Molten salt heat storage operation method and system

Abstract

The invention discloses a fused salt heat storage operation method and system, relates to the technical field of heat storage operation of a coal-fired power generation unit, and aims to determine an operation scheme of a heat storage system through an optimization algorithm so as to adapt to the change of an electric load and improve the adjustment flexibility and efficiency of the heat storage system. According to the method, a fused salt physical property model, a boundary condition model, a heat storage system model, a heat storage scheme optimization model and a heat release scheme optimization model are constructed, and the fused salt physical property model, the boundary condition model and the heat storage system model provide constraint conditions and application scenes for system heat storage and heat release optimization; according to the heat storage scheme of the heat storage system, the heat storage characteristic of each heat storage unit is fully combined, the heat of the coal-fired unit is efficiently stored, and renewable energy electric power is consumed; in the heat release stage, the maximum income of the power plant serves as the target, an MIQP algorithm is adopted for optimization calculation, and therefore each heat storage unit supplies heat to the coal-fired unit in good time and in proper amount; and finally outputting the optimal heat release scheme of the heat storage system, the heat storage scheme of the heat storage system and the operation scheme of the coal-fired unit by the optimization algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of heat storage operation of coal-fired power generation units, and in particular to a molten salt heat storage operation method and system. Background Art

[0002] my country is accelerating the construction of a new power system dominated by renewable energy. However, renewable energy sources such as photovoltaic and wind power are characterized by significant intermittency and volatility. Their widespread grid integration results in numerous uncertainties and rapidly changing operating conditions on the power system's source side. Therefore, under these new circumstances, coal-fired power plants are required to continuously improve their operational flexibility to facilitate the absorption of renewable energy generation.

[0003] Integrating energy storage systems into coal-fired power generation units is currently an effective means of improving their operational flexibility. Molten salt thermal storage systems, with their advantages of large energy storage capacity and high safety, have been widely used in concentrated solar power plants. With the deepening of peak-shaving retrofits for thermal power units, molten salt thermal storage technology is increasingly being used for deep peak-shaving coupled with thermal power units, achieving promising results. Molten salt thermal storage systems consist of thermal storage units of varying capacities, each with its own independently controllable operating status. For a population of thermal storage units with varying capacities, thermal capacity utilization is a key indicator of their operational status. Differences in remaining thermal storage capacity lead to differences in both heat storage and release rates, resulting in varying levels of regulation flexibility. With improvements in living standards and economic well-being, electricity consumption in the residential and tertiary industries is growing at a faster rate and exhibiting greater flexibility. On the other hand, the diverse production activities in industrial parks exacerbate significant fluctuations in electricity consumption over time. The varying peak-shaving flexibility of thermal storage units and the complex and volatile electricity loads inevitably challenge the stability and thermal efficiency of molten salt thermal storage systems. Summary of the Invention

[0004] In view of the problems existing in the existing molten salt heat storage operation and system, the present invention is proposed.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a molten salt heat storage operation method, which includes:

[0007] The operation plan of the heat storage system is determined through optimization algorithm to adapt to changes in electricity load and improve the adjustment flexibility and efficiency of the heat storage system.

[0008] As a preferred solution of the molten salt heat storage operation method described in the present invention, the optimization algorithm calculates and obtains the heat release plan of the heat storage system, the heat storage plan of the heat storage system and the operation plan of the coal-fired unit.

[0009] As a preferred solution of the molten salt heat storage operation method of the present invention, wherein: the heat storage solution of the heat storage system includes using renewable energy power to supplement the remaining capacity of the heat storage unit;

[0010] The heat storage capacity of each heat storage unit is determined based on the remaining capacity ratio, and the benefits of the heat storage stage are calculated.

[0011] As a preferred solution of the molten salt heat storage operation method of the present invention, wherein: the heat release solution of the heat storage system includes:

[0012] The load is allocated to the coal-fired unit and the heat storage unit according to the electrical load and unit parameters, and the maximum load of each heat storage unit is reset according to the functional relationship between the remaining capacity of the molten salt and the heat release rate, so as to reduce the cost of the heat release stage and calculate the benefits of the heat release stage.

[0013] As a preferred solution of the molten salt heat storage operation method of the present invention, wherein: the optimization algorithm is established based on the molten salt physical property model;

[0014] The molten salt physical property model includes a functional relationship between the molten salt remaining capacity and the heat release rate, a functional relationship between the molten salt remaining capacity and the heat storage rate, and a functional relationship between the molten salt remaining capacity and the electrical heat storage rate;

[0015] The molten salt physical property model is used to optimize heat storage and heat release schemes.

