Plateau isolated network disturbance coping method and system

Abstract

The invention relates to the technical field of isolated network disturbance coping, and discloses a plateau isolated network disturbance coping method and system.The plateau isolated network disturbance coping method comprises the steps that for large disturbance, state data, including temperature, pressure and flow, of a heat storage system are collected in real time through the monitoring and analysis technology; acquiring illumination condition data and short-term forecast weather data under the current large disturbance condition in real time; an energy supply prediction model is constructed, and power consumption, production efficiency and safety are predicted, studied and judged; adjusting operation parameters of production equipment of the lithium carbonate processing plant according to a prediction research and judgment result, and dynamically adjusting production load or safe shutdown; for the small disturbance, collecting real-time molten salt liquid level data, analyzing complex behaviors of the molten salt in the disturbance process, and determining the molten salt liquid level reduction amount caused by the small disturbance under different working conditions; when the illumination is sufficient, the lithium carbonate processing plant is actively dispatched for load reduction operation, and the molten salt reduction amount caused by small disturbance is supplemented. Through the method, a perfect disturbance coping plan and an intelligent scheduling strategy are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of dealing with island power grid disturbances, and particularly to a method and system for dealing with island power grid disturbances on the plateau. Background Art

[0002] In today's energy field and industrial production process, especially in industrial facilities such as lithium carbonate processing plants that rely on energy supply, stable energy supply and efficient energy management are crucial for ensuring the continuous operation of production, improving production efficiency, and ensuring production safety. The location of Zabuye Salt Lake is extremely rich in light resources, with long annual sunshine hours and high solar radiation intensity. Moreover, the vast land in this area provides sufficient space for the construction of large-scale solar power generation facilities. According to the natural conditions and energy demand characteristics of Zabuye Salt Lake, the "Integrated Energy Supply Project of Zabuye Source-Network-Load-Storage in Tibet" selects the technical route of "solar thermal + photovoltaic + energy storage (electric energy storage, heat storage)", which can effectively solve the local energy supply problem and promote the sustainable development of lithium carbonate processing plants.

[0003] However, on the one hand, solar photovoltaic power generation is significantly affected by lighting conditions and has intermittency and instability. For example, in the case of continuous rainy days or insufficient lighting, the power generation of photovoltaic power generation will drop significantly, which may lead to fluctuations in the power supply of the factory and thus affect the normal progress of production. On the other hand, as an important means of energy storage and regulation, the state of the heat storage system in the factory plays a key role in balancing energy supply and demand. At present, the monitoring and management level of the heat storage system still needs to be improved. Many factories can only achieve simple status monitoring, lack in-depth analysis and optimized utilization of the data of the heat storage system, and it is difficult to flexibly adjust the production plan and energy dispatch according to the real-time status of the heat storage system.

[0004] In addition, for the evaluation of the power consumption, production efficiency, and safety of the factory, traditional prediction methods are often too simple and rough to comprehensively consider various complex factors. These factors not only include the detailed status data of the heat storage system (such as temperature, pressure, flow rate, energy storage capacity, etc.), but also involve the dynamic changes in lighting conditions and future weather forecast information. In actual production, traditional methods usually rely on empirical rules or simple linear models and cannot accurately predict the energy demand and production efficiency under different operating modes, resulting in a lag in response to power supply fluctuations and the inability to formulate reasonable coping strategies in advance, which may cause energy waste or production interruption.

[0005] Meanwhile, when dealing with various disturbances (including large and small disturbances), the existing response plans also have deficiencies. For large disturbances, such as the large power disturbance caused by continuous rainy days, the existing dispatching decisions often rely on manual judgment and only based on the simple state of the thermal energy storage system and lighting conditions, lacking comprehensive and accurate system state assessment and prediction, making it difficult to ensure that the lithium carbonate plant makes the optimal load reduction or safe shutdown decision under complex energy supply conditions. For small disturbances, such as the fluctuations in the molten salt liquid level in the system due to various reasons, the current treatment plan lacks precise quantitative analysis and active dispatching adjustment. In actual operation, the use of electrochemical energy storage has not fully utilized its advantages, its potential as a stable power source and emergency power source has not been fully explored, and there is no systematic replenishment strategy and automatic control for the reduction of the molten salt liquid level caused by small disturbances, thus affecting the stability and safety of the entire system.

