Wind, light, water and storage coordinated comprehensive energy system control method and device

Abstract

This invention provides a control method and apparatus for a wind-solar-hydro-storage integrated energy system, comprising: acquiring real-time meteorological conditions, historical time-series operational data of wind and solar turbines, and real-time output feedback data of wind and solar turbines during operation; performing spatiotemporal correlation on the real-time meteorological conditions and historical time-series operational data to determine initial predicted time-series data for wind and solar power generation; using real-time output feedback data to correct errors in the initial predicted time-series data for wind and solar power generation to determine target predicted time-series data for wind and solar power generation; determining the initial output time-series data for wind and solar power generation corresponding to the target predicted time-series data; performing closed-loop feedback control by combining the real-time output feedback data and the initial output time-series data for wind and solar power generation to determine the target output time-series data for wind and solar power generation; and controlling the wind and solar turbines to operate according to the target output time-series data for wind and solar power generation. This invention can enhance the renewable energy absorption capacity and improve system stability.

Description

Technical Field

[0001] This invention relates to the field of energy control technology, and in particular to a control method for an integrated energy system that combines wind, solar, hydro, and storage, a control device for an integrated energy system that combines wind, solar, hydro, and storage, an electronic device, and a computer-readable storage medium. Background Technology

[0002] In technologies related to the coordinated energy control of wind, solar, hydro, and energy storage, static thresholds or fixed strategies are typically used to schedule hydropower and energy storage resources to compensate for fluctuations in wind and solar power. However, wind and solar power forecasting often relies on a single meteorological model without deep coupling of real-time output feedback, leading to accumulated forecast errors, delayed hydropower regulation, and frequent ineffective energy storage actions. The open-loop separation between forecasting and control, coupled with the lack of a multi-source coordination mechanism, results in limited wind and solar power absorption capacity and the spread of hidden faults, leading to insufficient system stability. Summary of the Invention

[0003] In view of the above problems, embodiments of the present invention are proposed to provide a control method for a wind-solar-hydro-storage integrated energy system that overcomes or at least partially solves the above problems, a control device for a wind-solar-hydro-storage integrated energy system, an electronic device, and a computer-readable storage medium.

[0004] To address the aforementioned problems, in a first aspect of this invention, an embodiment discloses a control method for a wind-solar-hydro-storage integrated energy system, wherein the integrated energy system includes wind and solar turbines, and the method includes: During operation, real-time weather conditions, historical time-series operation data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines are acquired. The real-time meteorological status and the historical time-series operational data are spatiotemporally correlated to determine the initial prediction time-series data for wind and solar power generation; The real-time output feedback data is used to correct the error in the initial prediction time series data of wind and solar power generation, and the target prediction time series data of wind and solar power generation is determined. Determine the initial output time series data of wind and solar power generation corresponding to the target prediction time series data of wind and solar power generation; By combining the real-time output feedback data and the initial output timing data of wind and solar power generation, closed-loop feedback control is performed to determine the target output timing data of wind and solar power generation. The wind and solar turbines are controlled to operate according to the wind and solar power generation target output timing data.

[0005] Optionally, the integrated energy system combining wind, solar, hydro, and storage further includes a hydro-storage unit. After the step of controlling the wind and solar turbines to operate according to the wind and solar power generation target output time-series data, the method further includes: Obtain the wind and solar output data of the wind and solar turbine units; Determine the first power deviation data between the target output time-series data of wind and solar power generation and the wind and solar output data; The water storage unit is controlled on multiple time scales based on the power deviation data.

[0006] Optionally, after the step of performing multi-timescale control of the water storage unit based on the power deviation data, the method further includes: Obtain the water storage output data of the water storage unit; The system output data is determined by combining the water storage output data and the wind and solar power output data; Determine the second power deviation data between the system output data and the target output time-series data of wind and solar power generation; Fault mapping is performed based on the second power deviation data to determine operational diagnostic data; Output the aforementioned operational diagnostic data.

[0007] Optionally, the method further includes: Analyze the operational diagnostic data to determine the fault compensation parameters; The fault compensation parameters are used to correct the initial prediction time series data and the initial output time series data of wind and solar power generation.

[0008] Optionally, the step of using the real-time output feedback data to correct errors in the initial prediction time series data of wind and solar power generation and determining the target prediction time series data of wind and solar power generation includes: Determine the error matrix between the real-time output feedback data and the initial prediction time series data of wind and solar power generation; Backpropagation is performed using the error matrix to correct the initial prediction time series data for wind and solar power generation and determine the target prediction time series data for wind and solar power generation.

[0009] Optionally, the step of combining the real-time output feedback data and the initial output timing data of wind and solar power generation to perform closed-loop feedback control and determine the target output timing data of wind and solar power generation includes: Determine the instantaneous deviation data between the initial output timing data of the wind and solar power generation and the real-time output feedback data; Determine the control parameters corresponding to the instantaneous deviation data; The target output timing data of wind and solar power generation is determined by using the control parameters, the initial output timing data of wind and solar power generation, and the real-time output feedback data for proportional-integral-derivative control.

[0010] Optionally, the step of performing multi-time-scale control of the water storage unit based on the power deviation data includes: In the event of high-frequency energy fluctuations in the power deviation data, the water storage unit is charged and discharged based on the power deviation data. When the power deviation data shows low-frequency energy fluctuations, gradient output control is applied to the water storage unit.

[0011] Optionally, the step of controlling the charging and discharging of the water storage unit based on the power deviation data when there are high-frequency energy fluctuations in the power deviation data includes: When the power deviation data exhibits high-frequency energy fluctuations and the power deviation data is a positive deviation, the energy storage unit is controlled to store energy. If the power deviation data exhibits high-frequency energy fluctuations and is negative, the water storage unit is controlled to discharge.

[0012] In a second aspect, embodiments of the present invention disclose a control device for a wind-solar-hydro-storage integrated energy system, wherein the integrated energy system includes wind and solar turbines, and the device includes: The acquisition module is used to acquire real-time weather conditions, historical time-series operation data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines during operation. The spatiotemporal correlation module is used to perform spatiotemporal correlation between the real-time meteorological status and the historical time-series operational data to determine the initial prediction time-series data for wind and solar power generation. The first correction module is used to correct the error of the initial prediction time series data of wind and solar power generation using the real-time output feedback data, and to determine the target prediction time series data of wind and solar power generation. The wind and solar forecasting module is used to determine the initial output time series data of wind and solar power generation corresponding to the wind and solar power generation target forecast time series data; The wind and solar output module is used to combine the real-time output feedback data and the initial output timing data of wind and solar power generation to perform closed-loop feedback control and determine the target output timing data of wind and solar power generation. The first control module is used to control the wind and solar turbines to operate according to the timing data output by the wind and solar power generation target.

[0013] In a third aspect of the present invention, an electronic device is disclosed, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When the computer program is executed by the processor, it implements the steps of the integrated energy system control method for wind, solar, hydro, and storage synergy as described above.

