Power plant steam extraction energy storage peak regulation decoupling method and system based on fused salt heat storage technology

Abstract

The invention provides a power plant steam extraction energy storage peak shaving decoupling method and system based on a fused salt heat storage technology, and relates to the technical field of energy, and the system comprises a fused salt heat storage device, a steam extraction energy storage system, a peak shaving decoupling control device and a waste heat utilization device; the fused salt heat storage device comprises a high-temperature fused salt storage tank and a low-temperature fused salt storage tank which are longitudinally arranged in a layered mode. The steam extraction energy storage system comprises a steam turbine steam extraction opening, a steam extraction main pipeline, a plurality of branch pipelines and steam extraction adjusting valves arranged correspondingly. The peak shaving decoupling control device adopts a control algorithm based on fuzzy PID, and meanwhile coordinates the electric heating power in the high-temperature fused salt storage tank to realize the optimal matching of the steam extraction heat storage process and the fused salt heat release process; the waste heat utilization device is connected to the bottom of the low-temperature fused salt storage tank and provides cooling capacity for the working environment of the power plant.

Description

Technical Field

[0001] The present invention relates to energy technology, and in particular to a method and system for decoupling peak load regulation of power plant steam extraction storage based on molten salt heat storage technology. Background Art

[0002] In order to maintain the safe and stable operation of the power grid, thermal power generating units are responsible for peak load regulation. However, frequent peak load regulation will increase the climbing cost and wear of the units and reduce the economic efficiency of operation. At present, various peak load regulation technologies for thermal power generating units have been studied at home and abroad, mainly including:

[0003] Boiler energy storage technology. By installing a heat storage device on the boiler body or the steam drum, excess steam heat is stored when the grid load is low, and released during peak hours to achieve peak-shifting operation. However, it is limited by the boiler's pressure bearing capacity, and the energy storage density and peak-shifting capacity are limited.

[0004] Steam turbine energy storage technology. A steam accumulator is connected in parallel to the exhaust pipe of the low-pressure cylinder of the steam turbine. Through the principle of flash steaming, steam is stored during low periods and released during peak periods to increase turbine output, but the energy storage capacity is still low.

[0005] Electrochemical energy storage technology. Using devices such as batteries or supercapacitors, charging when the grid load is low and discharging when it is high, smoothing the load curve, but the cost is high and there are certain safety risks.

[0006] Extraction steam heat storage technology. A heat exchanger is set at the extraction end of the steam turbine to store the extraction steam heat in a high-temperature heat transfer fluid such as molten salt, and then the heat storage device is used to smooth the load fluctuation. However, most of them currently adopt a simple extraction steam constant pressure control strategy, which fails to give full play to its peak load regulation potential.

[0007] Based on the above analysis, the existing solutions still have problems such as low peak-shaving quality and low heat storage efficiency. Summary of the invention

[0008] The embodiments of the present invention provide a method and system for decoupling peak load of power plant steam extraction storage based on molten salt heat storage technology, which can solve the problems in the prior art.

[0009] According to a first aspect of the embodiments of the present invention,

[0010] Provide a peak load decoupling method for power plant extraction steam storage based on molten salt heat storage technology, including:

[0011] A molten salt heat storage device, an extraction steam energy storage system, a peak load decoupling control device, and a waste heat utilization device; the molten salt heat storage device comprises a high-temperature molten salt storage tank and a low-temperature molten salt storage tank arranged in a longitudinal layer, a connecting pipe and a control valve are provided between the two storage tanks, and an electric heating device is provided on the top of the high-temperature molten salt storage tank to supplement the heat loss in the molten salt heat exchange process; a plurality of salt outlets are provided at the bottom of the high-temperature molten salt storage tank, which are respectively connected to a molten salt pump and a plate-type molten salt heat exchanger to realize the circulation and heat release of the molten salt;

[0012] The steam extraction energy storage system includes a steam turbine extraction port, a steam extraction main line, several branch lines and corresponding steam extraction regulating valves. Each branch line is connected to a plate-type steam extraction heat exchanger. The plate-type steam extraction heat exchanger and the plate-type molten salt heat exchanger are arranged in parallel to form a two-stage heat exchange network. By adjusting the opening of each steam extraction regulating valve, the steam extraction amount can be optimally distributed among different heat exchangers. The peak-shaving decoupling control device adopts a control algorithm based on fuzzy PID. According to the peak-shaving instructions of the power grid, the load changes of the power plant and the operating conditions of the steam turbine, it calculates the optimal steam extraction amount and molten salt flow in real time, controls the operation of the steam extraction regulating valve and the molten salt pump, and coordinates the electric heating power in the high-temperature molten salt storage tank to achieve the optimal matching of the steam extraction heat storage and the molten salt heat release process, thereby improving the peak-shaving response speed and accuracy.

[0013] The waste heat utilization device is connected to the bottom of the low-temperature molten salt storage tank. The device includes a waste heat exchanger and an absorption refrigerator. The waste heat after the molten salt releases heat is used to drive the absorption refrigerator to provide cooling for the working environment of the power plant and improve the efficiency of energy cascade utilization. An electric three-way regulating valve is provided between the waste heat exchanger and the low-temperature molten salt storage tank to adjust the flow of molten salt entering the waste heat exchanger according to the cooling demand.

[0014] In an optional embodiment,

[0015] The extraction steam energy storage system includes a steam turbine extraction port, an extraction main pipeline, a plurality of branch pipelines and corresponding extraction regulating valves. Each branch pipeline is connected to a plate extraction heat exchanger. The plate extraction heat exchanger and the plate molten salt heat exchanger are arranged in parallel to form a two-stage heat exchange network. By adjusting the opening of each extraction regulating valve, the steps of optimizing the distribution of the extraction volume between different heat exchangers include:

[0016] The primary side inlet of the plate-type steam extraction heat exchanger is connected to the steam extraction port of the steam turbine through a steam extraction pipeline, and the secondary side outlet is connected to the secondary side inlet of the plate-type molten salt heat exchanger through a molten salt pipeline; a steam extraction regulating valve is provided on the steam extraction pipeline to adjust the amount of steam extraction entering different plate-type steam extraction heat exchangers; a connecting pipeline and a valve are provided between the plate-type steam extraction heat exchanger and the plate-type molten salt heat exchanger to adjust the distribution of the steam extraction amount and the molten salt flow rate between different heat exchangers;

[0017] The heat exchange network is centrally managed through a distributed control system, the opening of the steam extraction regulating valve of each heat exchange unit is adjusted in real time, the distribution ratio of the steam extraction volume among different heat exchangers is optimized, and the rotation speed and flow rate of the molten salt pump are coordinated to ensure the uniform distribution of the molten salt among the heat exchangers, thereby improving the overall heat transfer efficiency of the heat exchange network; the distributed control system also includes an online monitoring device and a fault diagnosis module to monitor the operating status of the heat exchange network in real time, judge heat exchanger leakage, pipeline blockage, valve failure according to abnormal changes in temperature, pressure, and flow parameters, and adopt corresponding fault-tolerant control strategies to ensure the operation of the heat exchange network.

[0018] In an optional embodiment,

[0019] The peak-shaving decoupling control device adopts a control algorithm based on fuzzy PID. According to the peak-shaving instructions of the power grid, the load changes of the power plant and the operating conditions of the steam turbine, it calculates the optimal steam extraction volume and molten salt flow in real time, controls the operation of the steam extraction regulating valve and the molten salt pump, and coordinates the electric heating power in the high-temperature molten salt storage tank to achieve the optimal matching of the steam extraction heat storage and the molten salt heat release process. The steps to improve the peak-shaving response speed and accuracy include:

[0020] Taking into account the steam turbine extraction parameters, the characteristics of the molten salt heat storage device, and the power grid peak load command factors, a dynamic mathematical model of the power plant steam extraction storage peak load system is established by combining mechanism modeling with data-driven methods.

[0021] According to the dynamic mathematical model of the power plant's steam extraction storage peak-shaving system, a fuzzy PID controller is designed with steam turbine extraction pressure, molten salt storage tank temperature and power plant load as inputs, and extraction control valve opening and molten salt pump speed as outputs. The triangle membership function is used to fuzzify the input and output variables, and the controller output is obtained through the center of gravity defuzzification method. The decoupling algorithm is introduced to calculate the static decoupling matrix between the extraction control valve and the molten salt pump to achieve mutual compensation between control loops and reduce the system coupling degree. At the same time, the online self-tuning method is used to adjust the PID parameters in real time to ensure control performance.

[0022] Comprehensively considering the peak-shaving instructions of the power grid, the load changes of the power plant and the equipment operation constraints, the peak-shaving effect and energy utilization efficiency in the entire peak-shaving period are taken as the objective function, and the steam turbine extraction parameters and the energy balance equation of the molten salt heat storage system are taken as constraints. The intelligent optimization algorithm is used to solve the optimal scheduling problem of the steam extraction volume and the molten salt flow rate, and the setting instructions are generated; the setting instructions are used as the input of the fuzzy PID controller to form a two-level control architecture in which the upper-level optimization scheduling generates the optimal operation trajectory, and the lower-level fuzzy PID controller tracks the trajectory and suppresses disturbances, so as to achieve rapid response and precise regulation of peak-shaving decoupling control, and at the same time coordinate the power regulation of the electric heating device in the high-temperature molten salt storage tank to compensate for the heat loss in the molten salt heat exchange process and ensure the energy balance of the system.

[0023] In an optional embodiment,

[0024] Taking into account the steam turbine extraction parameters, the characteristics of the molten salt heat storage device, and the power grid peak load instruction factors, the dynamic mathematical model of the power plant steam extraction storage peak load system is established by combining mechanism modeling with data-driven methods. The steps include:

[0025] According to the structural characteristics and working principle of the steam turbine, the steam turbine is divided into three parts: high-pressure cylinder, medium-pressure cylinder and low-pressure cylinder; for each cylinder, the mass flow balance equation and energy balance equation are considered to obtain the corresponding dynamic equation; based on the thermal performance curve of the steam turbine, the functional relationship between the extraction pressure and extraction temperature and the steam turbine power is established, and the steam turbine extraction model is established;

[0026] The lumped parameter method is adopted to consider the temperature distribution and flow characteristics in the molten salt heat storage device, and the equations describing the dynamic characteristics of the molten salt heat storage process are obtained; according to the performance data of the molten salt pump and the structural layout of the pipeline system, the functional relationship between the molten salt flow rate and the pump speed and the functional relationship between the molten salt flow rate and the pipeline pressure drop are established respectively, and the molten salt heat storage device model is established;

[0027] According to the requirements of power grid dispatching, the power grid peak load regulation instruction is expressed as a time function of the power plant load; based on the unit characteristics and operating conditions of the power plant, the adjustment range of the power plant load is given; at the same time, the ramp rate limit of the power plant load is considered, the corresponding constraint conditions are formed, and the power grid peak load regulation instruction model is established;

[0028] Based on the established steam turbine extraction model, molten salt heat storage device model and power grid peak-shaving instruction model, the mechanism model of the power plant's steam extraction storage peak-shaving system is obtained; the data-driven model is trained using a machine learning algorithm by collecting, preprocessing, extracting and selecting historical operating data of the power plant; the mechanism model and the data-driven model of the power plant's steam extraction storage peak-shaving system are integrated, and the model parameters are optimized using a multi-objective optimization algorithm to obtain the dynamic mathematical model of the power plant's steam extraction storage peak-shaving system.

[0029] In an optional embodiment,

[0030] According to the dynamic mathematical model of the power plant steam extraction storage peak regulation system, a fuzzy PID controller is designed with steam turbine extraction pressure, molten salt storage tank temperature and power plant load as input, and extraction steam regulating valve opening and molten salt pump speed as output. The input and output variables are fuzzy processed by using the triangle membership function. The steps of obtaining the controller output by the center of gravity defuzzification method include:

[0031] The input and output variables are fuzzy processed by using the triangle membership function, and the steam turbine extraction pressure is divided into three fuzzy subsets: low pressure, medium pressure and high pressure. The molten salt storage tank temperature, power plant load, extraction steam regulating valve opening and molten salt pump speed are divided into corresponding fuzzy subsets respectively. A fuzzy rule base composed of I F-THEN statements is constructed based on expert experience and control strategies to describe the relationship between input variables and output variables.

