Power plant deep peak regulation and power generation optimization method and system based on steam extraction and energy storage

Abstract

The invention provides a power plant deep peak regulation and power generation optimization method and system based on steam extraction energy storage, and relates to the technical field of power grids, the method comprises the steps that high-temperature and high-pressure steam is generated through a steam turbine, the steam enters a steam extraction system, and the steam extraction system is used for extracting part of steam exhausted by the steam turbine when the load of the power grid is low; the extracted steam is conveyed into the energy storage device to be stored; based on historical power grid load data and real-time load prediction data, dynamic optimization adjustment of generated power is carried out by combining characteristics of a generator set of a power plant, and deep peak regulation operation of the generator set in a low-load operation state is realized by controlling a power generation dispatching control module of the power plant. The output power of each unit is adjusted through an optimization algorithm; when the load of the power grid is low, part of steam of the steam turbine is stored in the energy storage device through the steam extraction system, and when the load of the power grid is peak, the stored steam flows back to the steam turbine again to be reused.

Description

Technical Field

[0001] The present invention relates to power grid technology, and in particular to a method and system for deep peak regulation and power generation optimization of a power plant based on extracted steam energy storage. Background Art

[0002] It is difficult for traditional power plants to maintain efficient operation when the load is low, which easily leads to energy waste and reduced power generation efficiency. Therefore, how to optimize the operation of generator sets and improve energy utilization during deep peak regulation has become a key issue in the current operation of power plants.

[0003] As an effective means of peak load regulation, the extraction steam energy storage technology provides a flexible solution for power plants in the process of deep peak load regulation. The extraction steam energy storage system adjusts the steam extraction volume of the steam turbine to store excess steam in the energy storage device. When the grid load increases, the stored steam is returned to the steam turbine to increase the output power of the generator set. By reasonably controlling the steam extraction volume and steam return volume, the power plant can store excess energy when the load is low and release energy when the load is peak, so as to achieve the purpose of balancing power output and optimizing power generation efficiency.

[0004] Traditional peak-shaving methods in power plants mainly rely on power regulation of generator sets, usually by reducing fuel supply or lowering the operating load of generator sets to cope with load troughs. However, this strategy can easily lead to low operating efficiency of units, and may even cause problems such as increased equipment wear and increased power generation costs. In contrast, peak-shaving technology based on extraction steam energy storage can effectively alleviate the impact of load fluctuations on unit operation by dynamically adjusting the extraction steam system and energy storage device without significantly reducing the unit load, and significantly improve the peak-shaving capacity and energy utilization of power plants. Summary of the invention

[0005] The embodiments of the present invention provide a method and system for deep peak regulation and power generation optimization of a power plant based on extracted steam storage, which can solve the problems in the prior art.

[0006] According to a first aspect of the embodiments of the present invention,

[0007] Provides deep peak regulation and power generation optimization methods for power plants based on extracted steam storage, including:

[0008] The steam turbine generates high-temperature and high-pressure steam, which enters the steam extraction system. The steam extraction system is used to extract part of the steam discharged from the steam turbine when the power grid load is low, and transport the extracted steam to the energy storage device for storage. The energy storage device includes a high-temperature heat storage device and a high-pressure steam storage tank. The steam extraction system is connected to the energy storage device to achieve efficient storage of steam. When the power grid load is at a peak, the power generation dispatching control module adjusts the steam extraction system according to the real-time power grid load information, so that the steam stored in the energy storage device is returned to the medium-pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module for reuse. The thermal system is used to recover the residual heat generated by the steam turbine. The residual heat is transported to the condenser through a pipeline for condensation treatment, and the condensed water is returned to the boiler for recycling.

[0009] Based on historical grid load data and real-time load forecast data, combined with the characteristics of the power plant's generator sets, dynamic optimization and adjustment of power generation is carried out. By controlling the power generation dispatching control module of the power plant, deep peak-shaving operation of the generator sets under low-load operation is achieved, and the output power of each unit is adjusted through the optimization algorithm. During the deep peak-shaving process, various parameters of the steam turbine, steam extraction system and energy storage device are monitored and adjusted. According to the real-time operating conditions, the steam extraction volume, steam return volume, energy storage state of the energy storage device and the output power of the steam turbine are dynamically adjusted to achieve the deep peak-shaving goal of the power plant.

[0010] Based on historical data and real-time load change trends, future loads are predicted. Based on the load prediction results, combined with the energy storage status of the energy storage device and the operating status of the steam turbine, the operating strategy of the generator set is dynamically adjusted to achieve the power generation optimization goal of the power plant. When the grid load is low, part of the steam from the steam turbine is stored in the energy storage device through the steam extraction system. When the grid load is peak, the stored steam is returned to the steam turbine for reuse, thereby achieving peak load balance of the grid without increasing fuel consumption.

[0011] In an optional embodiment,

[0012] When the grid load is at its peak, the power generation dispatch control module adjusts the steam extraction system according to the real-time grid load information, so that the steam stored in the energy storage device is returned to the medium-pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module for reuse; the thermal system is used to recover the residual heat generated by the steam turbine, and the residual heat is transported to the condenser through a pipeline for condensation treatment, and the condensed water is returned to the boiler for recycling, including:

[0013] When the load of the power grid is at its peak, the power generation dispatch control module controls the steam extraction system to release the stored high-temperature and high-pressure steam from the energy storage device according to the load forecast result, and the high-temperature and high-pressure steam is introduced into the intermediate pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module. The steam distribution module dynamically adjusts the proportion of steam entering the intermediate pressure cylinder or low-pressure cylinder according to the current operating conditions of the steam turbine to optimize the energy output of the steam turbine;

[0014] The low-temperature and low-pressure steam generated by the steam turbine during operation is recovered through the thermal system for waste heat. The low-temperature and low-pressure steam enters the condenser through the pipeline of the thermal system. The condenser condenses the low-temperature and low-pressure steam into condensed water through cooling water or air. The condensed water then flows back to the boiler through the pipeline for cyclic heating treatment to form a closed-loop circulating steam supply system, thereby realizing efficient recovery and reuse of steam.

[0015] The power generation dispatching control module dynamically adjusts the steam extraction volume, steam return volume and output power of the steam turbine of the steam extraction system in combination with the operating status of the steam turbine, the energy storage status of the energy storage device and the condensing efficiency of the condenser to ensure efficient utilization of steam under different load conditions. The adjustment is based on real-time acquired system operating parameters and grid load changes, and real-time regulation of each system component is performed through an automated control system.

[0016] In an optional embodiment,

[0017] Based on historical grid load data and real-time load forecast data, combined with the characteristics of the power plant's generator sets, dynamic optimization and adjustment of power generation is carried out. By controlling the power generation dispatching control module of the power plant, deep peak-shaving operation of the generator sets under low-load operation is achieved, and the output power of each unit is adjusted through the optimization algorithm, including:

[0018] The historical grid load data of the grid is obtained through the grid load data acquisition module, wherein the historical grid load data includes daily load, monthly load and seasonal load, and the historical grid load data is cleaned and preprocessed to remove abnormal values ​​and process missing data to ensure the accuracy and continuity of the data;

[0019] The current grid load data is obtained in real time through the grid load monitoring module, and combined with the historical grid load data, a load forecasting algorithm is used to generate a forecast result of the future grid load. The load forecasting algorithm includes a time series model or a load forecasting model based on machine learning. The forecast result is used to guide the subsequent power adjustment of the generator set;

[0020] Obtain the operating characteristics of each generator set, including the load response speed, start-stop time, minimum stable output and power generation efficiency of the generator set, formulate the load adaptation strategy of the generator set in combination with the real-time load forecast results, and determine the power adjustment range and load adaptation plan of each generator set;

[0021] Based on the real-time load forecast results and the load adaptation plan of the generator set, the power generation dispatch control module dynamically adjusts the output power of each generator set through an optimization algorithm. The optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm, and the goal is to minimize fuel consumption or maximize power generation efficiency. The optimization algorithm calculates the optimized power distribution plan in real time according to the current grid load demand and the status of the generator set;

[0022] When the grid load forecast results show that the grid load is in a low state, the power generation dispatching control module performs deep peak-shaving operations according to the minimum stable output of each generator set. By shutting down some generator sets or controlling the output power of some generator sets to the minimum stable output, it avoids long-term low-load operation of the units, resulting in inefficiency or equipment loss.

