Cogeneration scheduling method coupling electric heating and molten salt heat storage and related equipment

Abstract

The present application relates to cogeneration system scheduling technical field, specifically to a kind of coupling electric heating and molten salt heat storage's cogeneration scheduling method and related equipment, the method includes obtaining the operating basic data of coupling electric heating and molten salt of cogeneration system and real-time data of power market, form data set;Optimal scheduling constraint condition is constructed to cover equipment operation constraint, energy conversion constraint and system boundary constraint;With the maximum system net profit as target, the net profit function is combined with the optimal scheduling constraint condition to construct system optimization scheduling model, the optimal scheduling parameter is obtained by solving the model, the system optimal scheduling strategy is generated, the maximum profit of cogeneration system is realized.The present application solves the technical problems that the existing coupling electric heating, molten salt's cogeneration system scheduling method cannot adapt to power spot market electricity price fluctuation, does not fully cooperate equipment operation constraint and profit target, leading to insufficient economic benefit of system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of combined heat and power system scheduling, in particular to a combined heat and power scheduling method coupling electric heating and molten salt heat storage and related equipment. BACKGROUND

[0002] With the advancement of power market reform, the electricity spot market is gradually established, and the electricity price presents the characteristics of real-time fluctuation. As an important form of energy comprehensive utilization, the combined heat and power system not only undertakes the task of power generation but also meets the demand for heat supply, and the rationality of its operation and scheduling directly affects the economic benefit and energy utilization efficiency of the system.

[0003] The current common combined heat and power system scheduling method in China is mainly based on fixed electric and heat load demand and real-time electricity price for scheduling. The scheduling strategy of electric heating equipment and molten salt storage and release is determined by logical conditions. When the electricity price is lower / higher than a certain limit value and the electric and heat load of the combined heat and power system meets the established conditions, the electric and heat load and the storage / release heat scheduling of the electric heating and molten salt equipment are carried out. Some of the conditions depend on artificial experience and have been set in advance, and cannot be dynamically modified according to the real-time operation parameters and market conditions of the combined heat and power system. In particular, in the scenario of large fluctuations in electricity price and other system performance parameters, the existing scheduling method is insufficient to achieve the global optimization of power generation, heat supply and molten salt storage and release. There is still room for further tapping the potential of the benefits brought by real-time electricity price fluctuations in the spot market. Therefore, how to combine the real-time electricity price in the electricity spot market to construct an optimal scheduling method that takes into account the dynamic constraints and profit maximization of the system has become a technical problem to be solved for the combined heat and power system coupling electric heating and molten salt. SUMMARY

[0004] In order to overcome the defects of the prior art, the purpose of the present application is to provide a combined heat and power scheduling method coupling electric heating and molten salt heat storage and related equipment, to solve the technical problem that the existing combined heat and power scheduling method coupling electric heating and molten salt cannot adapt to the electricity price fluctuation in the electricity spot market, and the device operation constraints and profit targets are not fully coordinated, resulting in insufficient economic benefit of the system.

[0005] The present application is realized by the following technical solutions: In a first aspect, the present application provides a combined heat and power scheduling method coupling electric heating and molten salt heat storage, comprising: obtaining the operation basic data of the combined heat and power system coupling electric heating and molten salt and the real-time data of the electricity market to form a data set; establishing a system net profit function based on the data set, and constructing optimization scheduling constraint conditions covering device operation constraints, energy conversion constraints and system boundary constraints; With the goal of maximizing the system's net profit, a system optimization scheduling model is constructed by combining the net profit function with the optimization scheduling constraints. The optimal scheduling parameters are obtained by solving the model. Based on the optimal scheduling parameters, the optimal scheduling strategy of the system is generated to maximize the profit of the cogeneration system.

[0006] Preferably, the basic operational data includes the electrothermal boundary parameters of the cogeneration turbine for heating operation, the maximum heat storage rate of molten salt, the maximum heat release rate of molten salt, and the heat storage capacity of molten salt; the real-time electricity market data includes real-time data on high or low electricity prices in the electricity spot market.

