Multi-stage heat release regulation optimization method and system based on steam extraction energy storage

Abstract

The invention provides a multi-stage heat release regulation optimization method and system based on steam extraction energy storage, and relates to the technical field of heat regulation, and the method comprises the steps: building a dynamic thermodynamic model of a heat release process of a steam extraction energy storage system containing a plurality of state variables of heat release power, heat release time, heat release efficiency and temperature change of a heat storage water tank; establishing a multi-objective optimization model by taking minimization of total energy consumption of the system and minimization of entropy production of the heat exchanger as optimization objectives and combining constraint conditions corresponding to the optimization variables; solving the multi-objective optimization model by adopting a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm, and determining an optimal regulation strategy set of the multi-stage heat release process of the steam extraction energy storage system; the valve position opening degree of the steam extraction electric adjusting valve and the water supply flow of the steam extraction temperature and pressure reduction device are controlled through the steam extraction energy storage control system, optimized adjustment of steam extraction parameters is achieved, meanwhile, the rotating speed of the cold source side water supply and return pump is controlled, and optimized matching of water supply and return parameters is achieved.

Description

Technical Field

[0001] The invention relates to a heat regulation technology, and in particular to a multi-stage heat release regulation optimization method and system based on steam extraction energy storage. Background Art

[0002] As the proportion of renewable energy power generation continues to increase, the power grid has put forward higher requirements for the peak and frequency regulation capabilities of conventional thermal power units. Extracted steam energy storage technology adds heat storage devices to cogeneration units, uses the surplus electricity during low load periods of the power grid to store heat, releases the stored heat during peak load periods, and increases the output of steam turbine units. It is an effective way to improve the flexibility and economy of thermal power units.

[0003] The existing extraction steam energy storage system has the following problems during the heat release process: (1) It is difficult to accurately adjust the extraction steam parameters and the supply and return water parameters, resulting in reduced heat release efficiency; (2) The peak and frequency regulation requirements of the steam turbine units are not fully considered, and there is a lack of methods to optimize the heat release strategy according to the grid demand; (3) The optimization control of the multi-stage heat release process is highly complex, and there is a lack of global optimization control solutions.

[0004] In summary, the existing technology has shortcomings in the multi-stage heat release regulation optimization of the steam extraction energy storage system. There is an urgent need for a global optimization control method that can adaptively select the best regulation strategy according to the peak and frequency regulation requirements of the power grid, coordinately optimize the steam extraction parameters and the supply and return water parameters, and achieve safe and efficient heat release. Summary of the invention

[0005] The embodiments of the present invention provide a multi-stage heat release regulation optimization method and system based on steam extraction energy storage, which can solve the problems in the prior art.

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

[0007] Provides a multi-stage heat release regulation optimization method based on extraction steam storage, including:

[0008] According to the heat transfer process with a finite temperature difference between the heat source, cold source and the heat exchanger wall of the steam turbine unit, the convective heat transfer coefficient between the heat source, cold source and the heat exchanger wall and the thermal conductivity of the heat exchanger wall are introduced to determine the convective heat transfer, heat conduction and external heat dissipation losses inside the heat exchanger, and the hot water storage tank is simplified into a lumped parameter model to establish a dynamic thermodynamic model of the heat release process of the extraction steam storage system including multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the hot water storage tank;

[0009] According to the dynamic thermodynamic model of the heat release process of the extraction steam storage system, minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger are taken as optimization objectives, the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage are taken as optimization variables, and combined with the constraint conditions corresponding to the optimization variables, a multi-objective optimization model is established; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the optimal regulation strategy set for the multi-stage heat release process of the extraction steam storage system is determined;

[0010] According to the peak-shaving and frequency-regulating requirements of the steam turbine unit, the best regulation strategy is selected from the optimal regulation strategy set, and the valve position opening of the steam extraction electric regulating valve and the feed water flow of the steam extraction temperature and pressure reduction device are controlled through the steam extraction energy storage control system to achieve optimal regulation of the steam extraction parameters. At the same time, the speed of the supply and return water pumps on the cold source side is controlled to achieve optimal matching of the supply and return water parameters, so that the steam extraction energy storage system always maintains the best operating condition during the multi-stage heat release process.

[0011] In an optional embodiment,

[0012] According to the heat transfer process with a finite temperature difference between the heat source, cold source and the wall of the heat exchanger of the steam turbine unit, the convective heat transfer coefficient between the heat source, cold source and the wall of the heat exchanger and the thermal conductivity of the wall of the heat exchanger are introduced to determine the convective heat transfer, heat conduction and external heat dissipation losses inside the heat exchanger, and simplify the hot water storage tank into a lumped parameter model. The dynamic thermodynamic model of the heat release process of the extraction steam storage system including multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the hot water storage tank is established, including:

[0013] Based on the finite-time thermodynamics theory, the heat transfer process with a finite temperature difference between the heat source, the cold source and the heat exchanger wall is considered, and the energy balance model and entropy balance model describing the heat release process are established;

[0014] The convective heat transfer coefficient between the heat source, the cold source and the heat exchanger wall and the thermal conductivity of the heat exchanger wall are introduced to establish a heat transfer balance model that describes the heat transfer process between the heat source, the cold source and the heat exchanger wall; the above heat transfer balance model is combined with the energy balance model and the entropy balance model to construct a comprehensive heat release model that reflects the inherent relationship between heat release power, heat release efficiency and heat release time;

[0015] Based on the thermal resistance theory, the various heat transfer forms of convection heat transfer, heat conduction and external heat dissipation loss in the heat exchanger are comprehensively considered, and a non-steady-state heat transfer partial differential equation describing the three-dimensional temperature distribution inside the heat exchanger is established; and combined with the preset initial conditions and boundary conditions, a temperature heat loss model describing the dynamic temperature distribution and heat dissipation loss inside the heat exchanger is constructed;

[0016] The hot water storage tank is simplified into a lumped parameter model. The heat absorbed by the hot water storage tank from the heat exchanger, the heat loss of the hot water storage tank, and the heat supplied by the hot water storage tank to the load are comprehensively considered to establish a water tank temperature change model that describes the dynamic change of the water temperature in the hot water storage tank.