[0016] As a preferred solution of the molten salt heat storage operation method of the present invention, wherein: the optimization algorithm also takes boundary conditions into consideration;

[0017] The boundary conditions include any one or more of regional electricity prices, coal prices, coal calorific value, new energy power, ambient temperature, electric load and unit parameters.

[0018] As a preferred solution of the molten salt heat storage operation method of the present invention, the operation method finally outputs multiple evaluation indicators.

[0019] In a second aspect, an embodiment of the present invention provides a molten salt heat storage operation system, which includes an optimization algorithm module, a heat storage scheme module, a molten salt physical property model module, and an evaluation index module;

[0020] The optimization algorithm module is responsible for calculating and optimizing the operation plan of the heat storage system, including the heat storage and heat release plans and the operation plan of the coal-fired unit;

[0021] The heat storage solution module involves specific operations in the heat storage phase, including using renewable energy electricity to supplement the remaining capacity of the heat storage unit and determining the heat storage capacity of each heat storage unit based on the remaining capacity ratio;

[0022] The molten salt physical property model module provides a functional relationship between the remaining capacity of the molten salt and the heat release rate, heat storage rate and electrical heat storage rate, which is used to optimize the heat storage and release schemes;

[0023] The evaluation index module is used to finally output a variety of evaluation indicators.

[0024] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned molten salt heat storage operation method is implemented.

[0025] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned molten salt heat storage operation method is implemented.

[0026] The present invention has the following beneficial effects: It constructs a model for optimizing molten salt properties, boundary conditions, a heat storage system, and a heat storage and release scheme. These molten salt properties, boundary conditions, and the heat storage system model provide constraints and application scenarios for optimizing the system's heat storage and release. The heat storage system's heat storage scheme fully integrates the heat storage characteristics of each heat storage unit, efficiently storing heat from coal-fired units and absorbing renewable energy power. During the release phase, an MIQP (mixed integer quadratic programming) algorithm is used to optimize the calculation, ensuring that each heat storage unit provides heat to the coal-fired units in a timely and appropriate manner, with the goal of maximizing power plant revenue. The optimization algorithm ultimately outputs the optimal heat storage system release and storage schemes, as well as the coal-fired unit operation plan. The algorithm also outputs evaluation metrics for peak-shaving capacity, peak-shaving depth, thermal efficiency, coal consumption rate, and power plant revenue for user reference. Based on the above method, a molten salt heat storage system with high thermal efficiency and peak-shaving flexibility is proposed. This invention significantly improves the peak-shaving flexibility and thermal efficiency of coal-fired units coupled with a molten salt heat storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0028] Figure 1 This is a flow chart of the molten salt heat storage operation method.

[0029] Figure 2 This is the algorithm design structure diagram for the molten salt heat storage operation method.

[0030] Figure 3 Schematic diagram of steam extraction and water return of the heat storage system.

[0031] Figure 4 Schematic diagram of the structural connection of the molten salt heat storage system.