[0014] In a fourth aspect, embodiments of the present invention disclose a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the integrated energy system control method for wind, solar, hydro, and storage synergy as described above.

[0015] The embodiments of the present invention have the following advantages: This invention, in its embodiments, acquires real-time weather conditions, historical time-series operational data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines during operation; performs spatiotemporal correlation on the real-time weather conditions and the historical time-series operational data to determine initial prediction time-series data for wind and solar power generation; uses the real-time output feedback data to correct errors in the initial prediction time-series data for wind and solar power generation to determine target prediction time-series data for wind and solar power generation; determines the initial output time-series data for wind and solar power generation corresponding to the target prediction time-series data; combines the real-time output feedback data and the initial output time-series data for wind and solar power generation to perform closed-loop feedback control to determine the target output time-series data for wind and solar power generation; and controls the wind and solar turbines to generate wind and solar power according to the wind and solar power generation schedule. The target output time-series data operation, through rolling correction of wind and solar power prediction based on environmental data and real-time output feedback data from wind and solar turbines, determines the target prediction time-series data for wind and solar power generation, providing a high-precision input source for coordinated control; through wind and solar complementary linkage, the initial output time-series data for wind and solar power generation is determined, and then combined with real-time output feedback data for closed-loop feedback control, which can achieve minute-level power fluctuation smoothing, effectively enhance the renewable energy absorption capacity, and significantly reduce wind and solar curtailment; significantly improve the system's stability in response to power fluctuations, and quickly smooth out the fluctuation risks caused by sudden changes in wind and solar output and load changes; under the premise of ensuring reliable energy supply, it achieves the coordinated improvement goals of maximizing fluctuation absorption, minimizing fault losses, and optimizing economic operation. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of an embodiment of the integrated energy system control method for wind, solar, hydro, and storage synergy according to the present invention. Figure 2 This is a flowchart illustrating the steps of another embodiment of the integrated energy system control method for wind, solar, hydro, and storage synergy of the present invention. Figure 3 This is a flowchart illustrating the steps of a control system example for a wind-solar-hydro-storage integrated energy system according to the present invention; Figure 4 This is a flowchart illustrating the steps of a control method for a wind-solar-hydro-storage integrated energy system according to the present invention. Figure 5 This is a structural block diagram of an embodiment of a wind-solar-hydro-storage integrated energy system control device of the present invention; Figure 6This is a structural block diagram of an electronic device provided in an embodiment of the present invention; Figure 7 This is a structural block diagram of a storage medium provided in an embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] In the coordinated energy control of wind, solar, hydro, and energy storage, static thresholds or fixed strategies are typically used to schedule hydropower and energy storage resources to compensate for fluctuations in wind and solar power. Wind and solar power prediction often relies on a single meteorological model and lacks deep coupling with real-time output feedback, leading to accumulated prediction errors, resulting in lag in hydropower regulation and frequent ineffective actions by energy storage. Simultaneously, the control logic of each energy subsystem is independent, lacking a dynamic coordination mechanism across multiple time scales, making it difficult to adapt to sudden changes in wind and solar power output and load fluctuations, easily leading to wind and solar curtailment or insufficient reserve capacity. Furthermore, fault diagnosis relies on direct monitoring by physical sensors, lacking the ability to indirectly identify gradual power anomalies caused by equipment performance degradation (such as aging photovoltaic panels and decreased efficiency in wind turbine drivetrains) based on energy deviations. This results in latent faults failing to provide early warning, ultimately leading to decreased system operational economy and increased reliability risks.

[0019] In summary, existing technologies suffer from limitations in wind and solar energy absorption capacity and the spread of latent faults due to the open-loop separation of prediction and control and the lack of multi-source collaborative mechanisms. Overcoming the operational bottlenecks caused by insufficient prediction accuracy, lack of multi-source dynamic coordination, and delayed latent fault identification in current wind-solar-hydro-storage coordinated control systems, and achieving efficient absorption of wind and solar fluctuations while ensuring system stability and economy, is a pressing technical problem in this field. This invention constructs a prediction-control-diagnosis closed-loop linkage mechanism to achieve synergistic unity between multi-energy complementary dynamic optimization and proactive equipment health protection.

[0020] Reference Figure 1 This document illustrates a flowchart of an embodiment of a wind-solar-hydro-storage integrated energy system control method according to the present invention. The integrated energy system includes wind and solar turbines. These turbines utilize wind energy to drive the rotation of turbine blades, and a generator converts mechanical energy into electrical energy, thus generating wind power. Solar energy is directly converted into electrical energy through photovoltaic panels. The output of wind and solar turbines is naturally complementary, smoothing the overall power generation curve and reducing dependence on a single energy source. The specific steps of the wind-solar-hydro-storage integrated energy system control method include: Step 101: During operation, acquire real-time weather conditions, historical time-series operation data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines; During the operation of the integrated energy system combining wind, solar, hydro, and storage, real-time weather conditions, historical time-series operational data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines can be acquired. Real-time weather conditions characterize the environmental conditions of the deployment site of the integrated energy system combining wind, solar, hydro, and storage. Real-time weather conditions include, but are not limited to, irradiance, wind speed, and temperature. Historical time-series operational data of the wind and solar turbines characterizes the correlation between the input and output states of the wind and solar turbines over a certain period of time. The length of the historical timeframe can be determined according to actual needs, and this embodiment of the invention does not impose a limitation. Real-time output feedback data of the wind and solar turbines characterizes the current output state of the wind and solar turbines.

[0021] Step 102: Perform spatiotemporal correlation between the real-time meteorological status and the historical time-series operational data to determine the initial prediction time-series data for wind and solar power generation; It can identify the spatiotemporal correlation between real-time meteorological conditions and historical time-series operational data, perform time-series predictions on these data, and determine the initial prediction time-series data for wind and solar power generation. This initial prediction time-series data characterizes the basic control objectives for the combined output of wind and solar turbines over a certain period in the future. The prediction timeframe can be determined based on actual needs, and this embodiment of the invention does not impose any limitations.

[0022] Step 103: Use the real-time output feedback data to correct the error in the initial prediction time series data of wind and solar power generation, and determine the target prediction time series data of wind and solar power generation. The real-time output feedback data can be compared with the corresponding time series data in the initial prediction time series data of wind and solar power generation to determine the error situation, and then the error correction can be performed on the initial prediction time series data of wind and solar power generation to obtain the target prediction time series data of wind and solar power generation.

[0023] Step 104: Determine the initial output time series data of wind and solar power generation corresponding to the wind and solar power generation target prediction time series data; Based on the predicted time-series data of solar power generation targets, the spatiotemporal complementarity of wind and solar power during the power generation process can be dynamically coordinated to determine the states that wind and solar turbines need to reach to achieve the predicted time-series data of wind and solar power generation targets. Initial output time-series data of wind and solar power generation is generated based on these states. This initial output time-series data characterizes the basic actions of the joint output of wind and solar turbines over a certain period of time in the future. The predicted time length can be determined according to actual needs, and this embodiment of the invention does not impose a limitation.

