A new energy and pumped storage coordinated cross-period energy balance method and system

Abstract

The application relates to the fields of power systems and energy storage technologies, and discloses a new energy and pumped storage coordinated cross-period energy balance method and system. The method comprises the following steps: extracting features from night monitoring data of wind power output and night monitoring data of solar residual heat power generation, and performing dynamic modeling to obtain a new energy surplus power prediction value; performing water level trend analysis and energy storage capacity evaluation on real-time water level data of an upper reservoir of pumped storage and basin inflow forecast data to obtain an upper limit value of pumped storage received power; adjusting power at a new energy power generation end based on a comparison result to obtain new energy actual output data; performing threshold value judgment and response coefficient calculation on a supply-demand deviation value to obtain a demand side adjustment scheme; performing dynamic programming optimization and constraint condition correction on periodized power data sequences to obtain a multi-energy coordinated power adjustment instruction sequence covering all periods. The application realizes accurate response to new energy output fluctuation and efficient utilization of pumped storage resources.

Description

Technical Field

[0001] The present invention relates to the field of power systems and energy storage technologies, and in particular to a cross-period energy balancing method and system for coordinated new energy and pumped storage. Background Art

[0002] Renewable energy generation is characterized by significant intermittency, volatility, and anti-peaking characteristics, making it difficult to match the actual demand of power loads in real time. If cross-period energy balance control cannot be achieved, the power system will face multiple risks, including rising power curtailment rates and insufficient system peak-shaving capacity. Pumped hydro storage, as a large-scale energy storage method, effectively bridges the temporal and spatial mismatches between renewable energy generation and power loads by pumping water to store energy during periods of abundant power and generating and releasing energy during periods of power shortages. This smoothes power fluctuations and plays a crucial role in achieving cross-period energy balance control.

[0003] Currently, mainstream energy management solutions rely on the independent regulation of a single energy storage device or simple load forecasting based on historical data. These solutions lack a comprehensive analysis of the randomness of renewable energy output, the operational constraints of energy storage systems, and the dynamic characteristics of the power grid. In actual operation, the uncertainty of renewable energy output and the inherent constraints of pumped storage systems create numerous challenges: 1) During the nighttime off-peak period, wind power output fluctuates dramatically due to abnormal changes in wind speed gradients, leading to grid power imbalances; 2) Due to the lag effect of heat exchange medium cooling, solar waste heat power generation systems continue to output residual power, further exacerbating energy surpluses; 3) The water level of pumped storage reservoirs is affected by factors such as upstream rainfall. When the water level is too high, the capacity and efficiency of reverse pumping storage are limited, weakening the system's ability to absorb excess energy. However, existing single control strategies are unable to effectively address the dynamic coupling of renewable energy output fluctuations, energy storage operational constraints, and user load demands, resulting in insufficient energy matching accuracy across time periods and reduced grid regulation flexibility.

[0004] Therefore, it is urgent to study a cross-period energy balance method that takes into account the active control of new energy and the coordinated response of the user side to improve the operating efficiency and safety level of the power system. Summary of the Invention

[0005] In response to the above-mentioned problems existing in the prior art, the present invention provides a cross-time energy balancing method and system that coordinates new energy and pumped storage.

[0006] In a first aspect, an embodiment of the present invention provides a method for balancing energy across time periods by coordinating new energy and pumped storage, including:

[0007] Obtaining nighttime monitoring data of wind power output and nighttime monitoring data of solar waste heat power generation at the renewable energy power generation end, and performing feature extraction and dynamic modeling on the nighttime monitoring data of wind power output and the nighttime monitoring data of solar waste heat power generation to obtain a predicted value of renewable energy excess power;

[0008] Acquiring real-time water level data of the upper pumped storage reservoir and water inflow forecast data of the river basin, and performing water level trend analysis and energy storage capacity assessment on the real-time water level data of the upper pumped storage reservoir and the water inflow forecast data of the river basin to obtain an upper limit value of the pumped storage power acceptance;

[0009] Comparing the predicted value of excess power of the renewable energy source with the upper limit of the power accepted by the pumped storage system, and adjusting the power of the renewable energy power generation end based on the comparison result to obtain actual output data of the renewable energy source;

[0010] Calculating a supply-demand deviation value based on the actual output data of the new energy, and performing threshold judgment and response coefficient calculation on the supply-demand deviation value to obtain a demand-side regulation plan;

[0011] The actual output data of the new energy and the demand-side regulation scheme are serialized to obtain a time-based power data sequence, and the time-based power data sequence is dynamically optimized and the constraint conditions are corrected to obtain a multi-energy collaborative power regulation instruction sequence covering the entire time period.

[0012] Preferably, after serializing the actual output data of the new energy and the demand-side regulation scheme to obtain a time-based power data sequence, and performing dynamic programming optimization and constraint condition correction on the time-based power data sequence to obtain a multi-energy coordinated power regulation instruction sequence covering the entire time period, the method further includes:

[0013] The multi-energy collaborative power regulation instruction sequence is executed by a multi-energy collaborative system, and the multi-energy collaborative power regulation instruction sequence is corrected based on the dynamic operation performance index of the multi-energy collaborative system to obtain an updated multi-energy collaborative power regulation instruction sequence.

[0014] Preferably, the acquiring of nighttime monitoring data of wind power output and nighttime monitoring data of solar waste heat power generation at the renewable energy power generation end, and performing feature extraction and dynamic modeling on the nighttime monitoring data of wind power output and the nighttime monitoring data of solar waste heat power generation to obtain a predicted value of renewable energy excess power includes:

[0015] Collect nighttime monitoring data on wind power output and solar waste heat power generation at renewable energy power generation terminals based on preset sampling intervals;

[0016] Performing frequency domain transformation on the nighttime monitoring data of wind power output to obtain wind power power fluctuation main frequency, and performing trend extrapolation on the wind power power fluctuation main frequency to obtain wind power forecast power;

[0017] Performing parameter identification on the nighttime monitoring data of the solar waste heat power generation to obtain dynamic characteristic parameters of the solar waste heat power generation, and constructing a transfer function on the dynamic characteristic parameters of the solar waste heat power generation to obtain a dynamic response model of the solar waste heat power generation;

[0018] The solar waste heat power generation dynamic response model is discretized and predicted to obtain the solar waste heat power generation predicted power, and the solar waste heat power generation predicted power and the wind power predicted power are weightedly fused and surplus calculated to obtain the new energy surplus power predicted value.

[0019] Preferably, the acquiring of real-time water level data of the upper pumped storage reservoir and water inflow forecast data of the river basin, and performing water level trend analysis and energy storage capacity assessment on the real-time water level data of the upper pumped storage reservoir and the water inflow forecast data of the river basin to obtain the upper limit of the pumped storage power acceptance includes:

[0020] Deploy sensors to collect real-time water level data from pumped storage reservoirs and connect to watershed inflow forecast data from meteorological monitoring systems;

[0021] Calculating the water level change rate based on the real-time water level data of the pumped storage upper reservoir, and calculating the predicted inflow flow increment based on the basin water inflow forecast data;

[0022] The water level change rate and the predicted inflow flow increment are subjected to trend prediction modeling to obtain a water level rise rate prediction value.