[0023] In an optional embodiment,

[0024] During the deep peak regulation process, various parameters of the steam turbine, steam extraction system and energy storage device are monitored and adjusted. The steam extraction volume, steam return volume, energy storage state of the energy storage device and output power of the steam turbine are dynamically adjusted according to the real-time working conditions to achieve the deep peak regulation goals of the power plant, including:

[0025] Through multiple sensors installed in the steam turbine, the steam extraction system and the energy storage device, real-time data collection is performed on multiple operating parameters such as the output power of the steam turbine, the steam extraction amount of the steam extraction system, the energy storage state of the energy storage device, and the steam return amount. The collected parameters include the pressure, temperature, flow rate of the steam and the state of the gas storage tank of the energy storage device;

[0026] According to the real-time collected data of the steam turbine load and the working condition of the steam extraction system, the power generation dispatching control module calculates the steam extraction amount required under the current load conditions; the calculation process is based on the real-time load demand of the power grid and the minimum stable output of the steam turbine, ensuring that the steam extraction amount matches the real-time load conditions, and adjusting the steam extraction amount by controlling the opening of the steam extraction valve, and the opening adjustment of the steam extraction valve is controlled by the automatic control system;

[0027] During the steam extraction process, the energy storage status of the energy storage device is monitored in real time, including the steam pressure and temperature in the gas storage tank; when the energy storage status of the energy storage device reaches the preset threshold, the automatic control system adjusts the steam extraction volume to prevent the energy storage device from being overloaded, while ensuring that the system has sufficient energy storage to support subsequent steam reflux operations during peak loads;

[0028] When the grid load forecast shows that the grid load will enter a peak stage, the power generation dispatch control module calculates the required steam return flow according to the power demand of the steam turbine and the energy storage status of the energy storage device, and adjusts the steam return flow by controlling the opening of the return valve; the return flow adjustment process needs to ensure that the pressure and temperature of the return steam match the current operating status of the steam turbine to avoid the return steam from impacting the steam turbine.

[0029] In an optional embodiment,

[0030] The method further comprises:

[0031] The power generation dispatch control module predicts future grid load changes through historical load data and real-time load change trends. When the load is low, the steam extraction volume is increased to store excess steam in the energy storage device. When the load is high, the steam extraction volume is reduced and steam reflux is increased to dynamically adjust the output power of the steam turbine to ensure that the power output of the steam turbine matches the grid load demand.

[0032] During the regulation of the extraction volume and steam return volume, the system forms a closed-loop feedback control mechanism by real-time monitoring of the operating data of the extraction system, energy storage device and steam turbine; the power generation dispatching control module further fine-tunes the extraction valve and return valve according to the feedback data to ensure that the extraction volume, steam return volume, energy storage status of the energy storage device and power output of the steam turbine are dynamically adjusted in real time to meet the changing needs of the power grid load.

[0033] In an optional embodiment,

[0034] Based on historical data and real-time load change trends, future loads are predicted. Based on the load prediction results, combined with the energy storage status of the energy storage device and the operating status of the steam turbine, the operating strategy of the generator set is dynamically adjusted to achieve the power generation optimization goals of the power plant, including:

[0035] Using a load forecasting model based on time series analysis, machine learning or neural network, based on historical load data and real-time load change trends, predict future power grid load; the load forecasting model generates a forecast result of future load demand by analyzing seasonal changes, periodic fluctuations and nonlinear relationships of power grid load;

[0036] The real-time operating data of the generator set is collected through multiple sensors installed in the power generation system, including the output power of the steam turbine, the steam extraction volume of the steam extraction system, the energy storage status of the energy storage device and the steam return volume; the collected real-time data is dynamically compared with the load prediction results to form operating condition feedback data;

[0037] The optimal steam extraction capacity under current load conditions is calculated based on the load forecast results and the energy storage status of the energy storage device. When the load forecast shows that the load is about to enter a valley, the automatic control system instructs the steam extraction system to increase the steam extraction capacity and store the excess steam in the energy storage device. When the load forecast shows that the load is about to rise, the system reduces the steam extraction capacity and starts steam reflux to ensure that the energy storage device can provide additional steam supply to the turbine during the peak load period.

[0038] Based on the load forecast results and real-time operating data, the control system adjusts the operating status of the steam turbine; when the load forecast results show that the future load will increase, the system increases the steam extraction volume and increases the steam return volume to increase the power output of the steam turbine; when the load forecast results show that the load will decrease, the system increases the steam extraction volume to reduce the power output of the steam turbine to avoid energy waste;

[0039] The automatic control system collects the operating conditions of the generator set in real time, including the steam pressure, temperature, flow rate of the steam turbine and the energy storage status of the energy storage device, and performs closed-loop control on the steam extraction volume, steam return volume and power output of the steam turbine; the closed-loop control system fine-tunes the power generation strategy based on the real-time feedback data to ensure that the output power of the generator set matches the load demand of the power grid and avoids excess or insufficient power;

[0040] The power generation dispatching control module generates a dynamic adjustment strategy based on the load forecast results, the energy storage status of the energy storage device and the real-time operating conditions of the steam turbine; the adjustment strategy realizes the dynamic operation optimization of the generator set under different load stages by controlling the opening of the steam extraction valve and the return valve, ensuring that the generator set stores excess steam at low loads and increases output power at peak loads, thereby realizing energy efficiency optimization of power plant operation.

[0041] According to a second aspect of the embodiments of the present invention,

[0042] Provide deep peak regulation and power generation optimization system for power plants based on extracted steam storage, including:

[0043] The first unit is used to generate high-temperature and high-pressure steam through a steam turbine. The steam enters a steam extraction system. The steam extraction system is used to extract part of the steam discharged from the steam turbine when the power grid load is low, and transport the extracted steam to an energy storage device for storage. The energy storage device includes a high-temperature heat storage device and a high-pressure steam storage tank. The steam extraction system is connected to the energy storage device to achieve efficient storage of steam. When the power grid load is at a peak, the power generation dispatching control module adjusts the steam extraction system according to the real-time power grid load information, so that the steam stored in the energy storage device is returned to the medium-pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module for reuse. The thermal system is used to recover the residual heat generated by the steam turbine. The residual heat is transported to the condenser through a pipeline for condensation treatment, and the condensed water is returned to the boiler for recycling.

[0044] The second unit is used to dynamically optimize and adjust the power generation based on historical grid load data and real-time load forecast data, combined with the characteristics of the power plant's generator sets. By controlling the power generation dispatching control module of the power plant, the deep peak-shaving operation of the generator sets under low-load operation is realized, and the output power of each unit is adjusted through the optimization algorithm; in the process of deep peak-shaving, various parameters of the steam turbine, steam extraction system and energy storage device are monitored and adjusted, and the steam extraction volume, steam return volume, energy storage state of the energy storage device and the output power of the steam turbine are dynamically adjusted according to the real-time operating conditions to achieve the deep peak-shaving goal of the power plant;

[0045] The third unit, for the deep peak-shaving optimization module further includes a load prediction module and a power generation optimization algorithm module. The load prediction module predicts future loads based on historical data and real-time load change trends. The power generation optimization algorithm module dynamically adjusts the operating strategy of the generator set based on the load prediction results, combined with the energy storage status of the energy storage device and the operating status of the steam turbine, so as to achieve the power generation optimization goal of the power plant; when the grid load is low, part of the steam of the steam turbine is stored in the energy storage device through the steam extraction system, and when the grid load is peak, the stored steam is returned to the steam turbine for reuse, thereby achieving peak-shaving balance of the grid load without increasing fuel consumption.

[0046] According to a third aspect of the embodiments of the present invention,

[0047] An electronic device is provided, comprising:

[0048] processor;

[0049] a memory for storing processor-executable instructions;

[0050] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0051] According to a fourth aspect of the embodiments of the present invention,

[0052] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0053] This technical solution achieves efficient scheduling and optimization of the power generation system during peak grid load. The extraction, storage, reuse of steam, and the recovery and recycling of heat form a closed-loop system to ensure efficient power generation and maximization of energy utilization under different load conditions.