[0017] Corresponding model weights are assigned to the heat release comprehensive model, the temperature heat loss model and the water tank temperature change model, respectively, and a dynamic thermodynamic model of the heat release process of the extraction steam storage system is established according to preset coupling conditions.

[0018] In an optional embodiment,

[0019] The energy balance model is shown in the following formula:

[0020]

[0021] The entropy balance model is shown in the following formula:

[0022]

[0023] In the formula, and are the heat transfer rates of the heat source and the cold source, respectively, is the output power of the heat release process, E is the total energy inside the heat exchanger, S is the total entropy inside the heat exchanger, T h and T c are the temperatures of the heat source and the cold source, respectively. is the entropy generation rate of the exothermic process, and t is the exothermic time;

[0024] The heat release comprehensive model reflecting the intrinsic relationship between heat release power, heat release efficiency and heat release time is constructed as shown in the following formula:

[0025]

[0026] A h and A c are the heat exchange areas on the heat source side and the cold source side, A w is the cross-sectional area of ​​the heat exchanger wall, δ is the thickness of the heat exchanger wall, T w,h and T w,c are the temperatures of the heat source side and the cold source side of the heat exchanger wall, is the heat transfer rate of the heat exchanger wall;

[0027] m is the mass of the working fluid in the heat exchanger, c p is the specific heat capacity of the working fluid;

[0028] The water tank temperature change model describing the dynamic change of water temperature in the hot water storage tank is established as shown in the following formula:

[0029]

[0030] in, Indicates the amount of heat absorbed by the water tank from the heat exchanger. Indicates the heat loss of the water tank and Indicates the heat supplied by the water tank to the load, T tank It represents the water temperature in the water tank, and M is the mass of the water in the water tank.

[0031] In an optional embodiment,

[0032] According to the dynamic thermodynamic model of the heat release process of the extraction steam storage system, minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger are taken as the optimization objectives, and the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage are taken as optimization variables, and combined with the constraint conditions corresponding to the optimization variables, a multi-objective optimization model is established; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the optimal regulation strategy set for the multi-stage heat release process of the extraction steam storage system is determined to include:

[0033] According to the dynamic thermodynamic model of the heat release process of the extraction steam energy storage system, minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger are taken as optimization objectives, and the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage are taken as optimization variables, and combined with the constraint conditions corresponding to the optimization variables, a multi-objective optimization model is established; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model;

[0034] The improved non-dominated sorting genetic algorithm comprises: randomly initializing a population including a plurality of individuals, each individual representing a set of feasible optimization variable combinations; performing fast non-dominated sorting and crowding calculation on the current population to obtain the non-dominated level and crowding distance of each individual; selecting individuals from the current population as the parent population according to the non-dominated level and crowding distance by using a binary tournament selection operator; performing simulated binary crossover and polynomial mutation on the parent population to generate new offspring individuals and construct a offspring population; merging the parent population and the offspring population, performing fast non-dominated sorting and crowding calculation on the merged population, and selecting excellent individuals as the new population; repeating iterations until a termination condition is met, and outputting the first non-dominated solution set as the optimization result of the improved non-dominated sorting genetic algorithm;

[0035] The multi-objective particle swarm optimization algorithm includes: taking the first non-dominated solution set as the initial population, and the position of each particle corresponds to a set of feasible optimization variable combinations; calculating the fitness value of each particle, that is, the value of the multi-objective optimization model, and updating the individual optimal position and the global optimal position of each particle; according to the individual optimal position and the global optimal position of the particle, using the speed update formula and the position update formula, updating the speed and position of each particle; performing boundary processing on the updated particle position to ensure that each optimization variable is within its definition domain; repeating the iteration until the termination condition is met, and outputting the second non-dominated solution set as the optimization result of the multi-objective particle swarm optimization algorithm;

[0036] The first non-dominated solution set and the second non-dominated solution set are combined to obtain a final non-dominated solution set as an optimal regulation strategy set for the multi-stage heat release process of the extraction steam storage system.

[0037] In an optional embodiment,

[0038] According to the dynamic thermodynamic model of the heat release process of the extraction steam energy storage system, with minimizing the total energy consumption of the system and minimizing the entropy generation of the heat exchanger as the optimization objectives, the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage as the optimization variables, and combined with the constraints corresponding to the optimization variables, a multi-objective optimization model is established, including:

[0039] The multi-objective optimization model includes:

[0040]

[0041] Among them, f1 and f2 are the total energy consumption of the system and the total entropy generation of the heat exchanger, respectively, N represents the number of stages of the heat release process, and L i 、h i , Δt i , They represent the steam extraction flow rate, steam extraction enthalpy, heat release time and heat exchange amount on the cold source side in the i-th stage respectively. represents the entropy generation rate of the heat exchanger in the i-th stage;

[0042] The constraints corresponding to the optimization variables include:

[0043]

[0044] in, Respectively represent the upper and lower limits of the extraction enthalpy value, Respectively represent the upper and lower limits of the extraction steam flow rate, They represent the upper and lower limits of the heat release time respectively.

