and the predicted data at the corresponding time using a preset sliding time window; Determine the error change rate based on the change in error values ​​within adjacent time windows; The period when the error change rate is greater than the preset error threshold is regarded as a high-risk period; For the high-risk period, the power gap value of the system is determined according to the error change rate, combined with the total load demand of the power grid and the measured data.

[0020] Specifically, the output of wind and solar power generation systems is greatly affected by weather conditions and has significant randomness and volatility characteristics. In actual operation, sudden changes in wind speed or cloud cover will cause a sharp change in power generation. This uncertainty poses a challenge to the stable operation of the power grid. This embodiment first obtains real-time monitoring data of wind and solar output and weather forecast data at the corresponding time through a data interface. For example, the data acquisition system obtains measured output data from the SCADA system of the wind farm and photovoltaic power station every 5 minutes. The measured data includes the active power of each wind turbine, the output power of each photovoltaic inverter, etc.; at the same time, meteorological parameters such as wind speed, wind direction, irradiance, cloud cover, temperature, humidity, etc. at the corresponding time are obtained from the weather forecast system. This embodiment can capture the law of output changes by monitoring wind and solar output data in real time and combining it with weather forecast information, providing a data basis for system imbalance risk assessment.

[0021] Furthermore, wind and solar output are predicted based on weather forecast data to obtain output forecast data. Predicting wind and solar output based on weather data is an existing technology for grid scheduling. Specifically, short-term forecast meteorological factors are first obtained, and then mapped to power output using physical, statistical, or machine learning models. For example, wind and solar output forecasts are obtained using a power curve model, where wind power has a cubic relationship with wind speed, and photovoltaic power has a linear relationship with irradiance.

[0022] Since the two types of data come from different sources, there may be deviations in the timestamps. Therefore, after acquiring the data, the data is time-series aligned according to the preset time interval. Specifically, the data with different sampling frequencies are unified to the same time scale through the interpolation algorithm.

[0023] Furthermore, a sliding time window is used to calculate the difference between the measured data and the predicted data obtained based on the meteorological forecast data, and the error value between the predicted value and the measured value of the wind and solar power output is obtained. Specifically, the sliding time window method can dynamically track the evolution of the prediction error. The time window length is set to 30 minutes, and it slides forward for 5 minutes each time. In each time window, the error value between the measured value of the wind and solar power output and the predicted value based on the meteorological forecast data is calculated. When the error values ​​of multiple consecutive time windows show an increasing trend and the rate of change exceeds the preset threshold, it indicates that the prediction accuracy is rapidly decreasing and the system faces a high risk of imbalance. Furthermore, after obtaining the error value, the error change rate is obtained by dividing the change in the error value in the adjacent time window by the time interval. If the error change rate is greater than the preset threshold, the period is marked as a high-risk period.

[0024] Furthermore, the system's power gap value reflects the balance between supply and demand. During high-risk periods, actual wind and solar power output may fall far short of expectations, 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 power, to generate a real-time power gap value, facilitating early identification of potential power supply shortage risks.

[0025] As one of the optional embodiments, determining 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 includes: Obtaining a power gap expansion rate according to the growth rate of the system power gap value within a continuous time window; Based on the high-risk period, determining a duration of the high-risk period; The error change rate, the power gap expansion rate and the duration of the high-risk period are normalized respectively, and the normalized error change rate, power gap expansion rate and the duration of the high-risk period are weighted and summed to obtain a comprehensive imbalance risk assessment value of a high-proportion new energy power system.

[0026] Specifically, the trend of the power gap expansion reflects the dynamic evolution of the imbalance between supply and demand in the power system. When the output of renewable energy decreases or the load suddenly increases, the difference between the total power generation of the system and the total load demand will gradually increase. This trend is quantified by the rate of change of the power gap value at consecutive time points, which provides an important basis for subsequent scheduling decisions. When the power gap continues to expand within a continuous time window, and the expansion rate exceeds the system's regulation capacity, it indicates that the system is developing towards an unbalanced state. In this embodiment, the gap expansion rate is obtained by the growth rate of the power gap value within the continuous time window; and the duration of the high-risk period is calculated as the key indicator for comprehensive imbalance risk assessment.

[0027] For the comprehensive imbalance risk assessment of a high-proportion new energy power system, an assessment is conducted based on three key indicators: the error change rate, the gap expansion rate, and the duration of the high-risk period. According to the experience of power grid operation and historical data analysis, the error change rate and the gap expansion rate have a greater impact on system stability. The corresponding weights are set to 0.4, and the weight of the duration of the high-risk period is set to 0.2. The comprehensive imbalance risk assessment value is obtained by weighted summation of the three. When the assessment value is lower than 0.3, it is low risk, between 0.3 and 0.7, it is medium risk, and more than 0.7, it is high risk. This embodiment quantifies the comprehensive imbalance risk of the system, which is conducive to taking corresponding prevention and response measures according to different risk levels, and improves the safe and stable operation level of the new energy power system.