[0054] By dynamically adjusting the steam extraction and steam return, the power plant can effectively balance the load demand during deep peak regulation and rationally utilize the steam resources in the energy storage device. The dynamic adjustment of the steam extraction ensures efficient energy utilization during low-load operation, while the dynamic adjustment of the steam return can increase the power output of the steam turbine during peak loads to meet the load requirements of the power grid. The synergy of the two is achieved through the automatic control system, ensuring that the entire system operates efficiently and stably under complex load conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a method for deep peak regulation and power generation optimization of a power plant based on extracted steam storage according to an embodiment of the present invention;

[0056] Figure 2 It is a structural schematic diagram of a deep peak regulation and power generation optimization system of a power plant based on extracted steam storage according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0059] Figure 1 FIG. 1 is a flow chart of a method for deep peak regulation and power generation optimization of a power plant based on extracted steam storage according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0060] S101. Generate high-temperature and high-pressure steam through the steam turbine, and the steam enters the steam extraction system. The steam extraction system is used to extract part of the steam discharged from the steam turbine when the power grid load is low, and transport the extracted steam to the energy storage device for storage. The energy storage device includes a high-temperature heat storage device and a high-pressure steam storage tank. The steam extraction system is connected to the energy storage device to achieve efficient storage of steam; when the power grid load is at a peak, the power generation dispatching control module adjusts the steam extraction system according to the real-time power grid load information, so that the steam stored in the energy storage device is returned to the medium-pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module for reuse; the thermal system is used to recover the residual heat generated by the steam turbine, and the residual heat is transported to the condenser through a pipeline for condensation treatment, and the condensed water is returned to the boiler for recycling;

[0061] S102. Based on historical grid load data and real-time load forecast data, combined with the characteristics of the power plant's generator sets, dynamically optimize and adjust the power generation power, and control the power generation dispatching control module of the power plant to achieve deep peak-shaving operation of the generator sets under low-load operation, and adjust the output power of each unit through the optimization algorithm; monitor and adjust various parameters of the steam turbine, steam extraction system and energy storage device during the deep peak-shaving process, and dynamically adjust the steam extraction volume, steam return volume, energy storage state of the energy storage device and the output power of the steam turbine according to the real-time operating conditions to achieve the deep peak-shaving target of the power plant;

[0062] S103. The deep peak-shaving optimization module further includes a load prediction module and a power generation optimization algorithm module. The load prediction module predicts future loads based on historical data and real-time load change trends. The power generation optimization algorithm module dynamically adjusts the operating strategy of the generator set based on the load prediction results, combined with the energy storage status of the energy storage device and the operating status of the steam turbine, so as to achieve the power generation optimization goal of the power plant; when the grid load is low, part of the steam of the steam turbine is stored in the energy storage device through the steam extraction system, and when the grid load is peak, the stored steam is returned to the steam turbine for reuse, thereby achieving peak-shaving balance of the grid load without increasing fuel consumption.

[0063] In an optional embodiment,

[0064] When the grid load is at its peak, the power generation dispatch control module adjusts the steam extraction system according to the real-time grid load information, so that the steam stored in the energy storage device is returned to the medium-pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module for reuse; the thermal system is used to recover the residual heat generated by the steam turbine, and the residual heat is transported to the condenser through a pipeline for condensation treatment, and the condensed water is returned to the boiler for recycling, including:

[0065] When the load of the power grid is at its peak, the power generation dispatch control module controls the steam extraction system to release the stored high-temperature and high-pressure steam from the energy storage device according to the load forecast result, and the high-temperature and high-pressure steam is introduced into the intermediate pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module. The steam distribution module dynamically adjusts the proportion of steam entering the intermediate pressure cylinder or low-pressure cylinder according to the current operating conditions of the steam turbine to optimize the energy output of the steam turbine;

[0066] The low-temperature and low-pressure steam generated by the steam turbine during operation is recovered through the thermal system for waste heat. The low-temperature and low-pressure steam enters the condenser through the pipeline of the thermal system. The condenser condenses the low-temperature and low-pressure steam into condensed water through cooling water or air. The condensed water then flows back to the boiler through the pipeline for cyclic heating treatment to form a closed-loop circulating steam supply system, thereby realizing efficient recovery and reuse of steam.

[0067] The power generation dispatching control module dynamically adjusts the steam extraction volume, steam return volume and output power of the steam turbine of the steam extraction system in combination with the operating status of the steam turbine, the energy storage status of the energy storage device and the condensing efficiency of the condenser to ensure efficient utilization of steam under different load conditions. The adjustment is based on real-time acquired system operating parameters and grid load changes, and real-time regulation of each system component is performed through an automated control system.

[0068] For example, in order to realize the reuse of steam stored in the energy storage device and the recovery and utilization of residual heat of the steam turbine during peak load of the power grid, the technical solution can be implemented in detail through the following steps:

[0069] When the grid load is at its peak, the power plant needs to dispatch according to the load changes to ensure that the output power of the generator sets matches the grid demand. To this end, the system first monitors the load data of the grid in real time through the power generation dispatch control module. This module obtains the grid load information at the current moment through data communication with the grid dispatch center, and predicts the load demand in the next few periods based on the change trend of the grid load.

[0070] Specifically, the power generation dispatch control module uses a load forecasting algorithm, combined with historical grid load data and current real-time load information, to dynamically adjust the operating status of the steam extraction system to adapt to the steam demand during peak grid load. This load forecasting algorithm can be based on a variety of technical means such as time series analysis and machine learning to ensure the accuracy of the forecast results, thereby providing a reliable basis for subsequent steps.

[0071] When the grid load reaches its peak, the power generation dispatch control module adjusts the steam extraction system according to the prediction results, so that the steam stored in the energy storage device is returned to the medium pressure cylinder or low pressure cylinder of the steam turbine through the steam distribution module for reuse. This process is divided into the following steps:

[0072] Energy storage steam release control: When the grid load is at its peak, the high-temperature and high-pressure steam in the energy storage device needs to be released on demand. The power generation dispatch control module controls the steam release rate by adjusting the release valve of the energy storage device to ensure that the amount of steam released matches the operating requirements of the steam turbine.

[0073] Adjustment of the steam distribution module: The released steam enters the steam turbine through the steam distribution module. The steam distribution module dynamically adjusts the steam to enter the medium pressure cylinder or low pressure cylinder according to the current operating status of the steam turbine. At peak load, due to the large load of the steam turbine, the reuse of steam helps to improve power generation efficiency and reduce fuel consumption.

[0074] Steam return path: After the steam is released from the energy storage device, it enters the steam distribution module through the high-temperature and high-pressure pipeline, and then the steam distribution module distributes the steam to the medium-pressure cylinder or low-pressure cylinder of the steam turbine. Generally, when more steam enters the medium-pressure cylinder, it is suitable for power plants with high loads and high power output. When the load is low, more steam enters the low-pressure cylinder to maintain the stable operation of the steam turbine.

[0075] When the steam turbine is working, it will generate a lot of residual heat, usually in the form of low-temperature and low-pressure steam. In order to improve energy efficiency, the thermal system is responsible for recovering this residual heat. The specific steps are as follows:

[0076] Export of low-temperature and low-pressure steam: After the steam in the steam turbine does work, most of its energy is converted into mechanical energy, and the remaining part is discharged from the steam turbine in the form of low-temperature and low-pressure steam. At this time, the thermal system exports this remaining steam through a special steam export pipeline.

[0077] Condensation treatment of condenser: The low-temperature and low-pressure steam is transported to the condenser through pipelines. The condenser is a device that condenses steam into water through cooling water or air. During the condensation process, the heat of the steam is taken away by the cooling water or air, causing the steam to cool rapidly and turn into liquid water.

[0078] Coolant circulation: The operation of the condenser depends on the circulation of the coolant. The coolant can be water or air, depending on the design of the power plant. Cooling water is usually taken from the cooling tower and enters the condenser through a circulating pump to exchange heat with the steam. The cooling water after heat exchange returns to the cooling tower, cools again and is reused. Air condensation uses a fan to force air through the condenser tube wall to remove the heat from the steam.

[0079] Condensate return: The condensed water flows back to the boiler through the pipeline for recycling. The boiler heats the condensed water and converts it into high-temperature and high-pressure steam again, which is supplied to the steam turbine for power generation. This process achieves efficient use of water resources and reduces the waste of cooling water.

[0080] In the whole system, steam extraction, energy storage, reuse and heat recovery form a closed-loop energy cycle. Through reasonable design and scheduling, the power plant can improve power generation efficiency without increasing additional fuel consumption, which is specifically reflected in the following aspects:

[0081] Efficient use of energy storage devices: When the load is low, the steam extraction system stores the excess steam in the turbine in the energy storage device; when the load is peak, the steam in the energy storage device is reused, realizing the allocation of energy across time periods.

[0082] Waste heat recovery of thermal system: By recovering the residual heat of the turbine and condensing it into water, and then sending it back to the boiler for recycling, the energy utilization efficiency is greatly improved and the heat waste is reduced.

[0083] Optimization of power generation efficiency: Through the dynamic allocation of steam by the power generation dispatching control module, the steam turbine can flexibly adjust its working state according to different load requirements to maximize power generation efficiency.

[0084] In order to ensure the stable operation of the system, the entire extraction steam energy storage and heat recovery process is controlled by an automatic control system. The system includes multiple sensors, actuators and control algorithms:

[0085] Sensor monitoring: The sensors in the control system monitor the temperature, pressure, flow and other parameters of each key node in real time. For example, the steam pressure of the energy storage device, the operating status of the steam turbine, the cooling efficiency of the condenser, etc.

[0086] Actuator adjustment: Based on the information fed back by the sensors, the control system uses actuators to adjust valves, pumps, fans and other equipment in real time to ensure that the system is in the best operating state. For example, it adjusts the steam release of the energy storage device and the cooling water flow of the condenser.