[0045] In an optional embodiment,

[0046] According to the peak and frequency regulation requirements of the steam turbine unit, the best regulation strategy is selected from the optimal regulation strategy set, and the valve position opening of the steam extraction electric regulating valve and the feed water flow of the steam extraction temperature and pressure reduction device are controlled through the steam extraction energy storage control system to achieve optimal regulation of the steam extraction parameters. At the same time, the speed of the supply and return water pumps on the cold source side is controlled to achieve optimal matching of the supply and return water parameters, so that the steam extraction energy storage system always maintains the best working condition during the multi-stage heat release process, including:

[0047] Through the communication interface with the power grid dispatching center, the peak-shaving and frequency-regulating requirements of the steam turbine unit are obtained in real time, and the peak-shaving and frequency-regulating requirements include target output, output change rate and regulation duration information, which are used as the basis for selecting the best regulation strategy; the applicability of each regulation strategy in the best regulation strategy set is evaluated according to the peak-shaving and frequency-regulating requirements, and the candidate regulation strategies that meet the peak-shaving and frequency-regulating requirements are screened out; the regulation strategy with the best comprehensive performance among the candidate regulation strategies is used as the best regulation strategy, and the comprehensive performance includes at least one performance indicator among heat release efficiency, response speed and stability;

[0048] According to the selected optimal regulation strategy, the time series of the valve position opening of the extraction steam electric regulating valve, the feed water flow of the extraction steam temperature and pressure reduction device, and the speed of the supply and return water pump on the cold source side are generated as the control instructions of the extraction steam energy storage control system;

[0049] The extraction steam energy storage control system adjusts the valve position opening of the extraction steam electric regulating valve through the PID control method based on adaptive gain according to the control instructions received, and adjusts the feed water flow of the extraction steam cooling and pressure reducing device through the fuzzy PID control method; the speed of the supply and return water pump on the cold source side is adjusted in real time by using the model predictive control algorithm, and the optimal speed sequence in the future several control cycles is obtained by solving the optimization problem including the dynamic model of pump end flow and speed and speed constraint, so as to achieve the optimal matching of supply and return water parameters with extraction steam parameters, so that the extraction steam energy storage system always maintains the best working condition during the multi-stage heat release process.

[0050] In an optional embodiment,

[0051] The method further comprises:

[0052] During the operation of the extraction steam storage system, the changes of the extraction steam parameters and the supply and return water parameters are monitored in real time, the actual values ​​are compared with the predicted values ​​of the optimal regulation strategy, and the tracking error is calculated;

[0053] When the tracking error exceeds the preset threshold, the feedback correction mechanism is triggered to adjust the control sequence of the optimal regulation strategy. At the same time, the operating status of the extraction steam storage system and the health status of key equipment are monitored, and potential fault hazards are diagnosed and handled in a timely manner to ensure that the extraction steam storage system always runs along the optimal control trajectory.

[0054] According to a second aspect of the embodiments of the present invention,

[0055] Provide a multi-stage heat release regulation optimization system based on extraction steam storage, including:

[0056] The first unit is used to determine the convective heat transfer coefficient between the heat source, the cold source and the heat exchanger wall and the thermal conductivity of the heat exchanger wall according to the heat transfer process with a finite temperature difference between the heat source, the cold source and the heat exchanger wall of the steam turbine unit, and to simplify the heat storage tank into a lumped parameter model, and to establish a dynamic thermodynamic model of the heat release process of the extraction steam storage system including multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the heat storage tank;

[0057] The second unit is used to establish a multi-objective optimization model based on the dynamic thermodynamic model of the heat release process of the extraction steam storage system, with minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger as the optimization objectives, with the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage as the optimization variables, and combined with the constraint conditions corresponding to the optimization variables; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model, and determine the optimal adjustment strategy set for the multi-stage heat release process of the extraction steam storage system;

[0058] The third unit is used to select the best regulation strategy from the optimal regulation strategy set according to the peak-shaving and frequency-regulating requirements of the steam turbine set, and control the valve position opening of the steam extraction electric regulating valve and the feed water flow of the steam extraction temperature and pressure reduction device through the steam extraction energy storage control system to achieve optimal regulation of the steam extraction parameters. At the same time, the speed of the supply and return water pumps on the cold source side is controlled to achieve optimal matching of the supply and return water parameters, so that the steam extraction energy storage system always maintains the best operating condition during the multi-stage heat release process.

[0059] According to a third aspect of the embodiments of the present invention,

[0060] An electronic device is provided, comprising:

[0061] processor;

[0062] a memory for storing processor-executable instructions;

[0063] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0064] According to a fourth aspect of the embodiments of the present invention,

[0065] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0066] This application constructs a dynamic thermodynamic model of the heat release process of the extraction steam energy storage system, comprehensively considers the thermodynamic characteristics and mutual coupling relationships of various components of the system, and can accurately reflect the dynamic heat release characteristics of the extraction steam energy storage system under variable operating conditions and variable parameters, providing reliable theoretical guidance for the optimal design and operation control of the system.

[0067] The optimal regulation strategy generation method proposed in this paper has the following advantages: (1) It adopts a multi-objective optimization model and comprehensively considers the system efficiency and thermodynamic performance, making the optimization results more comprehensive and reliable; (2) It introduces a hybrid optimization strategy and utilizes the advantages of the improved non-dominated sorting genetic algorithm and the multi-objective particle swarm optimization algorithm to improve the optimization efficiency and the quality of the solution; (3) The generated optimal regulation strategy set provides a flexible and optional strategy combination for practical engineering applications and has strong applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is a flow chart of a multi-stage heat release regulation optimization method based on extraction steam energy storage according to an embodiment of the present invention;