[0006] Moreover, in the research and management of the molten salt system, the analysis of the flow characteristics and liquid level changes of molten salt during the disturbance process is not deep enough. Although some factories use simple monitoring means, they lack advanced fluid dynamics simulation tools and analysis methods, unable to accurately grasp the performance changes of the molten salt system under different working conditions, difficult to precisely quantify the impact of small disturbances on the molten salt liquid level, resulting in a lack of scientific basis for the regulation and replenishment of the molten salt liquid level and affecting the operation stability of the system.

[0007] In summary, in current energy-intensive industrial scenarios such as lithium carbonate processing plants, there is an urgent need for a more comprehensive, intelligent, and precise disturbance response technical solution to ensure the efficient, safe, and stable operation of production. Summary of the Invention

[0008] The purpose of the present invention is to solve the above-mentioned shortcomings existing in the prior art, and provides a method and system for coping with disturbances in a high-altitude isolated power grid, which can comprehensively consider various factors such as the thermal energy storage system, lighting conditions, and forecast weather, and on this basis formulate a perfect disturbance response plan and intelligent dispatching strategy to achieve accurate prediction and control of power consumption, production efficiency, and safety, and improve energy utilization efficiency.

[0009] On the one hand, a method for coping with disturbances in a high-altitude isolated power grid, a method for coping with large and small disturbances is provided, wherein, For large disturbances: S10: Real-time collect the state data of the thermal energy storage system including temperature, pressure, and flow rate through monitoring and analysis technologies, and real-time obtain the lighting condition data and short-term forecast weather data under the current large disturbance situation; S11: Build an energy supply prediction model, and predict and judge the power consumption, production efficiency, and safety of the lithium carbonate processing plant under different operating modes based on the state data of the thermal energy storage system, lighting condition data, and forecast weather data; S12: Adjust the operating parameters of the lithium carbonate processing plant's production equipment according to the prediction and judgment results, dynamically adjust the production load or safely shut down the machine; For small disturbances: S20: Collect real-time molten salt liquid level data, construct a molten salt storage capacity prediction model based on fluid dynamics simulation, analyze the complex behavior of molten salt during the disturbance process, and determine the reduction in molten salt liquid level caused by small disturbances under different working conditions; S21: When the light is sufficient, actively dispatch the lithium carbonate processing plant to operate at a reduced load to supplement the reduction in molten salt caused by small disturbances.

[0010] Furthermore, in step S10, the state data of the heat storage system including temperature, pressure, and flow rate is collected in real time through monitoring and analysis technologies, and the light condition data under the current large disturbance is obtained in real time, further including: Through a distributed sensor network, the temperature, pressure, and flow rate data of each key part of the heat storage system are collected in real time and transmitted to the central control system for analysis and processing, so as to judge the current energy storage level, operating efficiency of the heat storage system, and whether there are potential fault risks; Use light monitoring equipment to obtain light condition data in real time, including light intensity, light duration, and cloud cover, and then predict the change trend of light resources in the short term.

[0011] Furthermore, in step S11, the construction of the energy supply prediction model includes: S110: Preprocess the collected data, including converting the obtained forecast weather into a numerical form recognizable by a computer using one-hot encoding, and then using the 3σ criterion to identify and correct outliers that deviate significantly from other data points for the state data of the heat storage system, light condition data, and the converted forecast weather data. At the same time, normalize the data to unify data of different magnitudes into the [0, 1] interval to eliminate the influence of dimensions and facilitate model training; S111: Construct the energy supply prediction model based on the long short-term memory network (LSTM). Divide the preprocessed data into a training set, a validation set, and a test set in chronological order. Select the mean squared error (MSE) as the loss function to train the energy supply prediction model, measure the difference between the model prediction value and the true value, and calculate the gradient of the loss function with respect to the model parameters through the backpropagation algorithm until the loss function no longer decreases on the validation set or reaches the preset maximum number of iterations; S112: Evaluate the trained model through the test set data, and use the energy supply prediction model with good evaluation and verification performance to predict the power consumption, production efficiency, and safety of the lithium carbonate processing plant under different operating modes.