[0087] Control algorithm optimization: The core algorithm in the control system is dynamically optimized based on real-time data to ensure the coordinated operation of steam extraction, energy storage, steam reuse, waste heat recovery and other links to maximize power generation efficiency and achieve grid load balance.

[0088] Through the above steps, this technical solution realizes the efficient scheduling and optimization of the power generation system during the peak load of the power grid. The extraction, storage, reuse of steam and the recovery and recycling of heat form a closed-loop system to ensure efficient power generation and maximization of energy utilization under different load conditions.

[0089] In an optional embodiment,

[0090] Based on historical grid load data and real-time load forecast data, combined with the characteristics of the power plant's generator sets, dynamic optimization and adjustment of power generation is carried out. By controlling the power generation dispatching control module of the power plant, deep peak-shaving operation of the generator sets under low-load operation is achieved, and the output power of each unit is adjusted through the optimization algorithm, including:

[0091] The historical grid load data of the grid is obtained through the grid load data acquisition module, wherein the historical grid load data includes daily load, monthly load and seasonal load, and the historical grid load data is cleaned and preprocessed to remove abnormal values ​​and process missing data to ensure the accuracy and continuity of the data;

[0092] The current grid load data is obtained in real time through the grid load monitoring module, and combined with the historical grid load data, a load forecasting algorithm is used to generate a forecast result of the future grid load. The load forecasting algorithm includes a time series model or a load forecasting model based on machine learning. The forecast result is used to guide the subsequent power adjustment of the generator set;

[0093] Obtain the operating characteristics of each generator set, including the load response speed, start-stop time, minimum stable output and power generation efficiency of the generator set, formulate the load adaptation strategy of the generator set in combination with the real-time load forecast results, and determine the power adjustment range and load adaptation plan of each generator set;

[0094] Based on the real-time load forecast results and the load adaptation plan of the generator set, the power generation dispatch control module dynamically adjusts the output power of each generator set through an optimization algorithm. The optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm, and the goal is to minimize fuel consumption or maximize power generation efficiency. The optimization algorithm calculates the optimized power distribution plan in real time according to the current grid load demand and the status of the generator set;

[0095] When the grid load forecast results show that the grid load is in a low state, the power generation dispatching control module performs deep peak-shaving operations according to the minimum stable output of each generator set. By shutting down some generator sets or controlling the output power of some generator sets to the minimum stable output, it avoids long-term low-load operation of the units, resulting in inefficiency or equipment loss.

[0096] For example, in order to achieve dynamic optimization and adjustment of power generation under changing grid load conditions, the system controls the power generation dispatching control module based on historical grid load data and real-time load forecast data, combined with the characteristics of the power plant's generator sets, especially during low-load operation, deep peak-shaving operations are achieved through intelligent algorithms, thereby adjusting the output power of each generator set. The following will focus on this technical solution and describe its implementation steps and specific technical details in detail.

[0097] First, the system needs to collect and analyze historical grid load data. Historical grid load data is an important basis for predicting future grid load change trends, especially in terms of seasonal fluctuations, day and night load changes, etc., which helps guide the power plant's power generation dispatching strategy. The specific steps are as follows:

[0098] Data collection: The power plant regularly obtains the load data of the power grid through the communication system with the power grid center. These data usually include multiple dimensions such as daily load, monthly load, seasonal load, etc., which can reflect the load change pattern of the power grid in different time periods. The data collection cycle can be hourly, daily, or monthly, depending on the refined needs of the power plant's power generation scheduling.

[0099] Data cleaning and preprocessing: The collected historical load data needs to be cleaned and preprocessed, including removing outliers in the data, processing missing data, etc. Commonly used data cleaning methods include interpolation and moving average methods to ensure the accuracy and continuity of the data.

[0100] Load data analysis: By analyzing the cleaned historical load data, the power generation dispatching system can discover the long-term trend of the power grid load, the periodic fluctuation characteristics, and the distribution of load peak and valley periods. Commonly used analysis methods include time series analysis and frequency domain analysis. These analysis results provide important references for subsequent load forecasting and power generation dispatching.

[0101] Based on the historical load data, the power generation dispatch control module also needs to combine the current real-time load data to predict the future power grid load. The accuracy of load prediction directly affects the power generation power regulation strategy of the power plant. The specific steps are as follows:

[0102] Real-time data acquisition: The system obtains the current grid load data in real time through the grid dispatch center. These data usually include instantaneous load, short-term load change trend, etc., which can reflect the current power demand of the grid. The frequency of real-time data acquisition is high, usually at the minute or second level, to ensure that the system can quickly respond to changes in grid load.

[0103] Establishment of load forecasting model: Based on historical load data and current real-time load data, the system establishes a load forecasting model. Commonly used forecasting models include time series models (such as ARI MA model) and machine learning-based forecasting models (such as long short-term memory network LSTM, support vector machine SVM, etc.). These models can effectively capture the trend of load changes and seasonal fluctuation characteristics.

[0104] Output of forecast results: The load forecast model outputs the load forecast value for a period of time in the future based on real-time data and historical load data. The forecast time span can be from a few hours to a few days, depending on the needs of the power plant. The forecast results provide a basis for the power regulation and peak load regulation of the generator set.

[0105] The generator sets in power plants have different operating characteristics, including load response speed, start-stop time, minimum stable output, etc. In order to operate efficiently under different load conditions, the system needs to combine the characteristics of the generator sets and reasonably allocate the output power of each unit. The specific steps are as follows:

[0106] Generator set characteristic data collection: The operating characteristics of each generator set can be obtained through historical operating data, including load response curve, start and stop duration, unit efficiency, etc. The system analyzes these data to understand the operating efficiency and response speed of each unit under different loads.

[0107] Load adaptation strategy: The system formulates a load adaptation strategy based on the characteristics of the generator set. For example, some units are suitable for operation under high load and have higher power generation efficiency, while some units are suitable for operation under low load and have better peak load regulation capabilities. Therefore, the system reasonably allocates the power generation tasks of different units based on the current and predicted grid load to ensure the maximization of the overall power generation efficiency.

[0108] Based on the load forecast results and the characteristics of the generator sets, the power generation dispatch control module dynamically optimizes and adjusts the power generation of the power plant. The core of this process is to adjust the power output of each generator set in real time through the optimization algorithm to adapt to the fluctuation of the grid load. The specific steps are as follows:

[0109] Establishment of power distribution model: The system establishes a power distribution model based on the load forecast results and the operating characteristics of the generator set. The goal of this model is to minimize fuel consumption or maximize power generation efficiency through optimization algorithms. Commonly used optimization algorithms include genetic algorithms, particle swarm optimization algorithms, etc.

[0110] Real-time power adjustment: The power generation dispatch control module adjusts the power output of each generator set in real time according to the output of the power distribution model. For example, when it is predicted that the grid load will increase, the system will start the units with fast response capabilities in advance and gradually increase their power output; when the load decreases, the system will gradually reduce the power output of the units, or shut down some units to avoid unnecessary energy consumption.

[0111] Feedback control and adjustment: The adjustment of power generation is a dynamic process. The system ensures the stability and accuracy of power adjustment through real-time feedback control. The actual power output of each generator set is monitored in real time by a power meter. The power generation dispatch control module further fine-tunes the output of the power distribution model based on the feedback data to ensure that the power generation of the power plant accurately matches the grid load demand.

[0112] When the grid load is low, the power plant needs to perform deep peak load regulation to avoid the unit being in a low load operation state for a long time, resulting in low efficiency or equipment loss. The specific steps of deep peak load regulation are as follows:

[0113] Determination of deep peak load regulation: When the load forecasting model predicts that the grid load will be at a low point for a long time, the system determines that deep peak load regulation is required. At this time, the power generation dispatching control module determines which units can enter the peak load regulation state based on the minimum stable output of the units.

[0114] Optimization of unit start and stop: During deep peak regulation, the system may need to shut down some generators to avoid energy consumption and loss during low-load operation. The power generation dispatch control module uses an optimization algorithm to determine the most suitable unit to shut down, and reasonably arranges the start and stop sequence of the units based on their start and stop time and load response speed.

[0115] Control of minimum stable output: For units that are still in operation, the system needs to control their power output near the minimum stable output. The minimum stable output is the lowest power output for stable operation of the generator set under low load. The system monitors the operating status of the unit in real time to ensure that its power output is not lower than this critical value to avoid unstable operation or shutdown of the unit due to low load.

[0116] The optimization algorithm plays a key role in the entire power generation regulation process. Through the optimization algorithm, the system maximizes power generation efficiency or minimizes fuel consumption while meeting the grid load requirements.

[0117] The specific steps are as follows:

[0118] Setting the objective function: The core of the optimization algorithm is to set a reasonable objective function. This function can be to minimize the fuel consumption of the power plant, maximize the power generation efficiency, or minimize the number of starts and stops of the unit. According to the operating objectives of the power plant, the system selects the appropriate optimization objective.