[0024] Step 105: Combine the real-time output feedback data and the initial output timing data of wind and solar power generation to perform closed-loop feedback control and determine the target output timing data of wind and solar power generation. Using the initial output timing data of wind and solar power generation as the control basis, and acquiring the current real-time output feedback data as the feedback input, closed-loop feedback control is performed to determine the target output timing data of wind and solar power generation. This target output timing data characterizes the target action of the combined output of the wind and solar turbines over a certain period of time in the future.

[0025] Step 106: Control the wind and solar turbine units to operate according to the wind and solar power generation target output timing data.

[0026] The target output timing data of wind and solar power generation is used as the control action. The wind and solar turbines are controlled to perform the corresponding actions in sequence according to the timing data of the target output timing data of wind and solar power generation to manage energy.

[0027] This invention, in its embodiments, acquires real-time weather conditions, historical time-series operational data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines during operation; performs spatiotemporal correlation on the real-time weather conditions and the historical time-series operational data to determine initial prediction time-series data for wind and solar power generation; uses the real-time output feedback data to correct errors in the initial prediction time-series data for wind and solar power generation to determine target prediction time-series data for wind and solar power generation; determines the initial output time-series data for wind and solar power generation corresponding to the target prediction time-series data; combines the real-time output feedback data and the initial output time-series data for wind and solar power generation to perform closed-loop feedback control to determine the target output time-series data for wind and solar power generation; and controls the wind and solar turbines to generate wind and solar power according to the wind and solar power generation schedule. The target output time-series data operation, through rolling correction of wind and solar power prediction based on environmental data and real-time output feedback data from wind and solar turbines, determines the target prediction time-series data for wind and solar power generation, providing a high-precision input source for coordinated control; through wind and solar complementary linkage, the initial output time-series data for wind and solar power generation is determined, and then combined with real-time output feedback data for closed-loop feedback control, which can achieve minute-level power fluctuation smoothing, effectively enhance the renewable energy absorption capacity, and significantly reduce wind and solar curtailment; significantly improve the system's stability in response to power fluctuations, and quickly smooth out the fluctuation risks caused by sudden changes in wind and solar output and load changes; under the premise of ensuring reliable energy supply, it achieves the coordinated improvement goals of maximizing fluctuation absorption, minimizing fault losses, and optimizing economic operation.

[0028] Reference Figure 2 This diagram illustrates a flowchart of another embodiment of the integrated energy system control method for wind-solar-hydro-storage synergy according to the present invention. The integrated energy system includes wind and solar turbines and hydro-storage units. The wind and solar turbines can generate wind power and photovoltaic power. The hydro-storage units can generate hydropower or store electrical energy through energy storage components such as batteries. The specific steps of the integrated energy system control method for wind-solar-hydro-storage synergy include: Step 201: During operation, acquire real-time weather conditions, historical time-series operation data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines; During the operation of an integrated energy system combining wind, solar, hydro, and storage, real-time meteorological conditions such as irradiance, wind speed, and temperature can be acquired, along with historical time-series operating data of wind and solar turbines and their real-time output feedback data. For example, a distributed meteorological sensor cluster can collect real-time meteorological conditions such as irradiance, wind speed, and temperature, while simultaneously retrieving historical time-series operating data from the historical output database of wind and solar turbines and reading real-time output feedback data from the turbines through sensors deployed at the output end.

[0029] Step 202: Perform spatiotemporal correlation between the real-time meteorological status and the historical time-series operational data to determine the initial prediction time-series data for wind and solar power generation; Real-time weather conditions and historical time-series operational data can be input into a time-series prediction model to capture the spatiotemporal correlation characteristics between meteorological elements in real-time weather conditions and wind and solar power generation, generating initial prediction time-series data for wind and solar power generation. The time-series prediction model can employ an LSTM or Transformer architecture, generating data by using deep learning to identify the spatiotemporal correlation characteristics between meteorological elements and power generation. For example, real-time weather conditions and historical time-series operational data can be used as input data. After processing such as anomaly filtering and normalization, the data is then input into the time-series prediction model. Based on the spatiotemporal correlation characteristics between meteorological elements and power generation, the time-series prediction model determines the wind and solar power generation for the next few hours as the initial prediction time-series data for wind and solar power generation.

[0030] Step 203: Use the real-time output feedback data to correct the error in the initial prediction time series data of wind and solar power generation, and determine the target prediction time series data of wind and solar power generation. The real-time output feedback data is then compared with the initial prediction time-series data for wind and solar power generation to determine the error at the corresponding time series. This error is then used to correct the corresponding initial prediction time-series data for wind and solar power generation by rolling over the time series, thus determining the target prediction time-series data for wind and solar power generation. This target prediction time-series data for wind and solar power generation provides an accurate reference input for coordinated control.

[0031] In an optional embodiment of this application, the step of using the real-time output feedback data to correct errors in the initial prediction time series data of wind and solar power generation and determining the target prediction time series data of wind and solar power generation includes: Sub-step S2031: Determine the error matrix between the real-time output feedback data and the initial prediction time series data of wind and solar power generation; Based on the same time series, data from the initial forecast time series data for wind and solar power generation can be compared one by one with the real-time output feedback data to calculate the error value and determine the error matrix. For example, the real-time power monitoring device at the grid connection point of the wind and solar power plant continuously uploads the actual output data stream of the wind turbine generators, i.e., the real-time output feedback data. Then, every 15 minutes, the latest real-time output feedback data is compared with the predicted value of the corresponding time period in the initial forecast time series data for wind and solar power generation to calculate the error matrix.

[0032] Sub-step S2032 involves backpropagation using the error matrix to correct the initial prediction time series data for wind and solar power generation and determine the target prediction time series data for wind and solar power generation.

[0033] Backpropagation using the error matrix updates the spatiotemporal correlation between meteorological elements and power generation, thereby correcting the original output of initial wind and solar power generation forecast time series data and determining the target forecast time series data for wind and solar power generation. For example, when using a time series prediction model to predict the initial forecast time series data for wind and solar power generation, after obtaining the error matrix, the error matrix can be backpropagated and fed back into the time series prediction model in real time. This drives the time series prediction model to update the hidden layer weight parameters online, thereby correcting the original output of the initial wind and solar power generation forecast time series data and determining the target forecast time series data for wind and solar power generation. Dynamic correction through rolling learning can correct prediction offsets caused by local meteorological changes or equipment state drift, ultimately outputting error-compensated target forecast time series data for wind and solar power generation, providing a reliable benchmark for subsequent coordinated control.