[0012] Further, in step S12, actively scheduling and adjusting the operating parameters of the production equipment in the lithium carbonate processing plant according to the prediction and judgment results, and dynamically adjusting the production load or safely shutting down the machine includes: If the prediction shows that in the next period of time, the light is sufficient, the energy storage system of the heat storage system is sufficient, and the weather forecast is good, then formulate a full-load production plan, make full use of the abundant energy supply, increase the output of lithium carbonate, give priority to using photovoltaic power generation to meet the electricity demand of the factory, and store the excess electricity in the electrochemical energy storage system for emergencies; If it is predicted that the light is insufficient, the energy storage of the heat storage system is also at a low level, and there are adverse weather conditions such as continuous rainy days in the weather forecast, then reasonably arrange for some production lines to operate at reduced load or safely shut down according to the key degree of the production process and the energy demand. At the same time, start the electrochemical energy storage system as an emergency power supply to make up for the power gap; If it is predicted that the weather changes frequently, the light conditions are unstable, and the state of the heat storage system fluctuates, then formulate a flexible production plan, adopt the method of dynamically adjusting the production load, build an intelligent energy scheduling system, combine the real-time light, heat storage, and grid power data, and formulate the optimal energy distribution plan through an optimization algorithm.

[0013] Further, step S20 includes: S200: Construct a molten salt storage prediction model based on the Navier - Stokes equation, which describes the momentum conservation of viscous Newtonian fluids, and the expression form is: Among them, is the fluid density, is the velocity vector, is the time, is the pressure, is the dynamic viscosity, is the volume force acting on the fluid, represents the local acceleration term, is the Hamiltonian operator, is the Laplace operator. By combining the specific structural parameters, operating conditions of the molten salt system, and the type and intensity of small perturbations, an accurate numerical model is constructed; S201: Divide the calculation area into a large number of small units through the discretization method, and numerically solve the Navier - Stokes equation for each small unit, so as to obtain the flow characteristics of the molten salt during the perturbation process, including the velocity field and pressure field distribution; S202: By tracking the movement of the molten salt - gas interface, use the Volume of Fluid method interface capture technology to accurately simulate the liquid level change. The Volume of Fluid method defines a volume fraction function is used to represent the volume ratio of molten salt in a certain unit. The transport equation of is expressed as follows: where indicates that the unit is completely filled with molten salt, indicates that there is no molten salt in the unit. By solving the transport equation of the interface position is tracked; S203: Determine the reduction amount of the molten salt liquid level caused by small disturbances under different working conditions through the constructed molten salt storage prediction model.

[0014] Further, step S21 includes: Build an intelligent power grid dispatching automation system based on the distributed control system DCS. Use the liquid level sensors installed in the molten salt system to monitor the molten salt liquid level data in real time. Combine the overall operation conditions of the power system, including load and the output power of each power generation equipment, and use linear programming to formulate an optimal dispatching plan, and accurately control the operation parameters of relevant equipment according to the dispatching plan.

[0015] Preferably, the use of linear programming to formulate an optimal dispatching plan includes: Set the objective function to minimize the cost brought by adjusting the power generation equipment power or operation mode on the premise of meeting production requirements. The constraint conditions include the power balance constraint of the power system, the operation limits of the equipment, and the restoration requirements of the molten salt liquid level; According to the dispatching plan, adjust the control parameters of the photovoltaic inverter to change the working point of the photovoltaic panels to achieve the adjustment of the output power, optimize the operation mode of the energy station, and accurately supplement the molten salt liquid level of the whole system on the premise of meeting production requirements.

[0016] Further, the safe shutdown includes: According to the characteristic analysis of the lithium carbonate processing plant, it takes a certain amount of time to shut down from full load or 60% load operation. The whole shutdown process is powered by molten salt. Arrange the shutdown plan in advance according to the molten salt liquid level to ensure the safe shutdown of the lithium carbonate processing plant.