[0028] As one of the optional embodiments, obtaining the upward regulation capacity of the thermal power unit, determining the power compensation amount required to be borne 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 regulation capacity of the hydropower unit include: Obtaining output data of the thermal power unit, and calculating the upward adjustment capacity of the thermal power unit 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 gap value is greater than the upward adjustment capacity of the thermal power unit, the power compensation amount required to be borne by the hydropower unit is calculated according to the difference between the power gap value and the upward adjustment capacity; Determining the response delay time required for the hydropower unit to increase from a current output to a target output based on the power compensation amount and the ramp speed of the hydropower unit; 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 regulation capacity of the hydropower unit; wherein, if the response delay time is less than the response time threshold and the available regulation capacity is greater than the power compensation amount, it is determined that the hydropower unit has dynamic response capability.

[0029] Specifically, the regulation capacity assessment of a thermal power unit is based on the unit's current operating status and technical limitations. The rated capacity of a thermal power unit represents its maximum power generation capacity, while the minimum technical output is the minimum output level required to ensure stable combustion and equipment safety. First, the thermal power unit's output data, including the rated capacity, minimum technical output, and current active power, is obtained. The upward regulation capacity is calculated based on the difference between the rated capacity and the current active power. The downward regulation capacity is calculated based on the difference between the current active power and the minimum technical output. The remaining regulation capacity range of the thermal power unit is determined based on the upward and downward regulation capacities.

[0030] 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 a crucial resource for frequency and peak regulation in the power grid. However, the determination of the range of remaining regulation capacity of thermal power units directly impacts the system's ability to cope with power shortfalls. If the power shortfall is 500MW and the thermal power unit's upward regulation capacity is only 400MW, even full thermal power generation cannot fully fill the shortfall. The remaining 100MW gap must be met by other power sources, and hydropower units, due to their rapid regulation characteristics, are the preferred supplementary power source.

[0031] Furthermore, by comparing the remaining regulation capacity range of the thermal power unit with the power gap value of the system, if the power gap value exceeds the upward regulation 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 regulation capacity. The power compensation amount that the hydropower unit needs to bear is obtained by the difference between the power gap value and the upward regulation capacity of the thermal power unit.

[0032] Furthermore, the calculation of the response delay time of a hydropower unit needs to consider the entire process from the unit receiving the dispatch instruction to the actual output change, which includes the steps of the governor receiving the signal, the guide vane opening adjustment, the water flow acceleration, the speed change to the power output, etc. Each step has a certain time delay, which is accumulated to form the total response delay time. For conventional hydropower units, this process is usually completed between a few seconds and tens of seconds. Specifically, according to the power compensation amount required by the hydropower unit, the climbing speed of the hydropower unit is obtained from the historical operation data, the current output level of the hydropower unit is read, and the time required to increase the output from the current output to the target output is obtained by dividing the power compensation amount by the climbing speed, that is, the response delay time of the hydropower unit.

[0033] The ramping speed of a hydropower unit refers to the rate of change of unit output per unit time. It is affected by factors such as turbine type, governor performance, and water diversion system characteristics. Historical operating data records actual ramping speeds under different operating conditions. For example, a hydropower unit can achieve a ramping speed of 50 MW / min under normal operating conditions. However, when an output increase of 100 MW is required, it takes two minutes to complete the power increase.

[0034] Furthermore, the dynamic response capability assessment of a hydropower unit requires a comprehensive consideration of both time and capacity. The response time threshold is set based on the system frequency stability requirements, typically requiring effective power support to be provided within 30 seconds after a disturbance occurs. It is also necessary to verify whether the available capacity of the hydropower unit meets the requirements. Specifically, the response delay time of the hydropower unit is compared with a preset response time threshold. If the response delay time is less than the response time threshold, and the difference between the rated capacity of the hydropower unit and the current output level is greater than the required power compensation, the hydropower unit is determined to have dynamic response capability. The output includes a dynamic response capability assessment result for the hydropower unit, including the response delay time, the adjustable capacity value, and the response capability determination result. For example, when a hydropower unit has a rated capacity of 800MW, a current output of 500MW, an available adjustable capacity of 300MW, which 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 dispatching system to quickly select appropriate regulation resources in emergency situations, helping to maintain safe and stable operation of the power grid.

[0035] As one of the optional embodiments, determining a system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and judging whether to reconfigure backup resources according to the over-limit range of the system comprehensive risk index, includes: Obtaining meteorological data of the wind and solar power stations, inputting the meteorological data into a preset risk perception model to obtain a real-time risk perception value; the risk perception model is obtained by training a neural network based on historical meteorological data; Obtaining a system comprehensive risk index based on a weighted calculation of the real-time risk perception value, the comprehensive imbalance risk assessment value, and the dynamic response capability of the hydropower unit; Calculate the risk excess margin based on the difference between the system comprehensive risk index and the preset risk threshold; If the risk exceeds the limit by less than or equal to zero, the normal operation of the system is maintained; if the risk exceeds the limit by greater than zero, it is determined that the backup resources need to be reconfigured.