[0034] Step 204: Determine the initial output time series data of wind and solar power generation corresponding to the wind and solar power generation target prediction time series data; Based on the target forecast time-series data for wind and solar power generation, and considering the time-series characteristics of wind and solar power generation (such as wind power compensating for solar troughs at night), adjustments can be made to wind and solar power generation to determine the initial output time-series data for achieving the corresponding target state. Specifically, the time-series characteristics of wind and solar power generation can be analyzed, and a complementary adjustment coefficient can be dynamically calculated. This coefficient comprehensively considers factors such as the anti-peak-shaving characteristics of wind power resources during periods of intraday solar irradiance decay and the spatiotemporal misalignment of output caused by regional micro-meteorological differences. The complementary adjustment coefficient is used to adjust the output of wind and solar power generation. Initial output time-series data for wind and solar power generation is generated when wind power compensates for solar troughs or solar power smooths out wind power fluctuations. This initial output time-series data serves as the basic control target for combined wind and solar power output.

[0035] Step 205: Combine the real-time output feedback data and the initial output timing data of wind and solar power generation to perform closed-loop feedback control and determine the target output timing data of wind and solar power generation. After obtaining the initial output timing data of wind and solar power generation, the deviation between the real-time output feedback data and the initial output timing data of wind and solar power generation can be used as a control reference factor to carry out closed-loop feedback control, such as PID (proportional integral derivative) control, and determine the target output timing data of wind and solar power generation.

[0036] In an optional embodiment of the present invention, the step of performing closed-loop feedback control by combining the real-time output feedback data and the initial output timing data of wind and solar power generation to determine the target output timing data of wind and solar power generation includes: Sub-step S2051: Determine the instantaneous deviation data between the initial output timing data of the wind and solar power generation and the real-time output feedback data; First, the deviation between the real-time output feedback data and the corresponding time-series parameters in the initial output time-series data of wind and solar power generation can be calculated. This deviation is the instantaneous deviation data.

[0037] For example, in the real-time operation of a wind-solar-hydro-storage integrated energy system, the power acquisition device at the grid connection point of the wind and solar power station uploads real-time output feedback data corresponding to the actual output at a frequency of minutes. The deviation is calculated with the predicted data at the corresponding time moment of the initial output time series data of wind and solar power generation, and the instantaneous power deviation is determined as the instantaneous deviation data.

[0038] Sub-step S2052: Determine the control parameters corresponding to the instantaneous deviation data; Control parameters for closed-loop feedback control can be determined based on the magnitude of instantaneous deviation data. For example, when the closed-loop feedback control is PID control, the proportional, integral, and derivative statistic strengths can be determined based on the magnitude of the instantaneous deviation data. In practical applications, instantaneous deviation data and its rate of change can be input into a fuzzy inference engine. The fuzzy inference engine has a built-in tuning rule library based on expert experience (e.g., when the instantaneous deviation data is large and increases rapidly, the proportional gain of the controller is significantly enhanced; when the instantaneous deviation data is consistently small and stable, the integral gain is reduced to avoid overshoot). Based on the input instantaneous deviation data, the engine dynamically outputs PID parameter adjustments according to the corresponding rules, optimizing the controller's response characteristics in real time.

[0039] Sub-step S2053: Using the control parameters, the initial output timing data of the wind and solar power generation, and the real-time output feedback data, proportional-integral-derivative control is performed to determine the target output timing data of the wind and solar power generation.

[0040] Then, the control parameters are used as control factors for proportional-integral-derivative (PID) control. The initial output time series data of wind and solar power generation is used as input, and the real-time output feedback data is used as feedback to perform PLD control to determine the target output time series data of wind and solar power generation.

[0041] Step 206: Control the wind and solar turbine units to operate according to the wind and solar power generation target output timing data; The system controls wind and solar turbines to operate according to the target output timing data for wind and solar power generation, dynamically adjusting the turbine output to smooth out fluctuations. For example, a PID controller with tuned parameters can be immediately applied to the power regulation unit of the wind and solar turbines. For wind power clusters, pitch and torque are coordinated to adjust the rotor to capture kinetic energy based on the pitch in the target output timing data for wind and solar power generation; for photovoltaic arrays, the maximum power point tracking trajectory is changed through inverter DC voltage modulation.

[0042] Step 207: Obtain the wind and solar output data of the wind and solar turbine units; After the wind and solar turbines are controlled to output according to the target output timing data for wind and solar power generation, the wind and solar output data of the turbines can be acquired in real time. The wind and solar output data can be used to characterize the output of the wind and solar turbines after control.

[0043] Step 208: Determine the first power deviation data between the target output time-series data of wind and solar power generation and the output data of wind and solar power generation; Based on the same time series, the deviation value between the target output time series data of wind and solar power generation and the wind and solar output data can be determined, namely the first power deviation data.

[0044] Step 209: Perform multi-time-scale control on the water storage unit based on the power deviation data; The magnitude of the first power deviation data can be used to control the hydro-storage unit for multi-time-scale control, and the output of the wind and solar units can be compensated from different time scales to meet the needs of the integrated energy system of wind, solar, hydro and storage, and optimize the resource scheduling efficiency of wind, solar, hydro and storage coordinated control.

[0045] In an optional embodiment of this application, the step of performing multi-timescale control of the water storage unit based on the power deviation data includes: Sub-step S2091: In the case of high-frequency energy fluctuations in the power deviation data, charge and discharge control of the water storage unit is performed based on the power deviation data; When high-frequency energy fluctuations exist in the power deviation data, specifically those on the order of seconds to minutes, an adaptive droop control strategy based on the power deviation data can dynamically generate charging and discharging commands to control the charging and discharging of the hydroelectric storage unit. This allows for rapid charging and discharging actions to address high-frequency power fluctuations on the order of seconds to minutes, achieving instantaneous power balance.

[0046] Specifically, the step of controlling the charging and discharging of the water storage unit based on the power deviation data when there are high-frequency energy fluctuations in the power deviation data includes: controlling the water storage unit to store energy when there are high-frequency energy fluctuations in the power deviation data and the power deviation data is a positive deviation; and controlling the water storage unit to discharge when there are high-frequency energy fluctuations in the power deviation data and the power deviation data is a negative deviation.

[0047] If the power deviation data shows high-frequency energy fluctuations and is positive, it indicates that the current wind and solar turbines are outputting too much energy. In this case, the energy storage capacity of the hydroelectric turbines can be controlled, such as by controlling the battery or flywheel for energy storage. If the power deviation data shows high-frequency energy fluctuations and is negative, it indicates that the current wind and solar turbine output is insufficient. In this case, the discharge capacity of the hydroelectric turbines can be controlled, such as by controlling the hydroelectric generator for power generation.

[0048] For example, if a positive power deviation is detected, meaning the actual output is higher than the planned value, the energy storage charging action is immediately triggered to absorb the excess energy; if a negative power deviation is detected, meaning the actual output is lower than the planned value, the hydroelectric generator's discharge mode is activated to supplement the output shortfall. Through hydroelectric storage units such as lithium batteries or flywheel energy storage units, precise energy throughput is executed according to demand, achieving dynamic balance of high-frequency disturbances in a very short time. Its response process strictly matches the fluctuation frequency characteristics, avoiding overshoot or response lag problems caused by traditional fixed threshold control.

[0049] Sub-step S2092: When the power deviation data shows low-frequency energy fluctuations, gradient output control is performed on the water storage unit.

