A method, system, equipment, and storage medium for controlling heat release from molten salt energy storage.

Abstract

This invention discloses a method, system, device, and storage medium for controlling the exothermic reaction of molten salt energy storage, relating to the field of energy storage technology. The method includes inputting the principal component parameters of the molten salt energy storage exothermic system at the current moment into an efficiency prediction model to obtain the steam generation efficiency and power generation efficiency of the molten salt energy storage exothermic system at the current moment. The efficiency prediction model is determined by training an artificial neural network based on historical datasets. Based on the steam generation efficiency and power generation efficiency at the current moment, a particle swarm optimization algorithm is used to maximize the energy utilization rate of the molten salt energy storage exothermic system at the current moment, obtaining the exothermic load and thermoelectric load ratio of the molten salt energy storage exothermic system corresponding to the maximization of energy utilization rate at the current moment. The molten salt energy storage exothermic system is then operated according to the exothermic load and thermoelectric load ratio at the current moment. This invention can guide and optimize the operation of molten salt systems, improving the overall energy utilization rate of molten salt systems.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a method, system, device and storage medium for controlling heat release in molten salt energy storage. Background Technology

[0002] Molten salt energy storage consists of two processes: heat storage and heat release. Heat storage: A smart complementary system uses wind power, solar power, off-peak electricity, and industrial waste heat as energy sources to heat the molten salt, storing renewable or off-peak electricity. Heat release: In the heat exchange system, high-temperature molten salt exchanges heat with water to generate steam, which drives a turbine to generate electricity.

[0003] Currently, the heat release process of molten salt energy storage heat release system is manually adjusted by operators, which has problems such as cumbersome operation, slow response speed, and high requirements for operators (such as system familiarity and operation experience). The heat release process is entirely based on experience and past operating data for manual judgment, resulting in long feedback cycles and failure to maximize energy utilization. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, device, and storage medium for controlling the heat release of molten salt energy storage, thereby improving energy utilization.

[0005] To achieve the above objectives, the present invention provides the following solution:

[0006] A method for controlling heat release from molten salt energy storage includes:

[0007] The principal component parameters of the molten salt energy storage and heat release system at the current moment are input into the efficiency prediction model to obtain the steam generation efficiency and power generation efficiency of the molten salt energy storage and heat release system at the current moment; the efficiency prediction model is determined by training an artificial neural network based on historical datasets;

[0008] Based on the current steam generation efficiency and power generation efficiency, the particle swarm optimization algorithm is used to maximize the energy utilization rate of the molten salt energy storage heat release system at the current moment, and the heat release load and thermoelectric load ratio of the molten salt energy storage heat release system corresponding to the maximization of energy utilization rate at the current moment are obtained.

[0009] The molten salt energy storage heat release system is operated according to the current heat release load and thermoelectric load ratio.

[0010] Optionally, before inputting the principal component parameters of the molten salt energy storage and heat release system at the current moment into the efficiency prediction model to obtain the steam generation efficiency and power generation efficiency of the molten salt energy storage and heat release system at the current moment, the method further includes:

[0011] Obtain historical operating status data of the molten salt energy storage and heat release system;

[0012] Principal component analysis was performed on the historical operating data to determine the principal component parameters that affect energy utilization.

[0013] Optionally, the historical operating status data includes molten salt flow rate, high-temperature molten salt temperature, low-temperature molten salt temperature, steam generation efficiency, power generation efficiency, main steam pressure, main steam temperature, turbine extraction steam volume, turbine extraction steam pressure, turbine extraction steam temperature, turbine exhaust steam pressure, turbine exhaust steam temperature, steam supply pressure, steam supply temperature, and the ambient temperature of the molten salt energy storage heat release system.