[0017] On the other hand, a plateau isolated network disturbance response system is provided, including a large disturbance response system and a small disturbance response system. Among them, the large disturbance response system includes: A data collection module is used to collect the state data of the heat storage system, including temperature, pressure and flow, in real time through monitoring and analysis technology, and obtain the light condition data and short-term forecast weather data under the current large disturbance situation in real time; The model construction module is used to construct an energy supply prediction model, and based on the state data of the heat storage system, the lighting condition data, and the forecast weather data, predict and judge the power consumption, production efficiency, and safety of the lithium carbonate processing plant under different operating modes; The scheduling adjustment module is used to adjust the operating parameters of the production equipment of the lithium carbonate processing plant according to the prediction and judgment results, and dynamically adjust the production load or perform a safety shutdown; The small disturbance system includes: The molten salt prediction and analysis module is used to collect real-time molten salt liquid level data, construct a molten salt storage prediction model based on fluid dynamics simulation, analyze the complex behavior of molten salt during the disturbance process, and determine the reduction amount of the molten salt liquid level caused by small disturbances under different working conditions; The scheduling supplement module is used to, when the light is sufficient, actively schedule the lithium carbonate processing plant to operate at a reduced load to supplement the reduction amount of molten salt caused by small disturbances.

[0018] Compared with the prior art, the beneficial effects of the present invention are: By constructing an energy supply prediction model and comprehensively considering the state data of the heat storage system, the lighting condition data, and the forecast weather data, the present invention can accurately predict the power consumption and production efficiency of the lithium carbonate processing plant under different operating modes; According to the real-time state of the heat storage system, the lighting conditions, and the operation information of the power system, the present invention accurately controls the operating parameters of the photovoltaic power generation equipment, the electrochemical energy storage system, and other electrical equipment to achieve the optimal allocation of energy; For large power disturbances caused by factors such as continuous rainy days, the scheduling scheme accurately judges whether the lithium carbonate plant needs to operate at a reduced load or perform a safety shutdown operation based on the comprehensive evaluation of the state of the heat storage system and the lighting conditions; In terms of dealing with small disturbances, through in-depth fluid dynamics simulation of the molten salt system, the present invention accurately quantifies the reduction amount of the molten salt liquid level caused by small disturbances, supplements the molten salt liquid level through an active scheduling strategy, and optimizes the use of electrochemical energy storage at the same time; When adjusting the operating parameters of the equipment and the production plan, the present invention fully considers the overall safety of the system. During the process of reducing the load or shutting down, it will operate according to a predetermined safety process to avoid damage to the equipment due to sudden power fluctuations or overload. Description of the Drawings

[0019] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings: Figure 1 It is a flowchart of a method for coping with disturbances in a high-altitude isolated power grid of the present invention; Figure 2Schematic diagram of the construction process of an energy supply prediction model according to the present invention; Figure 3 Schematic diagram of the molten salt storage capacity prediction process according to the present invention. Specific embodiments

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0021] The following describes the specific embodiments of the present invention with reference to the accompanying drawings and embodiments.

[0022] First Embodiment Please refer to Figure 1 , which is a technical solution of a method for coping with disturbances in a high-altitude isolated power grid provided in this embodiment, including methods for coping with large disturbances and small disturbances. Among them, For large disturbances: S10: Real-time collect the state data of the heat storage system including temperature, pressure, and flow rate through monitoring and analysis technologies, and obtain the illumination condition data and short-term forecast weather data under the current large disturbance situation in real time; S11: Construct an energy supply prediction model, and predict and judge the power consumption, production efficiency, and safety of the lithium carbonate processing plant under different operating modes based on the state data of the heat storage system, illumination condition data, and forecast weather data; S12: Adjust the operating parameters of the production equipment of the lithium carbonate processing plant according to the prediction and judgment results, and dynamically adjust the production load or safely shut down the machine; For small disturbances: S20: Collect real-time molten salt liquid level data, construct a molten salt storage capacity prediction model based on fluid dynamics simulation, analyze the complex behavior of molten salt during the disturbance process, and determine the reduction in molten salt liquid level caused by small disturbances under different working conditions; S21: When the illumination is sufficient, actively dispatch the lithium carbonate processing plant to operate at a reduced load to supplement the reduction in molten salt caused by small disturbances.