[0050] When low-frequency energy fluctuations exist in the power deviation data, gradient output control of the hydro-storage unit can be implemented on an hourly basis. This ensures a smooth transition of the turbine output to the target value, avoiding water hammer and mechanical losses. By generating gradient adjustment commands for the hydro-storage unit based on the hourly continuous energy deviation, compensation is achieved using the inertia of the hydropower. This allows for dynamic coordination of the hydropower and energy storage operation commands, ensuring that the actual system output closely follows the planned output curve. For example, based on model predictive control principles, combined with reservoir water level, flow constraints, and grid dispatch commands, the optimal output trajectory of the hydropower unit for the next few hours is calculated. Gradient control is achieved by adjusting the guide vane opening setpoint through phased adjustments, such as gradually increasing or decreasing by 5% of rated power. Furthermore, during this process, the state of charge of the energy storage and the regulation margin of the hydropower unit can be continuously evaluated, dynamically coordinating their operation timing and power distribution ratio.

[0051] By combining hydropower and energy storage, the actual output curve is ensured to closely track the scheduling plan, while minimizing equipment wear caused by frequent operation of hydropower units and deep circulation of energy storage.

[0052] Step 210: Obtain the water storage output data of the water storage unit; After adjustments are made to the wind and solar turbine units and the hydroelectric storage units, the water storage output data of the hydroelectric storage units can be acquired in real time. This water storage output data characterizes the output of the hydroelectric storage units after control.

[0053] Step 211: Combine the water storage output data and the wind and solar power output data to determine the system output data; The output data of water storage and wind and solar power are combined to form the system output data of a comprehensive energy system that integrates wind, solar, water and storage.

[0054] Step 212: Determine the second power deviation data between the system output data and the target output time series data of wind and solar power generation; The output power deviation between the system output data and the target output time-series data of wind and solar power generation can be determined, i.e., the second power deviation data. The second power deviation data is used to characterize the output situation where the power deviation still exists after water-storage co-optimization.

[0055] Step 213: Perform fault mapping based on the second power deviation data to determine operational diagnostic data; Fault mapping can be performed based on the second power deviation data, and the causes of faults leading to the second power deviation data can be classified and determined, including but not limited to fault type, fault location and confidence assessment results, to generate operational diagnostic data.

[0056] For example, when continuously monitoring the power deviation data stream, a fault diagnosis mechanism is triggered when a persistent abnormal deviation exceeding a preset error threshold is detected, such as a deviation exceeding 15% for two consecutive hours. The abnormal data, including the second power deviation data and system operating status, are input into a pre-trained machine learning model, such as LSTM or random forest. This model performs pattern matching and root cause analysis against a built-in fault feature library, including those for photovoltaic pollution and wind turbine blade damage. The final output is the specific fault type, location, and confidence assessment results, generating operational diagnostic data. Specifically, the diagnostic mechanism can be triggered when the second power deviation data continuously exceeds a reasonable fluctuation range and cannot be attributed to meteorological changes. At this point, the original deviation data is analyzed. Time-frequency analysis techniques are used to decompose the statistical characteristics of the deviation (such as fluctuation amplitude distribution, duration patterns, and energy concentration in specific frequency bands), constructing multi-dimensional feature vectors to represent abnormal patterns. These feature vectors are input into a pre-trained machine learning diagnostic model for deep analysis. Based on a feature mapping relationship established by a historical fault case library, the model performs similarity matching between real-time features and typical fault patterns. If the feature shows a systematic decrease in photovoltaic output under normal irradiance conditions, it is associated with surface contamination or hot spot effect of photovoltaic modules. If the feature shows that the power curve of the wind turbine is continuously distorted in the rated wind speed range, it is associated with blade damage or pitch mechanism jamming. Further, the fault type, possible location and confidence level assessment results are generated by probability weighted calculation to produce operation diagnostic data.

[0057] Step 214: Output the operational diagnostic data; The generated operational diagnostic data can be output as a readable diagnostic report and pushed to the system. For example, automatic push to the operation and maintenance system transforms traditional passive monitoring relying on physical sensors into a proactive protection system based on energy flow anomaly analysis, enabling early intervention in equipment performance degradation. For instance, operational diagnostic data can include, but is not limited to, triggering photovoltaic array cleaning work orders and locating priority cleaning areas for high-confidence pollution warnings, and prompting key checks on the corresponding wind turbine blade counterweights and surface integrity for blade aerodynamic imbalance alarms.

[0058] Step 215: Analyze the operational diagnostic data to determine the fault compensation parameters; It can analyze operational diagnostic data, identify equipment with latent faults, determine the influencing factors of the latent faults, and determine the corresponding fault compensation parameters.

[0059] Step 216: Correct the initial prediction time series data and the initial output time series data of wind and solar power generation using the fault compensation parameters.

[0060] The initial forecast time-series data for wind and solar power generation is corrected by fault compensation parameters, and the adjustment weights of hydropower and energy storage are dynamically increased to compensate for their output losses. The fault compensation parameters also feed equipment health status factors back to the prediction model for real-time updates, thereby correcting the initial output time-series data for wind and solar power generation.

[0061] This invention, through its embodiments, acquires real-time weather conditions, historical time-series operational data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines during operation; performs spatiotemporal correlation on the real-time weather conditions and the historical time-series operational data to determine initial prediction time-series data for wind and solar power generation; uses the real-time output feedback data to correct errors in the initial prediction time-series data for wind and solar power generation to determine target prediction time-series data for wind and solar power generation; determines the initial output time-series data for wind and solar power generation corresponding to the target prediction time-series data; performs closed-loop feedback control by combining the real-time output feedback data and the initial output time-series data for wind and solar power generation to determine the target output time-series data for wind and solar power generation; controls the wind and solar turbines to operate according to the target output time-series data for wind and solar power generation; and acquires the wind... The process involves: analyzing the wind and solar output data of the solar power unit; determining a first power deviation between the target output time-series data of the wind and solar power generation and the wind and solar output data; performing multi-time-scale control on the hydro-storage unit based on the power deviation data; acquiring the hydro-storage output data of the hydro-storage unit; combining the hydro-storage output data and the wind and solar output data to determine the system output data; determining a second power deviation between the system output data and the target output time-series data of the wind and solar power generation; performing fault mapping based on the second power deviation data to determine operational diagnostic data; outputting the operational diagnostic data; parsing the operational diagnostic data to determine fault compensation parameters; and using the fault compensation parameters to correct the initial prediction time-series data and the initial output time-series data of the wind and solar power generation. The embodiments of this invention can effectively enhance the renewable energy absorption capacity and significantly reduce wind and solar curtailment; significantly improve the system's stability in responding to power fluctuations and quickly mitigate the fluctuation risks caused by sudden changes in wind and solar output and load changes; achieve a breakthrough in early proactive warning of hidden equipment faults, preventing progressive degradation problems such as photovoltaic module pollution and wind turbine blade damage from developing in their infancy; and simultaneously optimize the scheduling efficiency of hydropower and energy storage resources, reducing ineffective action losses and frequent adjustment costs. Ultimately, under the premise of ensuring reliable energy supply, the invention achieves the synergistic improvement goals of maximizing fluctuation absorption, minimizing fault losses, and optimizing economic operation.