[0069] Figure 2 It is a structural schematic diagram of a multi-stage heat release regulation optimization system based on steam extraction energy storage according to an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0072] Figure 1 FIG. 1 is a flow chart of a multi-stage heat release regulation optimization method based on steam extraction energy storage according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0073] S101. According to the heat transfer process with a finite temperature difference between the heat source, the cold source and the wall of the heat exchanger of the steam turbine unit, the convective heat transfer coefficient between the heat source, the cold source and the wall of the heat exchanger and the thermal conductivity of the wall of the heat exchanger are introduced to determine the convective heat transfer, heat conduction and external heat dissipation losses inside the heat exchanger, and simplify the hot water storage tank into a lumped parameter model to establish a dynamic thermodynamic model of the heat release process of the extraction steam storage system including multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the hot water storage tank;

[0074] S102. According to the dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger are taken as optimization objectives, the steam extraction pressure, steam extraction temperature, steam extraction flow rate and heat release time of each stage are taken as optimization variables, and combined with the constraint conditions corresponding to the optimization variables, a multi-objective optimization model is established; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the optimal adjustment strategy set of the multi-stage heat release process of the steam extraction energy storage system is determined;

[0075] S103. According to the peak-shaving and frequency-regulating requirements of the steam turbine unit, the best regulation strategy is selected from the optimal regulation strategy set, and the valve position opening of the steam extraction electric regulating valve and the feed water flow of the steam extraction temperature and pressure reduction device are controlled through the steam extraction energy storage control system to achieve optimal regulation of the steam extraction parameters. At the same time, the speed of the supply and return water pumps on the cold source side is controlled to achieve optimal matching of the supply and return water parameters, so that the steam extraction energy storage system always maintains the best operating condition during the multi-stage heat release process.

[0076] In an optional embodiment,

[0077] According to the heat transfer process with a finite temperature difference between the heat source, cold source and the wall of the heat exchanger of the steam turbine unit, the convective heat transfer coefficient between the heat source, cold source and the wall of the heat exchanger and the thermal conductivity of the wall of the heat exchanger are introduced to determine the convective heat transfer, heat conduction and external heat dissipation losses inside the heat exchanger, and simplify the hot water storage tank into a lumped parameter model. The dynamic thermodynamic model of the heat release process of the extraction steam storage system including multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the hot water storage tank is established, including:

[0078] Based on the finite-time thermodynamics theory, the heat transfer process with a finite temperature difference between the heat source, the cold source and the heat exchanger wall is considered, and the energy balance model and entropy balance model describing the heat release process are established;

[0079] The convective heat transfer coefficient between the heat source, the cold source and the heat exchanger wall and the thermal conductivity of the heat exchanger wall are introduced to establish a heat transfer balance model that describes the heat transfer process between the heat source, the cold source and the heat exchanger wall; the above heat transfer balance model is combined with the energy balance model and the entropy balance model to construct a comprehensive heat release model that reflects the inherent relationship between heat release power, heat release efficiency and heat release time;

[0080] Based on the thermal resistance theory, the various heat transfer forms of convection heat transfer, heat conduction and external heat dissipation loss in the heat exchanger are comprehensively considered, and a non-steady-state heat transfer partial differential equation describing the three-dimensional temperature distribution inside the heat exchanger is established; and combined with the preset initial conditions and boundary conditions, a temperature heat loss model describing the dynamic temperature distribution and heat dissipation loss inside the heat exchanger is constructed;

[0081] The hot water storage tank is simplified into a lumped parameter model. The heat absorbed by the hot water storage tank from the heat exchanger, the heat loss of the hot water storage tank, and the heat supplied by the hot water storage tank to the load are comprehensively considered to establish a water tank temperature change model that describes the dynamic change of the water temperature in the hot water storage tank.

[0082] Corresponding model weights are assigned to the heat release comprehensive model, the temperature heat loss model and the water tank temperature change model, respectively, and a dynamic thermodynamic model of the heat release process of the extraction steam storage system is established according to preset coupling conditions.

[0083] In an optional embodiment,

[0084] The energy balance model is shown in the following formula:

[0085]

[0086] The entropy balance model is shown in the following formula:

[0087]

[0088] In the formula, and are the heat transfer rates of the heat source and the cold source, respectively, is the output power of the heat release process, E is the total energy inside the heat exchanger, S is the total entropy inside the heat exchanger, T h and T c are the temperatures of the heat source and the cold source, respectively. is the entropy generation rate of the exothermic process, and t is the exothermic time;

[0089] The heat release comprehensive model reflecting the intrinsic relationship between heat release power, heat release efficiency and heat release time is constructed as shown in the following formula:

[0090]

[0091] A h and Ac are the heat exchange areas on the heat source side and the cold source side, A w is the cross-sectional area of ​​the heat exchanger wall, δ is the thickness of the heat exchanger wall, T w,h and T w,c are the temperatures of the heat source side and the cold source side of the heat exchanger wall, is the heat transfer rate of the heat exchanger wall;

[0092] m is the mass of the working fluid in the heat exchanger, c p is the specific heat capacity of the working fluid;

[0093] The water tank temperature change model describing the dynamic change of water temperature in the hot water storage tank is established as shown in the following formula:

[0094]

[0095] in, Indicates the amount of heat absorbed by the water tank from the heat exchanger. Indicates the heat loss of the water tank and Indicates the heat supplied by the water tank to the load, T tank It represents the water temperature in the water tank, and M is the mass of the water in the water tank.

[0096] For example, after determining the optimal extraction position and extraction steam parameters on the heat source side, as well as the supply and return water temperature and pressure and other parameters on the cold source side, this paper constructs a dynamic thermodynamic model of the heat release process of the extraction steam storage system based on the finite-time thermodynamics theory and thermal resistance theory, which comprehensively considers the heat release power, heat release efficiency, heat release time, heat exchanger temperature distribution, heat dissipation loss and temperature change of the hot water storage tank.