[0023] Among them, in step S10, the real-time collection of the state data of the heat storage system including temperature, pressure, and flow rate through monitoring and analysis technologies, and the further acquisition of the illumination condition data under the current large disturbance situation include: Through a distributed sensor network, the temperature, pressure, and flow rate data of each key part of the heat storage system are collected in real time and transmitted to the central control system for analysis and processing, so as to judge the current energy storage level, operation efficiency, and whether there are potential failure risks of the heat storage system; Using light monitoring devices, such as solar radiation sensors, light intensity meters, etc., the light condition data are obtained in real time, including light intensity, light duration, and cloud cover conditions, and then the change trend of light resources in the short term is predicted.

[0024] The construction of the energy supply prediction model is as Figure 2 shown, and further includes: S110: Preprocess the collected data, including converting the obtained forecast weather into a numerical form recognizable by a computer using one-hot encoding. For example, assuming there are three weather conditions: sunny, cloudy, and rainy, then sunny can be encoded as [1, 0, 0], cloudy as [0, 1, 0], and rainy as [0, 0, 1]. Then, use the 3σ criterion to identify and correct the outliers that significantly deviate from other data points for the state data of the heat storage system, light condition data, and the converted forecast weather data. At the same time, normalize the data to unify different magnitude data into the [0, 1] interval to eliminate the influence of dimensions for model training. For example, in this embodiment, the normalization formula for temperature data is: where, and are the minimum and maximum values in the temperature data sequence respectively; S111: Build the energy supply prediction model based on the long short-term memory network LSTM. Considering that power consumption, production efficiency, and safety prediction have time series characteristics, the LSTM network can effectively handle the long-term dependence problem in sequence data and is suitable for such prediction tasks. Divide the preprocessed data into a training set, a validation set, and a test set in chronological order. Select the mean squared error MSE as the loss function to train the energy supply prediction model to measure the difference between the model prediction value and the true value. Calculate the gradient of the loss function with respect to the model parameters through the backpropagation algorithm until the loss function no longer decreases on the validation set or reaches the preset maximum number of iterations. In this embodiment, the optimizer selects the Adam optimizer. When updating the parameters, the calculation formula is as follows: where, is the first moment estimate of the bias correction, , is the second moment estimate of the bias correction, , is the gradient at the current moment, and is the attenuation coefficient, usually set to 0.9 and 0.999 respectively, is the learning rate, is a small constant to prevent the denominator from being zero; S112: Evaluate the trained model using the test set data, and use the power prediction model with good evaluation and verification performance to predict the power consumption, production efficiency and safety of the lithium carbonate processing plant under different operating modes.

[0025] In step S12, actively schedule and adjust the operating parameters of the production equipment of the lithium carbonate processing plant according to the prediction and research results. The dynamic adjustment of the production load or safe shutdown further includes: If the prediction shows that in the next period of time, the light is sufficient and the energy storage of the heat storage system is sufficient, and the weather forecast is good at the same time, then formulate a full-load production plan, make full use of the rich energy supply, increase the output of lithium carbonate, give priority to using photovoltaic power generation to meet the electricity demand of the factory, and store the excess electricity in the electrochemical energy storage system for future use; If it is predicted that the light is insufficient, the energy storage of the heat storage system is also at a low level, and there are adverse weather conditions such as continuous rainy days in the weather forecast, then reasonably arrange some production lines to operate at reduced load or shut down safely according to the key degree of the production process and the energy demand. At the same time, start the electrochemical energy storage system as an emergency power supply to make up for the power gap; If it is predicted that the weather changes frequently, the light conditions are unstable, and the state of the heat storage system fluctuates, then formulate a flexible production plan, adopt the method of dynamically adjusting the production load, build an intelligent energy scheduling system, combine the real-time light, heat storage, and grid power data, and formulate the optimal energy distribution plan through an optimization algorithm.