[0023] Preferably, after performing trend prediction modeling on the water level change rate and the predicted inflow flow increment to obtain a water level rise prediction value, the method further includes:

[0024] Based on the water level rise rate prediction value, a difference operation is performed on the total storage capacity of the pumped storage upper reservoir and the current storage capacity of the pumped storage upper reservoir to obtain the remaining storage capacity of the pumped storage upper reservoir;

[0025] Obtaining a historical inflow flow sequence of a pumped storage upper reservoir, and performing feature extraction and peak value calculation on the historical inflow flow sequence of the pumped storage upper reservoir to obtain a peak value of the inflow flow of the pumped storage upper reservoir;

[0026] Performing calculations on the remaining storage capacity of the pumped storage upper reservoir and the peak inflow rate of the pumped storage upper reservoir to obtain an estimated full reservoir time, and performing a threshold comparison on the estimated full reservoir time to obtain a degree of energy storage capacity limitation;

[0027] The pumped storage basic acceptance power is calculated based on the remaining storage capacity of the pumped storage upper reservoir, and the pumped storage basic acceptance power is graded and reduced according to the degree of limitation of the energy storage capacity to obtain the upper limit value of the pumped storage acceptance power.

[0028] Preferably, the comparing the predicted value of excess power of the renewable energy source with the upper limit of the power accepted by the pumped storage system, and adjusting the power of the renewable energy power generation end based on the comparison result to obtain the actual output data of the renewable energy source, includes:

[0029] Performing a differential operation on the predicted value of excess power of renewable energy and the upper limit of the pumped storage power acceptance to obtain a power reduction amount;

[0030] If the power reduction amount is a positive number, power reduction allocation is performed on the new energy power generation end based on the power reduction amount to obtain wind power reduction power and solar waste heat power generation reduction power;

[0031] Based on the wind power curtailment power, the pitch angle of the wind turbine at the renewable energy power generation end is adjusted to obtain actual output data of the wind turbine;

[0032] Based on the power reduction of the solar waste heat power generation, the cooling rate of the solar waste heat power generation system at the new energy power generation end is adjusted to obtain actual output data of the solar waste heat power generation system.

[0033] Preferably, the calculation of the supply-demand deviation value based on the actual output data of the new energy source, and performing threshold judgment and response coefficient calculation on the supply-demand deviation value to obtain a demand-side regulation solution include:

[0034] Performing a differential operation on the actual output data of the renewable energy and the real-time total load demand to obtain a supply-demand deviation value;

[0035] If the supply-demand deviation value exceeds a preset power balance deviation threshold, calculating a ratio of the supply-demand deviation value to the real-time load adjustable capacity, and representing the ratio as a response coefficient;

[0036] The response coefficient is mapped to a level quantitative level to obtain a corresponding demand-side regulation solution.

[0037] Preferably, the actual output data of the new energy and the demand-side regulation scheme are serialized to obtain a time-based power data sequence, and the time-based power data sequence is dynamically optimized and the constraint conditions are modified to obtain a multi-energy coordinated power regulation instruction sequence covering the entire time period, including:

[0038] Dividing the actual output data of the new energy and the demand-side regulation scheme according to preset time periods to obtain a periodized power data sequence, wherein the periodized power data sequence includes the actual output values ​​of the new energy and the demand-side load regulation values ​​of several time periods;

[0039] Dynamic programming is used to recursively calculate the periodized power data sequence to obtain an optimal power allocation sequence, wherein the optimal power allocation sequence includes the optimal actual output value of new energy and the optimal demand-side load regulation value for each period;

[0040] performing efficiency compensation calculation on the optimal power allocation sequence to obtain the actual power of the pumped storage, and performing power limiting correction on the optimal power allocation sequence based on a comparison result of the actual power of the pumped storage and the rated power of the pumped storage to obtain a corrected power allocation sequence;

[0041] Energy time series distribution is performed on the modified power distribution sequence to obtain a target energy distribution sequence, and multi-energy control parameter decomposition is performed on the target energy distribution sequence to obtain a multi-energy coordinated power adjustment instruction sequence covering the entire time period.

[0042] Preferably, executing the multi-energy collaborative power adjustment instruction sequence by the multi-energy collaborative system, and modifying the multi-energy collaborative power adjustment instruction sequence based on the dynamic operation performance index of the multi-energy collaborative system to obtain an updated multi-energy collaborative power adjustment instruction sequence, includes:

[0043] Executing the multi-energy collaborative power regulation instruction sequence through a multi-energy collaborative system using a standardized communication protocol;

[0044] performing a differential operation on the current grid frequency and the rated grid frequency to obtain a grid frequency deviation, and calculating a dispatch power deviation based on the real-time power data of the multi-energy collaborative system;

[0045] Based on the grid frequency deviation and the dispatch power deviation, the multi-energy collaborative power regulation instruction sequence is corrected by model predictive control to obtain an updated multi-energy collaborative power regulation instruction sequence.

[0046] In a second aspect, an embodiment of the present invention provides a cross-period energy balance system that coordinates new energy and pumped storage, including:

[0047] A new energy excess power prediction module is used to obtain nighttime monitoring data of wind power output and solar waste heat power generation from the new energy power generation end, and perform feature extraction and dynamic modeling on the wind power output nighttime monitoring data and the solar waste heat power generation nighttime monitoring data to obtain a new energy excess power prediction value;

[0048] A pumped storage power acceptance determination module is used to obtain real-time water level data of the pumped storage upper reservoir and water inflow forecast data of the basin, and perform water level trend analysis and energy storage capacity evaluation on the real-time water level data of the pumped storage upper reservoir and the water inflow forecast data of the basin to obtain an upper limit value of the pumped storage power acceptance;

[0049] A new energy actual output determination module is used to compare the predicted value of the new energy excess power with the upper limit of the pumped storage power acceptance, and adjust the power of the new energy power generation end based on the comparison result to obtain the new energy actual output data;

[0050] a demand-side regulation scheme determination module, configured to calculate a supply-demand deviation value based on the actual output data of the new energy source, and perform threshold determination and response coefficient calculation on the supply-demand deviation value to obtain a demand-side regulation scheme;

[0051] The power regulation instruction sequence determination module is used to serialize the actual output data of the new energy and the demand-side regulation plan to obtain a time-based power data sequence, and to perform dynamic planning optimization and constraint condition correction on the time-based power data sequence to obtain a multi-energy collaborative power regulation instruction sequence covering the entire time period.