[0097] First, consider the heat transfer process with a finite temperature difference between the heat source (extraction steam), the cold source (hot water supply) and the heat exchanger wall. According to the law of conservation of energy and the second law of thermodynamics, the energy balance model and entropy balance model describing the heat release process are established as follows:

[0098] Energy balance model:

[0099]

[0100] Entropy balance model:

[0101]

[0102] In the formula, and are the heat transfer rates of the heat source and the cold source, respectively, is the output power of the heat release process, E is the total energy inside the heat exchanger, S is the total entropy inside the heat exchanger, T h and T c are the temperatures of the heat source and the cold source, respectively. is the entropy generation rate of the exothermic process, and t is the exothermic time.

[0103] On the basis of the energy balance model and the entropy balance model, the convective heat transfer coefficient h between the heat source and the heat exchanger wall and between the cold source and the heat exchanger wall is introduced. h and h c , and the thermal conductivity λ of the heat exchanger wall, a heat transfer balance model describing the heat transfer process between the heat source, the cold source and the heat exchanger wall is established as follows:

[0104]

[0105] In the formula, A h and A c are the heat exchange areas on the heat source side and the cold source side, A w is the cross-sectional area of ​​the heat exchanger wall, δ is the thickness of the heat exchanger wall, T w,h and T w,c are the temperatures of the heat source side and the cold source side of the heat exchanger wall, is the heat transfer rate of the heat exchanger wall.

[0106] By combining the above heat transfer balance model with the energy balance model and the entropy balance model, and performing appropriate mathematical transformation and simplification, we can get the formula that reflects the heat release power. The comprehensive heat release model with the intrinsic relationship between heat release efficiency η and heat release time t is as follows:

[0107]

[0108] In the formula, m is the mass of the working fluid in the heat exchanger, c is p is the specific heat capacity of the working fluid.

[0109] The heat release comprehensive model reveals the intrinsic relationship between power, efficiency and time in the heat release process of the extraction steam storage system, and provides a theoretical basis for analyzing and optimizing the heat release performance of the system. It should be pointed out that in the actual extraction steam storage system, the structure and heat transfer characteristics of the heat exchanger are relatively complex. Although the heat release comprehensive model has been simplified to a certain extent, it can still better reflect the dynamic heat release characteristics of the system, which is of great significance for guiding the design and operation of the system.

[0110] In addition, in order to more comprehensively and accurately describe the heat release process of the extraction steam storage system, it is necessary to further consider the three-dimensional temperature distribution and dynamic heat dissipation loss inside the heat exchanger. Based on the thermal resistance theory, considering the various heat transfer forms such as convection heat transfer, heat conduction and external heat dissipation loss inside the heat exchanger, the non-steady-state heat transfer partial differential equation describing the temperature distribution T(x, y, z, t) inside the heat exchanger can be established, as shown below:

[0111]

[0112] Where ρ is the density of the heat exchanger wall, is the internal heat source term of the heat exchanger wall, reflecting the heat dissipation loss between the heat exchanger and the outside world. (x, y, z) represents the three-dimensional spatial coordinates of any point inside the heat exchanger.

[0113] For heat loss It can be described by Newton's law of cooling:

[0114]

[0115] In the formula, h loss is the convective heat transfer coefficient of the outer surface of the heat exchanger, A loss is the outer surface area of ​​the heat exchanger, T w is the average temperature of the heat exchanger wall, T a is the ambient temperature.

[0116] By introducing the heat loss as an internal heat source term into the heat transfer equation of the heat exchanger, and combining appropriate initial conditions and boundary conditions, a temperature heat loss model describing the dynamic temperature distribution and heat loss inside the heat exchanger can be constructed. This model needs to be solved by a numerical solution, which can obtain the three-dimensional temperature distribution inside the heat exchanger and the change law of heat loss over time, providing a more detailed and accurate basis for the design and optimization of the extraction steam storage system.

[0117] Finally, for the hot water storage tank, it can be simplified to a lumped parameter model, taking into account the heat absorbed by the tank from the heat exchanger Heat loss from water tank And the heat supplied by the water tank to the load Establish a description of the water temperature T in the water tank tank The dynamic water tank temperature change model is as follows:

[0118]

[0119] Where M is the mass of water in the water tank, c p is the specific heat capacity of the working fluid.

[0120] In summary, the dynamic thermodynamic model of the heat release process of the extraction steam storage system consists of three sub-models: the heat release comprehensive model, the temperature heat loss model, and the water tank temperature change model. In order to organically combine these three sub-models, it is necessary to reasonably determine the coupling conditions and boundary conditions between them. This paper adopts the weighted coupling method, that is, assigning corresponding model weights to the three sub-models respectively, and establishing a complete dynamic thermodynamic model of the heat release process of the extraction steam storage system according to the preset coupling conditions, as shown below:

[0121] minJ=w1J1+w2J2+w3J3;

[0122] Where J1, J2 and J3 are the objective functions of the heat release comprehensive model, temperature heat loss model and water tank temperature change model respectively; w1, w2 and w3 are the model weights corresponding to the three sub-models, reflecting the accuracy and complexity of each sub-model; the coupling conditions include the energy balance condition between the heat exchanger and the hot water storage tank, the temperature continuity condition, etc.

[0123] By reasonably selecting the model weights and solving the above optimization problem, the dynamic thermodynamic model of the heat release process of the extraction steam energy storage system can be obtained. This model comprehensively considers the thermodynamic characteristics and mutual coupling relationship of each component of the system, and can accurately reflect the dynamic heat release characteristics of the extraction steam energy storage system under variable operating conditions and variable parameters, providing reliable theoretical guidance for the optimal design and operation control of the system.