[0032] In the figure, 1 is the boiler; 2 is the high-pressure cylinder; 3 is the medium-pressure cylinder; 4 is the low-pressure cylinder; 5 is the generator; 6 is the deaerator; 7 is the condenser; 8 is the feed water pump; 9 is the condensate pump; 10 is the No. 1 high-pressure heater; 11 is the No. 2 high-pressure heater; 12 is the No. 3 high-pressure heater; 13 is the No. 4 low-pressure heater; 14 is the No. 5 low-pressure heater; 15 is the No. 6 low-pressure heater; 1-1 is the low-temperature molten salt tank 1, 1-2 is the molten salt-steam heat storage heat exchanger 1, 1-3 is the molten salt-steam Heat exchanger 1; 1-4, electric heater 1; 1-5, high temperature molten salt tank 1; 1-6, low temperature molten salt tank solenoid valve 1; 1-7, electric heater molten salt flow channel solenoid valve 1; 1-8, high temperature molten salt tank solenoid valve 1; 1-9, steam flow channel solenoid valve 1 for heat storage unit 1; 1-10, molten salt-steam heat exchanger solenoid valve 1 for heat storage unit 2; heat storage unit 2: 2-1, low temperature molten salt tank 2; 2-2, molten salt-steam storage Heat exchanger 2; 2-3, molten salt-steam heat exchanger 2; 2-4, electric heater 2; 2-5, high-temperature molten salt tank 2; 2-6, low-temperature molten salt tank solenoid valve 2; 2-7, electric heater molten salt flow channel solenoid valve 2; 2-8, high-temperature molten salt tank solenoid valve 2; 2-9, heat storage unit 1 steam flow channel solenoid valve 2; 2-10, molten salt-steam heat exchanger return water solenoid valve 2; heat storage unit 3: 3-1, low-temperature molten salt Salt tank 3; 3-2, molten salt-steam heat storage heat exchanger 3; 3-3, molten salt-steam heat release heat exchanger 3; 3-4, electric heater 3; 3-5, high-temperature molten salt tank 3; 3-6, low-temperature molten salt tank solenoid valve 3; 3-7, electric heater molten salt flow channel solenoid valve 3; 3-8, high-temperature molten salt tank solenoid valve 3; 3-9, heat storage unit 1 steam flow channel solenoid valve 3; 3-10, molten salt-steam heat release heat exchanger return water solenoid valve 3. DETAILED DESCRIPTION

[0033] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0034] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0035] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0036] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0037] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0038] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0039] Example 1

[0040] Reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a molten salt heat storage operation method, which determines the operation plan of the heat storage system through an optimization algorithm to adapt to changes in electric load and improve the adjustment flexibility and efficiency of the heat storage system.

[0041] In an optional embodiment, if Figure 1 As shown in the figure, when applying it, it is necessary to build a model of molten salt properties, boundary conditions, and heat storage system. The MIQP (mixed integer quadratic programming) algorithm is used to optimize the calculation to obtain the best heat release plan of the heat storage system, the heat storage plan of the heat storage system, and the operation plan of the coal-fired unit. The evaluation indicators are output according to the final plan for user reference.

[0042] The optimization algorithm calculates and obtains a heat release plan of the heat storage system, a heat storage plan of the heat storage system, and an operation plan of the coal-fired unit.

[0043] The heat storage scheme of the heat storage system includes using renewable energy electricity to supplement the remaining capacity of the heat storage unit; and determining the heat storage capacity of each heat storage unit according to the remaining capacity ratio, and calculating the benefits of the heat storage stage.

[0044] The heat storage system consists of multiple molten salt heat storage units of varying capacities, each capable of storing both steam and electricity. The heat storage period, heat release period, and storage power are determined by the electrical load and unit parameters. During the heat storage period, during periods of low electrical load (generally between 0:00 and 6:00), the coal-fired unit operates at the minimum permitted operating load, with the molten salt heat stored after deducting the electrical load. The remaining time, excluding the heat storage period, is the heat release period.

[0045] In an optional embodiment, the optimization algorithm used in this embodiment is to first allocate a load P0 to the coal-fired unit according to the electrical load P and the unit parameters at a certain moment, and then determine whether the allocated load P0 is within the minimum load P that the unit can provide. m-min and the maximum load P that the unit can provide m-max If not, return to redistribution until the allocated load P0 is within the interval, and then allocate loads P1 to P0 to each heat storage unit in turn. i Before distributing the electric load at the next moment, the maximum load that each heat storage unit can provide is first reset according to the functional relationship between the remaining capacity of the molten salt and the heat release rate. Then, the load is redistributed to the coal-fired units and each heat storage unit according to the above load distribution method, and all heat release periods are traversed in turn. Finally, the coal purchase cost (C m-f ) is the lowest target and the MIQP (mixed integer quadratic programming) algorithm is used to optimize and calculate the best heat storage unit heat release scheme and coal-fired unit operation scheme. The heat release phase benefit (S 0-f ), i.e. the income from electricity sold during the heat release phase (S e-f ) Calculate the coal purchase cost after deducting heat release (C m-f ).

[0046] The heat release scheme of the heat storage system includes allocating loads to coal-fired units and heat storage units based on electrical load and unit parameters, resetting the maximum load of each heat storage unit based on the functional relationship between the remaining capacity of the molten salt and the heat release rate, reducing the cost of the heat release stage, and calculating the benefits of the heat release stage.