[0062] To enable those skilled in the art to clearly understand the implementation process of the embodiments of the present invention, please refer to... Figure 3 The above steps are achieved through a prediction and correction module, a collaborative control module, a water storage scheduling module, a fault diagnosis module, and a closed-loop optimization module. For specific execution steps, please refer to [link / reference]. Figure 4 .

[0063] Step 1: Dynamic Prediction and Feedback Correction of Wind and Solar Power. Real-time weather conditions and historical time-series operating data of wind and solar turbines are collected and input into the time-series prediction model to generate short-term initial prediction time-series data for wind and solar power generation; real-time output feedback data from the wind and solar turbines is used to continuously correct the initial prediction time-series data for wind and solar power generation.

[0064] Step 2: Wind-Solar Hybridization and Adaptive Control. Based on real-time weather conditions and historical time-series operational data, the spatiotemporal complementary characteristics of wind and solar power are dynamically coordinated to generate initial predicted time-series data for wind and solar power generation. Based on the minute-level error between the initial predicted time-series data and the real-time output feedback data, fuzzy rules are used to tune the PID control parameters online, dynamically adjusting the output of the wind and solar turbines to determine the target output time-series data for wind and solar power generation, and implementing control to achieve primary fluctuation smoothing.

[0065] Step 3: Multi-timescale collaborative optimization of hydropower and storage. Based on the first power deviation data between the target output time series data of wind and solar power generation and the wind and solar output data, rapid charging and discharging actions are performed for high-frequency power fluctuations at the second to minute level to achieve instantaneous power balance; at the same time, for the continuous energy deviation at the hour level, gradient adjustment commands for hydropower and storage units are generated and compensated by the inertial support of hydropower.

[0066] Step 4: Hidden Fault Diagnosis Driven by Power Deviation. Continuously monitor system output data. Based on the system output data, perform pattern matching and root cause analysis against a built-in fault feature library containing information on photovoltaic pollution, wind turbine blade damage, etc. Finally, output specific fault types, locations, and confidence level assessment results, along with other operational diagnostic data. Automatically generate early warning commands for cleaning maintenance or shutdown repairs. Step 5: Closed-loop feedback and strategy optimization. The diagnostic results are then fed back to the control system to form a closed-loop optimization, correcting the initial prediction time series data and the initial output time series data of wind and solar power generation.

[0067] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0068] Reference Figure 5 The diagram illustrates a structural block diagram of an embodiment of a wind-solar-hydro-storage integrated energy system control device according to the present invention. The integrated energy system includes wind and solar turbines, and the control device specifically includes the following modules: The acquisition module 501 is used to acquire real-time weather conditions, historical time-series operation data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines during operation. The spatiotemporal correlation module 502 is used to perform spatiotemporal correlation between the real-time meteorological status and the historical time-series operational data to determine the initial prediction time-series data for wind and solar power generation. The first correction module 503 is used to correct the error of the initial prediction time series data of wind and solar power generation using the real-time output feedback data, and to determine the target prediction time series data of wind and solar power generation. The wind and solar prediction module 504 is used to determine the initial output time series data of wind and solar power generation corresponding to the wind and solar power generation target prediction time series data; The wind and solar output module 505 is used to combine the real-time output feedback data and the initial output timing data of wind and solar power generation to perform closed-loop feedback control and determine the target output timing data of wind and solar power generation. The first control module 506 is used to control the wind and solar turbine to operate according to the wind and solar power generation target output timing data.

[0069] In an optional embodiment of the present invention, the integrated energy system combining wind, solar, hydro, and storage further includes a hydro storage unit, and the device further includes: The first sampling module is used to acquire the wind and solar output data of the wind and solar turbines; The first deviation determination module is used to determine the first power deviation data between the wind and solar power generation target output timing data and the wind and solar output data; The second control module is used to perform multi-timescale control of the water storage unit based on the power deviation data.

[0070] In an optional embodiment of the present invention, the device further includes: The second sampling module is used to acquire the water storage output data of the water storage unit; The module is used to combine the water storage output data and the wind and solar output data to determine the system output data; The second deviation determination module is used to determine the second power deviation data between the system output data and the wind and solar power generation target output time series data; The mapping module is used to perform fault mapping based on the second power deviation data to determine operational diagnostic data; The diagnostic output module is used to output the operational diagnostic data.

[0071] In an optional embodiment of the present invention, the device further includes: The parsing module is used to parse the operational diagnostic data and determine the fault compensation parameters; The compensation module is used to correct the initial prediction time series data and the initial output time series data of wind and solar power generation using the fault compensation parameters.

[0072] In an optional embodiment of the present invention, the first correction module 503 includes: The error matrix determination submodule is used to determine the error matrix between the real-time output feedback data and the initial prediction time series data of wind and solar power generation; The backpropagation submodule is used to perform backpropagation using the error matrix to correct the initial prediction time series data of wind and solar power generation and determine the target prediction time series data of wind and solar power generation.

[0073] In an optional embodiment of the present invention, the wind and solar output module includes: The instantaneous deviation submodule is used to determine the instantaneous deviation data between the initial output timing data of the wind and solar power generation and the real-time output feedback data; The parameter tuning submodule is used to determine the control parameters corresponding to the instantaneous deviation data; The wind and solar output submodule is used to perform proportional-integral-derivative control using the control parameters, the initial output timing data of wind and solar power generation, and the real-time output feedback data to determine the target output timing data of wind and solar power generation.

[0074] In an optional embodiment of the present invention, the second control module includes: The charge / discharge control submodule is used to control the charge / discharge of the water storage unit based on the power deviation data when there are high-frequency energy fluctuations in the power deviation data. The gradient control submodule is used to perform gradient output control on the water storage unit when there are low-frequency energy fluctuations in the power deviation data.

[0075] In an optional embodiment of the present invention, the charge / discharge control submodule includes: An energy storage unit is used to control the energy storage of the water storage unit when the power deviation data has high-frequency energy fluctuations and the power deviation data is a positive deviation. The discharge unit is used to control the water storage unit to discharge when the power deviation data has high-frequency energy fluctuations and the power deviation data is a negative deviation.