[0052] Compared with the existing technology, the embodiment of the present invention provides a cross-time period energy balance method and system for coordinated new energy and pumped storage, which has the following beneficial effects: by extracting features and dynamically modeling night-time data of wind power output and night-time data of solar waste heat power generation, the excess power of new energy can be predicted in advance, and the upper limit of energy storage capacity can be evaluated in combination with the water level of the pumped storage reservoir and the inflow forecast data, so as to realize the dynamic matching of new energy output and pumped storage acceptance capacity, and effectively reduce the power abandonment rate; at the same time, based on the demand-side response mechanism and dynamic planning optimization of supply and demand deviation, a multi-energy coordinated power adjustment instruction sequence covering the entire time period can be generated, which effectively improves the accuracy of cross-time period energy balance on the basis of taking into account the active control of new energy and the load regulation on the user side, enhances the adaptability of the power grid to the intermittent and fluctuating output of new energy, and ultimately achieves the dual improvement of the operation efficiency and safety level of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of a method for balancing energy across time periods by coordinating new energy and pumped storage according to an embodiment of the present invention;

[0054] Figure 2 This is another flow chart of a method for balancing energy across time periods using a combination of new energy and pumped storage in accordance with an embodiment of the present invention;

[0055] Figure 3 This is a schematic structural diagram of a cross-period energy balance system that combines new energy and pumped storage in an embodiment of the present invention;

[0056] Reference numerals:

[0057] 1. New energy excess power prediction module; 2. Pumped storage power acceptance determination module; 3. New energy actual output determination module; 4. Demand-side regulation plan determination module; 5. Power regulation instruction sequence determination module. DETAILED DESCRIPTION

[0058] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0059] In the description of the present invention, it should be understood that the terms "first" and "second" etc. are used in the present invention to distinguish different objects rather than to describe a specific order.

[0060] In describing the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. Those skilled in the art will understand the specific meanings of the above terms in the present invention in specific circumstances.

[0061] like Figure 1 As shown in FIG, it is a flow chart of a cross-period energy balance method for the coordination of new energy and pumped storage according to an embodiment of the present invention. Figure 1 The embodiment of the present invention provides a cross-period energy balance method for coordinated new energy and pumped storage, comprising the steps of:

[0062] S1. Obtain nighttime monitoring data of wind power output and solar waste heat power generation from the renewable energy power generation end, and perform feature extraction and dynamic modeling on the wind power output and solar waste heat power generation nighttime monitoring data to obtain a predicted value of renewable energy excess power;

[0063] Specifically, step S1 includes:

[0064] 1) Collect nighttime monitoring data on wind power output and solar waste heat power generation from renewable energy power generation terminals based on preset sampling intervals;

[0065] During the acquisition of nighttime wind power output monitoring data, wind power exhibits significant random fluctuations, primarily influenced by factors such as changes in wind speed and direction, and the wind turbine's own regulation. Although solar waste heat power generation systems lack direct solar radiation at night, the waste heat stored in the heat storage device is continuously released and converted into electricity, resulting in a gradual decay of residual output.

[0066] Collecting nighttime monitoring data for wind power output and solar waste heat power generation at preset sampling intervals can resolve the issue of timestamp discrepancies between different data sources and ensure data comparability. For example, wind power data may be sampled every 5 minutes, while waste heat power generation data may be sampled every 10 minutes, both of which need to be unified to a 10-minute interval.

[0067] 2) Perform frequency domain transformation on the nighttime monitoring data of wind power output to obtain the main frequency of wind power fluctuation, and perform trend extrapolation on the main frequency of wind power fluctuation to obtain the predicted wind power;

[0068] Nighttime wind power output monitoring data, presented in the time domain, is converted to frequency domain data using a fast Fourier transform. After frequency domain transformation, the wind power output data displays an energy distribution of different frequency components. Low-frequency components typically correspond to slow changes in wind speed, while high-frequency components reflect the effects of rapid disturbances such as turbulence. By identifying the dominant fluctuation frequency (the main frequency of wind power fluctuations) and performing trend extrapolation on this main frequency, we can predict the dominant pattern of power fluctuations over the next period of time, thereby deriving the predicted wind power output.

[0069] 3) Parameter identification is performed on the nighttime monitoring data of solar waste heat power generation to obtain the dynamic characteristic parameters of solar waste heat power generation, and a transfer function is constructed for the dynamic characteristic parameters of solar waste heat power generation to obtain the dynamic response model of solar waste heat power generation;

[0070] This embodiment uses an autocorrelation function to calculate the correlation coefficient at different time intervals for the nighttime monitoring data of solar waste heat power generation. When the correlation coefficient decreases to 1 / e of the initial value, the corresponding time interval is determined as the time constant. This time constant reflects the thermal inertia characteristics of the solar waste heat power generation system. The larger the time constant, the slower the solar waste heat power generation system responds to external disturbances. The response lag parameter is obtained by calculating the time difference between the moment when the wind power changes and the moment when the solar waste heat power generation power responds. This response lag parameter reflects the delay time from the input change to the start of the output response. The time constant and the response lag parameter together constitute the dynamic characteristic parameters of solar waste heat power generation.

[0071] The first-order inertia link transfer function is a classic model for describing the response characteristics of a dynamic system. Therefore, this embodiment constructs a first-order inertia link transfer function based on the dynamic characteristic parameters of solar waste heat power generation to describe the dynamic response characteristics of solar waste heat power generation. Specifically, the following formula is used to characterize the first-order inertia link transfer function, i.e., the dynamic response model of solar waste heat power generation:

[0072]

[0073] in, represents the transfer function, represents the steady-state gain, represents the time constant, represents the response hysteresis parameter, It should be noted that the steady-state gain represents the ratio of the output to the input when the solar waste heat power generation system reaches a stable state.

[0074] 4) Discrete prediction calculation is performed on the dynamic response model of solar waste heat power generation to obtain the predicted power of solar waste heat power generation, and weighted fusion and excess calculation are performed on the predicted power of solar waste heat power generation and wind power prediction to obtain the predicted value of new energy excess power.

[0075] The Z transform is used to convert the continuous-domain transfer function (the solar waste heat power generation dynamic response model) into a discrete-domain difference equation. This difference equation is then iterated based on nighttime wind power output monitoring data to obtain the predicted solar waste heat power generation power within the prediction window. This prediction window is set to 4-6 hours in this embodiment, covering the main nighttime scheduling period.

[0076] The predicted power of solar waste heat power generation and wind power generation is weighted and integrated to obtain the total power value of renewable energy within the forecast window. The weight coefficient is determined by the inverse of the variance of the respective power fluctuations. That is, the weight coefficient corresponding to the predicted power of solar waste heat power generation is the inverse of the variance of the predicted power fluctuation of solar waste heat power generation, and the weight coefficient corresponding to the predicted power of wind power is the inverse of the variance of the predicted power fluctuation of wind power. Furthermore, the total power value of renewable energy is differentially calculated with the upper limit of the grid absorption capacity to obtain the predicted value of excess power of renewable energy.