[0026] The steps S20 for dealing with small disturbances further include: S200: Construct a molten salt storage prediction model based on the Navier - Stokes equation, which describes the momentum conservation of viscous Newtonian fluids, and the expression form is: Among them, is the fluid density, is the velocity vector, is the time, is the pressure, is the dynamic viscosity, is the volume force acting on the fluid, represents the local acceleration term, is the Hamiltonian operator, is the Laplace operator. By combining the specific structural parameters, operating conditions of the molten salt system, and the type and intensity of small disturbances, an accurate numerical model is constructed; S201: Divide the computational domain into a large number of small cells through a discretization method, and numerically solve the Navier-Stokes equations for each small cell to obtain the flow characteristics of the molten salt during the perturbation process, including the velocity field and pressure field distributions; S202: Precisely simulate the liquid level change by tracking the movement of the molten salt-gas interface and using the Volume of Fluid (VOF) method interface capturing technique. The VOF method represents the volume fraction of the molten salt in a certain cell by defining a volume fraction function to represent the proportion of the volume occupied by the molten salt in a certain cell, and the transport equation of : where represents that the cell is completely filled with molten salt, represents that there is no molten salt in the cell, and the interface position is tracked by solving the transport equation of ; S203: Determine the reduction in the molten salt liquid level caused by small perturbations under different working conditions through the constructed molten salt reserve prediction model.

[0027] Step S21 further includes: Construct an intelligent power grid dispatching automation system based on the distributed control system (DCS). Use the liquid level sensors installed in the molten salt system to monitor the molten salt liquid level data in real time. Combine the overall operation conditions of the power system, including load and the output power of each power generation device, and use linear programming to formulate an optimal dispatching plan, and precisely control the operation parameters of relevant devices according to the dispatching plan.

[0028] Among them, the use of linear programming to formulate an optimal dispatching plan further includes: Set the objective function to minimize the cost brought by adjusting the power generation device power or operation mode on the premise of meeting production requirements. The constraint conditions include the power balance constraint of the power system, the operation limits of the devices, and the requirements for the recovery of the molten salt liquid level; According to the dispatching plan, adjust the output power by changing the working point of the photovoltaic panels by adjusting the control parameters of the photovoltaic inverter, optimize the operation mode of the energy station, and precisely supplement the molten salt liquid level for the entire system on the premise of meeting production requirements.

[0029] In addition, according to the characteristic analysis of the lithium carbonate processing plant, it takes a certain amount of time, usually about 20 hours, to run from full load or 60% load to shutdown. The shutdown process is powered by molten salt. Arrange the shutdown plan in advance according to the molten salt liquid level to ensure the safe shutdown of the lithium carbonate processing plant.

[0030] This embodiment also provides a high-altitude isolated grid disturbance response system, including a large disturbance response system and a small disturbance response system. Among them, The large disturbance response system includes: A data collection module, which is used to collect the state data of the heat storage system including temperature, pressure and flow rate in real time through monitoring and analysis technologies, and obtain the light condition data and short-term forecast weather data under the current large disturbance situation in real time; A model construction module, which is used to construct an energy supply prediction model, and predict and judge the power consumption, production efficiency and safety of the lithium carbonate processing plant under different operating modes based on the state data of the heat storage system, light condition data and forecast weather data; A scheduling adjustment module, which is used to adjust the operating parameters of the production equipment of the lithium carbonate processing plant according to the prediction and judgment results, and dynamically adjust the production load or safely shut down; The small disturbance system includes: A molten salt prediction and analysis module, which is used to collect real-time molten salt liquid level data, construct a molten salt storage prediction model based on fluid dynamics simulation, analyze the complex behavior of molten salt during the disturbance process, and determine the reduction amount of molten salt liquid level caused by small disturbances under different working conditions; A scheduling supplement module, which is used to actively schedule the lithium carbonate processing plant to operate at a reduced load when the light is sufficient to supplement the reduction amount of molten salt caused by small disturbances.

[0031] Among them, the function implementation of each module and unit corresponds to each step in the above-mentioned embodiment of the high-altitude isolated grid disturbance response method, and its function and implementation process will not be elaborated here one by one.

[0032] Finally, it should be noted that the above are only the preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements should also be regarded as the protection scope of the present invention.