[0036] Specifically, a risk perception model based on a BP neural network is constructed in advance using meteorological forecast data for wind and solar power output. The input layer of the model receives wind speed, light intensity, temperature, and humidity data, and performs nonlinear mapping through a three-layer fully connected network. The output layer generates a normalized real-time risk perception value. Among them, the BP neural network, as a classic feedforward neural network, trains network weights through the backpropagation algorithm and can effectively handle nonlinear mapping relationships. In the risk perception model, the number of neurons in the input layer corresponds to the dimensions of wind and solar meteorological parameters, such as wind speed, light intensity, temperature, and humidity, each occupying one input node; the hidden layer uses a sigmoid activation function to implement nonlinear transformation, mapping multidimensional meteorological data to the risk feature space; the output layer uses a linear activation function to generate a normalized value between 0 and 1, which directly reflects the risk level of the current system. The calculation process of the real-time risk perception value integrates data from multiple sources. For example, when the wind speed suddenly drops from 15m / s to 5m / s, and 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 a sharp increase in system risk.

[0037] Furthermore, a weighted calculation is performed on the real-time risk perception value, the system's comprehensive imbalance risk assessment value, and the determination value of the hydropower unit's dynamic response capability to obtain the system's comprehensive risk index. For example, the weights corresponding to the real-time risk perception value, the system's comprehensive imbalance risk assessment value, and the hydropower unit's dynamic response capability are set to 0.3, 0.4, and 0.3, respectively. The weights reflect the relative importance of different risk factors. The imbalance risk assessment value is weighted at 0.4, reflecting the central role of supply and demand balance in grid stability. When the system power gap continues to expand and the imbalance risk assessment value increases, the comprehensive risk index will increase significantly, even if other indicators are normal. The real-time risk perception value and the dynamic response capability determination value each have a weight of 0.3, supplementing the system risk assessment from the two dimensions of forecast accuracy and regulation resource availability, respectively.

[0038] Furthermore, the risk excess amplitude is calculated based on the difference between the system comprehensive risk index and the preset risk threshold; the risk state is judged based on the positive and negative values ​​of the risk excess amplitude. If the risk excess amplitude is less than or equal to zero, the normal operation of the system is maintained; if the risk excess amplitude is greater than zero, it is determined that the 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 the system's tolerance. For example, by analyzing historical accident cases, it is found that when the comprehensive risk index exceeds 0.7, the probability of a cascading failure in the system increases sharply, so 0.7 is set as the risk threshold. When the comprehensive risk index calculated in real time is 0.85, it exceeds the risk threshold of 0.15. This difference is the risk excess amplitude, indicating that the system is already in a high-risk state. This embodiment uses the risk excess amplitude as a trigger mechanism, which reflects the transformation of the power system from passive response to active regulation, avoids frequent unnecessary adjustments, and ensures timely response at critical moments.

[0039] In an 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 excess range, combined with real-time monitoring data and historical operation data, and the capacity of backup resources that can be dispatched in the area is obtained. The target backup resource combination is matched, a backup resource scheduling instruction is generated, and the backup scheduling instruction is issued to the backup resource unit. Among them, according to the backup resource demand gap value, the startup time, unit adjustment cost, and access point electrical distance parameters of each resource unit are read from the backup resource information database. The startup time less than the preset threshold is assigned a priority weight of 0.5, the unit adjustment cost less than the preset threshold is assigned a weight of 0.3, and the electrical distance close to the preset threshold is assigned a weight of 0.2. The comprehensive score of each backup resource is obtained by weighted summation; the backup resource comprehensive score is used to sort from high to low, and the current dispatchable capacity of each backup resource is queried in turn. The dispatchable capacity of the selected resources is accumulated. If the accumulated value is less than the backup resource demand gap value, the next resource is selected until the accumulated value is greater than or equal to the demand gap value, and the target backup resource combination is obtained; based on the number of each unit in the target backup resource combination, the allocated dispatch capacity, and the expected response time, a dispatch instruction data packet containing the resource number, dispatch capacity value, and startup time is generated. After encryption, the backup dispatch instruction is issued to the corresponding backup resource unit via the power communication network. This embodiment ensures resource utilization efficiency and avoids excessive calls, and can achieve rapid and accurate allocation of backup resources.

[0040] As one of the optional embodiments, the configuration optimization is performed 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, and the output adjustment amount and scheduling priority sequence of each resource are determined, including: Taking the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices as decision variables, minimizing the total dispatch cost as the goal, constructing an objective function based on the unit capacity cost of each resource, and constructing constraints based on the regulation capacity of each resource; Perform iterative optimization based on the objective function and constraints to obtain the optimal backup capacity of the thermal power unit, the hydropower unit, and the energy storage device; Obtaining an optimized spare capacity allocation ratio based on the ratio of the optimal spare capacity to the total spare capacity of each resource, and calculating the difference between the current output and the target output of each resource based on the spare capacity allocation ratio to obtain the output adjustment of each resource; The scheduling priority sequence is determined based on the order of unit capacity cost of each resource from low to high.