[0119] Setting of constraints: During the optimization process, the system needs to set a series of constraints, such as the minimum stable output of the unit, start-stop time, load response speed, etc. These constraints ensure that the optimization results are feasible in actual operation.

[0120] Optimization algorithm solution: The system uses genetic algorithms, particle swarm optimization algorithms, etc. to solve the optimal solution of the power allocation model. Genetic algorithms continuously optimize the power allocation plan by simulating the selection, crossover and mutation process of biological evolution; particle swarm optimization algorithms gradually approach the optimal solution by simulating the cooperation of individuals in the cluster. These algorithms can quickly converge to the optimal or suboptimal solution, ensuring that the system can complete power regulation in a short time.

[0121] The dynamic optimization and adjustment of the entire power generation is achieved through an automated control system. The system integrates functions such as load forecasting, unit characteristics analysis, power regulation and feedback control, and realizes the intelligence and automation of the power generation process. The specific technical implementation is as follows:

[0122] Real-time monitoring and feedback system: The automated control system relies on sensors and monitoring systems to monitor key parameters such as the operating status of the generator set and the grid load in real time. Through feedback control, the system can adjust the power output of each unit in a timely manner to ensure that the power generation of the power plant matches the grid load demand.

[0123] Intelligent scheduling and control: The intelligent scheduling module in the automation control system formulates the power adjustment plan of the generator set according to the load forecast results and the output of the optimization algorithm, and controls the power output, start and stop status of each unit through the actuator.

[0124] Closed-loop control and optimization: The automated control system achieves dynamic optimization and adjustment of power generation through closed-loop control. The various modules of the system work together to ensure efficient operation of the power plant under different load conditions through real-time monitoring, dynamic adjustment, optimization algorithms and feedback control.

[0125] Based on historical grid load data and real-time load forecast data, combined with the characteristics of power plant generators, the power generation dispatch control module can achieve dynamic optimization and adjustment of power generation. Under low-load operation, through deep peak-shaving operations and the application of optimization algorithms, the power plant can effectively adjust the output power of each unit to ensure maximum power generation efficiency and minimum energy consumption.

[0126] In an optional embodiment,

[0127] During the deep peak regulation process, various parameters of the steam turbine, steam extraction system and energy storage device are monitored and adjusted. The steam extraction volume, steam return volume, energy storage state of the energy storage device and output power of the steam turbine are dynamically adjusted according to the real-time working conditions to achieve the deep peak regulation goals of the power plant, including:

[0128] Through multiple sensors installed in the steam turbine, the steam extraction system and the energy storage device, real-time data collection is performed on multiple operating parameters such as the output power of the steam turbine, the steam extraction amount of the steam extraction system, the energy storage state of the energy storage device, and the steam return amount. The collected parameters include the pressure, temperature, flow rate of the steam and the state of the gas storage tank of the energy storage device;

[0129] According to the real-time collected data of the steam turbine load and the working condition of the steam extraction system, the power generation dispatching control module calculates the steam extraction amount required under the current load conditions; the calculation process is based on the real-time load demand of the power grid and the minimum stable output of the steam turbine, ensuring that the steam extraction amount matches the real-time load conditions, and adjusting the steam extraction amount by controlling the opening of the steam extraction valve, and the opening adjustment of the steam extraction valve is controlled by the automatic control system;

[0130] During the steam extraction process, the energy storage status of the energy storage device is monitored in real time, including the steam pressure and temperature in the gas storage tank; when the energy storage status of the energy storage device reaches the preset threshold, the automatic control system adjusts the steam extraction volume to prevent the energy storage device from being overloaded, while ensuring that the system has sufficient energy storage to support subsequent steam reflux operations during peak loads;

[0131] When the grid load forecast shows that the grid load will enter a peak stage, the power generation dispatch control module calculates the required steam return flow according to the power demand of the steam turbine and the energy storage status of the energy storage device, and adjusts the steam return flow by controlling the opening of the return valve; the return flow adjustment process needs to ensure that the pressure and temperature of the return steam match the current operating status of the steam turbine to avoid the return steam from impacting the steam turbine.

[0132] In an optional embodiment, the method further includes:

[0133] The power generation dispatch control module predicts future grid load changes through historical load data and real-time load change trends. When the load is low, the steam extraction volume is increased to store excess steam in the energy storage device. When the load is high, the steam extraction volume is reduced and steam reflux is increased to dynamically adjust the output power of the steam turbine to ensure that the power output of the steam turbine matches the grid load demand.

[0134] During the regulation of the extraction volume and steam return volume, the system forms a closed-loop feedback control mechanism by real-time monitoring of the operating data of the extraction system, energy storage device and steam turbine; the power generation dispatching control module further fine-tunes the extraction valve and return valve according to the feedback data to ensure that the extraction volume, steam return volume, energy storage status of the energy storage device and power output of the steam turbine are dynamically adjusted in real time to meet the changing needs of the power grid load.

[0135] For example, in the process of deep peak regulation of power plants, dynamic regulation of steam extraction is one of the key links. When the power plant is at a low load, the excess steam is stored in the energy storage device by adjusting the steam extraction, and the steam extraction is reduced at peak load to increase the output power of the steam turbine. The dynamic regulation of steam extraction is mainly achieved by real-time monitoring of the operating status of the steam turbine, the load change trend, and the energy storage status of the energy storage device. Its core technical point lies in the precise control of the steam extraction valve and the automatic regulation of the steam extraction.

[0136] When the power grid is in a low-load state, the power plant needs to reduce the output power of the generator set to avoid energy waste and meet the peak load demand of the power grid. In this scenario, the power generation dispatch control module monitors the operating status of the steam turbine in real time, increases the steam extraction according to the current power grid load, and transmits the excess steam to the energy storage device through the steam extraction system for storage. The specific implementation process is as follows:

[0137] The system collects real-time operating data through the pressure sensor and temperature sensor installed at the turbine outlet. These data are used to determine the current steam status and turbine load operation status.

[0138] The power generation dispatch control module calculates the amount of excess steam based on the load peak regulation requirements and the real-time load data of the steam turbine. The calculation needs to take into account the minimum stable output of the steam turbine to ensure that the steam extraction operation does not cause unstable operation of the steam turbine.

[0139] After calculating the required steam extraction volume, the automatic control system adjusts the steam extraction volume by controlling the opening of the steam extraction valve. After receiving the command from the control module, the actuator adjusts the valve opening to match the steam extraction volume with the real-time load. The opening of the steam extraction valve is adjusted proportionally to ensure a smooth change in steam flow without impacting the operation of the turbine.

[0140] At the same time, the system also needs to monitor the energy storage status of the energy storage device, including the pressure and temperature of the gas tank, to ensure that the stored steam remains in a stable state in the energy storage device in preparation for subsequent load peaks.

[0141] When the grid load gradually recovers, the power plant needs to increase the output power of the generator set. At this time, the power generation dispatch control module gradually reduces the steam extraction according to the load forecast results and the power demand of the steam turbine to ensure that more steam is used to drive the steam turbine and achieve power increase. The specific technical process is as follows:

[0142] The system predicts load changes in the future through historical load data and real-time load change trends. When it is predicted that the grid load will increase, the system calculates the required power generation increment in advance.

[0143] The power generation dispatching module instructs the actuator to gradually close the steam extraction valve according to the power increment demand, reducing the steam extraction amount and ensuring that more steam flows into the turbine to increase its output power.

[0144] In the process of reducing the steam extraction, the system obtains the change of steam flow in real time through the pressure and flow sensors installed at the turbine air inlet. If the load response speed of the turbine is slow or too fast, the system will automatically adjust the opening of the steam extraction valve to ensure that the output power of the turbine matches the load demand of the power grid.

[0145] The regulation of steam extraction depends on the precise control of the steam extraction valve by the automatic control system. The steam extraction valve is opened by the actuator, and the actuator command is issued by the power generation dispatching control module according to the real-time load demand and the turbine operating status. The specific control logic is as follows:

[0146] The automatic control system adopts proportional-integral-differential (PID) control algorithm or fuzzy control algorithm, combined with real-time load peak regulation requirements, to accurately calculate the change range of steam extraction volume.

[0147] The actuator receives the signal from the control module and gradually adjusts the valve opening according to the set opening change curve. The response speed and accuracy of the actuator directly affect the stability of the steam extraction volume. Therefore, the system will smooth the control signal of the valve according to the load response characteristics of the steam turbine to prevent the opening adjustment from being too fast or too slow.

[0148] During the valve adjustment process, the system monitors the steam flow, pressure changes and other parameters in real time and feeds them back to the control module. The control module further fine-tunes the valve opening based on the feedback data to ensure that the steam extraction volume accurately matches the load demand, forming a closed-loop control.