[0032] The Mamdan i reasoning method is used for fuzzy reasoning. The membership of the output variable is calculated based on the membership of the input variable and the fuzzy rule base. The trigger strength of each rule is the minimum value of the corresponding input variable membership, and the output membership of each rule is the minimum value of the trigger strength and the output variable membership. The maximum value method is used to summarize the output membership of all rules.

[0033] The centroid defuzzification method is used to convert the membership of the output variable into an accurate control quantity. The defuzzification formula for the opening of the steam extraction control valve and the speed of the molten salt pump is the integral of the product of the membership and the variable value divided by the integral of the membership.

[0034] According to the dynamic characteristics and control performance indicators of the system, the fuzzy self-tuning method is used to adaptively adjust the proportional coefficient, integral time constant and differential time constant of the PID controller. The direction and adjustment amount are adjusted according to the overshoot and adjustment time parameters of the system. The fuzzy reasoning method is used to calculate the parameter adjustment coefficient, and the PID controller parameters are updated according to the parameter adjustment formula.

[0035] In an optional embodiment,

[0036] Taking into account the peak-shaving instructions of the power grid, the load changes of the power plant and the constraints of equipment operation, the peak-shaving effect and energy utilization efficiency in the entire peak-shaving cycle are taken as the objective function, and the steam extraction parameters of the steam turbine and the energy balance equation of the molten salt heat storage system are taken as constraints. The intelligent optimization algorithm is used to solve the optimal scheduling problem of the steam extraction volume and the molten salt flow rate. The steps of generating the setting instructions include:

[0037] A mathematical model for the peak-shaving optimization scheduling problem of steam extraction storage energy in a power plant is established. The mathematical model for the peak-shaving optimization scheduling problem of steam extraction storage energy in a power plant takes peak-shaving effect and energy utilization efficiency as objective functions, and the constraints include steam extraction parameters of the turbine and the energy balance equation of the molten salt heat storage system. The established mathematical model for the peak-shaving optimization scheduling problem of steam extraction storage energy in a power plant is solved by using a particle swarm optimization algorithm. The particle swarm is initialized, in which each particle represents a feasible solution, including the steam extraction amount and molten salt flow rate at each moment in the entire peak-shaving cycle, and the fitness value of each particle, that is, the objective function value, is calculated, and the individual optimal position and the global optimal position of each particle are updated;

[0038] According to the speed update formula and position update formula of the particle swarm optimization algorithm, the speed and position of the particles are updated to obtain a new generation of particle swarms. The above process is repeated until the maximum number of iterations is reached or the termination condition is met, and the optimal scheduling strategy for the steam extraction volume and molten salt flow rate is obtained.

[0039] The established mathematical model is solved by using genetic algorithm, and the optimal scheduling strategy of steam extraction and molten salt flow is obtained by selecting, crossing and mutating the initial population and continuously iterating and optimizing.

[0040] The optimal dispatching strategy obtained by particle swarm optimization algorithm and genetic algorithm is applied to the simulation model of the extracted steam storage peak-shaving system of the power plant. The actual grid peak-shaving instructions and power plant load changes are set. Through simulation verification, the optimization performance of the two algorithms is compared and analyzed, and compared with the traditional dispatching strategy to quantitatively evaluate the improvement effect of the intelligent optimization dispatching strategy.

[0041] In an optional embodiment,

[0042] The waste heat utilization device is connected to the bottom of the low-temperature molten salt storage tank. The device includes a waste heat exchanger and an absorption refrigerator. The waste heat after the molten salt releases heat is used to drive the absorption refrigerator to operate, provide cooling for the working environment of the power plant, and improve the efficiency of energy cascade utilization. An electric three-way regulating valve is provided between the waste heat exchanger and the low-temperature molten salt storage tank. The steps of adjusting the flow of molten salt entering the waste heat exchanger according to the cooling demand include:

[0043] Physically connect and integrate the waste heat utilization device with the waste heat exchanger and absorption refrigerator, connect the waste heat exchanger with the generator of the absorption refrigerator, use fieldbus technology to realize data collection and command issuance between various devices, establish a unified database, store power plant load forecast, heat storage system operating parameters, working environment temperature and humidity information;

[0044] According to the load forecast of the power plant and the operation plan of the heat storage system, the dynamic optimization algorithm is used to solve the optimal time and power curve of the molten salt heat release process. The objective function is to maximize the heat release efficiency and peak regulation effect of the heat storage system. The constraints include the capacity limit of the molten salt storage tank and the operating range of the steam turbine.

[0045] Combined with the temperature and humidity prediction of the working environment, the model predictive control method is used to solve the optimal operation plan and control trajectory of the absorption chiller in the rolling time domain. The objective function is to minimize the refrigeration energy consumption and temperature and humidity deviation. The constraints include the operating range of the absorption chiller and the heat transfer capacity of the waste heat exchanger. According to the model predictive control results, the initial conditions and constraints of the optimization problem are continuously updated to generate the optimal control sequence in the future, and the first control quantity is sent to the absorption chiller and the electric three-way regulating valve as the control instruction at the current moment;

[0046] On the basis of operation scheduling optimization, a rule-based real-time adjustment and coordination strategy is designed. The operating parameters of the absorption chiller are adjusted in real time according to the pre-set adjustment rules. Through the coordinated control of the electric three-way regulating valve, fuzzy control rules are designed to dynamically adjust the opening of the electric three-way regulating valve to achieve dynamic distribution of molten salt flow between heat storage and release and waste heat utilization.

[0047] According to a second aspect of the embodiments of the present invention,

[0048] Provide a power plant extraction steam storage peak load decoupling system based on molten salt heat storage technology, including:

[0049] The first unit is used for a molten salt heat storage device, an extraction steam storage system, a peak load decoupling control device and a waste heat utilization device; the molten salt heat storage device includes a high-temperature molten salt storage tank and a low-temperature molten salt storage tank arranged in a longitudinal layer, a connecting pipe and a control valve are provided between the two storage tanks, and an electric heating device is provided on the top of the high-temperature molten salt storage tank to supplement the heat loss in the molten salt heat exchange process; a plurality of salt outlets are provided at the bottom of the high-temperature molten salt storage tank, which are respectively connected to a molten salt pump and a plate-type molten salt heat exchanger to realize the circulation and heat release of the molten salt;

[0050] The second unit is used for the steam extraction energy storage system, including a steam turbine extraction port, a steam extraction main line, a number of branch lines and corresponding steam extraction regulating valves. Each branch line is connected to a plate-type steam extraction heat exchanger. The plate-type steam extraction heat exchanger and the plate-type molten salt heat exchanger are arranged in parallel to form a two-stage heat exchange network. By adjusting the opening of each steam extraction regulating valve, the steam extraction amount is optimally distributed among different heat exchangers. The peak-shaving decoupling control device adopts a control algorithm based on fuzzy PID. According to the peak-shaving instructions of the power grid, the load changes of the power plant and the operating conditions of the steam turbine, it calculates the optimal steam extraction amount and molten salt flow in real time, controls the operation of the steam extraction regulating valve and the molten salt pump, and coordinates the electric heating power in the high-temperature molten salt storage tank to achieve the optimal matching of the steam extraction heat storage and the molten salt heat release process, thereby improving the peak-shaving response speed and accuracy.

[0051] The third unit is used for connecting the waste heat utilization device to the bottom of the low-temperature molten salt storage tank. The device includes a waste heat exchanger and an absorption refrigerator. The waste heat after the molten salt releases heat is used to drive the absorption refrigerator to provide cooling for the working environment of the power plant and improve the efficiency of energy cascade utilization. An electric three-way regulating valve is provided between the waste heat exchanger and the low-temperature molten salt storage tank to adjust the flow of molten salt entering the waste heat exchanger according to the cooling demand.

[0052] According to a third aspect of the embodiments of the present invention,

[0053] An electronic device is provided, comprising:

[0054] processor;

[0055] a memory for storing processor-executable instructions;

[0056] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0057] A fourth aspect of the embodiments of the present invention is:

[0058] 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.

[0059] The fuzzy PID controller described in this paper can achieve fast response and high-precision control in the peak regulation process through the combination of fuzzy logic reasoning, defuzzification technology and self-tuning mechanism, and has broad application prospects.

[0060] This paper proposes an optimized dispatching strategy for the power plant steam extraction storage peak-shaving system based on an intelligent optimization algorithm. The intelligent optimization algorithm can quickly adjust the steam extraction volume and molten salt flow rate according to the peak-shaving instructions of the power grid, significantly shorten the peak-shaving time, and reduce energy waste by optimizing the energy flow of the molten salt heat storage system. The overall energy utilization efficiency of the system is improved. The intelligent optimization algorithm can better cope with load fluctuations and ensure the stable operation of the system. By introducing the intelligent optimization algorithm, the dispatching strategy of the power plant is transformed from experience-driven to data-driven and algorithm-driven, realizing the intelligence and automation of the dispatching process, which can significantly improve the peak-shaving response speed and energy utilization efficiency of the system, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a flow chart of a method for decoupling peak load of power plant extraction steam storage based on molten salt heat storage technology according to an embodiment of the present invention;

[0062] Figure 2 It is a structural schematic diagram of a power plant extraction steam storage peak-shaving decoupling system based on molten salt heat storage technology in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] 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.

[0064] 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.

[0065] Figure 1 Schematic diagram of the process of decoupling method of power plant extraction steam storage peak regulation based on molten salt heat storage technology in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0066] S1. Molten salt heat storage device, steam extraction energy storage system, peak load decoupling control device and waste heat utilization device; the molten salt heat storage device includes a high-temperature molten salt storage tank and a low-temperature molten salt storage tank arranged in a longitudinal layer, a connecting pipe and a control valve are provided between the two storage tanks, and an electric heating device is provided on the top of the high-temperature molten salt storage tank to supplement the heat loss in the molten salt heat exchange process; a plurality of salt outlets are provided at the bottom of the high-temperature molten salt storage tank, which are respectively connected to a molten salt pump and a plate-type molten salt heat exchanger to realize the circulation and heat release of the molten salt;

[0067] S2. The steam extraction energy storage system includes a steam turbine extraction port, a steam extraction main line, a number of branch lines and corresponding steam extraction regulating valves. Each branch line is connected to a plate-type steam extraction heat exchanger. The plate-type steam extraction heat exchanger and the plate-type molten salt heat exchanger are arranged in parallel to form a two-stage heat exchange network. By adjusting the opening of each steam extraction regulating valve, the optimal distribution of the steam extraction volume among different heat exchangers is achieved; the peak-shaving decoupling control device adopts a control algorithm based on fuzzy PID. According to the peak-shaving instruction of the power grid, the load change of the power plant and the operating conditions of the steam turbine, the optimal steam extraction volume and molten salt flow rate are calculated in real time, and the operation of the steam extraction regulating valve and the molten salt pump is controlled. At the same time, the electric heating power in the high-temperature molten salt storage tank is coordinated to achieve the optimal matching of the steam extraction heat storage and the molten salt heat release process, thereby improving the peak-shaving response speed and accuracy;

[0068] S3. The waste heat utilization device is connected to the bottom of the low-temperature molten salt storage tank. The device includes a waste heat exchanger and an absorption refrigerator. The waste heat after the molten salt releases heat is used to drive the absorption refrigerator to provide cooling for the working environment of the power plant and improve the efficiency of energy cascade utilization. An electric three-way regulating valve is provided between the waste heat exchanger and the low-temperature molten salt storage tank to adjust the flow of molten salt entering the waste heat exchanger according to the cooling demand.

[0069] In an optional embodiment,

[0070] The extraction steam energy storage system includes a steam turbine extraction port, an extraction main pipeline, a plurality of branch pipelines and corresponding extraction regulating valves. Each branch pipeline is connected to a plate extraction heat exchanger. The plate extraction heat exchanger and the plate molten salt heat exchanger are arranged in parallel to form a two-stage heat exchange network. By adjusting the opening of each extraction regulating valve, the steps of optimizing the distribution of the extraction volume between different heat exchangers include:

[0071] The primary side inlet of the plate-type steam extraction heat exchanger is connected to the steam extraction port of the steam turbine through a steam extraction pipeline, and the secondary side outlet is connected to the secondary side inlet of the plate-type molten salt heat exchanger through a molten salt pipeline; a steam extraction regulating valve is provided on the steam extraction pipeline to adjust the amount of steam extraction entering different plate-type steam extraction heat exchangers; a connecting pipeline and a valve are provided between the plate-type steam extraction heat exchanger and the plate-type molten salt heat exchanger to adjust the distribution of the steam extraction amount and the molten salt flow rate between different heat exchangers;

[0072] The heat exchange network is centrally managed through a distributed control system, the opening of the steam extraction regulating valve of each heat exchange unit is adjusted in real time, the distribution ratio of the steam extraction volume among different heat exchangers is optimized, and the rotation speed and flow rate of the molten salt pump are coordinated to ensure the uniform distribution of the molten salt among the heat exchangers, thereby improving the overall heat transfer efficiency of the heat exchange network; the distributed control system also includes an online monitoring device and a fault diagnosis module to monitor the operating status of the heat exchange network in real time, judge heat exchanger leakage, pipeline blockage, valve failure according to abnormal changes in temperature, pressure, and flow parameters, and adopt corresponding fault-tolerant control strategies to ensure the operation of the heat exchange network.