[0014] Optionally, the energy utilization rate of the molten salt energy storage and heat release system at time t is expressed as:

[0015]

[0016] Wherein, E(t) represents the energy utilization rate of the molten salt energy storage and heat release system at time t, Q(t) represents the total heat release of the molten salt energy storage and heat release system at time t, ∑p(t) represents the sum of the power of the electrical equipment in the molten salt energy storage and heat release system at time t, f1(t) represents the steam-energy conversion coefficient of the molten salt energy storage and heat release system at time t, e1(t) represents the steam generation efficiency of the molten salt energy storage and heat release system at time t, e2(t) represents the power generation efficiency of the molten salt energy storage and heat release system at time t, f2(t) represents the electrical-energy conversion coefficient of the molten salt energy storage and heat release system at time t, and r(t) represents the proportion of heat used for steam generation in the molten salt energy storage and heat release system at time t to the total heat release load.

[0017] Optionally, the training process of the artificial neural network includes:

[0018] The artificial neural network is trained using the principal component parameters in the historical dataset as input and the steam generation efficiency and power generation efficiency corresponding to the principal component parameters as output. The trained artificial neural network is then used as an efficiency prediction model.

[0019] This invention also discloses a molten salt energy storage and heat release control system, comprising:

[0020] The steam generation efficiency and power generation efficiency prediction module is used to input the principal component parameters of the molten salt energy storage and heat release system at the current moment into the efficiency prediction model to obtain the steam generation efficiency and power generation efficiency of the molten salt energy storage and heat release system at the current moment; the efficiency prediction model is determined by training an artificial neural network based on historical datasets;

[0021] The heat release load and thermoelectric load ratio determination module is used to maximize the energy utilization rate of the molten salt energy storage heat release system at the current moment based on the current steam generation efficiency and power generation efficiency, and to obtain the heat release load and thermoelectric load ratio of the molten salt energy storage heat release system corresponding to the current moment when the energy utilization rate is maximized.

[0022] The heat release load and thermoelectric load ratio application module is used to operate the molten salt energy storage heat release system according to the heat release load and thermoelectric load ratio at the current moment.

[0023] The present invention also discloses an electronic device, including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the molten salt energy storage heat release control method.

[0024] The present invention also discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the molten salt energy storage and heat release control method.

[0025] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0026] This invention improves the real-time prediction of steam production efficiency and power generation efficiency by predicting them in real time. Based on the real-time predicted steam production efficiency and power generation efficiency, the energy utilization rate is maximized. Based on the energy utilization rate, the corresponding heat release load and thermoelectric load ratio are maximized to operate the molten salt energy storage heat release system, thereby improving the energy utilization rate. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a schematic flowchart of a molten salt energy storage heat release control method provided in an embodiment of the present invention;

[0029] Figure 2 A schematic diagram illustrating the principle of a molten salt energy storage heat release control method provided in an embodiment of the present invention;

[0030] Figure 3 This is a schematic diagram of a molten salt energy storage and heat release control system provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] The purpose of this invention is to provide a method, system, device, and storage medium for controlling the heat release of molten salt energy storage, thereby improving energy utilization.

[0033] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0034] Example 1

[0035] like Figure 1 As shown, the molten salt energy storage heat release control method of the present invention includes the following steps.

[0036] Step 101: Input the principal component parameters of the molten salt energy storage and heat release system at the current moment into the efficiency prediction model to obtain the steam generation efficiency and power generation efficiency of the molten salt energy storage and heat release system at the current moment; the efficiency prediction model is determined by training an artificial neural network based on historical datasets.

[0037] Prior to step 101, the molten salt energy storage heat release control method of the present invention further includes:

[0038] Obtain historical operating status data of the molten salt energy storage and heat release system.

[0039] Principal component analysis was performed on the historical operating data to determine the principal component parameters that affect energy utilization.

[0040] The historical operating status data includes molten salt flow rate, high-temperature molten salt temperature, low-temperature molten salt temperature, steam generation efficiency, power generation efficiency, main steam pressure, main steam temperature, turbine extraction steam volume, turbine extraction steam pressure, turbine extraction steam temperature, turbine exhaust steam pressure, turbine exhaust steam temperature, steam supply pressure, steam supply temperature, and the ambient temperature of the molten salt energy storage heat release system.