[0124] In an optional embodiment,

[0125] According to the dynamic thermodynamic model of the heat release process of the extraction steam storage system, minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger are taken as the optimization objectives, and the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage are taken as optimization variables, and combined with the constraint conditions corresponding to the optimization variables, a multi-objective optimization model is established; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the optimal regulation strategy set for the multi-stage heat release process of the extraction steam storage system is determined to include:

[0126] According to the dynamic thermodynamic model of the heat release process of the extraction steam energy storage system, minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger are taken as optimization objectives, and the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage are taken as optimization variables, and combined with the constraint conditions corresponding to the optimization variables, a multi-objective optimization model is established; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model;

[0127] The improved non-dominated sorting genetic algorithm comprises: randomly initializing a population including a plurality of individuals, each individual representing a set of feasible optimization variable combinations; performing fast non-dominated sorting and crowding calculation on the current population to obtain the non-dominated level and crowding distance of each individual; selecting individuals from the current population as the parent population according to the non-dominated level and crowding distance by using a binary tournament selection operator; performing simulated binary crossover and polynomial mutation on the parent population to generate new offspring individuals and construct a offspring population; merging the parent population and the offspring population, performing fast non-dominated sorting and crowding calculation on the merged population, and selecting excellent individuals as the new population; repeating iterations until a termination condition is met, and outputting the first non-dominated solution set as the optimization result of the improved non-dominated sorting genetic algorithm;

[0128] The multi-objective particle swarm optimization algorithm includes: taking the first non-dominated solution set as the initial population, and the position of each particle corresponds to a set of feasible optimization variable combinations; calculating the fitness value of each particle, that is, the value of the multi-objective optimization model, and updating the individual optimal position and the global optimal position of each particle; according to the individual optimal position and the global optimal position of the particle, using the speed update formula and the position update formula, updating the speed and position of each particle; performing boundary processing on the updated particle position to ensure that each optimization variable is within its definition domain; repeating the iteration until the termination condition is met, and outputting the second non-dominated solution set as the optimization result of the multi-objective particle swarm optimization algorithm;

[0129] The first non-dominated solution set and the second non-dominated solution set are combined to obtain a final non-dominated solution set as an optimal regulation strategy set for the multi-stage heat release process of the extraction steam storage system.

[0130] In an optional embodiment,

[0131] According to the dynamic thermodynamic model of the heat release process of the extraction steam energy storage system, with minimizing the total energy consumption of the system and minimizing the entropy generation of the heat exchanger as the optimization objectives, the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage as the optimization variables, and combined with the constraints corresponding to the optimization variables, a multi-objective optimization model is established, including:

[0132] The multi-objective optimization model includes:

[0133]

[0134] Among them, f1 and f2 are the total energy consumption of the system and the total entropy generation of the heat exchanger, respectively, N represents the number of stages of the heat release process, and L i 、h i , Δt i , They represent the steam extraction flow rate, steam extraction enthalpy, heat release time and heat exchange amount on the cold source side in the i-th stage respectively. represents the entropy generation rate of the heat exchanger in the i-th stage;

[0135] The constraints corresponding to the optimization variables include:

[0136]

[0137] in, Respectively represent the upper and lower limits of the extraction enthalpy value, Respectively represent the upper and lower limits of the extraction steam flow rate, They represent the upper and lower limits of the heat release time respectively.

[0138] For example, in order to achieve optimal regulation of the multi-stage heat release process of the extraction steam storage system, this paper proposes an optimal regulation strategy generation method based on a multi-objective optimization model and a hybrid optimization algorithm. The specific steps are as follows:

[0139] Firstly, according to the dynamic thermodynamic model of the heat release process of the extraction steam storage system, considering the energy conversion efficiency and thermodynamic irreversibility of the system, the minimization of the total energy consumption of the system and the minimization of the entropy generation of the heat exchanger are selected as the optimization objectives. Among them, the total energy consumption of the system includes the electrical energy and thermal energy consumed by the extraction steam storage system in the multi-stage heat release process, and the entropy generation of the heat exchanger represents the irreversible loss in the heat transfer process.

[0140] Then, the extraction pressure, extraction temperature, extraction flow rate and heat release time of each stage are taken as optimization variables, which directly affect the operating state and energy conversion efficiency of the system. At the same time, considering the limitations of actual operating conditions, set the constraints of the optimization variables, such as the upper and lower limits of the extraction pressure, the range of the extraction temperature, the boundary of the extraction flow rate and the requirements of the heat release time.

[0141] By comprehensively considering the optimization objectives and constraints, a multi-objective optimization model for the multi-stage heat release process of the extraction steam energy storage system is established. The objective function of the model is to minimize the total energy consumption of the system and minimize the entropy generation of the heat exchanger. The decision variables are the extraction pressure, extraction temperature, extraction flow rate and heat release time of each stage. The constraints cover the value range of the optimization variables and the system operation requirements.

[0142] In order to efficiently solve the established multi-objective optimization model, this paper adopts a hybrid optimization strategy that combines an improved non-dominated sorting genetic algorithm with a multi-objective particle swarm optimization algorithm. This strategy fully utilizes the advantages of the two algorithms and can speed up the optimization process and improve the quality of the solution while ensuring the diversity of the solution.

[0143] The main steps of the improved non-dominated sorting genetic algorithm are as follows:

[0144] Initialize the population randomly. Generate an initial population containing multiple individuals, each of which represents a set of feasible optimization variable combinations, namely, the values ​​of extraction pressure, extraction temperature, extraction flow rate and heat release time. For example, the initial population can contain 100 individuals, each of which consists of 20 optimization variables.

[0145] Fast non-dominated sorting and crowding calculation. Perform fast non-dominated sorting on the current population and divide the population into multiple non-dominated levels according to the dominance relationship between individuals. Individuals in the first non-dominated level are not dominated by any other individuals, and so on. Then, the crowding distance of each individual is calculated, which indicates the distribution density of individuals in the same non-dominated level. The larger the crowding distance, the sparser the space around the individual.