[0041] Specifically, when the risk exceeds the limit by more than zero, the backup resources need to be reconfigured. First, the objective function is set to minimize the total dispatch cost, and the decision variables are the backup capacity values ​​of thermal power, hydropower, and energy storage devices. The unit capacity costs of various resources are read from the cost database to construct a linear objective function. Through real-time operation data, the current output and ramp rate of thermal power units, the relationship between the reservoir water level and power generation flow of hydropower units, the charge state and maximum charge and discharge power of energy storage devices are obtained. Combined with the upper and lower capacity limits in the rated equipment parameters, a linear inequality constraint group for the adjustment range of each resource is formed. Using the linear inequality constraint group and the linear objective function, the optimal backup capacity values ​​of thermal power, hydropower, and energy storage devices are obtained through iterative solution using the simplex method.

[0042] The unit capacity cost of a thermal power plant typically includes fuel costs and startup and shutdown losses. For hydropower plants, the primary consideration is the opportunity cost of water resources, which is typically lower. For energy storage devices, the cost of losses during the charge and discharge cycles must be calculated. These cost coefficients are multiplied by the corresponding backup capacity decision variables and summed to form the total cost function to be minimized. For example, the objective function formula is as follows: in, represents the unit reserve capacity cost of thermal power, Indicates the thermal power reserve capacity, Indicates the number of thermal power units; represents the unit reserve capacity cost of hydropower, Indicates the hydropower reserve capacity, Indicates the number of hydropower units; represents the unit backup capacity cost of the energy storage device, Indicates the energy storage reserve capacity, Indicates the number of energy storage devices.

[0043] The simplex method is an algorithm for solving linear programming problems. It iteratively searches for the optimal solution between vertices in the feasible region. Starting with an initial feasible solution, the algorithm uses a test value to determine whether it has reached optimality. If not, it moves to the next vertex in the direction that most rapidly decreases the objective function value. After a finite number of iterations, it finds a backup capacity allocation solution that minimizes total cost.

[0044] Furthermore, the optimal spare capacity of each type of resource is divided by the total spare capacity to obtain the spare capacity allocation ratio; the scheduling priority sequence is determined by sorting the units from low to high according to the unit capacity cost; based on the scheduling priority sequence and the spare 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 is generated including the resource type, the positive and negative sign of the output adjustment amount, and the absolute value of the adjustment amount.

[0045] For example, the calculation of reserve capacity allocation ratios 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. Furthermore, based on unit cost ranking, if hydropower has the lowest cost, it will be ranked first in the dispatch priority 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, while a negative sign indicates a decrease. Consequently, each power plant can accurately understand and implement dispatch requirements, ensuring rapid system response and stable operation under risk conditions. By optimizing the allocation of reserve capacity for different resource types, the technical and economic characteristics of each power source are fully utilized, improving the grid's ability to cope with fluctuations in renewable energy.

[0046] Furthermore, power adjustment instructions are issued to each power generation unit according to the scheduling priority sequence to ensure that the actual output adjustment of each power generation unit can meet the capacity allocation and scheduling priority requirements in the dynamic configuration plan. If the response speed of the energy storage device reaches the target speed, the energy storage backup capacity is called first to obtain real-time control effect of the system power balance recovery process. Specifically, the sorted resource list is read according to the scheduling priority sequence, and the identification of each power generation unit and the output adjustment instruction value generated by multivariate linear programming optimization are extracted in sequence starting from the first one in the list. An instruction data packet containing the unit number, target output value, and execution time is sent to each power generation unit through the scheduling communication channel; the actual response time of the energy storage device is detected based on the response time parameters of each power generation unit obtained after the instruction data packet is sent. If the response time of the energy storage device is less than a preset threshold, the output adjustment instruction of the energy storage device is executed first, and the energy storage backup capacity is called to compensate for the system power gap; after calling the energy storage backup capacity, the output adjustment of the remaining power generation units continues to be executed according to the scheduling priority sequence, and the actual output value of each unit is collected in real time. The consistency between the actual output value and the target output value is verified to ensure that the capacity allocation requirements are met; the difference between the real-time monitored system total power generation power and the total load demand is calculated. When the absolute value of the power difference is continuously less than the preset balance threshold, the system power balance recovery time is recorded, and the real-time control effect of the system power balance recovery process achieved by prioritizing the call of fast response resources and coordinating the output adjustment of other resources is obtained.

[0047] The system power balance is determined based on the principle of continuity. Simply because the power difference is less than the threshold at a given moment does not indicate that the system has stabilized. The difference must remain within the permitted range for multiple consecutive sampling periods. For example, a balance is considered achieved only when the difference between the system's total generated power and total load power remains within 10MW for five consecutive minutes. This determination method avoids misjudgments caused by transient fluctuations and ensures that the system has truly returned to a stable operating state. By recording the time from the occurrence of an imbalance to the restoration of balance, the effectiveness of the entire control process can be evaluated, providing data support for subsequent optimization of scheduling strategies.