[0041] Historical datasets are built based on the principal component parameters determined by principal component analysis.

[0042] Principal component analysis explores the degree of correlation between multiple potentially related variables, and seeks the direction of maximum or minimum correlation to achieve the purpose of data compression or noise reduction and dimensionality reduction.

[0043] The data processing steps for principal component analysis include the following:

[0044] The historical operation status data contains n status parameters, and each status parameter has m historical operation status data. Therefore, the historical operation status data can form a matrix D:

[0045]

[0046] Where, d ij This represents the m-th data of the n-th state parameter.

[0047] Standardize matrix D to obtain matrix X:

[0048]

[0049] in,

[0050]

[0051] and s j p represents the number of active sub-parameters.

[0052] Establish the correlation coefficient matrix R between parameters:

[0053]

[0054] in,

[0055] Calculate the eigenvalues ​​λ1≥λ2≥...λ of the correlation coefficient matrix R. m ≥0 and eigenvectors u1, u2, ... u m . and Let be the mean of the i-th and j-th columns in matrix X.

[0056] Among them, u j =(u 1j ,u 2j ,…u nj ) T Each feature vector is used to form m new index parameters:

[0057]

[0058] Where y1 is the first principal component, y2 is the second principal component, ..., y m It is the m-th principal component.

[0059] Write out the principal components and calculate the overall score. Calculate the principal component contribution rate and the cumulative contribution rate.

[0060] Principal component λ j Contribution rate:

[0061] Cumulative contribution rate of the first j principal components:

[0062] The principal components corresponding to the first p terms with a cumulative contribution rate of 85% to 95% are selected for comprehensive analysis, and the first p terms with a contribution rate of 85% to 95% are used as principal component parameters.

[0063] Based on the principal component parameters determined above, the e1(t) and e2(t) values ​​at time t are calculated based on the historical operating data of the molten salt energy storage heat release system and the parameters of the steam and power generation system, and a sample dataset (historical dataset) is established.

[0064] e1(t) and e2(t) are determined in the historical dataset using the following formula.

[0065]

[0066] Qs(t') represents the steam output at time t', which can be obtained through real-time measurement and converted into the steam output at time t using the hysteresis characteristics of the molten salt energy storage heat release system.

[0067] Qe(t') represents the amount of electricity generated at time t', which can be obtained through real-time measurement and converted into the amount of electricity generated at time t through the hysteresis characteristics of the molten salt energy storage heat release system.

[0068] The hysteresis characteristics of molten salt energy storage and heat release systems can be obtained through system commissioning.

[0069] The training process of the artificial neural network includes:

[0070] The artificial neural network is trained using the principal component parameters in the historical dataset as input and the steam generation efficiency and power generation efficiency corresponding to the principal component parameters as output. The trained artificial neural network is then used as an efficiency prediction model.

[0071] Step 102: Based on the current steam generation efficiency and power generation efficiency, use the particle swarm optimization algorithm to maximize the energy utilization rate of the molten salt energy storage heat release system at the current moment, and obtain the heat release load and thermoelectric load ratio of the molten salt energy storage heat release system corresponding to the maximization of energy utilization rate at the current moment.

[0072] The energy utilization rate of the molten salt energy storage and heat release system at time t is expressed as:

[0073]

[0074] Wherein, E(t) represents the energy utilization rate of the molten salt energy storage and heat release system at time t, Q(t) represents the total heat release of the molten salt energy storage and heat release system at time t, ∑p(t) represents the sum of the power of the electrical equipment in the molten salt energy storage and heat release system at time t, f1(t) represents the steam-energy conversion coefficient of the molten salt energy storage and heat release system at time t, e1(t) represents the steam generation efficiency of the molten salt energy storage and heat release system at time t, e2(t) represents the power generation efficiency of the molten salt energy storage and heat release system at time t, f2(t) represents the electrical-energy conversion coefficient of the molten salt energy storage and heat release system at time t, and r(t) represents the proportion of heat used for steam generation in the molten salt energy storage and heat release system at time t to the total heat release load.