[0146] Selection operation. Use the binary tournament selection operator to randomly select two individuals from the current population and compare their non-dominated ranks and crowding distances. Give priority to individuals with smaller non-dominated ranks. If the non-dominated ranks are the same, select individuals with larger crowding distances. Repeat the selection operation until a parent population with the same size as the initial population is selected.

[0147] Crossover and mutation operations. Perform crossover and mutation on the parent population to generate new offspring individuals. Use the simulated binary crossover operator to randomly select two parent individuals, and decide whether to perform a crossover operation based on the crossover probability to generate two offspring individuals. The crossover probability can be set to 0.9. Then, perform polynomial mutation on the offspring individuals, and decide whether to perform a mutation operation based on the mutation probability to perturb each optimized variable of the individual. The mutation probability can be set to 0.1.

[0148] Merge population and elite retention strategy. Merge the parent population and the child population to obtain a merged population containing two generations of individuals. Perform fast non-dominated sorting and crowding calculation on the merged population, and select the top ranked individuals as the new generation population according to the elite retention strategy. The elite retention strategy ensures that excellent individuals will not be lost during the evolution process. The size of the new generation population is the same as the initial population size.

[0149] Termination condition judgment. Repeat the iteration until the preset number of iterations is reached or other termination conditions are met. The first non-dominated solution set in the final population is taken as the optimization result of the improved non-dominated sorting genetic algorithm and recorded as the first non-dominated solution set.

[0150] The main steps of the multi-objective particle swarm optimization algorithm are as follows:

[0151] Initialize the particle swarm. The first non-dominated solution set obtained by the improved non-dominated sorting genetic algorithm is used as the initial population of the particle swarm, and the position of each particle corresponds to a set of feasible optimization variable combinations. For example, if the first non-dominated solution set contains 50 solutions, the size of the initial particle swarm is 50.

[0152] Calculate the fitness value and update the individual optimal position and the global optimal position. For each particle, calculate the objective function value of the multi-objective optimization model corresponding to its position, that is, the total energy consumption of the system and the entropy production of the heat exchanger, as the fitness value of the particle. Then, compare the current fitness value of the particle with its historical optimal fitness value, and update the individual optimal position of the particle. At the same time, compare the fitness values ​​of all particles from a global perspective and update the global optimal position.

[0153] Speed ​​and position update. According to the individual optimal position and global optimal position of the particle, the speed update formula and position update formula are used to update the speed and position of each particle in the search space. The speed update formula introduces inertia weight, individual learning factor and social learning factor, which respectively represent the degree to which the particle maintains the original motion trend, learns from the individual optimal position and learns from the global optimal position. The position update formula calculates the new position of the particle at the next moment based on the current position of the particle and the updated speed.

[0154] Boundary processing. For the updated particle positions, boundary processing is performed to ensure that each optimization variable is within its domain. If a dimension of the particle exceeds the range of the domain, it is restricted to the boundary.

[0155] Termination condition judgment. Repeat the iteration until the preset number of iterations is reached or other termination conditions are met. The non-dominated solution set in the final particle swarm is taken as the optimization result of the multi-objective particle swarm optimization algorithm and recorded as the second non-dominated solution set.

[0156] The first non-dominated solution set obtained by the improved non-dominated sorting genetic algorithm and the second non-dominated solution set obtained by the multi-objective particle swarm optimization algorithm are merged, and the duplicate solutions are removed to obtain the final non-dominated solution set. This solution set contains multiple non-dominated optimization solutions, each of which corresponds to a set of optimal extraction pressure, extraction temperature, extraction flow rate and heat release time combination, forming the optimal regulation strategy set for the multi-stage heat release process of the extraction steam storage system. The optimal regulation strategy set provides the basis for subsequent strategy selection and implementation.

[0157] Through the above steps, the improved non-dominated sorting genetic algorithm and multi-objective particle swarm optimization algorithm are comprehensively used to solve the multi-objective optimization model of the optimal regulation of the multi-stage heat release process of the extraction steam storage system, and a set of optimal regulation strategies with superior performance are generated. These strategies can effectively reduce the system energy consumption and irreversible losses of the heat exchanger while meeting the system operation constraints, and improve the economy and reliability of the system.

[0158] The optimal regulation strategy generation method proposed in this paper has the following advantages: (1) It adopts a multi-objective optimization model and comprehensively considers the system efficiency and thermodynamic performance, making the optimization results more comprehensive and reliable; (2) It introduces a hybrid optimization strategy and utilizes the advantages of the improved non-dominated sorting genetic algorithm and the multi-objective particle swarm optimization algorithm to improve the optimization efficiency and the quality of the solution; (3) The generated optimal regulation strategy set provides a flexible and optional strategy combination for practical engineering applications and has strong applicability.

[0159] It should be noted that in practical applications, the objective function, constraints and algorithm parameters in the multi-objective optimization model can be appropriately adjusted according to the specific parameters and operating conditions of the extraction steam storage system to obtain more accurate and reliable optimization results. At the same time, other optimization algorithms, such as differential evolution algorithm and ant colony algorithm, can also be considered to further improve the search efficiency and optimization performance of the optimal regulation strategy.