[0033] The technical features of the above-mentioned embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as the combinations of these technical features do not conflict, they should all be considered as the scope recorded in this specification.

Claims

1. A method for dealing with plateau isolated grid disturbance, characterized in that: Includes methods for dealing with large and small disturbances, among which: For large disturbances: S10: Through monitoring and analysis technology, real-time data on the status of the thermal storage system, including temperature, pressure and flow, is collected, and real-time data on light conditions and short-term weather forecasts under current large disturbances are obtained; S11: Construct an energy supply prediction model to predict the power consumption, production efficiency and safety of the lithium carbonate processing plant under different operating modes based on the status data of the thermal storage system, the light condition data and the forecast weather data; S12: Adjust the operating parameters of the lithium carbonate processing plant's production equipment based on the forecast and judgment results, and dynamically adjust the production load or shut down safely; For small disturbances: S20: Collect real-time molten salt level data, build a molten salt reserve prediction model based on fluid dynamics simulation, analyze the complex behavior of molten salt during disturbance, and determine the amount of molten salt level reduction caused by small disturbances under different working conditions; S21: When there is sufficient sunlight, the lithium carbonate processing plant is actively scheduled to operate at reduced load to compensate for the reduction in molten salt caused by small disturbances.

2. The plateau isolated network disturbance response method according to claim 1 is characterized in that: In step S10, real-time acquisition of status data of the heat storage system including temperature, pressure and flow rate through monitoring and analysis technology, and real-time acquisition of light condition data under current large disturbance conditions further include: Through the distributed sensor network, the temperature, pressure and flow data of key parts of the thermal storage system are collected in real time and transmitted to the central control system for analysis and processing to determine the current energy storage level, operating efficiency and potential failure risks of the thermal storage system; Use light monitoring equipment to obtain real-time light condition data, including light intensity, light duration, and cloud cover, to predict short-term trends in light resource changes.

3. The plateau isolated network disturbance response method according to claim 1 is characterized in that: In step S11, the building of the energy supply prediction model further includes: S110: preprocessing the collected data, including converting the obtained forecast weather into a numerical form recognizable by a computer using one-hot encoding, and then using the 3σ criterion to identify and correct abnormal values ​​that are obviously deviated from other data points for the state data of the thermal storage system, the light condition data, and the converted forecast weather data, and normalizing the data to unify data of different magnitudes into the [0, 1] interval to eliminate the dimension effect and facilitate model training; S111: constructing the energy supply prediction model based on the long short-term memory network LSTM, dividing the preprocessed data into a training set, a validation set and a test set in chronological order, selecting the mean square error MSE as the loss function to train the energy supply prediction model, measuring the difference between the model prediction value and the true value, and calculating the gradient of the loss function with respect to the model parameters through the back propagation algorithm until the loss function no longer decreases on the validation set or reaches a preset maximum number of iterations; S112: Evaluate the trained model through test set data, and use the energy supply prediction model with good evaluation and verification performance to predict the power consumption, production efficiency and safety of the lithium carbonate processing plant under different operating modes.

4. The plateau isolated network disturbance response method according to claim 1 is characterized in that: In step S12, adjusting the operating parameters of the lithium carbonate processing plant production equipment according to the prediction and judgment results, and dynamically adjusting the production load or safely shutting down further includes: If the forecast shows that there will be sufficient sunlight and energy storage in the thermal storage system in the future, and the weather is also good, a full-load production plan will be formulated to make full use of the abundant energy supply, increase the output of lithium carbonate, give priority to using photovoltaic power generation to meet the factory's electricity needs, and store the excess electricity in the electrochemical energy storage system for emergency use; If insufficient sunlight is predicted, the energy storage system is also at a low level, and adverse weather such as continuous rainy days is forecast, some production lines will be reasonably arranged to reduce load or shut down safely according to the criticality of the production process and energy demand, and the electrochemical energy storage system will be started as an emergency power supply to supplement the power gap; If it is predicted that the weather will change frequently, the lighting conditions will be unstable, and the state of the heat storage system will fluctuate, a flexible production plan will be formulated, and the production load will be dynamically adjusted to build an intelligent energy scheduling system. In combination with real-time lighting, heat storage, and grid power data, the optimal energy allocation plan will be formulated through optimization algorithms.