[0075] f1(t) is used to convert steam production into energy units for measurement (such as kW / MW / MWh); f2(t) is used to convert electricity generation into energy units for measurement, and is generally equal to 1.

[0076] This invention employs a particle swarm optimization algorithm to optimize the energy utilization function E(t), thereby obtaining the energy utilization rate in the molten salt energy storage and heat release system and maximizing the total energy utilization rate.

[0077] e1(t) and e2(t) are parameters related to the operation of the molten salt energy storage and heat release system, which can be calculated based on relevant parameters of the steam and power generation systems. However, due to the significant lag in system parameters during actual operation, the parameters cannot be calculated in real time. Therefore, this invention uses an artificial neural network algorithm (efficiency prediction model) to predict and calculate e1(t) and e2(t) during actual operation.

[0078] Figure 2 In this context, e1(Q,r) is e1(t), e2(Q,r) is e2(t), Q is Q(t), r is r(t), and f2 is f2(t).

[0079] This invention utilizes an efficiency prediction model to predict the steam generation efficiency and power generation efficiency in real time based on the system's real-time operating parameters. Based on the real-time predicted steam generation efficiency and power generation efficiency, the heat release load and thermoelectric load ratio of the corresponding molten salt energy storage heat release system are calculated.

[0080] This invention utilizes the particle swarm optimization algorithm to intelligently optimize formula (1), specifically including the following steps.

[0081] (1) Use the particle swarm optimization algorithm to generate an initial particle swarm for the optimized data combination and dataset.

[0082] (2) Set the initial optimal position of each ion in the initial particle swarm and the initial global optimal position.

[0083] (3) Calculate the fitness value of each ion in the particle swarm.

[0084] Specifically, the fitness value of each particle in the current particle swarm is calculated using the given fitness function expression F:

[0085]

[0086] Where m represents the number of particles in the current particle swarm; N i M represents the energy utilization rate corresponding to the i-th particle; i This represents the target energy utilization rate for the i-th particle.

[0087] (4) Determine the optimal position of each ion and the global optimal position of the entire population based on the fitness value;

[0088] Specifically, the optimal position of a particle is denoted as:

[0089] X bi =(X bi1 ,X bi2 ,…,X bin );

[0090] Its iterative formula is:

[0091]

[0092] After several iterations during the optimization process, the position of the globally optimal solution for the population is obtained, denoted as X. bz .

[0093] (5) Update the velocity and position of each particle in the current particle swarm according to the ion velocity update formula and the particle position update formula;

[0094] Specifically, the formula for iteratively updating particle velocity is:

[0095]

[0096] Where: β represents the inertia weight; C1 represents the cognitive factor, which is the acceleration weight for approaching its own extreme value; C2 represents the social factor, which is the acceleration weight for approaching the global extreme value; r1 and r2 are random numbers between [0,1]. This represents the velocity of the particle at step k; X represents the velocity of the particle at step k-1; bij X represents a particle whose optimal solution is known. bzj This indicates that the optimal solution for the particle swarm is known.

[0097] (6) Determine whether the current iteration number is equal to the maximum iteration number.

[0098] (7) If so, output the current particle swarm.

[0099] (8) If not, return to step “Calculate the fitness value of each ion in the current particle swarm” until the current iteration number reaches the iteration number.

[0100] (9) Calculate the fitness function. Initially, mean square error is chosen as the fitness function.

[0101] (10) If the number of iterations reaches the set maximum number of iterations or the fitness function reaches the set extreme value and converges, the iteration is terminated.

[0102] Step 103: Operate the molten salt energy storage heat release system according to the current heat release load and thermoelectric load ratio.

[0103] The steam generation efficiency and power generation efficiency of this invention are calculated in real time by the system algorithm, which is fast and accurate. The heat load and heat-to-electricity load ratio are determined by the intelligent algorithm, which is fast, accurate, and requires less experience from the operators.