[0077] S2. Obtain real-time water level data of the upper reservoir of the pumped storage power plant and water inflow forecast data of the river basin, and perform water level trend analysis and energy storage capacity assessment on the real-time water level data of the upper reservoir of the pumped storage power plant and the water inflow forecast data of the river basin to obtain the upper limit value of the pumped storage power acceptance;

[0078] Specifically, step S2 includes:

[0079] 1) Deploy sensors to collect real-time water level data from pumped storage reservoirs and watershed inflow forecast data from meteorological monitoring systems;

[0080] Water level data is collected through pressure sensors or ultrasonic water level gauges deployed at various locations in the pumped storage reservoir. These sensors record water level elevations at regular intervals. In addition, watershed inflow forecast data, namely upstream rainfall forecast data, is connected to the meteorological monitoring system.

[0081] 2) Calculate the water level change rate based on the real-time water level data of the pumped storage reservoir, and calculate the predicted inflow flow increment based on the basin inflow forecast data;

[0082] The water level change rate is obtained by calculating the ratio of the water level difference in the corresponding period of the real-time water level data of the pumped storage reservoir to the time interval, and the predicted inflow flow increment is obtained by calculating the product of the basin water inflow forecast data and the pre-calibrated basin confluence coefficient.

[0083] 3) Carry out trend prediction modeling on the water level change rate and the predicted inflow flow increment to obtain the predicted value of water level rise rate.

[0084] The water level trend is determined by multiplying the sum of the water level change rate and the predicted inflow increment by the cross-sectional area of ​​the upper pumped-storage reservoir. The cross-sectional area of ​​the upper pumped-storage reservoir changes with the water level elevation. The cross-sectional area at the corresponding water level can be obtained by querying the water level-reservoir capacity curve.

[0085] Furthermore, after step 3), the method further includes the following steps:

[0086] 4) Based on the predicted value of the water level rise rate, the total storage capacity of the pumped storage upper reservoir and the current storage capacity of the pumped storage upper reservoir are differentially calculated to obtain the remaining storage capacity of the pumped storage upper reservoir;

[0087] If the product of the predicted value of the water level rise rate and the predicted duration plus the current water level exceeds the preset safe water level threshold, the total storage capacity of the pumped storage upper reservoir and the current storage capacity of the pumped storage upper reservoir are differentially calculated to obtain the remaining storage capacity of the pumped storage upper reservoir.

[0088] 5) Obtain the historical inflow flow sequence of the pumped storage upper reservoir, and perform feature extraction and peak calculation on the historical inflow flow sequence of the pumped storage upper reservoir to obtain the peak inflow flow of the pumped storage upper reservoir;

[0089] The historical inflow flow sequence of the pumped storage upper reservoir is obtained from the historical database, and the inflow flow sequence under the same season and rainfall conditions is extracted from the historical inflow flow sequence of the pumped storage upper reservoir, and the peak value of the historical inflow flow, that is, the peak value of the inflow flow of the pumped storage upper reservoir, is calculated.

[0090] 6) Calculate the remaining storage capacity of the pumped storage upper reservoir and the peak inflow rate of the pumped storage upper reservoir to obtain the estimated full reservoir time, and compare the estimated full reservoir time with the threshold to obtain the degree of storage capacity limitation;

[0091] The estimated full reservoir time is obtained by calculating the ratio of the remaining storage capacity of the pumped storage upper reservoir and the peak inflow flow of the pumped storage upper reservoir, and the degree of storage capacity limitation is determined by comparing the relationship between the estimated full reservoir time and the first time threshold and the second time threshold.

[0092] It should be noted that the settings of the first and second time thresholds are closely related to the operating characteristics of the pumped-storage unit. The first time threshold generally corresponds to the minimum time required for the pumped-storage unit to complete a start-stop cycle, while the second time threshold corresponds to the standard cycle of the pumped-storage unit under the daily regulation operation mode. When the estimated full-storage time is less than the first time threshold, it is determined to be severely restricted; when the estimated full-storage time is between the first and second time thresholds, it is determined to be moderately restricted; when the estimated full-storage time is greater than the second time threshold, it is determined to be slightly restricted.

[0093] 7) The basic acceptance power of pumped storage is calculated based on the remaining storage capacity of the upper reservoir of pumped storage, and the basic acceptance power of pumped storage is graded and reduced according to the degree of storage capacity limitation to obtain the upper limit of the pumped storage acceptance power.

[0094] The pumped storage storable energy is calculated by multiplying the remaining storage capacity of the upper reservoir and the elevation difference between the upper reservoir and the pumped storage unit, divided by the pumping conversion coefficient. The pumped storage storable energy is then divided by the standard pumping time to obtain the pumped storage base capacity. The elevation difference between the upper reservoir and the pumped storage unit is the head height, and its product with the remaining storage capacity represents the potential gravitational potential energy. The pumping conversion coefficient takes into account factors such as pump efficiency, pipeline losses, and motor efficiency. The standard pumping time is determined based on the peak-shaving needs of the power grid and the economic efficiency of the pumped storage unit. A shorter time will result in frequent starts and stops, while a longer time will reduce regulatory flexibility.

[0095] The upper limit of the pumped storage capacity is obtained by multiplying the base pumped storage capacity acceptance by the corresponding reduction factor based on the degree of storage capacity limitation. Specifically, when the capacity is severely limited, the upper limit of the pumped storage capacity acceptance is obtained by multiplying the base pumped storage capacity acceptance by the first reduction factor; when the capacity is moderately limited, the upper limit of the pumped storage capacity acceptance is obtained by multiplying the base pumped storage capacity acceptance by the second reduction factor; and when the capacity is slightly limited, the upper limit of the pumped storage capacity acceptance is obtained by multiplying the base pumped storage capacity acceptance by the third reduction factor.

[0096] It's important to note that the three different reduction factors reflect a gradient in the setting of safety margins. Under severe constraints, the smallest reduction factor is used to ensure reservoir safety, while under mild constraints, a larger reduction factor can be used to fully utilize pumped storage capacity. This tiered approach not only ensures reservoir flood control safety, but also maximizes the role of pumped storage in absorbing excess renewable energy, achieving a balance between safety and cost-effectiveness.

[0097] S3. Compare the predicted value of excess power of renewable energy with the upper limit of the power accepted by pumped storage, and adjust the power of renewable energy generation end based on the comparison result to obtain the actual output data of renewable energy;

[0098] Specifically, step S3 includes:

[0099] 1) Perform a differential calculation on the predicted excess power of renewable energy and the upper limit of the pumped storage power acceptance value to obtain the power reduction amount;

[0100] 2) If the power reduction amount is a positive number, the power reduction allocation is performed on the renewable energy power generation end based on the power reduction amount to obtain the wind power reduction power and solar waste heat power generation power reduction power;

[0101] If the power reduction amount is a positive number, it means that the excess power of renewable energy exceeds the acceptance capacity of pumped storage, and the active control mode of renewable energy needs to be activated. Specifically, according to the power reduction amount, the reduction task is allocated according to the proportional relationship between the current output of wind power and the current output of solar waste heat power generation, and the wind power reduction power and solar waste heat power generation power reduction power are obtained. For example, if the current output of wind power accounts for 70% of the total output of renewable energy and solar waste heat power generation accounts for 30% of the total output of renewable energy, then the power reduction amount is also allocated in a ratio of 7:3. This proportional allocation method ensures that each power generation unit assumes the corresponding regulation responsibility, avoids a certain unit from bearing too heavy a reduction task, and is conducive to maintaining the overall stability of the system.