[0076] This invention, in its embodiments, acquires real-time weather conditions, historical time-series operational data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines during operation; performs spatiotemporal correlation on the real-time weather conditions and the historical time-series operational data to determine initial prediction time-series data for wind and solar power generation; uses the real-time output feedback data to correct errors in the initial prediction time-series data for wind and solar power generation to determine target prediction time-series data for wind and solar power generation; determines the initial output time-series data for wind and solar power generation corresponding to the target prediction time-series data; combines the real-time output feedback data and the initial output time-series data for wind and solar power generation to perform closed-loop feedback control to determine the target output time-series data for wind and solar power generation; and controls the wind and solar turbines to generate wind and solar power according to the wind and solar power generation schedule. The target output time-series data operation, through rolling correction of wind and solar power prediction based on environmental data and real-time output feedback data from wind and solar turbines, determines the target prediction time-series data for wind and solar power generation, providing a high-precision input source for coordinated control; through wind and solar complementary linkage, the initial output time-series data for wind and solar power generation is determined, and then combined with real-time output feedback data for closed-loop feedback control, which can achieve minute-level power fluctuation smoothing, effectively enhance the renewable energy absorption capacity, and significantly reduce wind and solar curtailment; significantly improve the system's stability in response to power fluctuations, and quickly smooth out the fluctuation risks caused by sudden changes in wind and solar output and load changes; under the premise of ensuring reliable energy supply, it achieves the coordinated improvement goals of maximizing fluctuation absorption, minimizing fault losses, and optimizing economic operation.

[0077] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0078] Reference Figure 6 The present invention also provides an electronic device, comprising: A processor 601 and a memory 602 are provided. The memory 602 stores a computer program executable by the processor 601. When the electronic device is controlled to run, the processor 601 executes the computer program to implement the integrated energy system control method for wind-solar-hydro-storage synergy as described in any embodiment of the present invention. The integrated energy system for wind-solar-hydro-storage synergy includes wind and solar turbines, and the integrated energy system control method for wind-solar-hydro-storage synergy includes: During operation, real-time weather conditions, historical time-series operation data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines are acquired. The real-time meteorological status and the historical time-series operational data are spatiotemporally correlated to determine the initial prediction time-series data for wind and solar power generation; The real-time output feedback data is used to correct the error in the initial prediction time series data of wind and solar power generation, and the target prediction time series data of wind and solar power generation is determined. Determine the initial output time series data of wind and solar power generation corresponding to the target prediction time series data of wind and solar power generation; By combining the real-time output feedback data and the initial output timing data of wind and solar power generation, closed-loop feedback control is performed to determine the target output timing data of wind and solar power generation. The wind and solar turbines are controlled to operate according to the wind and solar power generation target output timing data.

[0079] Optionally, the integrated energy system combining wind, solar, hydro, and storage further includes a hydro-storage unit. After the step of controlling the wind and solar turbines to operate according to the wind and solar power generation target output time-series data, the method further includes: Obtain the wind and solar output data of the wind and solar turbine units; Determine the first power deviation data between the target output time-series data of wind and solar power generation and the wind and solar output data; The water storage unit is controlled on multiple time scales based on the power deviation data.

[0080] Optionally, after the step of performing multi-timescale control of the water storage unit based on the power deviation data, the method further includes: Obtain the water storage output data of the water storage unit; The system output data is determined by combining the water storage output data and the wind and solar power output data; Determine the second power deviation data between the system output data and the target output time-series data of wind and solar power generation; Fault mapping is performed based on the second power deviation data to determine operational diagnostic data; Output the aforementioned operational diagnostic data.

[0081] Optionally, the method further includes: Analyze the operational diagnostic data to determine the fault compensation parameters; The fault compensation parameters are used to correct the initial prediction time series data and the initial output time series data of wind and solar power generation.

[0082] Optionally, the step of using the real-time output feedback data to correct errors in the initial prediction time series data of wind and solar power generation and determining the target prediction time series data of wind and solar power generation includes: Determine the error matrix between the real-time output feedback data and the initial prediction time series data of wind and solar power generation; Backpropagation is performed using the error matrix to correct the initial prediction time series data for wind and solar power generation and determine the target prediction time series data for wind and solar power generation.

[0083] Optionally, the step of combining the real-time output feedback data and the initial output timing data of wind and solar power generation to perform closed-loop feedback control and determine the target output timing data of wind and solar power generation includes: Determine the instantaneous deviation data between the initial output timing data of the wind and solar power generation and the real-time output feedback data; Determine the control parameters corresponding to the instantaneous deviation data; The target output timing data of wind and solar power generation is determined by using the control parameters, the initial output timing data of wind and solar power generation, and the real-time output feedback data for proportional-integral-derivative control.

[0084] Optionally, the step of performing multi-time-scale control of the water storage unit based on the power deviation data includes: In the event of high-frequency energy fluctuations in the power deviation data, the water storage unit is charged and discharged based on the power deviation data. When the power deviation data shows low-frequency energy fluctuations, gradient output control is applied to the water storage unit.

[0085] Optionally, the step of controlling the charging and discharging of the water storage unit based on the power deviation data when there are high-frequency energy fluctuations in the power deviation data includes: When the power deviation data exhibits high-frequency energy fluctuations and the power deviation data is a positive deviation, the energy storage unit is controlled to store energy. If the power deviation data exhibits high-frequency energy fluctuations and is negative, the water storage unit is controlled to discharge.

[0086] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0087] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0088] Reference Figure 7 This invention also provides a computer-readable storage medium 701, on which a computer program is stored. When executed by a processor, the computer program performs the integrated energy system control method for wind-solar-hydro-storage synergy as described in any one of the embodiments of this invention. The integrated energy system for wind-solar-hydro-storage synergy includes wind and solar turbines, and the integrated energy system control method for wind-solar-hydro-storage synergy includes: During operation, real-time weather conditions, historical time-series operation data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines are acquired. The real-time meteorological status and the historical time-series operational data are spatiotemporally correlated to determine the initial prediction time-series data for wind and solar power generation; The real-time output feedback data is used to correct the error in the initial prediction time series data of wind and solar power generation, and the target prediction time series data of wind and solar power generation is determined. Determine the initial output time series data of wind and solar power generation corresponding to the target prediction time series data of wind and solar power generation; By combining the real-time output feedback data and the initial output timing data of wind and solar power generation, closed-loop feedback control is performed to determine the target output timing data of wind and solar power generation. The wind and solar turbines are controlled to operate according to the wind and solar power generation target output timing data.

[0089] Optionally, the integrated energy system combining wind, solar, hydro, and storage further includes a hydro-storage unit. After the step of controlling the wind and solar turbines to operate according to the wind and solar power generation target output time-series data, the method further includes: Obtain the wind and solar output data of the wind and solar turbine units; Determine the first power deviation data between the target output time-series data of wind and solar power generation and the wind and solar output data; The water storage unit is controlled on multiple time scales based on the power deviation data.

[0090] Optionally, after the step of performing multi-timescale control of the water storage unit based on the power deviation data, the method further includes: Obtain the water storage output data of the water storage unit; The system output data is determined by combining the water storage output data and the wind and solar power output data; Determine the second power deviation data between the system output data and the target output time-series data of wind and solar power generation; Fault mapping is performed based on the second power deviation data to determine operational diagnostic data; Output the aforementioned operational diagnostic data.

[0091] Optionally, the method further includes: Analyze the operational diagnostic data to determine the fault compensation parameters; The fault compensation parameters are used to correct the initial prediction time series data and the initial output time series data of wind and solar power generation.