[0104] The predicted steam generation efficiency and power generation efficiency at time t are substituted into formula (1), and an intelligent optimization algorithm is used to calculate the heat release load and thermoelectric load ratio of the molten salt energy storage heat release system at time t, which corresponds to the maximum energy utilization rate. This allows for the determination of the heat release load and thermoelectric load ratio of the molten salt energy storage heat release system at its highest total energy utilization efficiency throughout the day. Figure 2 As shown, this invention guides the operation of the molten salt energy storage heat release system based on the heat release load and thermoelectric load ratio obtained at time t, thereby maximizing the total energy utilization efficiency.

[0105] Example 2

[0106] In order to implement the method corresponding to Embodiment 1 above and achieve the corresponding functions and technical effects, this embodiment discloses a molten salt energy storage heat release control system.

[0107] like Figure 3 As shown, a molten salt energy storage and heat release control system includes:

[0108] The steam generation efficiency and power generation efficiency prediction module 201 is used to input the principal component parameters of the molten salt energy storage and heat release system at the current moment into the efficiency prediction model to obtain the steam generation efficiency and power generation efficiency of the molten salt energy storage and heat release system at the current moment; the efficiency prediction model is determined by training an artificial neural network based on historical datasets.

[0109] The heat release load and thermoelectric load ratio determination module 202 is used to maximize the energy utilization rate of the molten salt energy storage heat release system at the current moment based on the current steam generation efficiency and power generation efficiency, and to obtain the heat release load and thermoelectric load ratio of the molten salt energy storage heat release system corresponding to the current moment when the energy utilization rate is maximized.

[0110] The heat release load and thermoelectric load ratio application module 203 is used to operate the molten salt energy storage heat release system according to the heat release load and thermoelectric load ratio at the current moment.

[0111] Example 3

[0112] This embodiment discloses an electronic device, including a memory and a processor. The memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform the molten salt energy storage heat release control method according to Embodiment 1.

[0113] This embodiment discloses a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the molten salt energy storage heat release control method as described in Embodiment 1.

[0114] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0115] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A molten salt energy storage and discharge control method, characterized by, The method comprises the following steps: inputting the principal component parameters of the molten salt energy storage and heat release system at the current time into an efficiency prediction model to obtain the steam generation efficiency and power generation efficiency of the molten salt energy storage and heat release system at the current time; the efficiency prediction model is determined by training an artificial neural network according to a historical data set; maximizing the energy utilization rate of the molten salt energy storage and heat release system at the current time according to the steam generation efficiency and power generation efficiency at the current time by using a particle swarm algorithm to obtain the heat release load and thermal power load ratio of the molten salt energy storage and heat release system corresponding to the maximum energy utilization rate at the current time; operating the molten salt energy storage and heat release system according to the heat release load and thermal power load ratio at the current time; The molten salt energy storage and discharge system t The energy utilization rate at the moment is expressed as: ； wherein, represents the molten salt energy storage and heat release system t represents the energy utilization rate at the moment, represents the molten salt energy storage and heat release system t represents the total heat release amount at the moment, p ( t ) represents the molten salt energy storage and heat release system t represents the sum of the power of the electrical equipment at the moment, f 1( t ) represents the molten salt energy storage and heat release system t represents the steam-energy conversion coefficient at the moment, represents the molten salt energy storage and heat release system t represents the steam generation efficiency at the moment, represents the molten salt energy storage and heat release system t represents the power generation efficiency at the moment, represents the molten salt energy storage and heat release system t represents the electrical energy-energy conversion coefficient at the moment, represents the molten salt energy storage and heat release system t represents the proportion of the heat used for steam generation to the total heat release load at the moment; The historical data set is determined by the following equation and ; ； wherein, Qs t is the steam production at time t, obtained by real-time measurement and converted into the steam production at time t+T by the hysteresis characteristic of the molten salt energy storage and heat release system, t t is the steam production at time t+T.​​ Qe t’ ) is t’ the power generation at the moment, which is obtained by real-time measurement and converted into t the power generation at the moment through the hysteresis characteristics of the molten salt energy storage and heat release system.​ 2. The molten salt energy storage discharge control method according to claim 1, wherein, Before the step of inputting the principal component parameters of the molten salt energy storage and heat release system at the current time into the efficiency prediction model to obtain the steam generation efficiency and power generation efficiency of the molten salt energy storage and heat release system at the current time, the method further comprises the following steps: obtaining historical operating state data of the molten salt energy storage and heat release system; performing principal component analysis on the historical operating state data to determine the principal component parameters affecting the energy utilization rate.