[0102] 3) Based on the wind power curtailment, the pitch angle of the wind turbine at the renewable energy power generation end is adjusted to obtain the actual output data of the wind turbine;

[0103] The target pitch angle required to achieve wind power curtailment is determined by querying a pre-built table of pitch angle and power output relationships. The pitch angle change rate is calculated by dividing the difference between the current and target pitch angles by the preset adjustment time. The pitch angle is then adjusted according to the pitch angle change rate using pitch control commands, yielding actual wind turbine output data.

[0104] It should be noted that the data table of the corresponding relationship between pitch angle and power output is obtained through factory testing and on-site calibration of the wind turbine. In one embodiment, when the wind speed remains constant, the power output of the wind turbine shows a nonlinear downward trend during the process of increasing the pitch angle from 0 degrees to 90 degrees; when the pitch angle is in the range of 0 degrees to 15 degrees, the power changes relatively slowly; when the pitch angle is in the range of 15 degrees to 45 degrees, the power reduction rate is significantly accelerated; after exceeding 45 degrees, the power output is close to zero. This nonlinear relationship requires the control system to select an appropriate pitch angle adjustment range according to different power reduction requirements. The mechanical characteristics of the pitch mechanism determine that there is an upper limit to the pitch angle adjustment speed. Excessive adjustment may cause damage to mechanical components or a decrease in control accuracy. Therefore, the preset adjustment time is usually between 30 seconds and 120 seconds. This time must meet the rapid response requirements of the power grid dispatching and ensure the smoothness of the pitch action.

[0105] 4) Based on the power reduction of solar waste heat power generation, the cooling rate of the solar waste heat power generation system at the new energy power generation end is adjusted to obtain the actual output data of the solar waste heat power generation system.

[0106] The power regulation of a solar waste heat power generation system is achieved by changing the temperature of the heat storage medium. When the power generation of the solar waste heat power generation system needs to be reduced, the cooling rate of the heat storage medium is accelerated to reduce the temperature, thereby reducing the amount of steam generated and achieving power reduction, thereby obtaining the actual output data of the solar waste heat power generation system.

[0107] It should be noted that wind power can achieve power changes within minutes through pitch angle adjustment, while solar waste heat power generation has a relatively slow power adjustment due to its large thermal inertia. Therefore, it is necessary to advance or delay control instructions based on the dynamic characteristics of each system to ensure that the power reduction effects of the two systems are consistent in time, thereby achieving coordinated control.

[0108] S4. Calculate the supply-demand deviation value based on the actual output data of new energy sources, and perform threshold determination and response coefficient calculation on the supply-demand deviation value to obtain a demand-side regulation plan;

[0109] Specifically, step S4 includes:

[0110] 1) Perform differential calculation on the actual output data of renewable energy and the real-time total load demand to obtain the supply-demand deviation value;

[0111] 2) If the supply-demand deviation exceeds the preset power balance deviation threshold, the ratio of the supply-demand deviation to the real-time load adjustable capacity is calculated and represented as the response coefficient;

[0112] If the supply-demand deviation exceeds the preset power balance deviation threshold, it is determined that the renewable energy power reduction cannot meet the power balance requirement. The response coefficient is calculated by dividing the supply-demand deviation by the real-time load adjustable capacity. In this embodiment, the preset power balance deviation threshold is 2% to 5% of the total system load. This range ensures grid frequency stability while preventing frequent demand response triggers.

[0113] If the supply-demand deviation value does not exceed the preset power balance deviation threshold or is a negative value, it is determined that the new energy power reduction meets the power balance requirements, and the current wind turbine pitch angle setting value and solar waste heat power generation cooling rate setting value are kept unchanged, maintaining the current new energy output and load configuration status.

[0114] 3) Perform quantitative mapping of the response coefficient to obtain the corresponding demand-side regulation plan.

[0115] Based on the response level table, the response coefficient is quantitatively mapped to obtain the corresponding demand-side regulation plan. Specifically, in this embodiment, when the response coefficient is less than 0.3, the first-level response is activated, and only voluntary interruptible loads are mobilized; when the response coefficient is between 0.3 and 0.7, the second-level response is activated, and some industrial production loads are mobilized; when the response coefficient exceeds 0.7, the third-level response is activated, and mandatory load reduction is adopted.

[0116] S5. Serialize the actual output data of new energy and the demand-side regulation plan to obtain a time-based power data sequence, and perform dynamic programming optimization and constraint condition correction on the time-based power data sequence to obtain a multi-energy coordinated power regulation instruction sequence covering the entire time period.

[0117] Specifically, step S5 includes:

[0118] 1) Divide the actual output data of renewable energy and the demand-side regulation plan into preset time periods to obtain a time-based power data series;

[0119] The time-based power data sequence includes the actual output value of renewable energy and the demand-side load adjustment value for several time periods. In this embodiment, the preset time period is 15 minutes, and the actual output data of renewable energy and the demand-side adjustment plan are divided into several 15-minute time-based power data sequences.

[0120] 2) Dynamic programming is used to recursively calculate the time-segmented power data sequence to obtain the optimal power allocation sequence;

[0121] Dynamic programming aims to achieve cross-period energy balance, taking into account multi-dimensional factors such as renewable energy generation characteristics, pumped storage operation constraints, and the dynamic characteristics of the power grid. By constructing an optimization model that includes a state transition equation that defines the state transition logic and an objective function that quantifies the energy balance deviation, the power allocation for each period is optimized step by step. In the recursive process, the optimal power allocation solution of the previous period is used as the initial condition for the optimization of the next period, and the calculation is iterated in sequence to finally obtain the optimal power allocation sequence covering the entire period. Among them, the core of state transition lies in how the decision of the current stage affects the state of the next stage. For example, choosing more pumped storage in the current period will reduce the power surplus in that period, but it will increase the reservoir water level and affect the pumping capacity of the next period.

[0122] The optimal power allocation sequence includes the optimal actual output of renewable energy and the optimal demand-side load regulation value for each time period. This sequence clearly defines the optimal actual output of renewable energy in each time period, which is used to accurately control the power of renewable energy generation. It also determines the optimal demand-side load regulation value, guiding demand-side response coordination, and achieving efficient cross-time coordination and energy balance control between renewable energy and pumped storage.

[0123] 3) Efficiency compensation calculation is performed on the optimal power allocation sequence to obtain the actual power of the pumped storage. Based on the comparison result of the actual power of the pumped storage and the rated power of the pumped storage, the optimal power allocation sequence is subjected to power limit correction to obtain a corrected power allocation sequence.