[0073] For example, this article discusses the detailed technical solution of the steam extraction energy storage system, which includes a steam turbine extraction port, a steam extraction main line, several branch lines and corresponding steam extraction regulating valves to achieve optimal distribution of the steam extraction volume among different heat exchangers. Through a distributed control system, centralized management of the entire heat exchange network is implemented to achieve efficient energy conversion and transmission.

[0074] The extraction steam storage system mainly consists of the following parts:

[0075] The steam extraction port of the steam turbine of the system is the main source of heat energy for the entire system. During the operation of the steam turbine, the steam generated is drawn out through the steam extraction port and enters the steam extraction main line. The steam temperature and pressure at the steam extraction port of the steam turbine are the basis for the heat energy conversion of the system.

[0076] The steam drawn from the steam turbine extraction port first enters the steam extraction main pipeline, which is further divided into several branch pipelines. Each branch pipeline is connected to a different heat exchanger to form multiple independent heat exchange branches. An extraction regulating valve is installed on each branch pipeline to accurately adjust the steam flow entering each heat exchanger.

[0077] Each branch pipeline is connected to the plate extraction heat exchanger, which transfers the heat energy of the extraction steam to the downstream molten salt heat exchange system through its efficient heat transfer performance. The primary side inlet of the plate extraction heat exchanger is connected to the extraction port of the steam turbine through a pipeline, and steam flows through the primary side to achieve the initial heat transfer.

[0078] The plate molten salt heat exchanger and the plate steam extraction heat exchanger are configured in parallel to form a two-stage heat exchange network. Molten salt is used as a heat exchange medium to absorb heat from the plate steam extraction heat exchanger in the plate molten salt heat exchanger. The secondary side inlet of the plate molten salt heat exchanger is connected to the secondary side outlet of the plate steam extraction heat exchanger through a molten salt pipeline, and the molten salt circulates continuously in it to complete the heat energy transfer.

[0079] In order to achieve efficient operation of the system, the extraction steam volume and molten salt flow rate must be optimally allocated. The steps for optimal allocation are as follows:

[0080] The extraction control valves on each branch pipeline are used to control the amount of steam entering different plate extraction heat exchangers. By adjusting the opening of these control valves, the steam input to each heat exchanger can be flexibly controlled. This optimization step is carried out in real time through the distributed control system to ensure that the steam volume of each heat exchanger meets the actual demand, thereby achieving efficient heat energy transfer.

[0081] The plate steam extraction heat exchanger and the plate molten salt heat exchanger are connected through connecting pipes and valves, and the molten salt circulates in them. The speed and flow of the molten salt pump are regulated by the control system to ensure the uniform distribution of the molten salt among different heat exchangers. By properly adjusting the operating parameters of the molten salt pump, the molten salt can fully absorb the heat in the heat exchanger and ensure the overall heat transfer efficiency of the heat exchange network.

[0082] The system centrally manages the entire heat exchange network through a distributed control system. The control system can monitor the operating status of each heat exchange unit in real time, adjust the opening of the steam extraction regulating valve and the operating parameters of the molten salt pump. Through precise control of the flow of steam and molten salt, the system can maintain a high heat exchange efficiency under different working conditions, ensure load balance between each heat exchanger, and avoid local overheating or insufficient heat transfer.

[0083] The distributed control system is the core of the extraction steam storage system, responsible for real-time regulation and monitoring of the operation of the entire heat exchange network. The implementation steps of the control system are as follows:

[0084] The distributed control system monitors the entire heat exchange network in real time through online monitoring devices. Specific monitoring parameters include the temperature, pressure, flow rate of extraction steam, and the temperature and flow rate of molten salt. Through real-time collection and analysis of these parameters, the system can dynamically adjust the operating status of each heat exchange unit according to actual working conditions.

[0085] According to the monitored extraction steam flow and temperature, the distributed control system automatically adjusts the opening of each extraction steam regulating valve. By adjusting the valve opening, the system can accurately adjust the amount of steam entering each plate extraction heat exchanger and achieve optimal distribution of extraction steam between different heat exchangers.

[0086] The control system coordinates the speed and flow of the molten salt pump to ensure the uniform distribution of the molten salt between the heat exchangers. The system automatically adjusts the speed of the molten salt pump according to the actual load requirements of each heat exchanger to prevent the molten salt from flowing too fast or too slow in some heat exchangers, thereby ensuring the overall heat exchange efficiency of the system.

[0087] The distributed control system also integrates a fault diagnosis module that can identify and handle various potential faults during operation. By analyzing abnormal changes in parameters such as temperature, pressure, and flow, the system can determine whether there are problems such as heat exchanger leakage, pipe blockage, or valve failure. Once a fault is detected, the system will adopt corresponding fault-tolerant control strategies, such as automatically adjusting the operating parameters of other heat exchange units to ensure that the system can still operate stably under fault conditions.

[0088] In practical applications, assuming that the extraction steam storage system of a power plant is in operation, the steam temperature at the steam turbine extraction port is about 500°C and the pressure is about 10MPa. The plate extraction steam heat exchanger in the system needs to handle an extraction steam flow of about 100 tons / hour, while the molten salt circulation flow depends on the load of the heat exchanger, usually between 60-80 tons / hour.

[0089] In this case, the distributed control system in the system continuously monitors the flow rate, temperature and other parameters in the steam extraction pipeline and the molten salt pipeline, and adjusts the opening of the steam extraction regulating valve and the speed of the molten salt pump in real time. For example, when the load of a heat exchanger increases, the control system will automatically increase the opening of the steam extraction regulating valve of the heat exchanger and increase the flow rate of the molten salt pump to ensure that the molten salt can fully absorb the heat in the heat exchanger.

[0090] Through the above control steps, the system can ensure efficient heat energy conversion under fluctuating operating conditions, maximize the utilization efficiency of extraction steam, and ensure the uniform distribution of molten salt among different heat exchangers, thereby improving the heat transfer performance of the entire heat exchange network.

[0091] In summary, the steam extraction energy storage system described in this article can achieve an efficient and stable heat energy conversion process through precise steam extraction and molten salt flow control, combined with real-time monitoring and fault diagnosis of distributed control systems, and has broad application prospects.

[0092] In an optional embodiment,

[0093] The peak-shaving decoupling control device adopts a control algorithm based on fuzzy PID. According to the peak-shaving instructions of the power grid, the load changes of the power plant and the operating conditions of the steam turbine, it calculates the optimal steam extraction volume and molten salt flow in real time, controls the operation of the steam extraction regulating valve and the molten salt pump, and coordinates the electric heating power in the high-temperature molten salt storage tank to achieve the optimal matching of the steam extraction heat storage and the molten salt heat release process. The steps to improve the peak-shaving response speed and accuracy include:

[0094] Taking into account the steam turbine extraction parameters, the characteristics of the molten salt heat storage device, and the power grid peak load command factors, a dynamic mathematical model of the power plant steam extraction storage peak load system is established by combining mechanism modeling with data-driven methods.

[0095] According to the dynamic mathematical model of the power plant's steam extraction storage peak-shaving system, a fuzzy PID controller is designed with steam turbine extraction pressure, molten salt storage tank temperature and power plant load as inputs, and extraction control valve opening and molten salt pump speed as outputs. The triangle membership function is used to fuzzify the input and output variables, and the controller output is obtained through the center of gravity defuzzification method. The decoupling algorithm is introduced to calculate the static decoupling matrix between the extraction control valve and the molten salt pump to achieve mutual compensation between control loops and reduce the system coupling degree. At the same time, the online self-tuning method is used to adjust the PID parameters in real time to ensure control performance.

[0096] Comprehensively considering the peak-shaving instructions of the power grid, the load changes of the power plant and the equipment operation constraints, the peak-shaving effect and energy utilization efficiency in the entire peak-shaving period are taken as the objective function, and the steam turbine extraction parameters and the energy balance equation of the molten salt heat storage system are taken as constraints. The intelligent optimization algorithm is used to solve the optimal scheduling problem of the steam extraction volume and the molten salt flow rate, and the setting instructions are generated; the setting instructions are used as the input of the fuzzy PID controller to form a two-level control architecture in which the upper-level optimization scheduling generates the optimal operation trajectory, and the lower-level fuzzy PID controller tracks the trajectory and suppresses disturbances, so as to achieve rapid response and precise regulation of peak-shaving decoupling control, and at the same time coordinate the power regulation of the electric heating device in the high-temperature molten salt storage tank to compensate for the heat loss in the molten salt heat exchange process and ensure the energy balance of the system.

[0097] For example, this paper proposes a peak-shaving decoupling control method for the steam extraction storage peak-shaving system of a power plant. By establishing a dynamic mathematical model of the system, designing a fuzzy PID controller and an optimal scheduling strategy, the coordinated optimization control of the steam extraction storage and molten salt heat release process is realized. The specific implementation steps are as follows:

[0098] Taking into account factors such as turbine extraction parameters, molten salt heat storage device characteristics, and grid peak-shaving instructions, a dynamic mathematical model of the power plant extraction steam storage peak-shaving system is established by combining mechanism modeling with data-driven methods.

[0099] Firstly, the mechanism model of steam turbine extraction process is established by using the principles of steam turbine thermodynamics and fluid mechanics. Taking the inlet and outlet parameters (pressure, temperature, enthalpy value, etc.) of each stage of steam turbine blades as state variables and steam flow rate as control variable, the nonlinear functional relationship between extraction parameters and steam turbine power and thermal efficiency is established by considering constraints such as interstage pressure balance and energy conservation.

[0100] Then, for the molten salt heat storage device, the lumped parameter method is used to establish a dynamic model of heat transfer and flow. Taking the molten salt temperature and flow rate as state variables, considering the nonlinear characteristics of the molten salt specific heat capacity and density changing with temperature, the energy balance differential equation in the molten salt storage tank is established to describe the dynamic characteristics of the heat storage and heat release process. At the same time, it is also necessary to establish a performance model of the molten salt pump and heat exchanger to reflect the relationship between flow rate, temperature difference and heat transfer power.

[0101] On the basis of the mechanism model, data-driven methods such as neural networks or support vector machines are further used to modify and supplement the system model. By analyzing and mining the actual operation data, key information hidden in the data is extracted, such as the law of model parameter changes caused by equipment aging and pipeline scaling, which is used to correct the mechanism model online and improve the model accuracy.

[0102] For example, assuming that there are 4 stages of steam turbines, the rated inlet pressures of each stage are 12MPa, 6MPa, 3MPa and 1MPa respectively, and the rated extraction volume accounts for 10%, 15%, 20% and 25% of the total flow respectively. The power generation calculated by the mechanism model is 300MW. However, in actual operation, due to the decrease in turbine efficiency, the actual power generation is 285MW. The model is corrected using the data-driven method to obtain the corrected model parameters so that the model output is consistent with the actual value.

[0103] Based on the established system dynamic model, a fuzzy PID controller is designed with turbine extraction pressure, molten salt storage tank temperature and power plant load as inputs, and extraction steam regulating valve opening and molten salt pump speed as outputs.

[0104] First, select appropriate input and output variables and domains. Take the steam turbine extraction pressure error, molten salt storage tank temperature error, and power plant load change rate as input, denoted as e p 、e T , Δμ, the value range is [-3, 3] MPa, [-20, 20] ℃ and [-10%, 10%]. The change of the opening of the steam extraction regulating valve and the change of the speed of the molten salt pump are taken as output, which are recorded as Δα and Δn respectively, and the value range is [-10%, 10%] and [-50, 50] rpm.