[0047] In another optional embodiment, a heat storage and release optimization algorithm is used to ultimately output a heat storage plan for each heat storage unit in the heat storage system, a heat release plan for each heat storage unit, and an operation plan for the coal-fired unit during the entire storage and release process. Simultaneously, the following evaluation indicators are output: peak shaving capacity, peak shaving depth, thermal efficiency, coal consumption rate, and power plant revenue. The relevant indicator formulas are as follows:

[0048] Peak shaving capacity and peak shaving depth of heat storage process:

[0049] ΔP c,t =P m-min -P c,t

[0050]

[0051] Peak shaving capacity and peak shaving depth of heat storage process:

[0052] ΔP f,t =P m-max -P f,t

[0053]

[0054] Thermal efficiency of the entire storage-release process:

[0055]

[0056] Coal consumption rate of the whole process of heat storage and release:

[0057]

[0058] Power plant revenue:

[0059] S0=S c +S f

[0060] Where: ΔP c,t With ΔP f,t are the peak load capacity of heat storage process and heat release process at time t respectively; P m-min 、P m-max They are the minimum operating load and 100% rated load respectively; c,t ,ξ f,t are the peak regulation depths of heat storage process and heat release process at time t respectively; η r is the thermal efficiency of the whole process of heat storage and heat release; t1 and t2 are the start and end time of the heat storage process respectively; t3 and t4 are the start and end time of the heat release process respectively; P c,t 、P f,t are the power generation during the heat storage process and the heat release process at time t; Q b1,t , Q b2,t are the heat loads output by the coal-fired unit during the heat storage and heat release processes at time t; η b,t is the thermal efficiency of the boiler at time t. B is the coal consumption rate of the whole heat storage-release process.

[0061] It's also worth noting that to simplify calculations, the algorithm optimizes the thermal storage system's heat storage and release schedule on an hourly basis. Furthermore, any remaining heat stored on the first day for use on the second day is not considered, meaning the system consumes all its stored heat on the same day. Renewable energy is freely available, with pricing determined by local policies.

[0062] The optimization algorithm is established based on a molten salt physical property model, which includes a functional relationship between the molten salt remaining capacity and the heat release rate, a functional relationship between the molten salt remaining capacity and the heat storage rate, and a functional relationship between the molten salt remaining capacity and the electrical heat storage rate;

[0063] The molten salt physical property model is used to optimize heat storage and heat release schemes.

[0064] In an optional embodiment, focusing on heat storage and release optimization based on a dynamic molten salt property model, a specific mathematical model, such as a polynomial or exponential function, and how to optimize it using the MIQP algorithm can be included. For example, it can describe how to adjust the heat storage rate based on the remaining capacity during the heat storage phase, how to adjust the heat release rate during the heat release phase, and how to optimize economic efficiency in combination with real-time electricity prices, as follows:

[0065] Heat storage and release optimization system based on dynamic molten salt physical property model, molten salt physical property model construction:

[0066] Establish a nonlinear functional relationship between the remaining capacity of molten salt and the heat release rate:

[0067]

[0068] Wherein, k1 is the heat release coefficient, n1 is the capacity sensitivity index, and α is the temperature attenuation coefficient;

[0069] Then the dynamic relationship between the remaining capacity of molten salt and the heat storage rate is established:

[0070]

[0071] K2 is the heat storage efficiency coefficient, n2 is the capacity gradient factor, and β is the temperature attenuation coefficient;

[0072] The improved MIQP algorithm uses the molten salt physical property model as a constraint to construct an objective function and dynamically update the boundary conditions of the heat storage unit: the upper limit of the heat storage and release rate is reset every 15 minutes based on the remaining capacity of the molten salt to ensure that the system operates within a safe temperature gradient (e.g., ±5°C / min);

[0073] During the off-peak period of the power grid (electricity price 0.3 yuan / kWh), the coal-fired units operate at 80% load, and the remaining capacity is supplemented by wind power, and the heat storage efficiency is increased to 92%; during the peak period (electricity price 1.2 yuan / kWh), the molten salt heat release rate dynamically matches the load demand, and the system's peak-shaving capacity reaches 35% of the rated capacity.