[0007] Preferably, the expression for the net profit function is as follows:

[0008] In the formula, The minimum time is 0.25 h; The net profit of the combined heat and power system is [amount in yuan]. The spot electricity price for the next 15-minute period in the electricity spot market, in yuan / kWh; For steam turbine power generation load, MW; The heating power load of the molten salt electric heater is in MW. For molten salt thermal storage / heat release decision, a value of 0 indicates that the next period is in thermal storage state, and a value of 1 indicates that the next period is in heat release state; Heat load for hot salt storage tank, MW; The heat load for thermal salt storage tanks is MW; For heating prices, the unit is yuan / t for industrial steam supply and yuan / GJ for heating supply. The unit for measuring the steam extraction load from the steam turbine for heating is MW; Fuel price, in yuan / ton; This represents the fuel consumption of a combined heat and power (CHP) system, in tons per hour (t / h).

[0009] Furthermore, the formula for calculating the fuel consumption of a combined heat and power (CHP) system is as follows:

[0010] In the formula, These are the calculation coefficients for the fuel consumption correlation formula. For a given heating steam turbine, all coefficients are known values. For steam turbine power generation load, MW; The unit for heating load is MW (watts) for steam extraction from the steam turbine.

[0011] Preferably, the optimized scheduling constraints include electric heater capacity constraints, molten salt heat release and storage rate constraints, remaining heat release capacity constraints of the hot salt storage tank, remaining heat storage capacity constraints of the hot salt storage tank, electric heating efficiency equation constraints, maximum main steam flow electrothermal boundary constraints of the steam turbine, minimum main steam flow electrothermal boundary constraints of the steam turbine, and minimum flow rate electrothermal boundary constraints of the steam turbine low-pressure cylinder, as expressed below:

[0012] In the formula, The maximum heating load of the molten salt electric heater is MW; The maximum exothermic load of molten salt is MW; The maximum thermal load for molten salt storage is MW; The remaining heat release from the hot salt storage tank in the next period, in MWh; The remaining heat storage capacity of the hot salt storage tank for the next period is MWh; The efficiency of the molten salt electric heater is % , These are the electrothermal boundary coefficients for the maximum main steam flow rate of the steam turbine; , These are the minimum main steam flow rate and the electrothermal boundary coefficient of the steam turbine; , These are the minimum flow rate and thermal boundary coefficient of the low-pressure cylinder of the steam turbine, respectively. The preferred expression for the system optimization scheduling model is as follows: Objective function: ; Constraints:

[0013] In the formula, The maximum heating load of the molten salt electric heater is MW; The maximum exothermic load of molten salt is MW; The maximum thermal load for molten salt storage is MW; The remaining heat release from the hot salt storage tank in the next period, in MWh; The remaining heat storage capacity of the hot salt storage tank for the next period is MWh; The efficiency of the molten salt electric heater is % , These are the electrothermal boundary coefficients for the maximum main steam flow rate of the steam turbine; , These are the minimum main steam flow rate and the electrothermal boundary coefficient of the steam turbine; , These are the minimum flow rate and thermal boundary coefficient of the low-pressure cylinder of the steam turbine, respectively.

[0014] Preferably, the optimal scheduling model is solved using an optimization algorithm to obtain the optimal values ​​of each optimization variable. Based on the optimal values, the turbine electrical load, turbine thermal load, electric heating-molten salt heat storage / release status, electric heating load, molten salt heat release load, and molten salt heat storage load under the electricity spot market are scheduled to generate the optimal scheduling strategy for the system.

[0015] Secondly, the present invention also provides a combined heat and power (CHP) dispatching system that couples electric heating and molten salt thermal storage, comprising: The data acquisition module is used to acquire basic operational data of the combined heat and power system with coupled electric heating and molten salt, as well as real-time data of the electricity market, to form a data set; The model processing module is used to establish a system net profit function based on the dataset and construct optimized scheduling constraints covering equipment operation constraints, energy conversion constraints and system boundary constraints. The strategy generation module is used to construct a system optimization scheduling model with the goal of maximizing the system's net profit, combining the net profit function and optimization scheduling constraints, solving the model to obtain the optimal scheduling parameters, and generating the system's optimal scheduling strategy based on the optimal scheduling parameters to maximize the profit of the cogeneration system.

[0016] Thirdly, the present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the cogeneration scheduling method for coupled electric heating and molten salt thermal storage as described above.

[0017] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the cogeneration scheduling method for coupled electric heating and molten salt thermal storage as described above.