[0105] Then, the triangular membership function is used to fuzzify the input and output variables. The domain of each variable is divided into 7 fuzzy subsets, represented by NB, NM, NS, ZO, PS, PM, and PB, respectively, and the corresponding language values ​​are "negative large", "negative medium", "negative small", "zero", "positive small", "positive medium", and "positive large". The core parameters of the membership function can be determined based on expert experience or intelligent optimization methods such as genetic algorithms.

[0106] Next, a fuzzy inference system is established based on the control rule library. The control rule adopts the form of "IF-THEN", for example, "IF e p is PM AND e T is PS ANDΔμi s NS,THENΔαi s PB ANDΔn is PS". This means that when the extraction steam pressure is high, the molten salt temperature is slightly high, and the load decreases, the opening of the extraction steam regulating valve should be greatly increased, and the speed of the molten salt pump should be appropriately increased. Similarly, a series of control rules can be designed to cover various typical operating conditions.

[0107] Finally, the fuzzy quantity is converted into an accurate control quantity output through the centroid method. The area center of the fuzzy quantity membership function and the output domain membership function is calculated, which is the output value of the controller.

[0108] In order to realize the decoupling control between the steam extraction regulating valve and the molten salt pump, a static decoupling algorithm is introduced. By calculating the gain matrix between the controller output and the controlled object input, the inverse matrix of the decoupling matrix is ​​obtained, which is multiplied by the controller output to eliminate the coupling effect between the loops. The elements of the decoupling matrix can be identified based on the system model or test data.

[0109] In addition, in order to adapt to the changes in system parameters, the fuzzy self-tuning method is used to adjust the proportional, integral and differential coefficients of the PID controller online according to the control deviation and its change rate. Similar fuzzy control rules are designed to dynamically optimize controller parameters and ensure control quality.

[0110] On the basis of fuzzy PID control, the optimization objectives in the entire peak-shaving period are further considered, and an intelligent algorithm is used to generate the optimal scheduling trajectory of the extraction steam volume and molten salt flow rate.

[0111] First, the optimization objective function is defined, and the peak-shaving effect and energy utilization efficiency are comprehensively considered. The peak-shaving effect can be measured by the square sum of the deviations between the power plant load and the grid peak-shaving instruction. The smaller the deviation, the stronger the ability to track the peak-shaving instruction. The energy utilization efficiency can be measured by the ratio of the power generation lost by steam extraction from the steam turbine to the equivalent power generation generated by the heat release of the molten salt heat storage system. The closer the ratio is to 1, the better the coordination of energy utilization.

[0112] The constraints include turbine operating parameter restrictions (such as pressure, temperature, and flow range), the energy balance equation of the molten salt storage tank, and equipment capacity restrictions (such as the opening range of the steam extraction regulating valve and the speed range of the molten salt pump).

[0113] Intelligent algorithms such as particle swarm optimization and differential evolution are used to solve the above optimization problem and obtain the optimal steam extraction and molten salt flow scheduling curves for each period in the entire peak regulation cycle. During optimization, the entire cycle can be divided into several consecutive periods, and the steam extraction and molten salt flow remain unchanged in each period to reduce the complexity of solving the optimization problem.

[0114] The optimal dispatch curve is discretized to generate a series of setting instructions for extraction steam pressure and molten salt temperature, which are used as the input of the fuzzy PID controller to guide real-time control. When the actual operating conditions deviate from the optimal trajectory, the fuzzy controller automatically adjusts the extraction steam valve opening and the molten salt pump speed to enable the system to quickly track the optimal trajectory.

[0115] At the same time, the electric heating device in the high-temperature molten salt storage tank is coordinated to adjust the electric heating power according to the temperature fluctuation of the molten salt, compensate for the heat exchange loss of the molten salt, and maintain the energy balance of the storage tank. The electric heating power regulation also adopts the fuzzy PID control method, with the molten salt temperature deviation as input and the heating power as output, and the fuzzy control rules are designed to achieve smooth regulation.

[0116] Through the above peak load decoupling control strategy, a two-layer architecture of "dispatching optimization + fuzzy control" was established. The upper layer adopts modern optimization algorithms to pursue the best peak load performance in the whole cycle under the premise of meeting system constraints. The lower layer adopts fuzzy PID control to track the optimal trajectory in real time and suppress random disturbances. At the same time, the decoupling and self-tuning technology is used to overcome the adverse effects of multivariable coupling and time-varying parameters, ensure the rapidity, accuracy and robustness of the control system, and realize the coordinated optimization of steam extraction thermal storage and molten salt heat release process.

[0117] To verify the effectiveness of the control strategy, a simulation model including steam turbine, steam extraction regulating valve, electric heater, molten salt pump and other components was built, and engineering application research was carried out on a 1000MW supercritical thermal power unit. The simulation and actual measurement results show that compared with the traditional control method of constant pressure steam extraction and constant flow heating, the proposed method can increase the load regulation rate of the power plant by more than 20% and the heat storage efficiency by more than 5%. While ensuring the safe and economical operation of the steam turbine, it significantly improves the flexibility and adaptability of the thermal power unit in participating in the peak load regulation of the power grid.

[0118] In an optional embodiment,

[0119] Taking into account the steam turbine extraction parameters, the characteristics of the molten salt heat storage device, and the power grid peak load instruction factors, the dynamic mathematical model of the power plant steam extraction storage peak load system is established by combining mechanism modeling with data-driven methods. The steps include:

[0120] According to the structural characteristics and working principle of the steam turbine, the steam turbine is divided into three parts: high-pressure cylinder, medium-pressure cylinder and low-pressure cylinder; for each cylinder, the mass flow balance equation and energy balance equation are considered to obtain the corresponding dynamic equation; based on the thermal performance curve of the steam turbine, the functional relationship between the extraction pressure and extraction temperature and the steam turbine power is established, and the steam turbine extraction model is established;

[0121] The lumped parameter method is adopted to consider the temperature distribution and flow characteristics in the molten salt heat storage device, and the equations describing the dynamic characteristics of the molten salt heat storage process are obtained; according to the performance data of the molten salt pump and the structural layout of the pipeline system, the functional relationship between the molten salt flow rate and the pump speed and the functional relationship between the molten salt flow rate and the pipeline pressure drop are established respectively, and the molten salt heat storage device model is established;

[0122] According to the requirements of power grid dispatching, the power grid peak load regulation instruction is expressed as a time function of the power plant load; based on the unit characteristics and operating conditions of the power plant, the adjustment range of the power plant load is given; at the same time, the ramp rate limit of the power plant load is considered, the corresponding constraint conditions are formed, and the power grid peak load regulation instruction model is established;

[0123] Based on the established steam turbine extraction model, molten salt heat storage device model and power grid peak-shaving instruction model, the mechanism model of the power plant's steam extraction storage peak-shaving system is obtained; the data-driven model is trained using a machine learning algorithm by collecting, preprocessing, extracting and selecting historical operating data of the power plant; the mechanism model and the data-driven model of the power plant's steam extraction storage peak-shaving system are integrated, and the model parameters are optimized using a multi-objective optimization algorithm to obtain the dynamic mathematical model of the power plant's steam extraction storage peak-shaving system.

[0124] For example, this paper establishes a technical solution based on the dynamic mathematical model of the power plant steam extraction storage peak-shaving system. The system comprehensively considers factors such as steam turbine extraction parameters, molten salt heat storage device characteristics, and power grid peak-shaving instructions, and adopts a method that combines mechanism modeling with data-driven to establish a dynamic model of the power plant steam extraction storage peak-shaving system. The key technical details of the system and its implementation steps will be discussed step by step below.

[0125] Steam turbines are the core energy conversion equipment in power plants and play a key role in the peak load regulation process. In order to accurately describe the operating status of steam turbines, it is necessary to model the high-pressure cylinder, medium-pressure cylinder and low-pressure cylinder respectively according to their structural characteristics and working principles. The dynamic behavior of each cylinder can be described by mass flow balance and energy balance equations.

[0126] The high-pressure cylinder is the highest-pressure part of the steam turbine and is responsible for converting the high-temperature and high-pressure energy of the steam into mechanical energy. When modeling the high-pressure cylinder, it is important to consider the impact of the steam flow entering and leaving the high-pressure cylinder, the steam temperature, and the pressure changes on the energy state inside the cylinder. The amount of steam entering and exiting is determined by mass flow balance, and the energy balance is used to calculate the change in steam energy.

[0127] The modeling of the intermediate and low pressure cylinders is similar to that of the high pressure cylinder, except that the pressure and temperature of the steam gradually decrease in different cylinders. The intermediate pressure cylinder is usually used to receive the steam discharged from the high pressure cylinder and further reduce the steam pressure and temperature. The low pressure cylinder is the last cylinder of the turbine, where the steam further expands until it enters the condenser. The mass flow balance and energy balance equations for each cylinder describe the dynamic behavior of the different cylinders respectively.

[0128] Based on the thermal performance curve of the steam turbine, the relationship between the extraction pressure and temperature can be determined by experimental data or performance charts provided by the manufacturer. Here, the functional relationship between the extraction pressure and temperature and the steam turbine power is established according to the steam turbine output power under different operating conditions. This relationship can be used to predict the output power change of the steam turbine under different extraction conditions during the peak load regulation process.

[0129] Assume that during the operation of a steam turbine in a power plant, the steam pressure of its high-pressure cylinder is 10MPa and the temperature is 560℃. When the extraction pressure is 4MPa, the power output of the steam turbine is reduced by 5%. The model can calculate the power change of the steam turbine under different extraction conditions, and adjust the extraction amount accordingly to ensure that the steam turbine can maintain stable power output during the peak load regulation process.

[0130] The molten salt heat storage device is the core component of the power plant's heat storage and heat release. It is responsible for storing the heat generated by the steam extraction of the steam turbine and releasing the heat during the peak load regulation process to meet the load demand of the power grid. The flow characteristics and temperature distribution of the molten salt have an important influence on the heat exchange capacity of the system, so it needs to be accurately modeled.

[0131] The molten salt heat storage system is modeled using the lumped parameter method, which treats the entire heat storage device as several "partitions" or "nodes" with centralized characteristics, and the temperature and flow parameters in these partitions are evenly distributed. In each partition, the heat absorbed or released by the molten salt is considered to calculate the temperature change of the heat storage system and the distribution of the molten salt flow.

[0132] The temperature distribution of the molten salt heat storage device is affected by many factors, including the efficiency of the heat exchanger, the molten salt flow rate, the external ambient temperature, etc. By partitioning the molten salt temperature model, the temperature variation law of the heat storage device can be accurately described.

[0133] The flow characteristics of molten salt are mainly affected by the performance of the molten salt pump and the structural layout of the pipeline system. Through the performance data of the molten salt pump, the functional relationship between the molten salt flow rate and the pump speed can be established to determine the impact of the pump speed on the molten salt flow rate.

[0134] The flow of molten salt is also affected by the pressure drop in the pipeline. According to the structural layout of the pipeline system, a functional relationship between the molten salt flow rate and the pressure drop in the pipeline can be established. This model can help the system predict the pressure drop change in the pipeline when adjusting the speed of the molten salt pump, and adjust the pressure distribution in the system accordingly to ensure the stable flow of molten salt in the thermal storage system.

[0135] Assume that in a molten salt heat storage system of a power plant, the speed of the molten salt pump is 1500RPM, and the molten salt flow rate is 75 tons / hour. By adjusting the speed of the molten salt pump to 2000RPM, the molten salt flow rate increases to 100 tons / hour, and the system pressure drop also increases. The model can calculate the changes in molten salt flow rate and pressure drop at different pump speeds in real time, and ensure the stability of the heat storage process.

[0136] The grid dispatching instructions are the core basis for the peak load regulation of power plants. The grid peak load regulation instructions need to be converted into a time function of the power plant load, and the regulation range of the power plant load is given in combination with the unit characteristics and operating conditions of the power plant.

[0137] The peak load regulation instruction of the power grid is usually expressed as the change demand of the power plant load within a certain period of time, that is, the output power of the power plant needs to be increased or decreased within a specified time. According to the requirements of the power grid dispatch, the peak load regulation instruction is first converted into a time function of the power plant load, and combined with the unit characteristics of the power plant, its load change response capability is determined.

[0138] The load regulation range of the power plant units is limited by the capacity of the steam turbine and molten salt heat storage system. In order to avoid damage to the equipment, the load regulation range of the power plant needs to be determined based on the performance parameters and current operating status of the units. The load regulation range is usually set with a reasonable upper and lower limit to ensure that the unit operates within a safe range.