[0124] For the recursively derived optimal power allocation sequence, the net power value is calculated as the difference between the optimal renewable energy actual output and the optimal demand-side load regulation value for each time period. The energy storage conversion loss coefficient is determined based on the pumping efficiency and power generation efficiency of the pumped storage unit. When the net power value is positive, the actual pumping power is obtained by dividing the net power value by the pumping efficiency. When the net power value is negative, the actual power generation is obtained by multiplying the net power value by the power generation efficiency. The actual pumping efficiency and actual power generation efficiency together constitute the actual power of the pumped storage unit.

[0125] By comparing the actual power of pumped storage with the rated power of pumped storage, if the actual power of pumped storage exceeds the rated power of pumped storage, the optimal actual output value of new energy and the optimal demand-side load regulation value in the current period are adjusted according to the ratio of rated power to actual demand power, and a corrected power allocation sequence that meets the power balance constraint of the pumped storage unit is obtained.

[0126] 4) Perform energy time series distribution on the modified power allocation sequence to obtain the target energy allocation sequence, and perform multi-energy control parameter decomposition on the target energy allocation sequence to obtain a multi-energy coordinated power adjustment instruction sequence covering the entire time period.

[0127] For the modified power allocation sequence, the target energy allocation sequence is generated by multiplying the power value of each time period with the corresponding adjustment duration, thereby realizing the quantitative allocation of energy across time periods.

[0128] Furthermore, the target energy allocation sequence is decomposed into multi-energy control parameters. Based on the operating characteristics of wind turbines, solar waste heat power generation systems, demand-side loads, and pumped storage, the total energy target is decoupled into specific adjustment parameters for each device. This ultimately forms a multi-energy coordinated power adjustment instruction sequence covering the entire time period. These multi-energy coordinated power adjustment instructions include wind turbine pitch angle adjustment instructions, solar waste heat power generation cooling rate adjustment instructions, demand-side load adjustment instructions, and pumped storage charging and discharging power adjustment instructions.

[0129] like Figure 2 As shown in FIG, it is another flow chart of a cross-period energy balance method for the coordination of new energy and pumped storage according to an embodiment of the present invention. Figure 2 The embodiment of the present invention provides a cross-period energy balance method for coordinated new energy and pumped storage, which, after step S5, further includes the following steps:

[0130] S6. Execute the multi-energy collaborative power regulation instruction sequence through the multi-energy collaborative system, and modify the multi-energy collaborative power regulation instruction sequence based on the dynamic operation performance index of the multi-energy collaborative system to obtain an updated multi-energy collaborative power regulation instruction sequence.

[0131] Specifically, step S6 includes:

[0132] 1) Execute multi-energy collaborative power regulation instruction sequences using standardized communication protocols through a multi-energy collaborative system;

[0133] Through the communication management module of the multi-energy collaborative system, a standardized communication protocol is used to perform protocol encapsulation and adaptation processing on the multi-energy collaborative power regulation instruction sequence covering the entire time period.

[0134] Specifically, based on the adjustment parameters of each device in the multi-energy collaborative power regulation instruction sequence, the data frame format of the corresponding protocol is encoded to generate a control instruction data packet that conforms to the device communication interface specification. Through the real-time communication network of the multi-energy collaborative system, the encapsulated control instruction data packet is sent to various terminal devices such as the wind turbine controller, solar waste heat power generation DCS system, demand-side load management terminal and pumped storage PCS device. After receiving the instruction, each terminal device decodes and executes the corresponding power regulation action through the protocol parsing module, and at the same time transmits the execution status and feedback data back to the multi-energy collaborative system according to the standardized protocol, forming a closed-loop control process of "instruction issuance-execution feedback" to ensure the accurate execution and real-time monitoring of the multi-energy collaborative power regulation instruction sequence.

[0135] 2) Perform differential calculation on the current grid frequency and the rated grid frequency to obtain the grid frequency deviation, and calculate the dispatch power deviation based on the real-time power data of the multi-energy coordinated system;

[0136] Grid frequency is a key indicator of the power generation and load balance. The grid frequency deviation is calculated by performing a differential operation on the current grid frequency and the rated grid frequency. Simultaneously, actual power data from each energy device is collected in real time. After data synchronization and timestamp alignment, the current measured power value is compared point by point with the target power value in the regulation instruction. A deviation calculation formula is used to generate a time series deviation sequence. High-frequency fluctuation noise is then smoothed using the Kalman filter algorithm. The final output is a dispatch power deviation that reflects the degree of deviation between the real-time operating status of the multi-energy coordinated system and the dispatch target.

[0137] 3) Based on the grid frequency deviation and dispatch power deviation, the multi-energy collaborative power regulation instruction sequence is corrected by model predictive control to obtain an updated multi-energy collaborative power regulation instruction sequence.

[0138] Using grid frequency deviation and dispatch power deviation as inputs for model predictive control, a predictive model is constructed that incorporates the dynamic constraints of the multi-energy collaborative system (renewable energy output fluctuation range, pumped storage power limit) and control objectives (frequency stability, deviation minimization). Utilizing a rolling optimization strategy, a multi-energy collaborative power regulation instruction sequence for a preset future time period is predicted and calculated within each control cycle. By solving a quadratic programming problem, the optimal control variable is obtained that accounts for the dynamic characteristics of the grid frequency and the correction of dispatch deviations. The original instruction sequence is then rolled over to generate an updated multi-energy collaborative power regulation instruction sequence that adapts to the real-time operating status of the grid.

[0139] The embodiment of the present invention provides a cross-time energy balance method for the coordination of new energy and pumped storage. By extracting features and dynamically modeling night-time data of wind power output and night-time data of solar waste heat power generation, the excess power of new energy can be predicted in advance. The upper limit of energy storage capacity is evaluated in combination with the water level of the pumped storage reservoir and the inflow forecast data, so as to realize the dynamic matching of the new energy output and the acceptance capacity of the pumped storage, and effectively reduce the power abandonment rate. At the same time, based on the demand-side response mechanism and dynamic planning optimization of the supply and demand deviation, a multi-energy coordinated power regulation instruction sequence covering the entire time period can be generated. On the basis of taking into account the active control of new energy and the load regulation on the user side, the accuracy of energy balance across time periods is effectively improved, and the adaptability of the power grid to the intermittent and fluctuating output of new energy is enhanced, thereby ultimately achieving a dual improvement in the operating efficiency and safety level of the power system.