[0149] When the load of the power grid rises, the high-temperature and high-pressure steam stored in the energy storage device can flow back to the steam turbine to increase its power generation capacity. The dynamic regulation of the steam return volume is an important step in achieving deep peak regulation of power plants. Especially at peak loads, reasonable regulation of the steam return volume can effectively increase the power output of the power plant and meet the load requirements of the power grid. The key technical points of steam return volume regulation will be discussed in detail below.

[0150] When the grid load forecast results show that the grid load will enter the peak stage, the power generation dispatch control module starts the steam reflux operation in the energy storage device according to the power demand of the steam turbine. The specific process is as follows:

[0151] The power generation dispatch control module calculates the required turbine power increment based on the load forecast results and the current grid load demand. Based on the power increment, the system further calculates the amount of steam that needs to flow back from the energy storage device.

[0152] The system controls the amount of steam reflux in the accumulator through the reflux valve. The automatic control system adjusts the opening of the reflux valve according to the steam storage status of the accumulator and the real-time power demand of the steam turbine to ensure that the amount of reflux steam matches the power demand of the steam turbine.

[0153] During the steam reflux process, the system monitors the steam pressure and temperature in the energy storage device in real time to ensure that the state of the reflux steam can meet the operating requirements of the steam turbine. At the same time, the system monitors the steam state through the temperature and pressure sensors at the turbine inlet to ensure that the reflux steam will not cause impact on the steam turbine.

[0154] When the steam stored in the energy storage device flows back to the steam turbine, its pressure and temperature must match the current operating status of the steam turbine, otherwise the steam turbine may be damaged due to parameter mismatch. To this end, the system needs to accurately control the pressure and temperature of the steam during the steam return process. The specific technical implementation is as follows:

[0155] The steam status in the energy storage device is monitored in real time by the temperature and pressure sensors installed on the gas storage tank. Based on these data, the power generation dispatch control module determines whether the return steam needs further adjustment.

[0156] If the steam temperature or pressure in the energy storage device does not meet the requirements of the turbine, the system will adjust the return steam by adjusting the pressure regulating valve and heating device in the return pipe to ensure that its temperature and pressure when entering the turbine match the current operating parameters of the turbine.

[0157] In order to avoid the impact of sudden large-scale steam return on the turbine, the system adopts a gradual return mechanism, that is, the amount of return steam is gradually increased according to the load demand of the turbine to ensure a smooth transition of the return process.

[0158] The dynamic regulation of steam return volume is mainly achieved through the automatic control system. The system integrates multiple functions such as load forecasting, energy storage device status monitoring, and turbine operation monitoring. It can intelligently adjust the opening of the return valve according to the load demand of the power grid and the power demand of the turbine to ensure that the return steam volume matches the power demand of the turbine. The specific technical details are as follows:

[0159] The system uses advanced control algorithms, such as fuzzy logic control or adaptive control algorithms, to automatically adjust the opening of the return valve based on the energy storage status of the energy storage device and the load demand of the steam turbine. These control algorithms can dynamically adjust the return steam volume based on real-time data to ensure that the system maintains efficient operation under changing load conditions.

[0160] During the steam return process, the system forms a closed-loop feedback control mechanism by real-time monitoring of the return steam flow, temperature, pressure and other parameters. If the state of the return steam does not meet the turbine requirements, the system will automatically adjust the opening of the return valve or pre-treat the steam to ensure that the state of the return steam is always within the optimal range.

[0161] By dynamically adjusting the steam extraction and steam return, the power plant can effectively balance the load demand during deep peak regulation and rationally utilize the steam resources in the energy storage device. The dynamic adjustment of the steam extraction ensures efficient energy utilization during low-load operation, while the dynamic adjustment of the steam return can increase the power output of the steam turbine during peak loads to meet the load requirements of the power grid. The synergy of the two is achieved through the automatic control system, ensuring that the entire system operates efficiently and stably under complex load conditions.

[0162] In an optional embodiment,

[0163] Based on historical data and real-time load change trends, future loads are predicted. Based on the load prediction results, combined with the energy storage status of the energy storage device and the operating status of the steam turbine, the operating strategy of the generator set is dynamically adjusted to achieve the power generation optimization goals of the power plant, including:

[0164] Using a load forecasting model based on time series analysis, machine learning or neural network, based on historical load data and real-time load change trends, predict future power grid load; the load forecasting model generates a forecast result of future load demand by analyzing seasonal changes, periodic fluctuations and nonlinear relationships of power grid load;

[0165] The real-time operating data of the generator set is collected through multiple sensors installed in the power generation system, including the output power of the steam turbine, the steam extraction volume of the steam extraction system, the energy storage status of the energy storage device and the steam return volume; the collected real-time data is dynamically compared with the load prediction results to form operating condition feedback data;

[0166] The optimal steam extraction capacity under current load conditions is calculated based on the load forecast results and the energy storage status of the energy storage device. When the load forecast shows that the load is about to enter a valley, the automatic control system instructs the steam extraction system to increase the steam extraction capacity and store the excess steam in the energy storage device. When the load forecast shows that the load is about to rise, the system reduces the steam extraction capacity and starts steam reflux to ensure that the energy storage device can provide additional steam supply to the turbine during the peak load period.

[0167] Based on the load forecast results and real-time operating data, the control system adjusts the operating status of the steam turbine; when the load forecast results show that the future load will increase, the system increases the steam extraction volume and increases the steam return volume to increase the power output of the steam turbine; when the load forecast results show that the load will decrease, the system increases the steam extraction volume to reduce the power output of the steam turbine to avoid energy waste;

[0168] The automatic control system collects the operating conditions of the generator set in real time, including the steam pressure, temperature, flow rate of the steam turbine and the energy storage status of the energy storage device, and performs closed-loop control on the steam extraction volume, steam return volume and power output of the steam turbine; the closed-loop control system fine-tunes the power generation strategy based on the real-time feedback data to ensure that the output power of the generator set matches the load demand of the power grid and avoids excess or insufficient power;

[0169] The power generation dispatching control module generates a dynamic adjustment strategy based on the load forecast results, the energy storage status of the energy storage device and the real-time operating conditions of the steam turbine; the adjustment strategy realizes the dynamic operation optimization of the generator set under different load stages by controlling the opening of the steam extraction valve and the return valve, ensuring that the generator set stores excess steam at low loads and increases output power at peak loads, thereby realizing energy efficiency optimization of power plant operation.

[0170] For example, in the process of deep peak regulation of power plants, load forecasting is a key link in optimizing the operating strategy of generator sets. By using historical load data and real-time load change trends, the system can predict future loads, thereby providing a reliable basis for the operation adjustment of generator sets. The load forecast results need to be combined with the energy storage status of the energy storage device and the current operating status of the steam turbine to dynamically adjust the operating strategy of the generator set to ensure that the power generation efficiency and energy utilization of the power plant are maximized. This article will focus on the key points in load forecasting technology and explore its specific application in optimizing the operation of generator sets.

[0171] The accuracy of load forecasting directly affects the operation optimization effect of the generator set. To this end, power plants usually use a variety of load forecasting models to improve the accuracy of forecasting. These models mainly include time series analysis models, machine learning models, and intelligent forecasting models based on neural networks.

[0172] The model captures the seasonal and cyclical characteristics of load changes by statistically analyzing historical load data. Commonly used methods include the autoregressive moving average model (ARIMA) and the exponential smoothing model (ETS). These models have strong adaptability to seasonal fluctuations in power grid load and are particularly suitable for medium- and short-term load forecasting.

[0173] Machine learning models such as support vector regression (SVR) and random forest can make more flexible load forecasts by learning nonlinear relationships in a large amount of historical load data. Compared with traditional time series models, machine learning models show better prediction capabilities when dealing with complex nonlinear load changes.

[0174] In recent years, deep learning models based on artificial neural networks (ANN), such as long short-term memory networks (LSTM) and convolutional neural networks (CNN), have been widely used in load forecasting. These models can process a large amount of historical data and capture the long-term dependencies in power grid load changes, which are particularly suitable for scenarios where load changes frequently and have no obvious periodicity.

[0175] The results of load forecasting provide a basis for the operation strategy of the generator set. The power plant dynamically adjusts the power generation strategy by combining the predicted load demand with the current status of the power generation facilities to achieve load balance and energy efficiency optimization.

[0176] The load forecast results need to be combined with the energy storage status of the energy storage device to optimize the operation plan of the generator set. When the load forecast shows that there will be a low load in the future, the system can increase the steam extraction volume in advance and store the excess steam in the energy storage device for use during the subsequent load peak; when the load forecast shows that there will be a load peak in the future, the system can reduce the steam extraction volume and increase the steam return volume to ensure that the turbine obtains additional steam supply and improve its output power.