[0048] As one of the optional embodiments, after performing power grid dispatch, the method further includes: Obtaining the total power generated and the total load power during the system power balance recovery process during grid dispatch, and determining the power balance recovery time according to the changes in the total power generated and the total load power; Calculating a time deviation value based on the difference between the power balance recovery time and a preset target time; If the time deviation value is greater than the preset time threshold, the current allocation ratio of the spare capacity of the thermal power unit, the hydropower unit, and the energy storage device, as well as the actual ramp rate of each type of resource during the scheduling process, are obtained; Calculating a deviation rate based on a ratio of the time deviation value to the target time; Calculating a capacity ratio correction amount according to the product of the deviation rate and the current allocation ratio, and obtaining a new spare capacity allocation ratio according to the current allocation ratio and the capacity ratio correction amount; The actual ramp rate is used as a new adjustment rate limit value, a configuration parameter group is generated according to the new spare capacity allocation ratio and the adjustment rate limit value, and the configuration parameter group is used as a resource configuration optimization benchmark for the next scheduling period.

[0049] Specifically, the total power generation power, total load power, 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 time when the power difference recovers to the allowable range is recorded as the power balance recovery time, and the time for the frequency deviation to recover to the preset range is recorded as the frequency stabilization time. The frequency stabilization time reflects the inertial response characteristics of the system. The power balance recovery time and the frequency stabilization time together constitute a quantitative evaluation of the actual response effect.

[0050] For example, during power balance restoration, the system collects total generated power and total load power data once per second and calculates the difference between the two. When a sudden drop in renewable energy output creates a 500MW power gap, this is recorded as the start of the imbalance. Monitoring continues until the power difference returns to within 10MW and remains stable. This duration is the power balance restoration time.

[0051] Furthermore, a time deviation value is calculated based on the power balance restoration time compared with a preset target time. The preset target time is determined based on the requirements for safe and stable power grid operation. According to power system stability guidelines, power imbalance should be restored within 5 minutes, and frequency deviation should return to the allowable range within 2 minutes. If the actual restoration time is 7 minutes, exceeding the target time by 2 minutes, it indicates that the current backup resource configuration is insufficient. Furthermore, if the time deviation value exceeds the preset time threshold, the backup capacity allocation ratios for thermal power, hydropower, and energy storage, as well as the actual ramp rates of each resource during the scheduling process, are extracted from the current operating data. The time deviation value is divided by the target time to obtain the deviation rate. The deviation rate is multiplied by the current backup capacity allocation ratio to obtain the capacity ratio correction. The correction value is added to the current allocation ratio to obtain the new allocation ratio, and the actual ramp rate is set as the new regulation rate limit. Based on the new backup capacity allocation ratio and regulation rate limit, combined with the upper and lower rated capacity limits of each resource, a configuration parameter group is formed, including the thermal power capacity ratio, hydropower capacity ratio, energy storage capacity ratio, and the corresponding regulation rates. This parameter group is then determined as the resource allocation optimization benchmark for the next scheduling cycle.

[0052] The updated rate limit reflects the discrepancy between the actual equipment capacity and theoretical parameters. For example, in real-world scheduling, due to factors such as equipment aging, the actual ramp rate may be lower than the theoretical value. By setting the actual value as the new rate limit, control deviations caused by overestimating unit capacity in subsequent scheduling are avoided.

[0053] For example, assume the current reserve capacity allocation ratios for thermal power, hydropower, and energy storage are 50%, 30%, and 20%, respectively. The power balance restoration time deviates by 2 minutes, while the target time is 5 minutes, resulting in a 40% deviation. Considering that energy storage has the fastest response but limited capacity, the energy storage ratio is increased by 40% × 20% = 8%, adjusting it to 28%. The slower-responding thermal power ratio is also reduced, thereby fully utilizing the fast-responding resources. The updated parameter set includes the capacity ratios of 45% for thermal power, 27% for hydropower, and 28% for energy storage, as well as the actual regulation rates for each resource. This set of parameters serves as the initial configuration for the next scheduling cycle, enabling a more rapid and effective restoration of balance 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 fluctuations in renewable energy sources.

[0054] The embodiment of the present invention obtains the predicted data and measured data of the output of the wind and solar power stations, determines the error change rate and the power gap value of the system according to the error between the predicted data and the measured data, and determines 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. It can reflect the stability of the power system under the condition of a high proportion of new energy in real time, accurately quantify the fluctuation of wind and solar power generation output, reduce scheduling errors caused by prediction deviation, and improve the operation reliability of the system; obtain the upward adjustment capacity of the thermal power unit, and determine the power compensation amount that the hydropower unit needs to bear in combination with the power gap value, and determine the power compensation amount that the hydropower unit needs to bear according to the power compensation amount and the available adjustment capacity of the hydropower unit. The dynamic response capability of the hydropower units is determined, 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 system comprehensive risk index is determined, and whether the backup resources need to be reconfigured is determined according to the excess range of the system comprehensive risk index; if the backup resources need to be reconfigured, the configuration optimization is carried out based on the backup capacity values ​​of the thermal power units, hydropower units, and energy storage devices with the goal of minimizing the total dispatch cost, and the output adjustment amount and dispatch priority sequence of various resources are determined for power grid dispatch, which provides complete risk management and dispatch support, can respond quickly to sudden fluctuations in new energy, and improve the safe and stable operation capability of the system.

[0055] Correspondingly, the present invention also provides a power grid flexibility resource scheduling device that can implement all processes of the power grid flexibility resource scheduling method in the above embodiment.