[0140] like Figure 3 As shown in FIG, it is a structural diagram of a cross-period energy balance system for the coordination of new energy and pumped storage according to an embodiment of the present invention. Figure 3 The embodiment of the present invention provides a cross-period energy balance system for coordinated new energy and pumped storage, including:

[0141] New energy excess power prediction module 1 is used to obtain nighttime monitoring data of wind power output and solar waste heat power generation at the new energy power generation end, and perform feature extraction and dynamic modeling on the nighttime monitoring data of wind power output and solar waste heat power generation to obtain the predicted value of new energy excess power;

[0142] Pumped storage power acceptance determination module 2 is used to obtain real-time water level data of the pumped storage upper reservoir and water inflow forecast data of the basin, and perform water level trend analysis and energy storage capacity evaluation on the real-time water level data of the pumped storage upper reservoir and water inflow forecast data of the basin to obtain the upper limit of the pumped storage power acceptance;

[0143] The new energy actual output determination module 3 is used to compare the predicted value of the new energy excess power with the upper limit of the pumped storage power acceptance, and adjust the power of the new energy power generation end based on the comparison result to obtain the new energy actual output data;

[0144] The demand-side regulation scheme determination module 4 is used to calculate the supply-demand deviation value based on the actual output data of new energy, and perform threshold judgment and response coefficient calculation on the supply-demand deviation value to obtain the demand-side regulation scheme;

[0145] The power regulation instruction sequence determination module 5 is used to serialize the actual output data of new energy and the demand-side regulation plan to obtain a time-based power data sequence, and to perform dynamic planning optimization and constraint condition correction on the time-based power data sequence to obtain a multi-energy coordinated power regulation instruction sequence covering the entire time period.

[0146] It should be noted that each module in the above-mentioned cross-period energy balance system for the coordination of a new energy source and pumped storage can be fully or partially implemented by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of the cross-period energy balance system for the coordination of a new energy source and pumped storage, please refer to the definition of the cross-period energy balance method for the coordination of a new energy source and pumped storage above. The two have the same functions and effects and will not be repeated here.

[0147] In summary, the embodiment of the present invention provides a cross-time energy balance method and system for the coordination of new energy and pumped storage. By extracting features and dynamically modeling night-time data of wind power output and night-time data of solar waste heat power generation, the excess power of new energy can be predicted in advance. The upper limit of energy storage capacity is evaluated in combination with the water level of the pumped storage reservoir and the inflow forecast data, thereby achieving dynamic matching of new energy output and pumped storage acceptance capacity, and effectively reducing the power abandonment rate. At the same time, based on the demand-side response mechanism and dynamic planning optimization of supply and demand deviations, a multi-energy coordinated power regulation instruction sequence covering the entire time period can be generated. On the basis of taking into account the active control of new energy and the load regulation on the user side, the accuracy of energy balance across time periods is effectively improved, and the adaptability of the power grid to the intermittent and fluctuating output of new energy is enhanced, thereby ultimately achieving a dual improvement in the operating efficiency and safety level of the power system.

[0148] Each embodiment in this specification is described in a progressive manner, and the same or similar parts of each embodiment can be directly referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the various technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0149] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and substitutions can be made without departing from the technical principles of the present invention. These improvements and substitutions should also be regarded as the scope of protection of the present invention.

Claims

1. A cross-period energy balance method for the coordination of new energy and pumped storage, characterized in that: include: Obtaining nighttime monitoring data of wind power output and nighttime monitoring data of solar waste heat power generation at the renewable energy power generation end, and performing feature extraction and dynamic modeling on the nighttime monitoring data of wind power output and the nighttime monitoring data of solar waste heat power generation to obtain a predicted value of renewable energy excess power; Acquiring real-time water level data of the upper pumped storage reservoir and water inflow forecast data of the river basin, and performing water level trend analysis and energy storage capacity assessment on the real-time water level data of the upper pumped storage reservoir and the water inflow forecast data of the river basin to obtain an upper limit value of the pumped storage power acceptance; Comparing the predicted value of excess power of the renewable energy source with the upper limit of the power accepted by the pumped storage system, and adjusting the power of the renewable energy power generation end based on the comparison result to obtain actual output data of the renewable energy source; Calculating a supply-demand deviation value based on the actual output data of the new energy, and performing threshold judgment and response coefficient calculation on the supply-demand deviation value to obtain a demand-side regulation plan; Serializing the actual output data of the new energy and the demand-side regulation scheme to obtain a time-based power data sequence, and performing dynamic programming optimization and constraint condition correction on the time-based power data sequence to obtain a multi-energy collaborative power regulation instruction sequence covering the entire time period; The supply-demand deviation value is calculated based on the actual output data of the new energy, and a threshold value judgment and response coefficient calculation are performed on the supply-demand deviation value to obtain a demand-side adjustment plan, including: Performing a differential operation on the actual output data of the renewable energy and the real-time total load demand to obtain a supply-demand deviation value; If the supply-demand deviation value exceeds a preset power balance deviation threshold, calculating a ratio of the supply-demand deviation value to the real-time load adjustable capacity, and representing the ratio as a response coefficient; The response coefficient is mapped to a level quantitative level to obtain a corresponding demand-side regulation solution.

2. The cross-period energy balance method for coordinated new energy and pumped storage according to claim 1 is characterized in that: After serializing the actual output data of the new energy and the demand-side regulation scheme to obtain a time-based power data sequence, and performing dynamic programming optimization and constraint condition correction on the time-based power data sequence to obtain a multi-energy coordinated power regulation instruction sequence covering the entire time period, the method further includes: The multi-energy collaborative power regulation instruction sequence is executed by a multi-energy collaborative system, and the multi-energy collaborative power regulation instruction sequence is corrected based on the dynamic operation performance index of the multi-energy collaborative system to obtain an updated multi-energy collaborative power regulation instruction sequence.

3. The cross-period energy balance method for coordinated new energy and pumped storage according to claim 1 is characterized in that: The method of obtaining nighttime monitoring data of wind power output and nighttime monitoring data of solar waste heat power generation from a new energy power generation terminal, and performing feature extraction and dynamic modeling on the nighttime monitoring data of wind power output and the nighttime monitoring data of solar waste heat power generation to obtain a predicted value of excess power of new energy includes: Collect nighttime monitoring data on wind power output and solar waste heat power generation at renewable energy power generation terminals based on preset sampling intervals; Performing frequency domain transformation on the nighttime monitoring data of wind power output to obtain wind power power fluctuation main frequency, and performing trend extrapolation on the wind power power fluctuation main frequency to obtain wind power forecast power; Performing parameter identification on the nighttime monitoring data of the solar waste heat power generation to obtain dynamic characteristic parameters of the solar waste heat power generation, and constructing a transfer function on the dynamic characteristic parameters of the solar waste heat power generation to obtain a dynamic response model of the solar waste heat power generation; The solar waste heat power generation dynamic response model is discretized and predicted to obtain the solar waste heat power generation predicted power, and the solar waste heat power generation predicted power and the wind power predicted power are weightedly fused and surplus calculated to obtain the new energy surplus power predicted value.