[0018] Compared with the prior art, the present invention has the following beneficial technical effects: This invention provides a cogeneration scheduling method that couples electric heating and molten salt thermal storage. By acquiring basic operational data of the cogeneration system and real-time electricity market data, it accurately captures the characteristics of electricity spot market price fluctuations, providing data support for adapting to market changes and solving the problem that existing methods cannot respond to price fluctuations. Based on this dataset, a system net profit function is established to accurately calculate the revenue from power generation, heating, and molten salt thermal storage, as well as fuel costs. Simultaneously, comprehensive constraints covering equipment operation, energy conversion, and system boundaries are constructed, effectively coordinating profit targets and operational constraints, and avoiding conflicts between scheduling strategies and equipment limits. An optimized scheduling model is constructed and solved with the goal of maximizing net profit, outputting optimal scheduling parameters and strategies. This achieves coordinated optimization of power generation load, heating load, and molten salt thermal storage status, significantly improving system economic efficiency. This technical solution forms a complete logical chain from data acquisition, function and constraint construction to model solving, ensuring both the economy of the scheduling strategy and the safety of system operation through comprehensive constraints, effectively solving the technical problems of low economic efficiency and insufficient scheduling safety in existing methods. Attached Figure Description

[0019] Figure 1 This is a flowchart of the cogeneration scheduling method for coupling electric heating and molten salt thermal storage in an embodiment of the present invention; Figure 2 This is a schematic diagram of a combined heat and power (CHP) dispatching system that couples electric heating and molten salt thermal storage in an embodiment of the present invention. In the diagram: 1. Data acquisition module; 2. Model processing module; 3. Strategy generation module. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0021] The purpose of this invention is to provide a cogeneration scheduling method and related equipment that couples electric heating and molten salt thermal storage, in order to solve the technical problem that existing cogeneration system scheduling methods that couple electric heating and molten salt cannot adapt to fluctuations in electricity spot market prices and do not fully coordinate equipment operation constraints and profit targets, resulting in insufficient system economic benefits.

[0022] The present invention will now be described in further detail with reference to the accompanying drawings: Example 1 See Figure 1In one embodiment of the present invention, a cogeneration scheduling method for coupled electric heating and molten salt thermal storage is provided, comprising: Step 1: Obtain basic operational data of the combined heat and power system with coupled electric heating and molten salt, as well as real-time electricity market data, to form a dataset; Specifically, the basic operational data includes the electrothermal boundary parameters of the combined heat and power turbine for heating operation, the maximum heat storage rate of molten salt, the maximum heat release rate of molten salt, and the heat storage capacity of molten salt; the real-time electricity market data includes real-time data on high or low electricity prices in the electricity spot market.

[0023] Step 2: Based on the dataset, establish a system net profit function and construct optimized scheduling constraints covering equipment operation constraints, energy conversion constraints, and system boundary constraints; Specifically, the expression for the net profit function is as follows:

[0024] In the formula, The minimum time is 0.25 h; The net profit of the combined heat and power system is [amount in yuan]. The spot electricity price for the next 15-minute period in the electricity spot market, in yuan / kWh; For steam turbine power generation load, MW; The heating power load of the molten salt electric heater is in MW. For molten salt thermal storage / heat release decision, a value of 0 indicates that the next period is in thermal storage state, and a value of 1 indicates that the next period is in heat release state; Heat load for hot salt storage tank, MW; The heat load for thermal salt storage tanks is MW; For heating prices, the unit is yuan / t for industrial steam supply and yuan / GJ for heating supply. The unit for measuring the steam extraction load from the steam turbine for heating is MW; Fuel price, in yuan / ton; This represents the fuel consumption of a combined heat and power (CHP) system, in tons per hour (t / h).

[0025] The formula for calculating the fuel consumption of a combined heat and power (CHP) system is as follows:

[0026] In the formula, These are the calculation coefficients for the fuel consumption correlation formula. For a given heating steam turbine, all coefficients are known values. For steam turbine power generation load, MW; The unit for heating load is MW (watts) for steam extraction from the steam turbine.