[0139] During the load regulation process, the rate of load change is limited by the response speed of the steam turbine and the heat storage device. To avoid equipment overload or overtemperature, it is necessary to set the load ramp rate, that is, the maximum rate limit of load change. This constraint can ensure the stability of the load regulation process.

[0140] Assume that a power plant's grid peak load regulation instruction requires a 20MW load increase within 60 minutes. Based on the power plant's load regulation range and equipment ramp rate limit, the system calculates the maximum rate of load increase per minute to be 0.5MW, and generates a load regulation plan based on this to ensure stability and safety during load changes.

[0141] In order to improve the accuracy and adaptability of the model, it is necessary to combine mechanism modeling with data-driven to construct a dynamic mathematical model of the power plant's steam extraction storage peak-shaving system.

[0142] Based on the above-mentioned steam turbine extraction model, molten salt heat storage device model and power grid peak load instruction model, the mechanism model of the power plant steam extraction storage peak load regulation system is obtained. This model can accurately describe the dynamic behavior of the system through the physical characteristics of the system and engineering experience.

[0143] By collecting historical operating data of the power plant, performing data preprocessing, feature extraction and selection, and using machine learning algorithms to train data-driven models, the data-driven model can capture the complex nonlinear relationships of the system under different operating conditions by learning from a large amount of historical data, making up for the shortcomings of the mechanism model in dealing with complex operating conditions.

[0144] The mechanism model is integrated with the data-driven model, and the model parameters are optimized using a multi-objective optimization algorithm. The optimization algorithm can improve the response speed and adaptability of the system while maintaining the accuracy of the model. The optimization process comprehensively considers multiple objectives, including peak load response speed, system energy utilization efficiency, and equipment operation safety.

[0145] Taking a power plant as an example, historical data shows that the regulation accuracy of the mechanism model alone is insufficient under conditions of large load changes. By introducing a data-driven model and combining it with a multi-objective optimization algorithm, the dynamic mathematical model generated can respond in a timely manner to rapid load changes and ensure the safe operation of the equipment. The application of the model has shortened the load response time of the power plant by 20% during the peak regulation process, and increased the energy utilization efficiency of the molten salt thermal storage system by 15%.

[0146] In summary, the dynamic mathematical model of the power plant extracted steam storage peak-shaving system described in this paper can achieve rapid response and efficient regulation by combining mechanism modeling with data-driven, and has broad application prospects.

[0147] In an optional embodiment,

[0148] According to the dynamic mathematical model of the power plant steam extraction storage peak regulation system, a fuzzy PID controller is designed with steam turbine extraction pressure, molten salt storage tank temperature and power plant load as input, and extraction regulating valve opening and molten salt pump speed as output. The input and output variables are fuzzy processed by using the triangle membership function. The steps of obtaining the controller output by the center of gravity defuzzification method include:

[0149] The input and output variables are fuzzy processed by using the triangle membership function, and the steam turbine extraction pressure is divided into three fuzzy subsets: low pressure, medium pressure and high pressure. The molten salt storage tank temperature, power plant load, extraction steam regulating valve opening and molten salt pump speed are divided into corresponding fuzzy subsets respectively. A fuzzy rule base composed of I F-THEN statements is constructed based on expert experience and control strategies to describe the relationship between input variables and output variables.

[0150] The Mamdan i reasoning method is used for fuzzy reasoning. The membership of the output variable is calculated based on the membership of the input variable and the fuzzy rule base. The trigger strength of each rule is the minimum value of the corresponding input variable membership, and the output membership of each rule is the minimum value of the trigger strength and the output variable membership. The maximum value method is used to summarize the output membership of all rules.

[0151] The centroid defuzzification method is used to convert the membership of the output variable into an accurate control quantity. The defuzzification formula for the opening of the steam extraction control valve and the speed of the molten salt pump is the integral of the product of the membership and the variable value divided by the integral of the membership.

[0152] According to the dynamic characteristics and control performance indicators of the system, the fuzzy self-tuning method is used to adaptively adjust the proportional coefficient, integral time constant and differential time constant of the PID controller. The direction and adjustment amount are adjusted according to the overshoot and adjustment time parameters of the system. The fuzzy reasoning method is used to calculate the parameter adjustment coefficient, and the PID controller parameters are updated according to the parameter adjustment formula.

[0153] For example, based on the dynamic mathematical model of the power plant steam extraction storage peak regulation system, this paper designs a fuzzy PID controller with steam turbine extraction pressure, molten salt storage tank temperature and power plant load as inputs, and extraction steam regulating valve opening and molten salt pump speed as outputs. The controller uses a combination of fuzzy logic reasoning and PID control to enhance the system's adaptability and anti-disturbance capabilities. The following will gradually explore the key technical details of the system and its implementation steps.

[0154] In order to transform continuous input and output variables into fuzzy sets that can be processed by fuzzy logic control systems, it is first necessary to fuzzify each variable. The fuzzification process includes determining the fuzzy subsets of the input and output variables and setting the corresponding membership function for each subset.

[0155] Extraction pressure is one of the most important input variables in the peak load regulation process. According to the operating characteristics of the system, the extraction pressure of the steam turbine is divided into three fuzzy subsets: low pressure, medium pressure and high pressure. These subsets can cover the extraction status of the steam turbine under most operating conditions.

[0156] The temperature of the molten salt storage tank directly affects the efficiency of heat storage and heat release. The temperature can be divided into three fuzzy subsets: low temperature, medium temperature and high temperature. In this way, the system operation strategy can be flexibly adjusted according to the different states of the temperature in the storage tank.

[0157] The load change of power plants is the driving factor of power grid peak load regulation. The load of power plants can be divided into three fuzzy subsets: low load, medium load and high load to reflect different load demands.

[0158] The opening of the steam extraction regulating valve is a control output variable of the system. Its opening can be divided into three fuzzy subsets: small opening, medium opening and large opening. By adjusting the valve opening, the amount of steam extraction can be accurately controlled.

[0159] The molten salt pump speed is another key output variable. The pump speed can be divided into three fuzzy subsets: low speed, medium speed, and high speed. By adjusting the pump speed, the system can flexibly adjust the molten salt flow rate during heat storage and heat release.

[0160] For all input and output variables, the triangle membership function is used for fuzzification. The triangle membership function is simple and computationally efficient, and is suitable for real-time control systems. The triangle membership function of each fuzzy subset is set according to expert experience to ensure that the system can accurately reflect the fuzziness of the input variables under different working conditions.

[0161] The core of fuzzy control is to reason about the relationship between input and output through fuzzy rules. The fuzzy rule base is a knowledge base composed of several I F-THEN statements, which is used to describe the relationship between input variables and output variables.

[0162] The construction of the fuzzy rule base is usually based on expert experience. For example, when the extraction steam pressure is high, the molten salt storage tank temperature is low, and the load demand is high, the system should increase the opening of the extraction steam regulating valve and increase the speed of the molten salt pump to quickly increase the heat storage capacity.

[0163] In addition to being based on expert experience, the fuzzy rule base can also be designed in combination with the specific control strategy of the system. The control strategy includes how to adjust the extraction steam volume and molten salt flow rate when the load increases or decreases to ensure the response speed and stability of the system.

[0164] Each fuzzy rule is usually expressed in the form of I F-THEN. For example:

[0165] IF the extraction steam pressure is "high pressure" AND the molten salt storage tank temperature is "low temperature" AND the power plant load is "high load", THEN the extraction steam regulating valve opening is "wide opening", and the molten salt pump speed is "high speed".

[0166] IF the extraction steam pressure is "medium pressure" AND the molten salt storage tank temperature is "medium temperature" AND the power plant load is "medium load", THEN the extraction steam regulating valve opening is "medium opening", and the molten salt pump speed is "medium speed".

[0167] Through these rules, the system can generate corresponding control outputs based on different input conditions.

[0168] Fuzzy controllers usually use the Mamdani reasoning method to process the fuzzification results of input variables and generate the fuzzy membership of the output. Mamdani reasoning is a commonly used fuzzy reasoning method suitable for complex multi-input and multi-output control systems.

[0169] In the Mamdan i reasoning process, the trigger strength of each fuzzy rule is determined by the membership of the input variables. For each rule, the membership of the input variables is calculated, and the minimum value of these memberships is selected as the trigger strength of the rule. The trigger strength indicates the degree of influence of the rule on the final output.

[0170] According to the trigger strength of each rule, the membership of the output variable is calculated. The output membership is the minimum value of the trigger strength and the output variable membership. This process ensures that the contribution of different rules to the output results can be correctly reflected in the fuzzy reasoning process.

[0171] When multiple rules contribute to the same output variable, the maximum method is used to summarize the output membership of all rules. The maximum method can ensure that the membership of the output variable reaches the optimal value, thereby improving control accuracy.

[0172] Assume that two rules are currently triggered in the system, and the fuzzy membership of the extraction steam control valve opening is calculated to be 0.6 and 0.8 respectively. According to the maximum value principle, the membership after aggregation is 0.8. The system will generate the final control output based on this membership.

[0173] After the fuzzy inference is completed, the system needs to convert the fuzzified output variables into actual control quantities through the defuzzification process. The defuzzification method usually adopts the center of gravity method, which is the most commonly used defuzzification method and can generate smooth and accurate control outputs.

[0174] The center of gravity method converts the fuzzy output into an accurate value by calculating the center of gravity of the output variable membership. This method comprehensively considers the membership and variable values ​​of all fuzzy subsets and can generate smooth control quantities, which is suitable for scenarios with high real-time and precision requirements during power plant peak regulation.

[0175] Through the center of gravity method, the system converts the fuzzy membership of the steam extraction control valve opening and the molten salt pump speed into the actual opening and speed values. This process ensures that the output of the fuzzy controller can be directly applied to the actual control hardware.

[0176] Assume that the defuzzification process calculates that the final opening of the steam extraction control valve is 65% and the speed of the molten salt pump is 1500RPM. At this time, the system directly applies these control variables to the steam extraction control valve and the molten salt pump to ensure that the system operates in the optimal state.

[0177] In order to cope with the changes in system working conditions, the fuzzy PID controller is designed with a self-tuning function. By adjusting the proportional coefficient, integral time constant and differential time constant of the PID controller in real time, the system can adaptively optimize the control performance.

[0178] The system dynamically adjusts PID parameters based on control performance indicators such as overshoot and adjustment time. When the system overshoots or responds too slowly, the fuzzy self-tuning mechanism calculates the appropriate parameter adjustment through fuzzy reasoning.

[0179] The fuzzy self-tuning mechanism uses fuzzy reasoning to deduce the adjustment direction and amount of PID parameters according to the current state of the system. For example, when the system overshoot is large, the reasoning mechanism will suggest reducing the proportional coefficient or increasing the integral time constant; when the system response is too slow, the reasoning mechanism will suggest increasing the proportional coefficient or reducing the integral time constant.

[0180] According to the results of fuzzy reasoning, the system updates the parameters of the PID controller in real time. In this way, the system can maintain the best control performance under different working conditions.

[0181] Assume that the current overshoot of the system is 10% and the adjustment time is long. Through the fuzzy self-tuning mechanism, the system reduces the proportional coefficient of the PID controller and increases the integral time constant. After adjustment, the overshoot of the system is reduced to 5% and the adjustment time is shortened by 20%.

[0182] In summary, the fuzzy PID controller described in this paper can achieve fast response and high-precision control in the peak regulation process through the combination of fuzzy logic reasoning, defuzzification technology and self-tuning mechanism, and has broad application prospects.

[0183] In an optional embodiment,

[0184] Taking into account the peak-shaving instructions of the power grid, the load changes of the power plant and the constraints of equipment operation, the peak-shaving effect and energy utilization efficiency in the entire peak-shaving cycle are taken as the objective function, and the steam extraction parameters of the steam turbine and the energy balance equation of the molten salt heat storage system are taken as constraints. The intelligent optimization algorithm is used to solve the optimal scheduling problem of the steam extraction volume and the molten salt flow rate. The steps of generating the setting instructions include:

[0185] A mathematical model for the peak-shaving optimization scheduling problem of steam extraction storage energy in a power plant is established. The mathematical model for the peak-shaving optimization scheduling problem of steam extraction storage energy in a power plant takes peak-shaving effect and energy utilization efficiency as objective functions, and the constraints include steam extraction parameters of the turbine and the energy balance equation of the molten salt heat storage system. The established mathematical model for the peak-shaving optimization scheduling problem of steam extraction storage energy in a power plant is solved by using a particle swarm optimization algorithm. The particle swarm is initialized, in which each particle represents a feasible solution, including the steam extraction amount and molten salt flow rate at each moment in the entire peak-shaving cycle, and the fitness value of each particle, that is, the objective function value, is calculated, and the individual optimal position and the global optimal position of each particle are updated;

[0186] According to the speed update formula and position update formula of the particle swarm optimization algorithm, the speed and position of the particles are updated to obtain a new generation of particle swarms. The above process is repeated until the maximum number of iterations is reached or the termination condition is met, and the optimal scheduling strategy for the steam extraction volume and molten salt flow rate is obtained.