4. The cross-period energy balance method for coordinated new energy and pumped storage according to claim 1 is characterized in that: The obtaining of real-time water level data of the upper pumped storage reservoir and water inflow forecast data of the river basin, and performing water level trend analysis and energy storage capacity evaluation on the real-time water level data of the upper pumped storage reservoir and the water inflow forecast data of the river basin to obtain the upper limit of the pumped storage power acceptance includes: Deploy sensors to collect real-time water level data from pumped storage reservoirs and connect to watershed inflow forecast data from meteorological monitoring systems; Calculating the water level change rate based on the real-time water level data of the pumped storage upper reservoir, and calculating the predicted inflow flow increment based on the basin water inflow forecast data; The water level change rate and the predicted inflow flow increment are subjected to trend prediction modeling to obtain a water level rise rate prediction value.

5. The cross-period energy balance method for coordinated new energy and pumped storage according to claim 4 is characterized in that: After performing trend prediction modeling on the water level change rate and the predicted inflow flow increment to obtain a water level rise rate prediction value, the method further includes: Based on the water level rise rate prediction value, a difference operation is performed on the total storage capacity of the pumped storage upper reservoir and the current storage capacity of the pumped storage upper reservoir to obtain the remaining storage capacity of the pumped storage upper reservoir; Obtaining a historical inflow flow sequence of a pumped storage upper reservoir, and performing feature extraction and peak value calculation on the historical inflow flow sequence of the pumped storage upper reservoir to obtain a peak value of the inflow flow of the pumped storage upper reservoir; Performing calculations on the remaining storage capacity of the pumped storage upper reservoir and the peak inflow rate of the pumped storage upper reservoir to obtain an estimated full reservoir time, and performing a threshold comparison on the estimated full reservoir time to obtain a degree of energy storage capacity limitation; The pumped storage basic acceptance power is calculated based on the remaining storage capacity of the pumped storage upper reservoir, and the pumped storage basic acceptance power is graded and reduced according to the degree of limitation of the energy storage capacity to obtain the upper limit value of the pumped storage acceptance power.

6. The cross-period energy balance method for coordinated new energy and pumped storage according to claim 1 is characterized in that: The comparing the predicted value of excess power of the renewable energy source with the upper limit of the power accepted by the pumped storage unit, and adjusting the power of the renewable energy power generation end based on the comparison result to obtain the actual output data of the renewable energy source, includes: Performing a differential operation on the predicted value of excess power of renewable energy and the upper limit of the pumped storage power acceptance to obtain a power reduction amount; If the power reduction amount is a positive number, power reduction allocation is performed on the new energy power generation end based on the power reduction amount to obtain wind power reduction power and solar waste heat power generation reduction power; Based on the wind power curtailment power, the pitch angle of the wind turbine at the renewable energy power generation end is adjusted to obtain actual output data of the wind turbine; Based on the power reduction of the solar waste heat power generation, the cooling rate of the solar waste heat power generation system at the new energy power generation end is adjusted to obtain actual output data of the solar waste heat power generation system.

7. The cross-period energy balance method for coordinated new energy and pumped storage according to claim 1 is characterized in that: The actual output data of the new energy and the demand-side regulation scheme are serialized to obtain a time-based power data sequence, and the time-based power data sequence is dynamically optimized and constraint conditions are modified to obtain a multi-energy collaborative power regulation instruction sequence covering the entire time period, including: Dividing the actual output data of the new energy and the demand-side regulation scheme according to preset time periods to obtain a periodized power data sequence, wherein the periodized power data sequence includes the actual output values ​​of the new energy and the demand-side load regulation values ​​of several time periods; Dynamic programming is used to recursively calculate the periodized power data sequence to obtain an optimal power allocation sequence, wherein the optimal power allocation sequence includes the optimal actual output value of new energy and the optimal demand-side load regulation value for each period; performing efficiency compensation calculation on the optimal power allocation sequence to obtain the actual power of the pumped storage, and performing power limiting correction on the optimal power allocation sequence based on a comparison result of the actual power of the pumped storage and the rated power of the pumped storage to obtain a corrected power allocation sequence; Energy time series distribution is performed on the modified power distribution sequence to obtain a target energy distribution sequence, and multi-energy control parameter decomposition is performed on the target energy distribution sequence to obtain a multi-energy coordinated power adjustment instruction sequence covering the entire time period.

8. The cross-period energy balance method for coordinated new energy and pumped storage according to claim 2 is characterized in that: The method of executing the multi-energy collaborative power regulation instruction sequence through the multi-energy collaborative system and correcting the multi-energy collaborative power regulation instruction sequence based on the dynamic operation performance index of the multi-energy collaborative system to obtain an updated multi-energy collaborative power regulation instruction sequence includes: Executing the multi-energy collaborative power regulation instruction sequence through a multi-energy collaborative system using a standardized communication protocol; performing a differential operation on the current grid frequency and the rated grid frequency to obtain a grid frequency deviation, and calculating a dispatch power deviation based on the real-time power data of the multi-energy collaborative system; Based on the grid frequency deviation and the dispatch power deviation, the multi-energy collaborative power regulation instruction sequence is corrected by model predictive control to obtain an updated multi-energy collaborative power regulation instruction sequence.

9. A cross-period energy balance system that combines new energy and pumped storage, characterized in that: include: A new energy excess power prediction module is used to obtain nighttime monitoring data of wind power output and solar waste heat power generation from the new energy power generation end, and perform feature extraction and dynamic modeling on the wind power output nighttime monitoring data and the solar waste heat power generation nighttime monitoring data to obtain a new energy excess power prediction value; A pumped storage power acceptance determination module is used to obtain real-time water level data of the pumped storage upper reservoir and water inflow forecast data of the basin, and perform water level trend analysis and energy storage capacity evaluation on the real-time water level data of the pumped storage upper reservoir and the water inflow forecast data of the basin to obtain an upper limit value of the pumped storage power acceptance; A new energy actual output determination module is used to compare the predicted value of the new energy excess power with the upper limit of the pumped storage power acceptance, and adjust the power of the new energy power generation end based on the comparison result to obtain the new energy actual output data; a demand-side regulation scheme determination module, configured to calculate a supply-demand deviation value based on the actual output data of the new energy source, and perform threshold determination and response coefficient calculation on the supply-demand deviation value to obtain a demand-side regulation scheme; A power regulation instruction sequence determination module is used to serialize the actual output data of the new energy and the demand-side regulation scheme to obtain a time-based power data sequence, and to perform dynamic programming optimization and constraint condition correction on the time-based power data sequence to obtain a multi-energy coordinated power regulation instruction sequence covering the entire time period; The supply-demand deviation value is calculated based on the actual output data of the new energy, and a threshold value judgment and response coefficient calculation are performed on the supply-demand deviation value to obtain a demand-side adjustment plan, including: Performing a differential operation on the actual output data of the renewable energy and the real-time total load demand to obtain a supply-demand deviation value; If the supply-demand deviation value exceeds a preset power balance deviation threshold, calculating a ratio of the supply-demand deviation value to the real-time load adjustable capacity, and representing the ratio as a response coefficient; The response coefficient is mapped to a level quantitative level to obtain a corresponding demand-side regulation solution.

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