[0074] In another embodiment, a multi-physics coupled intelligent heat storage system expands the molten salt physical property model to introduce a coupling relationship between the remaining capacity of the molten salt and the electrical heat storage rate:

[0075]

[0076] Where γ is a proportional coefficient, Q 储 (t) is the heat storage rate of the molten salt at time t, C 剩余 The remaining heat storage capacity of the current molten salt, C 额定 represents the rated heat storage capacity of the molten salt heat storage system, U is the unit mass of molten salt, and I(t) is the input power at time t;

[0077] Build a real-time feedback model for molten salt state parameters (temperature, viscosity, specific heat capacity) and correct the physical property parameters using the Kalman filter algorithm;

[0078] The system configuration and control adopts a modular heat storage unit design. Each unit is equipped with: a dual-tank molten salt system (high-temperature tank 560°C, low-temperature tank 290°C); phase change thermal storage material (NaNO3-KNO3 eutectic salt, melting point 220°C); and a solenoid valve matrix (response time ≤ 0.5s, flow regulation accuracy ±2%).

[0079] Intelligent control module integration: real-time electricity price prediction unit (based on LSTM neural network); equipment life loss assessment unit (based on rain flow counting method); multi-objective optimization unit (NSGA-II algorithm solves Pareto frontier);

[0080] Thermal storage stage: Prioritize the use of abandoned wind power from wind farms (with an electricity price of 0.15 yuan / kWh), maintain the minimum stable load (40%) of coal-fired units, and reduce the thermal storage cost to 0.08 yuan / kWhth;

[0081] Heat release phase: The heat release rate is dynamically adjusted according to the grid AGC instructions, with a response time of <10s and a 27% increase in peak-valley arbitrage earnings.

[0082]

[0083] In the above two optional embodiments, through specific mathematical models, system configurations and operation strategies, the deep coupling of molten salt physical property models and optimization algorithms is demonstrated, which can provide customized solutions for different application scenarios.

[0084] The optimization algorithm also takes into account boundary conditions;

[0085] The boundary conditions include any one or more of regional electricity prices, coal prices, coal calorific value, new energy power, ambient temperature, electric load and unit parameters.

[0086] In an optional embodiment, the regional electricity price of the coal-fired and molten salt heat storage combined optimization system based on multiple boundary condition constraints is as follows: peak-valley electricity price curve (1.2 yuan / kWh during peak hours and 0.3 yuan / kWh during valley hours); coal price: the real-time coal price fluctuation range is 600-800 yuan / ton, which is equivalent to a unit heat cost of 0.05-0.07 yuan / kWhth; new energy electricity: the cost of wind power abandonment is 0.15 yuan / kWh, and the cost of photovoltaic power abandonment is 0.2 yuan / kWh; coal calorific value: 4800-5200kcal / kg, corresponding to a power generation efficiency of 38%-42%; ambient temperature: the impact of high temperature in summer (35°C) and low temperature in winter (-10°C) on molten salt heat loss is dynamically adjusted through the correction coefficient (k_{\text{environment}}); unit parameters: the minimum stable load of the coal-fired unit is 40%, and the maximum load is 100%.

[0087] Electric load curve: Daytime peak load is 90% of rated capacity, and nighttime valley load is 50% of rated capacity.

[0088] During off-peak hours (electricity price 0.3 yuan / kWh), wind power curtailment (0.15 yuan / kWh) is preferentially used for heat storage, and the heat storage cost is reduced to 0.08 yuan / kWhth.

[0089] During peak hours (electricity price 1.2 yuan / kWh), the molten salt heat release rate matches the peak-shaving demand of the power grid, and the peak-valley arbitrage income increases by 35%.

[0090] The system's overall operating costs were reduced by 12%, and the average annual revenue increased by approximately RMB 5 million.

[0091] In another alternative embodiment, the boundary conditions are modeled:

[0092] Regional electricity prices: A time-of-use electricity price strategy is adopted, with a flat-rate electricity price of 0.6 yuan / kWh, a peak price of 1.5 yuan / kWh, and a valley price of 0.2 yuan / kWh; coal prices: quarterly fluctuations range from 700-900 yuan / ton, equivalent to a unit heat cost of 0.06-0.08 yuan / kWhth; new energy electricity: the cost of abandoned photovoltaic power is 0.2 yuan / kWh, and the cost of abandoned wind power is 0.15 yuan / kWh, and there is random volatility.

[0093] Coal calorific value: 5000kcal / kg, corresponding to a power generation efficiency of 40%; ambient temperature: the effects of extreme high temperature (40°C) and low temperature (-20°C) on molten salt heat loss are dynamically compensated through a nonlinear correction model; unit parameters: the minimum load of the coal-fired unit is 35% and the maximum load is 100%; electric load curve: the daytime peak load is 85% of the rated capacity, and the nighttime valley load is 40% of the rated capacity.