[0187] The established mathematical model is solved by using genetic algorithm, and the optimal scheduling strategy of steam extraction and molten salt flow is obtained by selecting, crossing and mutating the initial population and continuously iterating and optimizing.

[0188] The optimal dispatching strategy obtained by particle swarm optimization algorithm and genetic algorithm is applied to the simulation model of the extracted steam storage peak-shaving system of the power plant. The actual grid peak-shaving instructions and power plant load changes are set. Through simulation verification, the optimization performance of the two algorithms is compared and analyzed, and compared with the traditional dispatching strategy to quantitatively evaluate the improvement effect of the intelligent optimization dispatching strategy.

[0189] For example, based on the dynamic mathematical model of the power plant steam extraction energy storage peak-shaving system, this paper comprehensively considers the grid peak-shaving instructions, power plant load changes and equipment operation constraints, and proposes an optimization scheduling strategy with peak-shaving effect and energy utilization efficiency as the goal. Two intelligent optimization algorithms, particle swarm optimization algorithm (PSO) and genetic algorithm (GA), are used to solve the optimal scheduling problem of steam extraction volume and molten salt flow, and the performance of the two algorithms is verified by simulation. The following will gradually explore the detailed implementation steps of this technical solution.

[0190] In order to achieve the optimal dispatch of the peak load regulation process of the power plant, it is necessary to first establish a mathematical model of the optimization problem. The model takes the peak load regulation effect and energy utilization efficiency as the objective function, and the constraints include the steam turbine extraction parameters and the energy balance equation of the molten salt heat storage system.

[0191] The objective function is designed to optimize system performance throughout the peak load period. Specific objectives include:

[0192] Peak load regulation effect: ensure that the power plant can effectively respond to the peak load regulation instructions of the power grid and that the load changes meet the grid dispatching requirements.

[0193] Energy utilization efficiency: Maximize the energy utilization efficiency of the molten salt thermal storage system and reduce energy waste.

[0194] Steam turbine extraction parameters: During the peak load regulation process, the steam turbine extraction capacity must meet the physical constraints of the system, such as the extraction pressure and temperature limits.

[0195] Energy balance of molten salt heat storage system: The energy flow of the molten salt system must satisfy the dynamic balance of heat storage and heat release to ensure the stable operation of the heat storage system.

[0196] Particle swarm optimization (PSO) is an optimization algorithm based on swarm intelligence. It searches for the global optimal solution by simulating the dynamic behavior of individuals (particles) in the swarm in the search space.

[0197] First, the particle swarm is initialized, where each particle represents a feasible solution. The solution of each particle contains the steam extraction volume and molten salt flow rate at each moment in the entire peak regulation period. During initialization, the speed and position of the particles are randomly assigned to ensure the diversity of the solution space.

[0198] For each particle, calculate its fitness value, that is, the objective function value corresponding to the current solution. The fitness value reflects the quality of the particle in the current solution space. Specifically, the fitness value is determined by the peak-shaving effect and energy utilization efficiency. The quality of the peak-shaving effect is measured by the deviation between the load change of the power plant and the grid instruction, and the energy utilization efficiency is evaluated by the heat utilization of the molten salt system.

[0199] According to the fitness value, update the individual optimal position and global optimal position of each particle. Then, according to the speed update formula and position update formula of the particle swarm optimization algorithm, update the speed and position of all particles. The updated particle swarm will gradually converge to the global optimal solution.

[0200] Repeat the above process and iteratively update the particle speed and position until the maximum number of iterations is reached or the convergence condition is met. Finally, the optimal scheduling strategy for steam extraction and molten salt flow is obtained.

[0201] Assuming that the particle swarm size is 50, each particle contains 30 moments of steam extraction and molten salt flow. After 100 iterations, the PSO algorithm converges to the optimal solution, and the final output scheduling strategy can shorten the peak load time by 10% and improve the energy utilization efficiency by 8%.

[0202] Genetic Algorithm (GA) is an optimization algorithm that simulates natural selection and genetic mechanisms. It continuously optimizes individual solutions in the population through selection, crossover and mutation operations to find the global optimal solution.

[0203] The algorithm first initializes a population, each individual represents a feasible solution, including the steam extraction volume and molten salt flow rate at each moment in the entire peak regulation period. The initial value of the individual is randomly generated to ensure the diversity of the population.

[0204] According to the fitness value of each individual, a selection operation is adopted to select individuals with higher fitness from the population for reproduction according to the principle of survival of the fittest. Common selection methods include roulette selection and tournament selection.

[0205] After selecting the good individuals, a crossover operation is performed, that is, a new offspring individual is generated by exchanging some genes (steam extraction volume and molten salt flow rate) of the two individuals. The crossover operation can introduce new solutions and increase the diversity of the population.

[0206] In order to prevent the population from falling into a local optimal solution, the genetic algorithm also performs mutation operations on some individuals. The mutation operation randomly adjusts the gene value (steam extraction volume or molten salt flow rate) of the individual to generate new possible solutions.

[0207] Through selection, crossover and mutation operations, the population is continuously iterated and optimized until the maximum number of iterations is reached or the population converges, and finally the optimal scheduling strategy for steam extraction and molten salt flow is obtained.

[0208] In order to verify the performance of the intelligent optimization algorithm, it is necessary to apply the optimal scheduling strategy obtained by PSO and GA to the simulation model of the power plant's extracted steam storage peak-shaving system and compare and analyze it with the traditional scheduling strategy.

[0209] The simulation model settings include the actual grid peak load instructions and the load changes of the power plant. The simulation process simulates the operation status of the power plant during the entire peak load period, focusing on analyzing the impact of changes in steam extraction and molten salt flow on peak load effects and energy utilization efficiency.

[0210] Through simulation verification, the optimization performance of PSO and GA algorithms is evaluated respectively, mainly comparing the following aspects:

[0211] Compare the response speed of different algorithms to grid instructions during the peak-shaving process and analyze the reduction of peak-shaving time.

[0212] Evaluate the energy efficiency of molten salt thermal energy storage systems and analyze the performance of different algorithms in optimizing energy flow.

[0213] Analyze the convergence speed of PSO and GA in the solution space and the stability of the global optimal solution.

[0214] Assume that the simulation results show that the PSO algorithm can shorten the peak load time by 10% and improve the energy efficiency by 8%, while the GA algorithm can shorten the peak load time by 12% and improve the energy efficiency by 10%. Compared with the traditional scheduling strategy, these two intelligent optimization algorithms show significant optimization effects.

[0215] In order to further quantify the improvement effect of the intelligent optimization scheduling strategy, it is necessary to compare the PSO and GA algorithms with the traditional scheduling strategy in detail.

[0216] Traditional dispatching strategies usually adopt regularized dispatching methods to adjust the steam extraction and molten salt flow based on experience or simple linear models. Although such methods are easy to implement, they may lead to delayed peak load response or energy waste due to ignoring the nonlinearity and complexity of the system.

[0217] By comparing the simulation results of the intelligent optimization algorithm and the traditional scheduling strategy, the performance difference between the two can be quantified. The following indicators are mainly analyzed:

[0218] Intelligent optimization algorithms can respond to grid peak-shaving instructions more quickly and shorten peak-shaving time.

[0219] The intelligent optimization algorithm improves energy utilization efficiency and reduces heat loss by precisely controlling the molten salt flow rate.

[0220] Intelligent optimization algorithms can maintain higher system stability in the face of load fluctuations.

[0221] The simulation results show that the peak-shaving time of the traditional scheduling strategy is 30 minutes and the energy utilization efficiency is 75%. In contrast, the PSO algorithm shortens the peak-shaving time to 27 minutes and increases the energy utilization efficiency to 83%; the GA algorithm shortens the peak-shaving time to 26 minutes and increases the energy utilization efficiency to 85%.

[0222] In summary, this paper proposes an optimal dispatching strategy for the power plant steam extraction energy storage peak-shaving system based on an intelligent optimization algorithm. The intelligent optimization algorithm can quickly adjust the steam extraction volume and molten salt flow rate according to the peak-shaving instructions of the power grid, significantly shorten the peak-shaving time, and reduce energy waste by optimizing the energy flow of the molten salt heat storage system. The overall energy utilization efficiency of the system is improved, and the intelligent optimization algorithm can better cope with load fluctuations and ensure the stable operation of the system. By introducing the intelligent optimization algorithm, the dispatching strategy of the power plant is transformed from experience-driven to data-driven and algorithm-driven, realizing the intelligence and automation of the dispatching process, which can significantly improve the peak-shaving response speed and energy utilization efficiency of the system, and has broad application prospects.

[0223] In an optional embodiment,

[0224] The waste heat utilization device is connected to the bottom of the low-temperature molten salt storage tank. The device includes a waste heat exchanger and an absorption refrigerator. The waste heat after the molten salt releases heat is used to drive the absorption refrigerator to operate, provide cooling for the working environment of the power plant, and improve the efficiency of energy cascade utilization. An electric three-way regulating valve is provided between the waste heat exchanger and the low-temperature molten salt storage tank. The steps of adjusting the flow of molten salt entering the waste heat exchanger according to the cooling demand include:

[0225] Physically connect and integrate the waste heat utilization device with the waste heat exchanger and absorption refrigerator, connect the waste heat exchanger with the generator of the absorption refrigerator, use fieldbus technology to realize data collection and command issuance between various devices, establish a unified database, store power plant load forecast, heat storage system operating parameters, working environment temperature and humidity information;

[0226] According to the load forecast of the power plant and the operation plan of the heat storage system, the dynamic optimization algorithm is used to solve the optimal time and power curve of the molten salt heat release process. The objective function is to maximize the heat release efficiency and peak regulation effect of the heat storage system. The constraints include the capacity limit of the molten salt storage tank and the operating range of the steam turbine.

[0227] Combined with the temperature and humidity prediction of the working environment, the model predictive control method is used to solve the optimal operation plan and control trajectory of the absorption chiller in the rolling time domain. The objective function is to minimize the refrigeration energy consumption and temperature and humidity deviation. The constraints include the operating range of the absorption chiller and the heat transfer capacity of the waste heat exchanger. According to the model predictive control results, the initial conditions and constraints of the optimization problem are continuously updated to generate the optimal control sequence in the future, and the first control quantity is sent to the absorption chiller and the electric three-way regulating valve as the control instruction at the current moment;

[0228] On the basis of operation scheduling optimization, a rule-based real-time adjustment and coordination strategy is designed. The operating parameters of the absorption chiller are adjusted in real time according to the pre-set adjustment rules. Through the coordinated control of the electric three-way regulating valve, fuzzy control rules are designed to dynamically adjust the opening of the electric three-way regulating valve to achieve dynamic distribution of molten salt flow between heat storage and release and waste heat utilization.

[0229] For example, the technical solution of this article revolves around the waste heat utilization device of the molten salt heat storage system, aiming to use the waste heat in the low-temperature molten salt storage tank to drive the absorption chiller to operate, thereby improving the cascade utilization efficiency of the power plant energy. The solution achieves efficient utilization of waste heat and optimized scheduling of refrigeration equipment through physical connection and information integration, combined with dynamic optimization algorithms and model predictive control. The following will describe in detail the technical implementation steps of this system.

[0230] The waste heat utilization device includes a waste heat exchanger and an absorption refrigerator, and the device is connected to the bottom of the low-temperature molten salt storage tank through a pipeline. After releasing heat, the molten salt in the low-temperature molten salt storage tank enters the waste heat exchanger, and the waste heat exchanger transfers the remaining heat energy to the generator of the absorption refrigerator. In this way, the waste heat of the molten salt is used to drive the absorption refrigerator to provide cooling for the power plant.

[0231] In order to realize the intelligent operation of the system, the various devices of the waste heat utilization device need to be integrated. Through fieldbus technology, the operating status and parameters of the waste heat exchanger, absorption chiller, electric three-way regulating valve and other equipment can be collected in real time and uploaded to the central control system. The system has established a unified database to store the following key information:

[0232] Power plant load forecast: the load demand of the power plant in the future.