5. The method for coping with plateau isolated grid disturbance according to claim 1 is characterized in that: Step S20 further includes: S200: A molten salt reserve prediction model is constructed based on the Navier-Stokes equation, which describes the conservation of momentum of viscous Newtonian fluids and is expressed as: in, is the fluid density, is the velocity vector, For time, For pressure, is the dynamic viscosity, is the volume force acting on the fluid, represents the local acceleration term, is the Hamiltonian operator, For the Laplace operator, an accurate numerical model is constructed by combining the specific structural parameters of the molten salt system, operating conditions, and the type and intensity of small perturbations; S201: The calculation area is divided into a large number of small units by discretization method. The Navier-Stokes equation is numerically solved for each small unit to obtain the flow characteristics of molten salt during the disturbance process, including the velocity field and pressure field distribution; S202: By tracking the movement of the molten salt-gas interface, the Volume of Fluid method interface capture technology is used to accurately simulate the liquid level change. The Volume of Fluid method defines a volume fraction function To express the volume ratio of molten salt in a unit, The transport equation is expressed as follows: : in, Indicates that the cell is completely filled with molten salt, Indicates that there is no molten salt in this unit. By solving The transport equations of track the interface position; S203: Determine the reduction in molten salt level caused by small disturbances under different working conditions through the constructed molten salt reserve prediction model.

6. The plateau isolated grid disturbance response method according to claim 1 is characterized in that: Step S21 further includes: Based on the distributed control system DCS, a smart grid dispatching automation system is built. The liquid level sensor installed in the molten salt system is used to monitor the molten salt level data in real time. Combined with the overall operation of the power system including the load and the output power of each power generation equipment, linear programming is used to formulate the optimal dispatching plan, and the operating parameters of related equipment are accurately controlled according to the dispatching plan.

7. The method for dealing with plateau isolated grid disturbance according to claim 6 is characterized in that: The method of formulating the optimal scheduling scheme by linear programming further includes: The objective function is set to minimize the cost of adjusting the power or operation mode of the power generation equipment under the premise of meeting the production demand. The constraints include the power balance constraint of the power system, the operation limit of the equipment and the recovery requirement of the molten salt level. According to the scheduling plan, the output power is regulated by adjusting the control parameters of the photovoltaic inverter to change the working point of the photovoltaic panel, optimize the operation mode of the energy station, and enable the entire system to accurately replenish the molten salt level while meeting production needs.

8. The plateau isolated network disturbance response method according to claim 1 is characterized in that: The safety shutdown further includes: According to the characteristics analysis of the lithium carbonate processing plant, it takes a certain amount of time to go from full load or 60% load to shutdown. The entire shutdown process is powered by molten salt. The shutdown plan is arranged in advance according to the molten salt liquid level to ensure the safe shutdown of the lithium carbonate processing plant.

9. A plateau isolated network disturbance response system, characterized in that: It includes large disturbance response system and small disturbance response system, among which, The large disturbance response system includes: The data collection module is used to collect the status data of the thermal storage system including temperature, pressure and flow rate in real time through monitoring and analysis technology, and obtain the light condition data and short-term weather forecast data under the current large disturbance in real time; The model building module is used to build an energy supply prediction model to predict the power consumption, production efficiency and safety of the lithium carbonate processing plant under different operating modes based on the status data of the thermal storage system, the light condition data and the forecast weather data; The scheduling adjustment module is used to adjust the operating parameters of the lithium carbonate processing plant's production equipment according to the prediction and judgment results, and dynamically adjust the production load or shut down safely; The small disturbance system comprises: The molten salt prediction and analysis module is used to collect real-time molten salt level data, build a molten salt reserve prediction model based on fluid dynamics simulation, analyze the complex behavior of molten salt during disturbance, and determine the amount of molten salt level reduction caused by small disturbances under different working conditions; The scheduling and replenishment module is used to actively schedule the lithium carbonate processing plant to reduce load when there is sufficient sunlight, so as to compensate for the reduction in molten salt caused by small disturbances.

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