[0094] Build a multi-scenario optimization framework:

[0095] Scenario 1: During off-peak hours (electricity price 0.2 yuan / kWh), with the goal of maximizing heat storage, priority is given to the use of curtailed wind power.

[0096] Scenario 2: During the flat period (electricity price 0.6 yuan / kWh), balance the load of coal-fired units and the heat release power of molten salt.

[0097] Scenario 3: During peak hours (electricity price 1.5 yuan / kWh), the molten salt thermal storage energy is released first with the goal of maximizing peak-valley arbitrage profits.

[0098] Dynamically update boundary conditions:

[0099] The objective function weight is updated every hour based on real-time electricity prices, coal prices and the availability of renewable energy power; the upper limit of the heat storage and release rate is adjusted every 15 minutes based on the ambient temperature and molten salt state parameters.

[0100] During off-peak periods, the heat storage cost is reduced to 0.07 yuan / kWh-th by using the abandoned wind power for heat storage, and the new energy consumption rate is increased to 95%.

[0101] During peak hours, the molten salt heat release rate reaches 30% of the rated power, and the peak-valley arbitrage income increases by 40%.

[0102] During the system's full-year operation, the average load rate of coal-fired units increased to 75%, with significant energy-saving and emission-reduction benefits, reducing carbon dioxide emissions by approximately 100,000 tons annually.

[0103] The molten salt heat storage operation method finally outputs a variety of evaluation indicators.

[0104] In an optional embodiment, the output evaluation index may be a combination of economy, technology, environment and reliability, specifically:

[0105] Economic indicators, namely, peak-valley arbitrage income (yuan) and new energy consumption income (yuan);

[0106] Technical indicators, namely, system peak-shaving capacity (MW) and molten salt utilization rate (%);

[0107] Environmental indicators, i.e., carbon dioxide emission reduction (tons);

[0108] Reliability indicators, namely system response time (seconds) and equipment life loss rate (%);

[0109] Optimization process

[0110] During off-peak hours (electricity price 0.3 yuan / kWh), the heat storage cost is reduced to 0.08 yuan / kWh, and the new energy consumption rate reaches 92%.

[0111] During peak hours (electricity price 1.2 yuan / kWh), the molten salt heat release rate reaches 30% of the rated power, and the system's peak-shaving capacity is increased to 40% of the rated capacity.

[0112] Economic efficiency: Peak-valley arbitrage income is 2.8 million yuan / year, and new energy consumption income is 1.5 million yuan / year.

[0113] Technical characteristics: The molten salt utilization rate is 85%, and the system peak-shaving capacity is 120MW.

[0114] Environmental: Annual carbon dioxide emissions were reduced by 85,000 tons.

[0115] Reliability: System response time is 8 seconds and equipment life loss rate is 5%.

[0116] In summary, a model was constructed to optimize the molten salt properties, boundary conditions, heat storage system, and heat storage and release schemes. These molten salt properties, boundary conditions, and heat storage system models provide constraints and application scenarios for optimizing the system's heat storage and release. The heat storage scheme of the heat storage system fully integrates the heat storage characteristics of each heat storage unit, efficiently storing heat from the coal-fired unit and absorbing renewable energy power. During the release phase, the MIQP (Mixed Integer Quadratic Programming) algorithm is used to optimize the calculations, ensuring that each heat storage unit provides heat to the coal-fired unit in a timely and appropriate manner, with the goal of maximizing power plant revenue. The optimization algorithm ultimately outputs the optimal heat storage system release and storage schemes, as well as the coal-fired unit operation plan. The algorithm also outputs evaluation metrics for peak-shaving capacity, peak-shaving depth, thermal efficiency, coal consumption rate, and power plant revenue for user reference. Based on the above method, a molten salt heat storage system with high thermal efficiency and peak-shaving flexibility is proposed. This invention significantly improves the peak-shaving flexibility and thermal efficiency of coal-fired units coupled with a molten salt heat storage system.