[0233] Thermal storage system operating parameters: including molten salt temperature, molten salt flow rate, heat storage and release status, etc.

[0234] Working environment temperature and humidity information: used to optimize the operating parameters of the refrigerator.

[0235] During the molten salt heat release process, the main optimization goal of the system is to maximize the heat release efficiency of the heat storage system and the peak load regulation effect of the power plant. The constraints of the optimization problem include:

[0236] Molten salt storage tank capacity limit: Ensure that the system does not exceed the tank capacity limit during heat release.

[0237] Steam turbine operating range: The steam extraction volume and load need to be kept within the safe operating range of the steam turbine.

[0238] In order to solve the optimal heat release time and heat release power curve, a dynamic optimization algorithm is introduced. According to the load forecast of the power plant and the operation plan of the heat storage system, the algorithm adjusts the heat release time and heat release power of the molten salt in real time to ensure that the system can release enough heat energy before the load peak arrives.

[0239] Assuming that the load of the power plant will increase by 30MW in the next hour, the dynamic optimization algorithm calculates the best molten salt heat release strategy to gradually increase the molten salt heat release power in the next 30 minutes, ultimately reaching the goal of 50GJ of heat release per hour to meet the load demand.

[0240] The operation of refrigeration equipment not only needs to consider the load demand of the power plant, but also needs to be combined with the temperature and humidity prediction of the working environment. By real-time monitoring and prediction of the temperature and humidity of the working environment, the system can dynamically adjust the operating parameters of the absorption chiller to ensure the cooling effect.

[0241] Model predictive control (MPC) solves the optimal operation plan of the absorption chiller in real time in the rolling time domain. Its optimization goal is to minimize the cooling energy consumption and reduce the temperature and humidity deviation. The constraints include:

[0242] Operating range of absorption chiller: The refrigeration power of the chiller must vary within the range allowed by the equipment.

[0243] Heat transfer capacity of waste heat exchanger: The heat transfer capacity of the heat exchanger limits the heat release rate of the molten salt and affects the output power of the refrigerator.

[0244] The MPC algorithm uses a rolling time domain optimization method, that is, at each moment, based on the current system state and the predicted temperature and humidity conditions, the optimal control sequence for a period of time in the future is solved. In each calculated sequence, the first control quantity is sent to the absorption chiller and the electric three-way regulating valve as the control instruction at the current moment.

[0245] Assuming the current ambient temperature is 30°C and the humidity is 70%, the optimal strategy calculated by MPC is to keep the cooling power of the absorption chiller at 80% in the next 15 minutes, maintaining the ambient temperature at around 25°C with low energy consumption. The control instruction of the first step is to set the chiller power to 70% and the opening of the electric three-way regulating valve to 50% to control the molten salt flow rate.

[0246] On the basis of optimized scheduling, the system also designs a set of rule-based real-time adjustment and coordination strategies to cope with sudden load changes or environmental factors.

[0247] To ensure the flexibility of the system, the operating parameters of the absorption chiller are adjusted in real time according to the pre-set regulation rules. The rules are designed to be dynamically adjusted according to the real-time data of the power plant load, the state of the thermal storage system and the working environment. For example, when the power plant load suddenly increases, the system will immediately increase the heat release power of the molten salt to meet the additional energy demand.

[0248] The function of the electric three-way regulating valve is to control the molten salt flow entering the waste heat exchanger. The system dynamically adjusts the opening of the regulating valve through fuzzy control rules to ensure that the molten salt flow is reasonably distributed between heat storage and heat release and waste heat utilization.

[0249] The fuzzy control rules determine the opening change of the regulating valve based on multiple input variables (such as current load demand, molten salt temperature in the storage tank, refrigeration demand, etc.). Different input combinations will trigger different fuzzy rules to dynamically adjust the valve opening. For example:

[0250] If the load demand is high and the tank temperature is low, the regulating valve opening is increased.

[0251] If the load demand is low and the tank temperature is high, the regulating valve opening is reduced.

[0252] The optimization process of the entire system includes the following steps:

[0253] Data collection and information integration: Real-time collection of data such as power plant load, thermal storage system operating status, working environment temperature and humidity, and storage in a central database.

[0254] Dynamic optimization to solve the molten salt heat release strategy: Use the dynamic optimization algorithm to solve the optimal heat release time and power curve of the molten salt to ensure that the system can release heat efficiently when the load changes.

[0255] Model predictive control to solve the cooling strategy: Based on the prediction of ambient temperature and humidity, the MPC algorithm is used to solve the optimal operation plan of the absorption chiller and update the control sequence in a rolling manner.

[0256] Real-time regulation based on rules: According to the preset regulation rules, the operating parameters of the absorption chiller are adjusted in real time, and the opening of the electric three-way regulating valve is dynamically adjusted through fuzzy control rules.

[0257] Coordinated control and execution: Send the optimal control instructions to each device to achieve dynamic adjustment and coordinated control of the system.

[0258] In summary, this paper realizes the efficient operation of the molten salt heat storage system and the absorption chiller through the physical connection, information integration and intelligent optimization scheduling strategy of the waste heat utilization device, reduces the waste of heat energy, and the dynamic optimization algorithm can ensure that the heat release process of the molten salt matches the load demand of the power plant, thereby enhancing the peak-shaving capacity of the system. By using model predictive control and fuzzy control rules, the system can respond quickly to load changes and environmental fluctuations to ensure the cooling effect and energy utilization efficiency. Through information integration and data acquisition, the system realizes intelligent automatic control, reduces manual intervention, improves the reliability and stability of operation, and has broad application prospects.

[0259] Figure 2 Schematic diagram of the structure of the power plant extraction steam storage peak load decoupling system based on the molten salt heat storage technology according to the embodiment of the present invention. Figure 2 As shown, the system comprises:

[0260] The first unit is used for a molten salt heat storage device, an extraction steam storage system, a peak load decoupling control device and a waste heat utilization device; the molten salt heat storage device includes a high-temperature molten salt storage tank and a low-temperature molten salt storage tank arranged in a longitudinal layer, a connecting pipe and a control valve are provided between the two storage tanks, and an electric heating device is provided on the top of the high-temperature molten salt storage tank to supplement the heat loss in the molten salt heat exchange process; a plurality of salt outlets are provided at the bottom of the high-temperature molten salt storage tank, which are respectively connected to a molten salt pump and a plate-type molten salt heat exchanger to realize the circulation and heat release of the molten salt;

[0261] The second unit is used for the steam extraction energy storage system, including a steam turbine extraction port, a steam extraction main line, a number of branch lines and corresponding steam extraction regulating valves. Each branch line is connected to a plate-type steam extraction heat exchanger. The plate-type steam extraction heat exchanger and the plate-type molten salt heat exchanger are arranged in parallel to form a two-stage heat exchange network. By adjusting the opening of each steam extraction regulating valve, the steam extraction amount is optimally distributed among different heat exchangers. The peak-shaving decoupling control device adopts a control algorithm based on fuzzy PID. According to the peak-shaving instructions of the power grid, the load changes of the power plant and the operating conditions of the steam turbine, it calculates the optimal steam extraction amount and molten salt flow in real time, controls the operation of the steam extraction regulating valve and the molten salt pump, and coordinates the electric heating power in the high-temperature molten salt storage tank to achieve the optimal matching of the steam extraction heat storage and the molten salt heat release process, thereby improving the peak-shaving response speed and accuracy.

[0262] The third unit is used for connecting the waste heat utilization device to the bottom of the low-temperature molten salt storage tank. The device includes a waste heat exchanger and an absorption refrigerator. The waste heat after the molten salt releases heat is used to drive the absorption refrigerator to provide cooling for the working environment of the power plant and improve the efficiency of energy cascade utilization. An electric three-way regulating valve is provided between the waste heat exchanger and the low-temperature molten salt storage tank to adjust the flow of molten salt entering the waste heat exchanger according to the cooling demand.

[0263] According to a third aspect of the embodiments of the present invention,

[0264] An electronic device is provided, comprising:

[0265] processor;

[0266] a memory for storing processor-executable instructions;

[0267] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0268] According to a fourth aspect of the embodiments of the present invention,

[0269] 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.

[0270] 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.

[0271] 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 decoupling peak load regulation of power plant steam extraction storage based on molten salt heat storage technology, characterized in that: include: A molten salt heat storage device, an extraction steam energy storage system, a peak load decoupling control device, and a waste heat utilization device; the molten salt heat storage device comprises a high-temperature molten salt storage tank and a low-temperature molten salt storage tank arranged in a longitudinal layer, a connecting pipe and a control valve are provided between the two storage tanks, and an electric heating device is provided on the top of the high-temperature molten salt storage tank to supplement the heat loss in the molten salt heat exchange process; a plurality of salt outlets are provided at the bottom of the high-temperature molten salt storage tank, which are respectively connected to a molten salt pump and a plate-type molten salt heat exchanger to realize the circulation and heat release of the molten salt; The steam extraction energy storage system includes a steam turbine extraction port, a steam extraction main line, several branch lines and corresponding steam extraction regulating valves. Each branch line is connected to a plate-type steam extraction heat exchanger. The plate-type steam extraction heat exchanger and the plate-type molten salt heat exchanger are arranged in parallel to form a two-stage heat exchange network. By adjusting the opening of each steam extraction regulating valve, the steam extraction amount can be optimally distributed among different heat exchangers. The peak-shaving decoupling control device adopts a control algorithm based on fuzzy PID. According to the peak-shaving instructions of the power grid, the load changes of the power plant and the operating conditions of the steam turbine, it calculates the optimal steam extraction amount and molten salt flow in real time, controls the operation of the steam extraction regulating valve and the molten salt pump, and coordinates the electric heating power in the high-temperature molten salt storage tank to achieve the optimal matching of the steam extraction heat storage and the molten salt heat release process, thereby improving the peak-shaving response speed and accuracy. The waste heat utilization device is connected to the bottom of the low-temperature molten salt storage tank. The device includes a waste heat exchanger and an absorption refrigerator. The waste heat after the molten salt releases heat is used to drive the absorption refrigerator to provide cooling for the working environment of the power plant and improve the efficiency of energy cascade utilization. An electric three-way regulating valve is provided between the waste heat exchanger and the low-temperature molten salt storage tank to adjust the flow of molten salt entering the waste heat exchanger according to the cooling demand.

2. The method according to claim 1, characterized in that The extraction steam energy storage system includes a steam turbine extraction port, an extraction main pipeline, a plurality of branch pipelines and corresponding extraction regulating valves. Each branch pipeline is connected to a plate extraction heat exchanger. The plate extraction heat exchanger and the plate molten salt heat exchanger are arranged in parallel to form a two-stage heat exchange network. By adjusting the opening of each extraction regulating valve, the steps of optimizing the distribution of the extraction volume between different heat exchangers include: The primary side inlet of the plate-type steam extraction heat exchanger is connected to the steam extraction port of the steam turbine through a steam extraction pipeline, and the secondary side outlet is connected to the secondary side inlet of the plate-type molten salt heat exchanger through a molten salt pipeline; a steam extraction regulating valve is provided on the steam extraction pipeline to adjust the amount of steam extraction entering different plate-type steam extraction heat exchangers; a connecting pipeline and a valve are provided between the plate-type steam extraction heat exchanger and the plate-type molten salt heat exchanger to adjust the distribution of the steam extraction amount and the molten salt flow rate between different heat exchangers; The heat exchange network is centrally managed through a distributed control system, the opening of the steam extraction regulating valve of each heat exchange unit is adjusted in real time, the distribution ratio of the steam extraction volume among different heat exchangers is optimized, and the rotation speed and flow rate of the molten salt pump are coordinated to ensure the uniform distribution of the molten salt among the heat exchangers, thereby improving the overall heat transfer efficiency of the heat exchange network; the distributed control system also includes an online monitoring device and a fault diagnosis module to monitor the operating status of the heat exchange network in real time, judge heat exchanger leakage, pipeline blockage, valve failure according to abnormal changes in temperature, pressure, and flow parameters, and adopt corresponding fault-tolerant control strategies to ensure the operation of the heat exchange network.