[0027] Specifically, the optimization scheduling constraints include electric heater capacity constraints, molten salt heat release and storage rate constraints, remaining heat release capacity constraints of the hot salt storage tank, remaining heat storage capacity constraints of the hot salt storage tank, electric heating efficiency equation constraints, maximum main steam flow electrothermal boundary constraints of the steam turbine, minimum main steam flow electrothermal boundary constraints of the steam turbine, and minimum flow rate electrothermal boundary constraints of the steam turbine low-pressure cylinder. The expressions are as follows:

[0028] In the formula, The maximum heating load of the molten salt electric heater is MW; The maximum exothermic load of molten salt is MW; The maximum heat storage load of molten salt is MW; The remaining heat release from the hot salt storage tank in the next period, in MWh; The remaining heat storage capacity of the hot salt storage tank for the next period is MWh; The efficiency of the molten salt electric heater is % , These are the electrothermal boundary coefficients for the maximum main steam flow rate of the steam turbine; , These are the minimum main steam flow rate and the electrothermal boundary coefficient of the steam turbine; , These are the minimum flow rate electrothermal boundary coefficients for the low-pressure cylinder of the steam turbine. For a given heating steam turbine, the coefficients for all three types of electrothermal boundaries are known; for a given molten salt electric heater, , all, , , and The values ​​are known; the optimization variables required in the scheduling model are as follows: , , , , , .

[0029] Step 3: With the goal of maximizing the system's net profit, construct a system optimization scheduling model by combining the net profit function with the optimization scheduling constraints, solve the model to obtain the optimal scheduling parameters, and generate the system's optimal scheduling strategy based on the optimal scheduling parameters to maximize the profit of the cogeneration system.

[0030] Specifically, the expression for the system optimization scheduling model is as follows: Objective function: ; Constraints:

[0031] In the formula, The maximum heating load of the molten salt electric heater is MW; The maximum exothermic load of molten salt is MW; The maximum thermal load for molten salt storage is MW; The remaining heat release from the hot salt storage tank in the next period, in MWh; The remaining heat storage capacity of the hot salt storage tank for the next period is MWh; The efficiency of the molten salt electric heater is % , These are the electrothermal boundary coefficients for the maximum main steam flow rate of the steam turbine; , These are the minimum main steam flow rate and the electrothermal boundary coefficient of the steam turbine; , These are the minimum flow rate and thermal boundary coefficient of the low-pressure cylinder of the steam turbine, respectively.

[0032] By employing an optimization algorithm to solve the above-mentioned optimized scheduling model for the electric heating-molten salt cogeneration system, the optimal values ​​of various optimization variables can be obtained: , , , , , Based on this, the turbine electrical load, turbine thermal load, electric heating-molten salt heat storage / release strategy, electric heating load, molten salt heat release load, and molten salt heat storage load under the electricity spot market are scheduled to maximize unit profits.

[0033] In summary, this embodiment provides a combined heat and power (CHP) scheduling method that couples electric heating and molten salt thermal storage. By acquiring basic operational data of the CHP system and real-time electricity market data, it accurately captures the characteristics of electricity spot market price fluctuations, providing data support for adapting to market changes and solving the problem that existing methods cannot respond to price fluctuations. Based on this dataset, a system net profit function is established to accurately calculate the revenue from power generation, heating, and molten salt thermal storage, as well as fuel costs. Simultaneously, comprehensive constraints covering equipment operation, energy conversion, and system boundaries are constructed, effectively coordinating profit targets and operational constraints, and avoiding conflicts between scheduling strategies and equipment limits. An optimized scheduling model is constructed and solved with the goal of maximizing net profit, outputting optimal scheduling parameters and strategies. This achieves coordinated optimization of power generation load, heating load, and molten salt thermal storage status, significantly improving system economic efficiency. This technical solution forms a complete logical chain from data acquisition, function and constraint construction to model solving, ensuring both the economy of the scheduling strategy and the safety of system operation through comprehensive constraints, effectively solving the technical problems of low economic efficiency and insufficient scheduling safety in existing methods.

[0034] Example 2 according toFigure 2 As shown, this embodiment also provides a combined heat and power (CHP) dispatching system that couples electric heating and molten salt thermal storage, including: Data acquisition module 1 is used to acquire basic operational data of the combined heat and power system with coupled electric heating and molten salt, as well as real-time data of the electricity market, to form a data set; Model processing module 2 is used to establish a system net profit function based on the data set and construct optimized scheduling constraints covering equipment operation constraints, energy conversion constraints and system boundary constraints; Strategy generation module 3 is used to construct a system optimization scheduling model with the goal of maximizing the system's net profit, combining the net profit function and optimization scheduling constraints, solving the model to obtain the optimal scheduling parameters, and generating the system's optimal scheduling strategy based on the optimal scheduling parameters to maximize the profit of the cogeneration system.

[0035] Example 3 The present invention also provides a mobile terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, such as a cogeneration scheduling program for coupled electric heating and molten salt thermal storage.