[0117] Example 2

[0118] Based on the first embodiment, this embodiment further provides a molten salt heat storage operation system, including an optimization algorithm module, a heat storage scheme module, a molten salt physical property model module, and an evaluation index module;

[0119] The optimization algorithm module is responsible for calculating and optimizing the operation plan of the heat storage system, including the heat storage and heat release plans and the operation plan of the coal-fired unit;

[0120] The heat storage solution module involves specific operations in the heat storage phase, including using renewable energy electricity to supplement the remaining capacity of the heat storage unit and determining the heat storage capacity of each heat storage unit based on the remaining capacity ratio;

[0121] The molten salt physical property model module provides a functional relationship between the remaining capacity of the molten salt and the heat release rate, heat storage rate and electrical heat storage rate, which is used to optimize the heat storage and heat release schemes;

[0122] The evaluation index module is used to finally output a variety of evaluation indicators.

[0123] This embodiment also provides a computer device suitable for the molten salt heat storage operation method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the molten salt heat storage operation method proposed in the above embodiment.

[0124] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0125] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing molten salt heat storage operation proposed in the above embodiment is implemented.

[0126] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0127] Example 3

[0128] Based on the first two embodiments, refer to Figure 3 and Figure 4 shown.

[0129] Figure 3 The steam source for the heat storage system is provided. Numerous extraction nodes are available for the heat storage process, including main steam, reheat steam, high-pressure cylinder exhaust, and intermediate-pressure cylinder exhaust. Return water can flow back to the deaerator. Similarly, the heat release process involves water coming from the deaerator, generating steam that enters the high-pressure, intermediate-pressure, or low-pressure cylinders. The corresponding heat storage unit is activated by opening the corresponding solenoid valve based on the operating plan derived from the system optimization algorithm.

[0130] Heat storage process: The steam flows into several paths through the solenoid valve of the steam flow channel of the heat storage unit, and then enters the molten salt-steam heat storage heat exchanger to exchange heat with the low-temperature molten salt. The steam turns into water and enters the deaerator. The low-temperature molten salt heats up and turns into high-temperature molten salt and enters the high-temperature molten salt tank.

[0131] Electric heating process: The low-temperature molten salt flows out of the low-temperature molten salt tank and flows through the solenoid valve of the molten salt flow channel of the electric heater. After being heated by the electric heater, it becomes high-temperature molten salt and then enters the high-temperature molten salt tank.

[0132] Heat release process: The water in the deaerator is divided into several paths and passes through the return water solenoid valve of the molten salt-steam heat exchanger. Then it enters the molten salt-steam heat exchanger to exchange heat with high-temperature molten salt. The water turns into steam and enters the steam turbine. The high-temperature molten salt is cooled down to become low-temperature molten salt and enters the low-temperature molten salt tank.

[0133] A brief introduction to the MIQP model can be used as a reference:

[0134] Since 0-1 quadratic programming problems are generally NP-h, it's impossible to find a good, efficient algorithm. Therefore, enumeration is the only solution. However, the enormous computational complexity of enumeration presents an insurmountable challenge in practical applications. To maximize computational efficiency, reducing the number of enumerations is a crucial issue. Currently, the branch-and-bound method, proposed by Balas and Dakin, is commonly used in programming to solve 0-1 quadratic mixed programming problems. By bounding certain subproblems, unnecessary branches can be removed, effectively improving computational efficiency. Therefore, the bounding method is crucial. If it is well-defined and sufficiently close to the true optimal solution, the algorithm can eliminate many unnecessary computational branches, resulting in high computational efficiency. Furthermore, it can determine how far a feasible solution is from the optimal solution and whether it is an acceptable approximation.

[0135] The lagrange relaxation method is a basic bounding method. In order to describe the process of the Lagrange relaxation method, the following optimization problem is considered:

[0136] The Lagrange relaxation method will relax some constraints. For the problem (P) just now, we relax the difficult constraint g(x) and multiply it by the coefficient u and add it to the objective function of the original problem.

[0137] is a Lagrange function, but since some constraints are removed, the solution to the Lagrange subproblem may not be a feasible solution to the original problem. This leads to an improved solution method for this problem.

[0138] Lagrange relaxation can be used to find a lower bound on an optimization problem. Therefore, applying this method to mixed programming, relaxing the integer constraints, can yield a continuous programming problem. This is a method for obtaining a lower bound on the problem. This provides a path to solving integer programming problems. We can relax some of the integer constraints and use subgradient methods to solve a series of relaxed continuous programming problems. The original problem is often much more difficult than the relaxed problem.