[0056] See also Figure 2 , Figure 2 : is a schematic diagram of the structure of a power grid flexibility resource scheduling device provided by an embodiment of the present invention. The power grid flexibility resource scheduling device includes: The system imbalance assessment module 201 is configured to obtain predicted data and the measured data of the wind and solar power station output, determine an error change rate and a power gap value of the system based on an error between the predicted data and the measured data, and determine a 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 evaluation module 202 is configured to obtain the upward regulation capacity of the thermal power unit, determine the power compensation amount required by the hydropower unit based on the power gap value, and determine the dynamic response capability of the hydropower unit based on the power compensation amount and the available regulation capacity of the hydropower unit; A comprehensive risk assessment module 203 is configured to determine a system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and determine whether backup resources need to be reconfigured based on the excess range of the system comprehensive risk index; The grid resource scheduling module 204 is used to optimize the configuration of backup resources if reconfiguration of backup resources is required, based on the backup capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total scheduling cost, and determine the output adjustment amount and scheduling priority sequence of each type of resource to perform grid scheduling.

[0057] Preferably, the obtaining of predicted data and measured data of wind and solar power station output, and determining the error change rate and the power gap value of the system according to the error between the predicted data and the measured data, includes: Obtaining weather forecast data for the wind and solar power station, and performing wind and solar output forecasting based on the weather forecast data to obtain forecast data for the wind and solar power station output; Obtaining measured data of the output of the wind and solar power station, and calculating the error value between the measured data and the predicted data at the corresponding time using a preset sliding time window; Determine the error change rate based on the change in error values ​​within adjacent time windows; The period when the error change rate is greater than the preset error threshold is regarded as a high-risk period; For the high-risk period, the power gap value of the system is determined according to the error change rate, combined with the total load demand of the power grid and the measured data.

[0058] Preferably, determining 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 includes: Obtaining a power gap expansion rate according to the growth rate of the system power gap value within a continuous time window; Based on the high-risk period, determining a duration of the high-risk period; The error change rate, the power gap expansion rate and the duration of the high-risk period are normalized respectively, and the normalized error change rate, power gap expansion rate and the duration of the high-risk period are weighted and summed to obtain a comprehensive imbalance risk assessment value of a high-proportion new energy power system.

[0059] Preferably, the obtaining of the upward regulation capacity of the thermal power unit, determining the power compensation amount required to be borne by the hydropower unit in combination with the power gap value, and determining the dynamic response capability of the hydropower unit according to the power compensation amount and the available regulation capacity of the hydropower unit include: Obtaining output data of the thermal power unit, and calculating the upward adjustment capacity of the thermal power unit 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 gap value is greater than the upward adjustment capacity of the thermal power unit, the power compensation amount required to be borne by the hydropower unit is calculated according to the difference between the power gap value and the upward adjustment capacity; Determining the response delay time required for the hydropower unit to increase from a current output to a target output based on the power compensation amount and the ramp speed of the hydropower unit; 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 regulation capacity of the hydropower unit; wherein, if the response delay time is less than the response time threshold and the available regulation capacity is greater than the power compensation amount, it is determined that the hydropower unit has dynamic response capability.

[0060] Preferably, determining a system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and judging whether to reconfigure backup resources according to the over-limit range of the system comprehensive risk index, includes: Obtaining meteorological data of the wind and solar power stations, inputting the meteorological data into a preset risk perception model to obtain a real-time risk perception value; the risk perception model is obtained by training a neural network based on historical meteorological data; Obtaining a system comprehensive risk index based on a weighted calculation of the real-time risk perception value, the comprehensive imbalance risk assessment value, and the dynamic response capability of the hydropower unit; Calculate the risk excess margin based on the difference between the system comprehensive risk index and the preset risk threshold; If the risk exceeds the limit by less than or equal to zero, the normal operation of the system is maintained; if the risk exceeds the limit by greater than zero, it is determined that the backup resources need to be reconfigured.

[0061] Preferably, the configuration optimization is performed based on the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices with the goal of minimizing the total dispatching cost, and the output adjustment amount and dispatching priority sequence of each resource are determined, including: Taking the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices as decision variables, minimizing the total dispatch cost as the goal, constructing an objective function based on the unit capacity cost of each resource, and constructing constraints based on the regulation capacity of each resource; Perform iterative optimization based on the objective function and constraints to obtain the optimal backup capacity of the thermal power unit, the hydropower unit, and the energy storage device; Obtaining an optimized spare capacity allocation ratio based on the ratio of the optimal spare capacity to the total spare capacity of each resource, and calculating the difference between the current output and the target output of each resource based on the spare capacity allocation ratio to obtain the output adjustment of each resource; The scheduling priority sequence is determined based on the order of unit capacity cost of each resource from low to high.