3. The molten salt energy storage discharge control method according to claim 2, wherein, The historical operating state data comprises molten salt flow, high-temperature molten salt temperature, low-temperature molten salt temperature, steam generation efficiency, power generation efficiency, main steam pressure, main steam temperature, steam turbine extraction amount, steam turbine extraction pressure, steam turbine extraction temperature, steam turbine exhaust pressure, steam turbine exhaust temperature, steam supply pressure, steam supply temperature, and ambient temperature of the molten salt energy storage and heat release system.

4. The molten salt energy storage discharge control method of claim 1, wherein, The training process of the artificial neural network comprises the following steps: training the artificial neural network by taking the principal component parameters in the historical data set as input and the steam generation efficiency and power generation efficiency corresponding to the principal component parameters as output, and taking the trained artificial neural network as the efficiency prediction model.

5. A molten salt energy storage and discharge control system characterized by, The method comprises the following steps: a steam generation efficiency and power generation efficiency prediction module is configured to input the principal component parameters of the molten salt energy storage and heat release system at the current time into an efficiency prediction model to obtain the steam generation efficiency and power generation efficiency of the molten salt energy storage and heat release system at the current time; the efficiency prediction model is determined by training an artificial neural network according to a historical data set; a heat release load and thermal power load ratio determination module is configured to maximize the energy utilization rate of the molten salt energy storage and heat release system at the current time according to the steam generation efficiency and power generation efficiency at the current time by using a particle swarm algorithm to obtain the heat release load and thermal power load ratio of the molten salt energy storage and heat release system corresponding to the maximum energy utilization rate at the current time; a heat release load and thermal power load ratio application module is configured to operate the molten salt energy storage and heat release system according to the heat release load and thermal power load ratio at the current time; The molten salt energy storage and discharge system t The energy utilization rate at the moment is expressed as: ； in, This refers to the molten salt energy storage and heat release system. t Energy utilization rate at any time This refers to the molten salt energy storage and heat release system. t The total heat released at each moment, ∑ p ( t ) indicates the molten salt energy storage and heat release system t The sum of the power consumption of electrical equipment at all times. f 1( t ) indicates the molten salt energy storage and heat release system t The steam-energy conversion coefficient at time t, This indicates the molten salt energy storage and heat release system. t Steam production efficiency at any given moment This indicates the molten salt energy storage and heat release system. t Power generation efficiency at any given time This refers to the molten salt energy storage and heat release system. t The electrical energy to energy conversion coefficient at time t. This refers to the molten salt energy storage and heat release system. t The proportion of heat used for steam production to the total heat release load at any given time; The historical data set is determined by the following equation and ; ； wherein, Qs t is the steam production at time t, obtained by real-time measurement and converted into the steam production at time t+T by the hysteresis characteristic of the molten salt energy storage and heat release system; t t is the steam production at time t+T.​​ Qe t’ ) for t’ the power generation at time t, which is obtained by real-time measurement and converted to t the power generation at time t by the hysteresis characteristic of the molten salt energy storage and heat release system.​ 6. An electronic device, comprising: The electronic device comprises a memory and a processor, the memory is configured to store a computer program, and the processor is configured to run the computer program to enable the electronic device to perform the molten salt energy storage and heat release control method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer program stored in the memory is configured to be executed by the processor to implement the molten salt energy storage and heat release control method according to any one of claims 1 to 4.

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