[0139] Since the explicit expression of the Lagrange function L(u) is generally unknown, the general explicit function optimization method cannot be used to calculate the optimal value of L(u). However, the gradient of L(u) at u can be obtained, so the following subgradient method is used: In dealing with the Lagrange multiplier problem, suppose we are at a point u, whose Lagrange function L(u) = min{f(x)+u T The solution of g(x)|x∈X} is x 0 Then the gradient of L(u) at u is g(x 0 ).

[0140] In solving the optimization problem of "nonlinear objective function f(x)", the following method is often used: define the gradient ▽f(x)=(δf / δx1,δf / δx2,δf / δx3,…δf / δx n ), and from advanced mathematics we know that: when θ→0, (f(x+θ·d)-f(x)) / θ=▽f(x)·d, so if we choose a direction d so that ▽f(x)·d>0, and take a sufficiently small step θ in that direction, that is, x becomes x+θ·d, this will make the objective function rise. This type of method is called the gradient method in nonlinear problems.

[0141] Here θ is the step size, which represents the length of u's movement in the gradient direction. According to the definition of subgradient, the change of u is related to g(x) as follows:

[0142] gi(x) = 0, then the i-th component of u is fixed;

[0143] If gi(x)<0, reduce the i-th component of u;

[0144] If gi(x)>0, then increase the i-th component of u;

[0145] If u0 is taken as the initial point, according to u k+1 =u k +θ·g(x k ) This recursive formula determines the optimal solution of each uk,xk Lagrange subproblem when u=uk, θ k is the step length taken at the kth step.

[0146] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A molten salt heat storage operation method, characterized in that: The operation plan of the heat storage system is determined through optimization algorithm to adapt to changes in electricity load and improve the adjustment flexibility and efficiency of the heat storage system.

2. The molten salt heat storage operation method according to claim 1, characterized in that: The optimization algorithm calculates and obtains a heat release plan of the heat storage system, a heat storage plan of the heat storage system, and an operation plan of the coal-fired unit.

3. The molten salt heat storage operation method according to claim 2, characterized in that: The heat storage scheme of the thermal storage system includes using renewable energy electricity to supplement the remaining capacity of the thermal storage unit; The heat storage capacity of each heat storage unit is determined based on the remaining capacity ratio, and the benefits of the heat storage stage are calculated.

4. The molten salt heat storage operation method according to claim 3, characterized in that: The heat storage system heat release scheme includes: The load is allocated to the coal-fired unit and the heat storage unit according to the electrical load and unit parameters, and the maximum load of each heat storage unit is reset according to the functional relationship between the remaining capacity of the molten salt and the heat release rate, so as to reduce the cost of the heat release stage and calculate the benefits of the heat release stage.

5. The molten salt heat storage operation method according to claim 4, characterized in that: The optimization algorithm is established based on the molten salt physical property model; The molten salt physical property model includes a functional relationship between the molten salt remaining capacity and the heat release rate, a functional relationship between the molten salt remaining capacity and the heat storage rate, and a functional relationship between the molten salt remaining capacity and the electrical heat storage rate; The molten salt physical property model is used to optimize heat storage and heat release schemes.

6. The molten salt heat storage operation method according to claim 5, characterized in that: The optimization algorithm also takes into account boundary conditions; The boundary conditions include any one or more of regional electricity prices, coal prices, coal calorific value, new energy power, ambient temperature, electric load and unit parameters.

7. The molten salt heat storage operation method according to claim 6, characterized in that: The molten salt heat storage operation method finally outputs a variety of evaluation indicators.

8. A molten salt heat storage operation system, based on the molten salt heat storage operation method according to any one of claims 1 to 7, characterized in that: It includes optimization algorithm module, heat storage scheme module, molten salt physical property model module, and evaluation index module; The optimization algorithm module is responsible for calculating and optimizing the operation plan of the heat storage system, including the heat storage and heat release plans and the operation plan of the coal-fired unit; The heat storage solution module involves specific operations in the heat storage phase, including using renewable energy electricity to supplement the remaining capacity of the heat storage unit and determining the heat storage capacity of each heat storage unit based on the remaining capacity ratio; The molten salt physical property model module provides a functional relationship between the remaining capacity of the molten salt and the heat release rate, heat storage rate and electrical heat storage rate, which is used to optimize the heat storage and release schemes; The evaluation index module is used to finally output a variety of evaluation indicators.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the molten salt heat storage operation method according to any one of claims 1 to 7 are implemented.

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

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