[0062] Preferably, the power grid flexibility resource scheduling device is further used to: Obtaining the total power generated and the total load power during the system power balance recovery process during grid dispatch, and determining the power balance recovery time according to the changes in the total power generated and the total load power; Calculating a time deviation value based on the difference between the power balance recovery time and a preset target time; If the time deviation value is greater than the preset time threshold, the current allocation ratio of the spare capacity of the thermal power unit, the hydropower unit, and the energy storage device, as well as the actual ramp rate of each type of resource during the scheduling process, are obtained; Calculating a deviation rate based on a ratio of the time deviation value to the target time; Calculating a capacity ratio correction amount according to the product of the deviation rate and the current allocation ratio, and obtaining a new spare capacity allocation ratio according to the current allocation ratio and the capacity ratio correction amount; The actual ramp rate is used as a new adjustment rate limit value, a configuration parameter group is generated according to the new spare capacity allocation ratio and the adjustment rate limit value, and the configuration parameter group is used as a resource configuration optimization benchmark for the next scheduling period.

[0063] In the specific implementation, the working principle, control process and technical effect achieved by the power grid flexibility resource scheduling device provided by the embodiment of the present invention are the same as those of the power grid flexibility resource scheduling method in the above embodiment, and will not be repeated here.

[0064] See also Figure 3 , Figure 3 This is a 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, the steps of the aforementioned power grid flexibility resource scheduling method embodiment are implemented. Alternatively, when the processor 301 executes the computer program, the functions of the modules / units in the aforementioned apparatus embodiments are implemented.

[0065] 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 implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the computer device.

[0066] The computer device may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will appreciate that the schematic diagram is merely an example of a computer device and does not limit the computer device. The computer device may include more or fewer components than shown, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, and the like.

[0067] The processor 301 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may 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 using various interfaces and lines.

[0068] The memory 302 can be used to store the computer programs and / or modules. The processor 301 implements the various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302 and accessing 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 an operating system and at least one application required for a function (such as a sound playback function or an image playback function); the data storage area may store data generated based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory 302 may include high-speed random access memory and non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0069] If the module / unit integrated into the computer device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by the processor 301, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, software distribution medium, etc.

[0070] An embodiment of the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the power grid flexibility resource scheduling method described in any of the above embodiments.

[0071] The present invention provides a method, device, equipment and storage medium for dispatching flexible resources of a power grid, which has the following beneficial effects: by obtaining the predicted data and measured data of the output of wind and solar power stations, determining the error change rate and the power gap value of the system according to the error between the predicted data and the measured data, and determining 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, it can reflect the stability of the power system under the condition of a high proportion of new energy in real time, can accurately quantify the fluctuation of wind and solar power output, reduce the dispatching errors caused by prediction deviation, and improve the operation reliability of the system; obtain the upward adjustment capacity of the thermal power unit, and determine the power compensation amount that the hydropower unit needs to bear in combination with the power gap value, and determine the power compensation amount that the hydropower unit needs to bear according to the power gap value. The compensation amount and the available regulating capacity of the hydropower unit determine the dynamic response capability of the hydropower unit, 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 unit, the system comprehensive risk index is determined, and whether the backup resources need to be reconfigured is determined according to the over-limit range of the system comprehensive risk index; if the backup resources need to be reconfigured, the configuration optimization is carried out based on the backup capacity values ​​of the thermal power units, hydropower units, and energy storage devices with the goal of minimizing the total dispatch cost, and the output adjustment amount and dispatch priority sequence of each type of resource are determined for power grid dispatch, providing complete risk control and dispatch support, and being able to respond quickly to sudden fluctuations in new energy, thereby improving the safe and stable operation capability of the system.

[0072] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for scheduling power grid flexibility resources, characterized in that: include: Obtaining predicted data and measured data on the output of a wind and solar power station, determining an error change rate and a power gap value of the system based on an error between the predicted data and the measured data, and determining a comprehensive imbalance risk assessment value of the high-proportion new energy power system based on the error change rate and the power gap value; Obtaining the upward regulation 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 regulation capacity of the hydropower unit; Determining a system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and judging whether it is necessary to reconfigure backup resources according to the excess range of the system comprehensive risk index; If reserve resources need to be reconfigured, configuration optimization is performed 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 each type of resource are determined to carry out grid dispatch.

2. The grid flexibility resource scheduling method according to claim 1, characterized in that: The obtaining of predicted data and measured data of the wind and solar power station output, and determining the error change rate and the power gap value of the system according to the error between the predicted data and the measured data, includes: Obtaining weather forecast data for the wind and solar power station, and performing wind and solar output forecasting based on the weather forecast data to obtain forecast data for the wind and solar power station output; Obtaining measured data of the output of the wind and solar power station, and calculating the error value between the measured data and the predicted data at the corresponding time using a preset sliding time window; Determine the error change rate based on the change in error values ​​within adjacent time windows; The period when the error change rate is greater than the preset error threshold is regarded as a high-risk period; For the high-risk period, the power gap value of the system is determined according to the error change rate, combined with the total load demand of the power grid and the measured data.

3. The grid flexibility resource scheduling method according to claim 2, wherein: Determining 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 includes: Obtaining a power gap expansion rate according to the growth rate of the system power gap value within a continuous time window; Based on the high-risk period, determining a duration of the high-risk period; The error change rate, the power gap expansion rate and the duration of the high-risk period are normalized respectively, and the normalized error change rate, power gap expansion rate and the duration of the high-risk period are weighted and summed to obtain a comprehensive imbalance risk assessment value of a high-proportion new energy power system.