[0036] When the processor executes the computer program, it implements the above-described cogeneration scheduling method that combines electric heating and molten salt thermal storage, for example: Acquire basic operational data of the combined heat and power system with coupled electric heating and molten salt, as well as real-time electricity market data, to form a dataset; Based on the aforementioned dataset, a system net profit function is established, and optimized scheduling constraints covering equipment operation constraints, energy conversion constraints, and system boundary constraints are constructed. With the goal of maximizing the system's net profit, a system optimization scheduling model is constructed by combining the net profit function with the optimization scheduling constraints. The optimal scheduling parameters are obtained by solving the model. Based on the optimal scheduling parameters, the optimal scheduling strategy of the system is generated to maximize the profit of the cogeneration system.

[0037] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above system, for example: Data acquisition module 1 is used to acquire basic operational data of the combined heat and power system with coupled electric heating and molten salt, as well as real-time data of the electricity market, to form a data set; Model processing module 2 is used to establish a system net profit function based on the data set and construct optimized scheduling constraints covering equipment operation constraints, energy conversion constraints and system boundary constraints; Strategy generation module 3 is used to construct a system optimization scheduling model with the goal of maximizing the system's net profit, combining the net profit function and optimization scheduling constraints, solving the model to obtain the optimal scheduling parameters, and generating the system's optimal scheduling strategy based on the optimal scheduling parameters to maximize the profit of the cogeneration system.

[0038] For example, the computer program can be divided into a data acquisition module 1, a model processing module 2, and a strategy generation module 3; The specific functions of each module are as follows: Data acquisition module 1 is used to acquire basic operational data of the combined heat and power system with coupled electric heating and molten salt, as well as real-time data of the electricity market, to form a data set; Model processing module 2 is used to establish a system net profit function based on the data set and construct optimized scheduling constraints covering equipment operation constraints, energy conversion constraints and system boundary constraints; Strategy generation module 3 is used to construct a system optimization scheduling model with the goal of maximizing the system's net profit, combining the net profit function and optimization scheduling constraints, solving the model to obtain the optimal scheduling parameters, and generating the system's optimal scheduling strategy based on the optimal scheduling parameters to maximize the profit of the cogeneration system.

[0039] The mobile terminal can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The mobile terminal may include, but is not limited to, a processor and memory.

[0040] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the mobile terminal, connecting various parts of the mobile terminal via various interfaces and lines.

[0041] The memory can be used to store the computer program and / or module. The processor implements various functions of the mobile terminal by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory.

[0042] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMediaCards (SMC), Secure Digital (SD) cards, FlashCards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0043] Example 4 The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned cogeneration scheduling method for coupled electric heating and molten salt thermal storage.

[0044] If the modules / units integrated in the mobile terminal are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0045] Based on this understanding, all or part of the processes in the above method can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the above-described aggregated reinforcement learning resource scheduling method. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate form.

[0046] The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0047] It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0048] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for cogeneration scheduling that couples electric heating and molten salt thermal storage, characterized in that, include: Acquire basic operational data of the combined heat and power system with coupled electric heating and molten salt, as well as real-time electricity market data, to form a dataset; Based on the aforementioned dataset, a system net profit function is established, and optimized scheduling constraints covering equipment operation constraints, energy conversion constraints, and system boundary constraints are constructed. With the goal of maximizing the system's net profit, a system optimization scheduling model is constructed by combining the net profit function with the optimization scheduling constraints. The optimal scheduling parameters are obtained by solving the model. Based on the optimal scheduling parameters, the optimal scheduling strategy of the system is generated to maximize the profit of the cogeneration system.

2. The cogeneration scheduling method for coupled electric heating and molten salt thermal storage according to claim 1, characterized in that, The basic operational data includes the electrothermal boundary parameters of the cogeneration turbine for heating operation, the maximum heat storage rate of molten salt, the maximum heat release rate of molten salt, and the heat storage capacity of molten salt; the real-time electricity market data includes real-time data on high or low electricity prices in the electricity spot market.