[0177] The real-time operating status of the steam turbine is also crucial in dynamically adjusting the power generation strategy. When the load forecast shows that the peak load is about to arrive, the system needs to monitor the operating status of the steam turbine in real time, including parameters such as steam pressure, temperature and flow, to ensure that it can maintain stable operation when the load increases. At the same time, the load forecast results can also be used to guide the pre-maintenance plan of the steam turbine, avoid equipment failures during high-load operation, and ensure the reliability of power plant operation.

[0178] Based on load forecasting, the dynamic adjustment strategy of the generator set is realized through the automatic control system. The automatic control system comprehensively analyzes the load forecast results, the energy storage status of the energy storage device and the real-time operating data of the steam turbine, generates the optimal operation strategy, and performs closed-loop control through a real-time feedback mechanism.

[0179] The automatic control system generates an operation adjustment plan for the generator set based on the load forecast results and real-time operating data. For example, during the off-peak load period, the system will automatically instruct the actuator to increase the steam extraction volume and introduce the excess steam into the energy storage device; during the peak load period, the system will reduce the steam extraction volume and increase the steam return volume by adjusting the return valve to increase the turbine power output.

[0180] During the dynamic adjustment process, the system forms a closed-loop feedback mechanism by monitoring the operating data of each link in real time. If a deviation is found between the actual load and the predicted load during the execution process, the system will adjust the preset strategy based on the real-time data to ensure that the generator set can flexibly respond to load changes and avoid energy waste or power shortage.

[0181] The dynamic adjustment strategy of the generator set based on load forecasting achieves the power generation optimization goal of the power plant by combining the energy storage status of the energy storage device and the real-time operation status of the steam turbine. The selection and optimization of the load forecasting model is directly related to the load regulation efficiency of the power plant. Through the linkage adjustment of the automatic control system, the power plant can achieve efficient energy utilization and optimization of power output at different stages of load changes. This dynamic adjustment mechanism based on load forecasting not only improves the overall operating efficiency of the power plant, but also enhances the flexibility and reliability of the power plant in dealing with complex grid load changes.

[0182] Figure 2 FIG. 1 is a schematic diagram of the structure of a power plant deep peak regulation and power generation optimization system based on extracted steam storage according to an embodiment of the present invention. Figure 2 As shown, the system comprises:

[0183] The first unit is used to generate high-temperature and high-pressure steam through a steam turbine. The steam enters a steam extraction system. The steam extraction system is used to extract part of the steam discharged from the steam turbine when the power grid load is low, and transport the extracted steam to an energy storage device for storage. The energy storage device includes a high-temperature heat storage device and a high-pressure steam storage tank. The steam extraction system is connected to the energy storage device to achieve efficient storage of steam. When the power grid load is at a peak, the power generation dispatching control module adjusts the steam extraction system according to the real-time power grid load information, so that the steam stored in the energy storage device is returned to the medium-pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module for reuse. The thermal system is used to recover the residual heat generated by the steam turbine. The residual heat is transported to the condenser through a pipeline for condensation treatment, and the condensed water is returned to the boiler for recycling.

[0184] The second unit is used to dynamically optimize and adjust the power generation based on historical grid load data and real-time load forecast data, combined with the characteristics of the power plant's generator sets. By controlling the power generation dispatching control module of the power plant, the deep peak-shaving operation of the generator sets under low-load operation is realized, and the output power of each unit is adjusted through the optimization algorithm; in the process of deep peak-shaving, various parameters of the steam turbine, steam extraction system and energy storage device are monitored and adjusted, and the steam extraction volume, steam return volume, energy storage state of the energy storage device and the output power of the steam turbine are dynamically adjusted according to the real-time operating conditions to achieve the deep peak-shaving goal of the power plant;

[0185] The third unit, for the deep peak-shaving optimization module further includes a load prediction module and a power generation optimization algorithm module. The load prediction module predicts future loads based on historical data and real-time load change trends. The power generation optimization algorithm module dynamically adjusts the operating strategy of the generator set based on the load prediction results, combined with the energy storage status of the energy storage device and the operating status of the steam turbine, so as to achieve the power generation optimization goal of the power plant; when the grid load is low, part of the steam of the steam turbine is stored in the energy storage device through the steam extraction system, and when the grid load is peak, the stored steam is returned to the steam turbine for reuse, thereby achieving peak-shaving balance of the grid load without increasing fuel consumption.

[0186] According to a third aspect of the embodiments of the present invention,

[0187] An electronic device is provided, comprising:

[0188] processor;

[0189] a memory for storing processor-executable instructions;

[0190] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0191] According to a fourth aspect of the embodiments of the present invention,

[0192] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0193] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0194] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for deep peak regulation and power generation optimization of a power plant based on extracted steam storage, characterized in that: include: The steam turbine generates high-temperature and high-pressure steam, which enters the steam extraction system. The steam extraction system is used to extract part of the steam discharged from the steam turbine when the power grid load is low, and transport the extracted steam to the energy storage device for storage. The energy storage device includes a high-temperature heat storage device and a high-pressure steam storage tank. The steam extraction system is connected to the energy storage device to achieve efficient storage of steam. When the power grid load is at a peak, the power generation dispatching control module adjusts the steam extraction system according to the real-time power grid load information, so that the steam stored in the energy storage device is returned to the medium-pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module for reuse. The thermal system is used to recover the residual heat generated by the steam turbine. The residual heat is transported to the condenser through a pipeline for condensation treatment, and the condensed water is returned to the boiler for recycling. Based on historical grid load data and real-time load forecast data, combined with the characteristics of the power plant's generator sets, dynamic optimization and adjustment of power generation is carried out. By controlling the power generation dispatching control module of the power plant, deep peak-shaving operation of the generator sets under low-load operation is achieved, and the output power of each unit is adjusted through the optimization algorithm. During the deep peak-shaving process, various parameters of the steam turbine, steam extraction system and energy storage device are monitored and adjusted. According to the real-time operating conditions, the steam extraction volume, steam return volume, energy storage state of the energy storage device and the output power of the steam turbine are dynamically adjusted to achieve the deep peak-shaving goal of the power plant. Based on historical data and real-time load change trends, future loads are predicted. Based on the load prediction results, combined with the energy storage status of the energy storage device and the operating status of the steam turbine, the operating strategy of the generator set is dynamically adjusted to achieve the power generation optimization goal of the power plant. When the grid load is low, part of the steam from the steam turbine is stored in the energy storage device through the steam extraction system. When the grid load is peak, the stored steam is returned to the steam turbine for reuse, thereby achieving peak load balance of the grid without increasing fuel consumption.

2. The method according to claim 1, characterized in that When the grid load is at its peak, the power generation dispatch control module adjusts the steam extraction system according to the real-time grid load information, so that the steam stored in the energy storage device is returned to the medium-pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module for reuse; the thermal system is used to recover the residual heat generated by the steam turbine, and the residual heat is transported to the condenser through a pipeline for condensation treatment, and the condensed water is returned to the boiler for recycling, including: When the load of the power grid is at its peak, the power generation dispatch control module controls the steam extraction system to release the stored high-temperature and high-pressure steam from the energy storage device according to the load forecast result, and the high-temperature and high-pressure steam is introduced into the intermediate pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module. The steam distribution module dynamically adjusts the proportion of steam entering the intermediate pressure cylinder or low-pressure cylinder according to the current operating conditions of the steam turbine to optimize the energy output of the steam turbine; The low-temperature and low-pressure steam generated by the steam turbine during operation is recovered through the thermal system for waste heat. The low-temperature and low-pressure steam enters the condenser through the pipeline of the thermal system. The condenser condenses the low-temperature and low-pressure steam into condensed water through cooling water or air. The condensed water then flows back to the boiler through the pipeline for cyclic heating treatment to form a closed-loop circulating steam supply system, thereby realizing efficient recovery and reuse of steam. The power generation dispatching control module dynamically adjusts the steam extraction volume, steam return volume and output power of the steam turbine of the steam extraction system in combination with the operating status of the steam turbine, the energy storage status of the energy storage device and the condensing efficiency of the condenser to ensure efficient utilization of steam under different load conditions. The adjustment is based on real-time acquired system operating parameters and grid load changes, and real-time regulation of each system component is performed through an automated control system.