[0092] Optionally, the step of using the real-time output feedback data to correct errors in the initial prediction time series data of wind and solar power generation and determining the target prediction time series data of wind and solar power generation includes: Determine the error matrix between the real-time output feedback data and the initial prediction time series data of wind and solar power generation; Backpropagation is performed using the error matrix to correct the initial prediction time series data for wind and solar power generation and determine the target prediction time series data for wind and solar power generation.

[0093] Optionally, the step of combining the real-time output feedback data and the initial output timing data of wind and solar power generation to perform closed-loop feedback control and determine the target output timing data of wind and solar power generation includes: Determine the instantaneous deviation data between the initial output timing data of the wind and solar power generation and the real-time output feedback data; Determine the control parameters corresponding to the instantaneous deviation data; The target output timing data of wind and solar power generation is determined by using the control parameters, the initial output timing data of wind and solar power generation, and the real-time output feedback data for proportional-integral-derivative control.

[0094] Optionally, the step of performing multi-time-scale control of the water storage unit based on the power deviation data includes: In the event of high-frequency energy fluctuations in the power deviation data, the water storage unit is charged and discharged based on the power deviation data. When the power deviation data shows low-frequency energy fluctuations, gradient output control is applied to the water storage unit.

[0095] Optionally, the step of controlling the charging and discharging of the water storage unit based on the power deviation data when there are high-frequency energy fluctuations in the power deviation data includes: When the power deviation data exhibits high-frequency energy fluctuations and the power deviation data is a positive deviation, the energy storage unit is controlled to store energy. If the power deviation data exhibits high-frequency energy fluctuations and is negative, the water storage unit is controlled to discharge.

[0096] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0097] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0101] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0102] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0103] The foregoing has provided a detailed description of a wind-solar-hydro-storage integrated energy system control method, a wind-solar-hydro-storage integrated energy system control device, an electronic device, and a computer-readable storage medium provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The above description of the embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A control method for a synergistic wind-solar-hydro-storage integrated energy system, characterized in that, The integrated energy system combining wind, solar, hydro, and storage includes wind and solar turbines, and the method includes: During operation, real-time weather conditions, historical time-series operation data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines are acquired. The real-time meteorological status and the historical time-series operational data are spatiotemporally correlated to determine the initial prediction time-series data for wind and solar power generation; The real-time output feedback data is used to correct the error in the initial prediction time series data of wind and solar power generation, and the target prediction time series data of wind and solar power generation is determined. Determine the initial output time series data of wind and solar power generation corresponding to the target prediction time series data of wind and solar power generation; By combining the real-time output feedback data and the initial output timing data of wind and solar power generation, closed-loop feedback control is performed to determine the target output timing data of wind and solar power generation. The wind and solar turbines are controlled to operate according to the wind and solar power generation target output timing data.

2. The method according to claim 1, characterized in that, The integrated energy system combining wind, solar, hydro, and storage also includes a hydro-storage unit. After the step of controlling the wind and solar turbines to operate according to the wind and solar power generation target output time-series data, the method further includes: Obtain the wind and solar output data of the wind and solar turbine units; Determine the first power deviation data between the target output time-series data of wind and solar power generation and the wind and solar output data; The water storage unit is controlled on multiple time scales based on the power deviation data.

3. The method according to claim 1, characterized in that, After the step of performing multi-timescale control of the water storage unit based on the power deviation data, the method further includes: Obtain the water storage output data of the water storage unit; The system output data is determined by combining the water storage output data and the wind and solar power output data; Determine the second power deviation data between the system output data and the target output time-series data of wind and solar power generation; Fault mapping is performed based on the second power deviation data to determine operational diagnostic data; Output the aforementioned operational diagnostic data.

4. The method according to claim 3, characterized in that, The method further includes: Analyze the operational diagnostic data to determine the fault compensation parameters; The fault compensation parameters are used to correct the initial prediction time series data and the initial output time series data of wind and solar power generation.

5. The method according to any one of claims 1-4, characterized in that, The step of using the real-time output feedback data to correct errors in the initial prediction time series data of wind and solar power generation, and determining the target prediction time series data of wind and solar power generation, includes: Determine the error matrix between the real-time output feedback data and the initial prediction time series data of wind and solar power generation; Backpropagation is performed using the error matrix to correct the initial prediction time series data for wind and solar power generation and determine the target prediction time series data for wind and solar power generation.

6. The method according to any one of claims 1-4, characterized in that, The step of combining the real-time output feedback data and the initial output timing data of wind and solar power generation to perform closed-loop feedback control and determine the target output timing data of wind and solar power generation includes: Determine the instantaneous deviation data between the initial output timing data of the wind and solar power generation and the real-time output feedback data; Determine the control parameters corresponding to the instantaneous deviation data; The target output timing data of wind and solar power generation is determined by using the control parameters, the initial output timing data of wind and solar power generation, and the real-time output feedback data for proportional-integral-derivative control.

7. The method according to any one of claims 2-4, characterized in that, The step of performing multi-time-scale control of the water storage unit based on the power deviation data includes: In the event of high-frequency energy fluctuations in the power deviation data, the water storage unit is charged and discharged based on the power deviation data. When the power deviation data shows low-frequency energy fluctuations, gradient output control is applied to the water storage unit.

8. The method according to claim 7, characterized in that, The step of controlling the charging and discharging of the water storage unit based on the power deviation data when there are high-frequency energy fluctuations in the power deviation data includes: When the power deviation data exhibits high-frequency energy fluctuations and the power deviation data is a positive deviation, the energy storage unit is controlled to store energy. If the power deviation data exhibits high-frequency energy fluctuations and is negative, the water storage unit is controlled to discharge.

9. A control device for a combined wind, solar, hydro, and storage energy system, characterized in that, The integrated energy system combining wind, solar, hydro, and storage includes wind and solar turbines, and the device includes: The acquisition module is used to acquire real-time weather conditions, historical time-series operation data of the wind and solar turbines, and real-time output feedback data of the wind and solar turbines during operation. The spatiotemporal correlation module is used to perform spatiotemporal correlation between the real-time meteorological status and the historical time-series operational data to determine the initial prediction time-series data for wind and solar power generation. The first correction module is used to correct the error of the initial prediction time series data of wind and solar power generation using the real-time output feedback data, and to determine the target prediction time series data of wind and solar power generation. The wind and solar forecasting module is used to determine the initial output time series data of wind and solar power generation corresponding to the wind and solar power generation target forecast time series data; The wind and solar output module is used to combine the real-time output feedback data and the initial output timing data of wind and solar power generation to perform closed-loop feedback control and determine the target output timing data of wind and solar power generation. The first control module is used to control the wind and solar turbines to operate according to the timing data output by the wind and solar power generation target.

10. An electronic device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor. When executed by the processor, the computer program implements the steps of the integrated energy system control method for wind, solar, hydro, and storage synergy as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, it implements the steps of the integrated energy system control method for wind, solar, hydro, and storage synergy as described in any one of claims 1-8.

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