3. The cogeneration scheduling method for coupled electric heating and molten salt thermal storage according to claim 1, characterized in that, The expression for the net profit function is as follows: In the formula, The minimum time is 0.25 h; The net profit of the combined heat and power system is [amount in yuan]. The spot electricity price for the next 15-minute period in the electricity spot market, in yuan / kWh; For steam turbine power generation load, MW; The heating power load of the molten salt electric heater is in MW. For molten salt thermal storage / heat release decision, a value of 0 indicates that the next period is in thermal storage state, and a value of 1 indicates that the next period is in heat release state; Heat load for hot salt storage tank, MW; The heat load for thermal salt storage tanks is MW; For heating prices, the unit is yuan / t for industrial steam supply and yuan / GJ for heating supply. The unit for measuring the steam extraction load from the steam turbine for heating is MW; Fuel price, in yuan / ton; This represents the fuel consumption of a combined heat and power (CHP) system, in tons per hour (t / h).

4. The cogeneration scheduling method for coupled electric heating and molten salt thermal storage according to claim 3, characterized in that, The formula for calculating the fuel consumption of the combined heat and power system is as follows: In the formula, These are the calculation coefficients for the fuel consumption correlation formula. For a given heating steam turbine, all coefficients are known values. For steam turbine power generation load, MW; The unit for heating load is MW (watts) for steam extraction from the steam turbine.

5. The cogeneration scheduling method for coupled electric heating and molten salt thermal storage according to claim 1, characterized in that, The optimized scheduling constraints include electric heater capacity constraints, molten salt heat release and storage rate constraints, remaining heat release capacity constraints of the hot salt storage tank, remaining heat storage capacity constraints of the hot salt storage tank, electric heating efficiency equation constraints, maximum main steam flow rate electrothermal boundary constraints of the turbine, minimum main steam flow rate electrothermal boundary constraints of the turbine, and minimum flow rate electrothermal boundary constraints of the turbine low-pressure cylinder. The expressions are as follows: In the formula, The maximum heating load of the molten salt electric heater is MW; The maximum exothermic load of molten salt is MW; The maximum thermal load for molten salt storage is MW; The remaining heat release from the hot salt storage tank in the next period, in MWh; The remaining heat storage capacity of the hot salt storage tank for the next period is MWh; The efficiency of the molten salt electric heater is % , These are the electrothermal boundary coefficients for the maximum main steam flow rate of the steam turbine; , These are the minimum main steam flow rate and the electrothermal boundary coefficient of the steam turbine; , These are the minimum flow rate and thermal boundary coefficient of the low-pressure cylinder of the steam turbine, respectively.

6. The cogeneration scheduling method for coupled electric heating and molten salt thermal storage according to claim 1, characterized in that, The expression for the system optimization scheduling model is as follows: Objective function: ; Constraints: In the formula, The maximum heating load of the molten salt electric heater is MW; The maximum exothermic load of molten salt is MW; The maximum thermal load for molten salt storage is MW; The remaining heat release from the hot salt storage tank in the next period, in MWh; The remaining heat storage capacity of the hot salt storage tank for the next period is MWh; The efficiency of the molten salt electric heater is % , These are the electrothermal boundary coefficients for the maximum main steam flow rate of the steam turbine; , These are the minimum main steam flow rate and the electrothermal boundary coefficient of the steam turbine; , These are the minimum flow rate and thermal boundary coefficient of the low-pressure cylinder of the steam turbine, respectively.

7. A cogeneration scheduling method for coupled electric heating and molten salt thermal storage according to claim 1, characterized in that, The optimal scheduling model is solved using an optimization algorithm to obtain the optimal values ​​of each optimization variable. Based on the optimal values, the turbine electrical load, turbine thermal load, electric heating-molten salt heat storage / release status, electric heating load, molten salt heat release load, and molten salt heat storage load under the electricity spot market are scheduled to generate the optimal scheduling strategy for the system.

8. A combined heat and power (CHP) dispatching system that couples electric heating and molten salt thermal storage, characterized in that, include: The data acquisition module is used to acquire basic operational data of the combined heat and power system with coupled electric heating and molten salt, as well as real-time data of the electricity market, to form a data set; The model processing module is used to establish a system net profit function based on the dataset and construct optimized scheduling constraints covering equipment operation constraints, energy conversion constraints and system boundary constraints. The strategy generation module is used to construct a system optimization scheduling model with the goal of maximizing the system's net profit, combining the net profit function and optimization scheduling constraints, solving the model to obtain the optimal scheduling parameters, and generating the system's optimal scheduling strategy based on the optimal scheduling parameters to maximize the profit of the cogeneration system.

9. A mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the cogeneration scheduling method for coupled electric heating and molten salt thermal storage as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the cogeneration scheduling method for coupled electric heating and molten salt thermal storage as described in any one of claims 1-7.

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