3. The method according to claim 1, characterized in that Based on historical grid load data and real-time load forecast data, combined with the characteristics of the power plant's generator sets, dynamic optimization and adjustment of power generation is carried out. By controlling the power generation dispatching control module of the power plant, deep peak-shaving operation of the generator sets under low-load operation is achieved, and the output power of each unit is adjusted through the optimization algorithm, including: The historical grid load data of the grid is obtained through the grid load data acquisition module, wherein the historical grid load data includes daily load, monthly load and seasonal load, and the historical grid load data is cleaned and preprocessed to remove abnormal values ​​and process missing data to ensure the accuracy and continuity of the data; The current grid load data is obtained in real time through the grid load monitoring module, and combined with the historical grid load data, a load forecasting algorithm is used to generate a forecast result of the future grid load. The load forecasting algorithm includes a time series model or a load forecasting model based on machine learning. The forecast result is used to guide the subsequent power adjustment of the generator set; Obtain the operating characteristics of each generator set, including the load response speed, start-stop time, minimum stable output and power generation efficiency of the generator set, formulate the load adaptation strategy of the generator set in combination with the real-time load forecast results, and determine the power adjustment range and load adaptation plan of each generator set; Based on the real-time load forecast results and the load adaptation plan of the generator set, the power generation dispatch control module dynamically adjusts the output power of each generator set through an optimization algorithm. The optimization algorithm is a genetic algorithm or a particle swarm optimization algorithm. The goal is to minimize fuel consumption or maximize power generation efficiency. The optimization algorithm calculates the optimized power distribution plan in real time according to the current grid load demand and the status of the generator set.

4. The method according to claim 1, characterized in that: The method further comprises: When the grid load forecast results show that the grid load is in a low state, the power generation dispatching control module performs deep peak-shaving operations according to the minimum stable output of each generator set. By shutting down some generator sets or controlling the output power of some generator sets to the minimum stable output, it avoids long-term low-load operation of the units, resulting in inefficiency or equipment loss.

5. The method according to claim 1, characterized in that During the deep peak regulation process, various parameters of the steam turbine, steam extraction system and energy storage device are monitored and adjusted. The steam extraction volume, steam return volume, energy storage state of the energy storage device and output power of the steam turbine are dynamically adjusted according to the real-time working conditions to achieve the deep peak regulation goals of the power plant, including: Through multiple sensors installed in the steam turbine, the steam extraction system and the energy storage device, real-time data collection is performed on multiple operating parameters such as the output power of the steam turbine, the steam extraction amount of the steam extraction system, the energy storage state of the energy storage device, and the steam return amount. The collected parameters include the pressure, temperature, flow rate of the steam and the state of the gas storage tank of the energy storage device; According to the real-time collected data of the steam turbine load and the working condition of the steam extraction system, the power generation dispatching control module calculates the steam extraction amount required under the current load conditions; the calculation process is based on the real-time load demand of the power grid and the minimum stable output of the steam turbine, ensuring that the steam extraction amount matches the real-time load conditions, and adjusting the steam extraction amount by controlling the opening of the steam extraction valve, and the opening adjustment of the steam extraction valve is controlled by the automatic control system; During the steam extraction process, the energy storage status of the energy storage device is monitored in real time, including the steam pressure and temperature in the gas storage tank; when the energy storage status of the energy storage device reaches the preset threshold, the automatic control system adjusts the steam extraction volume to prevent the energy storage device from being overloaded, while ensuring that the system has sufficient energy storage to support subsequent steam reflux operations during peak loads; When the grid load forecast shows that the grid load will enter a peak stage, the power generation dispatch control module calculates the required steam return flow according to the power demand of the steam turbine and the energy storage status of the energy storage device, and adjusts the steam return flow by controlling the opening of the return valve; the return flow adjustment process needs to ensure that the pressure and temperature of the return steam match the current operating status of the steam turbine to avoid the return steam from impacting the steam turbine.

6. The method according to claim 5, characterized in that The method further comprises: The power generation dispatch control module predicts future grid load changes through historical load data and real-time load change trends. When the load is low, the steam extraction volume is increased to store excess steam in the energy storage device. When the load is high, the steam extraction volume is reduced and steam reflux is increased to dynamically adjust the output power of the steam turbine to ensure that the power output of the steam turbine matches the grid load demand. During the regulation of the extraction volume and steam return volume, the system forms a closed-loop feedback control mechanism by real-time monitoring of the operating data of the extraction system, energy storage device and steam turbine; the power generation dispatching control module further fine-tunes the extraction valve and return valve according to the feedback data to ensure that the extraction volume, steam return volume, energy storage status of the energy storage device and power output of the steam turbine are dynamically adjusted in real time to meet the changing needs of the power grid load.

7. The method according to claim 1, characterized in that Based on historical data and real-time load change trends, future loads are predicted. Based on the load prediction results, combined with the energy storage status of the energy storage device and the operating status of the steam turbine, the operating strategy of the generator set is dynamically adjusted to achieve the power generation optimization goals of the power plant, including: Using a load forecasting model based on time series analysis, machine learning or neural network, based on historical load data and real-time load change trends, predict future power grid load; the load forecasting model generates a forecast result of future load demand by analyzing seasonal changes, periodic fluctuations and nonlinear relationships of power grid load; The real-time operating data of the generator set is collected through multiple sensors installed in the power generation system, including the output power of the steam turbine, the steam extraction volume of the steam extraction system, the energy storage status of the energy storage device and the steam return volume; the collected real-time data is dynamically compared with the load prediction results to form operating condition feedback data; The optimal steam extraction capacity under current load conditions is calculated based on the load forecast results and the energy storage status of the energy storage device. When the load forecast shows that the load is about to enter a valley, the automatic control system instructs the steam extraction system to increase the steam extraction capacity and store the excess steam in the energy storage device. When the load forecast shows that the load is about to rise, the system reduces the steam extraction capacity and starts steam reflux to ensure that the energy storage device can provide additional steam supply to the turbine during the peak load period. Based on the load forecast results and real-time operating data, the control system adjusts the operating status of the steam turbine; when the load forecast results show that the future load will increase, the system increases the steam extraction volume and increases the steam return volume to increase the power output of the steam turbine; when the load forecast results show that the load will decrease, the system increases the steam extraction volume to reduce the power output of the steam turbine to avoid energy waste; The automatic control system collects the operating conditions of the generator set in real time, including the steam pressure, temperature, flow rate of the steam turbine and the energy storage status of the energy storage device, and performs closed-loop control on the steam extraction volume, steam return volume and power output of the steam turbine; the closed-loop control system fine-tunes the power generation strategy based on the real-time feedback data to ensure that the output power of the generator set matches the load demand of the power grid and avoids excess or insufficient power; The power generation dispatching control module generates a dynamic adjustment strategy based on the load forecast results, the energy storage status of the energy storage device and the real-time operating conditions of the steam turbine; the adjustment strategy realizes the dynamic operation optimization of the generator set under different load stages by controlling the opening of the steam extraction valve and the return valve, ensuring that the generator set stores excess steam at low loads and increases output power at peak loads, thereby realizing energy efficiency optimization of power plant operation.

8. A deep peak regulation and power generation optimization system for a power plant based on extracted steam storage, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The first unit is used to generate high-temperature and high-pressure steam through a steam turbine. The steam enters a steam extraction system. The steam extraction system is used to extract part of the steam discharged from the steam turbine when the power grid load is low, and transport the extracted steam to an energy storage device for storage. The energy storage device includes a high-temperature heat storage device and a high-pressure steam storage tank. The steam extraction system is connected to the energy storage device to achieve efficient storage of steam. When the power grid load is at a peak, the power generation dispatching control module adjusts the steam extraction system according to the real-time power grid load information, so that the steam stored in the energy storage device is returned to the medium-pressure cylinder or low-pressure cylinder of the steam turbine through the steam distribution module for reuse. The thermal system is used to recover the residual heat generated by the steam turbine. The residual heat is transported to the condenser through a pipeline for condensation treatment, and the condensed water is returned to the boiler for recycling. The second unit is used to dynamically optimize and adjust the power generation based on historical grid load data and real-time load forecast data, combined with the characteristics of the power plant's generator sets. By controlling the power generation dispatching control module of the power plant, the deep peak-shaving operation of the generator sets under low-load operation is realized, and the output power of each unit is adjusted through the optimization algorithm; in the process of deep peak-shaving, various parameters of the steam turbine, steam extraction system and energy storage device are monitored and adjusted, and the steam extraction volume, steam return volume, energy storage state of the energy storage device and the output power of the steam turbine are dynamically adjusted according to the real-time operating conditions to achieve the deep peak-shaving goal of the power plant; The third unit, for the deep peak-shaving optimization module further includes a load prediction module and a power generation optimization algorithm module. The load prediction module predicts future loads based on historical data and real-time load change trends. The power generation optimization algorithm module dynamically adjusts the operating strategy of the generator set based on the load prediction results, combined with the energy storage status of the energy storage device and the operating status of the steam turbine, so as to achieve the power generation optimization goal of the power plant; when the grid load is low, part of the steam of the steam turbine is stored in the energy storage device through the steam extraction system, and when the grid load is peak, the stored steam is returned to the steam turbine for reuse, thereby achieving peak-shaving balance of the grid load without increasing fuel consumption.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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