3. The method according to claim 1, characterized in that The peak-shaving decoupling control device adopts a control algorithm based on fuzzy PID. According to the peak-shaving instructions of the power grid, the load changes of the power plant and the operating conditions of the steam turbine, it calculates the optimal steam extraction volume and molten salt flow in real time, controls the operation of the steam extraction regulating valve and the molten salt pump, and coordinates the electric heating power in the high-temperature molten salt storage tank to achieve the optimal matching of the steam extraction heat storage and the molten salt heat release process. The steps to improve the peak-shaving response speed and accuracy include: Taking into account the steam turbine extraction parameters, the characteristics of the molten salt heat storage device, and the power grid peak load command factors, a dynamic mathematical model of the power plant steam extraction storage peak load system is established by combining mechanism modeling with data-driven methods. According to the dynamic mathematical model of the power plant's steam extraction storage peak-shaving system, a fuzzy PID controller is designed with steam turbine extraction pressure, molten salt storage tank temperature and power plant load as inputs, and extraction valve opening and molten salt pump speed as outputs. The input and output variables are fuzzy processed using a triangular membership function, and the controller output is obtained through the center of gravity defuzzification method. A decoupling algorithm is introduced to calculate the static decoupling matrix between the extraction regulating valve and the molten salt pump to achieve mutual compensation between control loops and reduce system coupling. At the same time, an online self-tuning method is used to adjust PID parameters in real time to ensure control performance. Comprehensively considering the peak-shaving instructions of the power grid, the load changes of the power plant and the equipment operation constraints, the peak-shaving effect and energy utilization efficiency in the entire peak-shaving period are taken as the objective function, and the steam turbine extraction parameters and the energy balance equation of the molten salt heat storage system are taken as constraints. An intelligent optimization algorithm is used to solve the optimal scheduling problem of the steam extraction volume and the molten salt flow rate, and the setting instructions are generated; the setting instructions are used as the input of the fuzzy PID controller to form a two-level control architecture in which the upper-level optimization scheduling generates the optimal operation trajectory, and the lower-level fuzzy PID controller tracks the trajectory and suppresses disturbances, so as to achieve rapid response and precise regulation of peak-shaving decoupling control, and at the same time coordinate the power regulation of the electric heating device in the high-temperature molten salt storage tank to compensate for the heat loss in the molten salt heat exchange process and ensure the energy balance of the system.

4. The method according to claim 3, characterized in that Taking into account the steam turbine extraction parameters, the characteristics of the molten salt heat storage device, and the power grid peak load instruction factors, the dynamic mathematical model of the power plant steam extraction storage peak load system is established by combining mechanism modeling with data-driven methods. The steps include: According to the structural characteristics and working principle of the steam turbine, the steam turbine is divided into three parts: high-pressure cylinder, medium-pressure cylinder and low-pressure cylinder; for each cylinder, the mass flow balance equation and energy balance equation are considered to obtain the corresponding dynamic equation; based on the thermal performance curve of the steam turbine, the functional relationship between the extraction pressure and extraction temperature and the steam turbine power is established, and the steam turbine extraction model is established; The lumped parameter method is adopted to consider the temperature distribution and flow characteristics in the molten salt heat storage device, and the equations describing the dynamic characteristics of the molten salt heat storage process are obtained; according to the performance data of the molten salt pump and the structural layout of the pipeline system, the functional relationship between the molten salt flow rate and the pump speed and the functional relationship between the molten salt flow rate and the pipeline pressure drop are established respectively, and the molten salt heat storage device model is established; According to the requirements of power grid dispatching, the power grid peak load regulation instruction is expressed as a time function of the power plant load; based on the unit characteristics and operating conditions of the power plant, the adjustment range of the power plant load is given; at the same time, the ramp rate limit of the power plant load is considered, the corresponding constraint conditions are formed, and the power grid peak load regulation instruction model is established; Based on the established steam turbine extraction model, molten salt heat storage device model and power grid peak-shaving instruction model, the mechanism model of the power plant's steam extraction storage peak-shaving system is obtained; the data-driven model is trained using a machine learning algorithm by collecting, preprocessing, extracting and selecting historical operating data of the power plant; the mechanism model and the data-driven model of the power plant's steam extraction storage peak-shaving system are integrated, and the model parameters are optimized using a multi-objective optimization algorithm to obtain the dynamic mathematical model of the power plant's steam extraction storage peak-shaving system.

5. The method according to claim 3, characterized in that: According to the dynamic mathematical model of the power plant steam extraction storage peak regulation system, a fuzzy PID controller is designed with steam turbine extraction pressure, molten salt storage tank temperature and power plant load as input, and extraction regulating valve opening and molten salt pump speed as output. The input and output variables are fuzzy processed by using the triangle membership function. The steps of obtaining the controller output by the center of gravity defuzzification method include: The input and output variables are fuzzified using the triangular membership function, and the steam turbine extraction pressure is divided into three fuzzy subsets: low pressure, medium pressure and high pressure. The molten salt storage tank temperature, power plant load, extraction steam regulating valve opening and molten salt pump speed are divided into corresponding fuzzy subsets respectively. A fuzzy rule base consisting of IF-THEN statements is constructed based on expert experience and control strategies to describe the relationship between input variables and output variables. The Mamdani reasoning method is used for fuzzy reasoning. The membership of the output variable is calculated based on the membership of the input variable and the fuzzy rule base. The trigger strength of each rule is the minimum value of the corresponding input variable membership, and the output membership of each rule is the minimum value of the trigger strength and the output variable membership. The maximum value method is used to summarize the output membership of all rules. The centroid defuzzification method is used to convert the membership of the output variable into an accurate control quantity. The defuzzification formula for the opening of the steam extraction control valve and the speed of the molten salt pump is the integral of the product of the membership and the variable value divided by the integral of the membership. According to the dynamic characteristics and control performance indicators of the system, the fuzzy self-tuning method is used to adaptively adjust the proportional coefficient, integral time constant and differential time constant of the PID controller. The direction and adjustment amount are adjusted according to the overshoot and adjustment time parameters of the system. The fuzzy reasoning method is used to calculate the parameter adjustment coefficient, and the PID controller parameters are updated according to the parameter adjustment formula.

6. The method according to claim 3, characterized in that Taking into account the peak-shaving instructions of the power grid, the load changes of the power plant and the constraints of equipment operation, the peak-shaving effect and energy utilization efficiency in the entire peak-shaving cycle are taken as the objective function, and the steam extraction parameters of the steam turbine and the energy balance equation of the molten salt heat storage system are taken as constraints. The intelligent optimization algorithm is used to solve the optimal scheduling problem of the steam extraction volume and the molten salt flow rate. The steps of generating the setting instructions include: A mathematical model for the peak-shaving optimization scheduling problem of steam extraction storage energy in a power plant is established. The mathematical model for the peak-shaving optimization scheduling problem of steam extraction storage energy in a power plant takes peak-shaving effect and energy utilization efficiency as objective functions, and the constraints include steam extraction parameters of the turbine and the energy balance equation of the molten salt heat storage system. The established mathematical model for the peak-shaving optimization scheduling problem of steam extraction storage energy in a power plant is solved by using a particle swarm optimization algorithm. The particle swarm is initialized, in which each particle represents a feasible solution, including the steam extraction amount and molten salt flow rate at each moment in the entire peak-shaving cycle, and the fitness value of each particle, that is, the objective function value, is calculated, and the individual optimal position and the global optimal position of each particle are updated; According to the speed update formula and position update formula of the particle swarm optimization algorithm, the speed and position of the particles are updated to obtain a new generation of particle swarms. The above process is repeated until the maximum number of iterations is reached or the termination condition is met, and the optimal scheduling strategy for the steam extraction volume and molten salt flow rate is obtained. The established mathematical model is solved by using genetic algorithm, and the optimal scheduling strategy of steam extraction and molten salt flow is obtained by selecting, crossing and mutating the initial population and continuously iterating and optimizing. The optimal dispatching strategy obtained by particle swarm optimization algorithm and genetic algorithm is applied to the simulation model of the extracted steam storage peak-shaving system of the power plant. The actual grid peak-shaving instructions and power plant load changes are set. Through simulation verification, the optimization performance of the two algorithms is compared and analyzed, and compared with the traditional dispatching strategy to quantitatively evaluate the improvement effect of the intelligent optimization dispatching strategy.

7. The method according to claim 1, characterized in that The waste heat utilization device is connected to the bottom of the low-temperature molten salt storage tank. The device includes a waste heat exchanger and an absorption refrigerator. The waste heat after the molten salt releases heat is used to drive the absorption refrigerator to operate, provide cooling for the working environment of the power plant, and improve the efficiency of energy cascade utilization. An electric three-way regulating valve is provided between the waste heat exchanger and the low-temperature molten salt storage tank. The steps of adjusting the molten salt flow entering the waste heat exchanger according to the refrigeration demand include: Physically connect and integrate the waste heat utilization device with the waste heat exchanger and absorption refrigerator, connect the waste heat exchanger with the generator of the absorption refrigerator, use fieldbus technology to realize data collection and command issuance between various devices, establish a unified database, store power plant load forecast, heat storage system operating parameters, working environment temperature and humidity information; According to the load forecast of the power plant and the operation plan of the heat storage system, the dynamic optimization algorithm is used to solve the optimal time and power curve of the molten salt heat release process. The objective function is to maximize the heat release efficiency and peak regulation effect of the heat storage system. The constraints include the capacity limit of the molten salt storage tank and the operating range of the steam turbine. Combined with the temperature and humidity prediction of the working environment, the model predictive control method is used to solve the optimal operation plan and control trajectory of the absorption chiller in the rolling time domain. The objective function is to minimize the refrigeration energy consumption and temperature and humidity deviation. The constraints include the operating range of the absorption chiller and the heat transfer capacity of the waste heat exchanger. According to the model predictive control results, the initial conditions and constraints of the optimization problem are continuously updated to generate the optimal control sequence in the future, and the first control quantity is sent to the absorption chiller and the electric three-way regulating valve as the control instruction at the current moment; On the basis of operation scheduling optimization, a rule-based real-time adjustment and coordination strategy is designed. The operating parameters of the absorption chiller are adjusted in real time according to the pre-set adjustment rules. Through the coordinated control of the electric three-way regulating valve, fuzzy control rules are designed to dynamically adjust the opening of the electric three-way regulating valve to achieve dynamic distribution of molten salt flow between heat storage and release and waste heat utilization.

8. A power plant steam extraction storage peak load decoupling system based on molten salt heat storage technology, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The first unit is used for a molten salt heat storage device, an extraction steam storage system, a peak load decoupling control device and a waste heat utilization device; the molten salt heat storage device includes a high-temperature molten salt storage tank and a low-temperature molten salt storage tank arranged in a longitudinal layer, a connecting pipe and a control valve are provided between the two storage tanks, and an electric heating device is provided on the top of the high-temperature molten salt storage tank to supplement the heat loss in the molten salt heat exchange process; a plurality of salt outlets are provided at the bottom of the high-temperature molten salt storage tank, which are respectively connected to a molten salt pump and a plate-type molten salt heat exchanger to realize the circulation and heat release of the molten salt; The second unit is used for the steam extraction energy storage system, including a steam turbine extraction port, a steam extraction main line, a number of branch lines and corresponding steam extraction regulating valves. Each branch line is connected to a plate-type steam extraction heat exchanger. The plate-type steam extraction heat exchanger and the plate-type molten salt heat exchanger are arranged in parallel to form a two-stage heat exchange network. By adjusting the opening of each steam extraction regulating valve, the steam extraction amount is optimally distributed among different heat exchangers. The peak-shaving decoupling control device adopts a control algorithm based on fuzzy PID. According to the peak-shaving instructions of the power grid, the load changes of the power plant and the operating conditions of the steam turbine, it calculates the optimal steam extraction amount and molten salt flow in real time, controls the operation of the steam extraction regulating valve and the molten salt pump, and coordinates the electric heating power in the high-temperature molten salt storage tank to achieve the optimal matching of the steam extraction heat storage and the molten salt heat release process, thereby improving the peak-shaving response speed and accuracy. The third unit is used for connecting the waste heat utilization device to the bottom of the low-temperature molten salt storage tank. The device includes a waste heat exchanger and an absorption refrigerator. The waste heat after the molten salt releases heat is used to drive the absorption refrigerator to provide cooling for the working environment of the power plant and improve the efficiency of energy cascade utilization. An electric three-way regulating valve is provided between the waste heat exchanger and the low-temperature molten salt storage tank to adjust the flow of molten salt entering the waste heat exchanger according to the cooling demand.

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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