4. The method for dispatching power grid flexibility resources according to claim 1, wherein: The step of obtaining the upward regulation 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 regulation capacity of the hydropower unit includes: Obtaining output data of the thermal power unit, and calculating the upward adjustment capacity of the thermal power unit 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 gap value is greater than the upward adjustment capacity of the thermal power unit, the power compensation amount required to be borne by the hydropower unit is calculated according to the difference between the power gap value and the upward adjustment capacity; Determining the response delay time required for the hydropower unit to increase from a current output to a target output based on the power compensation amount and the ramp speed of the hydropower unit; 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 regulation capacity of the hydropower unit; wherein, if the response delay time is less than the response time threshold and the available regulation capacity is greater than the power compensation amount, it is determined that the hydropower unit has dynamic response capability.

5. The grid flexibility resource scheduling method according to claim 1, wherein: Determining a system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and judging whether it is necessary to reconfigure backup resources according to the over-limit range of the system comprehensive risk index, includes: Obtaining meteorological data of the wind and solar power stations, inputting the meteorological data into a preset risk perception model to obtain a real-time risk perception value; the risk perception model is obtained by training a neural network based on historical meteorological data; Obtaining a system comprehensive risk index based on a weighted calculation of the real-time risk perception value, the comprehensive imbalance risk assessment value, and the dynamic response capability of the hydropower unit; Calculate the risk excess margin based on the difference between the system comprehensive risk index and the preset risk threshold; If the risk exceeds the limit by less than or equal to zero, the normal operation of the system is maintained; if the risk exceeds the limit by greater than zero, it is determined that the backup resources need to be reconfigured.

6. The method for dispatching power grid flexibility resources according to claim 1, wherein: The configuration optimization is performed 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, and the output adjustment amount and dispatch priority sequence of each resource are determined, including: Taking the reserve capacity values ​​of thermal power units, hydropower units, and energy storage devices as decision variables, minimizing the total dispatch cost as the goal, constructing an objective function based on the unit capacity cost of each resource, and constructing constraints based on the regulation capacity of each resource; Perform iterative optimization based on the objective function and constraints to obtain the optimal backup capacity of the thermal power unit, the hydropower unit, and the energy storage device; Obtaining an optimized spare capacity allocation ratio based on the ratio of the optimal spare capacity to the total spare capacity of each resource, and calculating the difference between the current output and the target output of each resource based on the spare capacity allocation ratio to obtain the output adjustment of each resource; The scheduling priority sequence is determined based on the order of unit capacity cost of each resource from low to high.

7. The grid flexibility resource scheduling method according to claim 1, wherein: After performing power grid dispatching, the method further includes: Obtaining the total power generated and the total load power during the system power balance recovery process during grid dispatch, and determining the power balance recovery time according to the changes in the total power generated and the total load power; Calculating a time deviation value based on the difference between the power balance recovery time and a preset target time; If the time deviation value is greater than the preset time threshold, the current allocation ratio of the spare capacity of the thermal power unit, the hydropower unit, and the energy storage device, as well as the actual ramp rate of each type of resource during the scheduling process, are obtained; Calculating a deviation rate based on a ratio of the time deviation value to the target time; Calculating a capacity ratio correction amount according to the product of the deviation rate and the current allocation ratio, and obtaining a new spare capacity allocation ratio according to the current allocation ratio and the capacity ratio correction amount; The actual ramp rate is used as a new adjustment rate limit value, a configuration parameter group is generated according to the new spare capacity allocation ratio and the adjustment rate limit value, and the configuration parameter group is used as a resource configuration optimization benchmark for the next scheduling period.

8. A power grid flexibility resource scheduling device, characterized in that: include: a system imbalance assessment module, configured to obtain predicted data and measured data on the output of wind and solar power stations, determine an error change rate and a power gap value of the system based on an error between the predicted data and the measured data, and determine a comprehensive imbalance risk assessment value of a high-proportion new energy power system based on the error change rate and the power gap value; a hydropower response assessment module, configured to obtain the upward regulation 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 regulation capacity of the hydropower unit; a comprehensive risk assessment module, configured to determine a system comprehensive risk index based on the comprehensive imbalance risk assessment value and the dynamic response capability of the hydropower unit, and determine whether it is necessary to reconfigure backup resources based on an over-limit range of the system comprehensive risk index; The grid resource scheduling module is used to optimize the configuration of backup resources if they need to be reconfigured. This module is based on the backup capacity values ​​of thermal power units, hydropower units, and energy storage devices, with the goal of minimizing the total scheduling cost. It also determines the output adjustment amount and scheduling priority sequence of each type of resource to carry out grid scheduling.

9. A computer device, characterized in that: It includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is configured to be executed by the processor, and when the processor executes the computer program, it implements 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 where the computer-readable storage medium is located executes the computer program, the power grid flexibility resource scheduling method according to any one of claims 1 to 7 is implemented.

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