[0160] In an optional embodiment,

[0161] According to the peak and frequency regulation requirements of the steam turbine unit, the best regulation strategy is selected from the optimal regulation strategy set, and the valve position opening of the steam extraction electric regulating valve and the feed water flow of the steam extraction temperature and pressure reduction device are controlled through the steam extraction energy storage control system to achieve optimal regulation of the steam extraction parameters. At the same time, the speed of the supply and return water pumps on the cold source side is controlled to achieve optimal matching of the supply and return water parameters, so that the steam extraction energy storage system always maintains the best working condition during the multi-stage heat release process, including:

[0162] Through the communication interface with the power grid dispatching center, the peak-shaving and frequency-regulating requirements of the steam turbine unit are obtained in real time, and the peak-shaving and frequency-regulating requirements include target output, output change rate and regulation duration information, which are used as the basis for selecting the best regulation strategy; the applicability of each regulation strategy in the best regulation strategy set is evaluated according to the peak-shaving and frequency-regulating requirements, and the candidate regulation strategies that meet the peak-shaving and frequency-regulating requirements are screened out; the regulation strategy with the best comprehensive performance among the candidate regulation strategies is used as the best regulation strategy, and the comprehensive performance includes at least one performance indicator among heat release efficiency, response speed and stability;

[0163] According to the selected optimal regulation strategy, the time series of the valve position opening of the extraction steam electric regulating valve, the feed water flow of the extraction steam temperature and pressure reduction device, and the speed of the supply and return water pump on the cold source side are generated as the control instructions of the extraction steam energy storage control system;

[0164] The extraction steam energy storage control system adjusts the valve position opening of the extraction steam electric regulating valve through the PID control method based on adaptive gain according to the control instructions received, and adjusts the feed water flow of the extraction steam cooling and pressure reducing device through the fuzzy PID control method; the speed of the supply and return water pump on the cold source side is adjusted in real time by using the model predictive control algorithm, and the optimal speed sequence in the future several control cycles is obtained by solving the optimization problem including the dynamic model of pump end flow and speed and speed constraint, so as to achieve the optimal matching of supply and return water parameters with extraction steam parameters, so that the extraction steam energy storage system always maintains the best working condition during the multi-stage heat release process.

[0165] For example, in the multi-stage heat release process of the extraction steam energy storage system, in order to adapt to the peak and frequency regulation requirements of the power grid, it is necessary to select the best regulation strategy from the optimal regulation strategy set, and optimize the steam extraction parameters and supply and return water parameters through the extraction steam energy storage control system to keep the system in the best working condition. The specific implementation steps are as follows:

[0166] First, through the communication interface with the power grid dispatching center, the peak and frequency regulation requirements of the steam turbine units are obtained in real time, including target output, output change rate, regulation duration, etc. This information will serve as an important basis for selecting the best regulation strategy.

[0167] According to the obtained peak and frequency regulation requirements, evaluate the applicability of each strategy in the optimal regulation strategy set. For each regulation strategy, consider the following factors:

[0168] Whether the target state of the strategy meets the peak load and frequency regulation requirements; whether the control sequence of the strategy can track the target state within the required time; whether the implementation of the strategy will cause the extraction steam storage system to exceed the operating constraints.

[0169] By comprehensively evaluating the above factors, candidate regulation strategies that meet the peak and frequency regulation needs are screened out.

[0170] Among the candidate regulation strategies that meet the peak and frequency regulation requirements, the performance indicators of each strategy, such as heat release efficiency, response speed, stability, etc., are further compared, and the strategy with the best comprehensive performance is selected as the optimal regulation strategy.

[0171] According to the selected optimal regulation strategy, the time series of control parameters such as the valve position opening of the steam extraction electric regulating valve, the feed water flow of the steam extraction temperature and pressure reduction device, and the speed of the supply and return water pumps on the cold source side are generated as control instructions of the steam extraction energy storage control system.

[0172] The steam extraction energy storage control system optimizes the steam extraction pressure, temperature and other parameters by adjusting the valve position opening of the steam extraction electric regulating valve and the water flow rate of the steam extraction temperature and pressure reduction device according to the control instructions received, ensuring that the steam extraction state always changes along the optimal trajectory.

[0173] Specifically, the valve position opening adjustment of the steam extraction electric control valve adopts a PID control method based on adaptive gain, and the output of the controller is the valve position opening change Δu(k):

[0174]

[0175] Where, e(k) is the difference between the set value and the actual value of the extraction steam pressure, K p (k), K i (k), K d (k) are proportional, integral and differential gains, respectively, which are adaptively adjusted according to the dynamic characteristics of the system.

[0176] The feedwater flow regulation of the extraction steam temperature reduction and pressure reduction device adopts a control method based on fuzzy PID. According to the deviation between the feedwater flow and the extraction steam temperature, the parameters of the PID controller are dynamically adjusted through fuzzy reasoning to achieve precise control of the extraction steam temperature.

[0177] In order to ensure the efficient operation of the steam extraction energy storage system, it is necessary to adjust the speed of the supply and return water pumps on the cold source side in real time according to the changes in the steam extraction parameters to achieve optimal matching of the supply and return water parameters with the steam extraction parameters.

[0178] The speed regulation of the supply and return water pumps adopts the method based on model predictive control to establish a dynamic model of pump end flow and speed:

[0179] Q(k+1)=aQ(k)+bn(k);

[0180] Among them, Q(k) and n(k) are the pump end flow and speed at time k, respectively, and a and b are model coefficients.

[0181] In each control cycle, according to the predicted value of the extraction steam flow, the following optimization problem is solved to obtain the optimal speed sequence {n(k),…,n(k+N―1)} in the next N control cycles:

[0182]

[0183] Among them, Q ref (k+i) is the predicted value of the extraction steam flow rate at time k+i, and λ is the weighting coefficient of the control increment.

[0184] The solution of the optimization problem is used as the speed control instruction of the supply and return water pumps to achieve dynamic matching of the supply and return water flow rates and the extraction steam flow rate.

[0185] Through the above steps, the selection and implementation of the optimal regulation strategy based on peak load and frequency regulation requirements are realized, so that the extraction steam storage system always maintains the optimal operating condition during the multi-stage heat release process, improves the operating efficiency and flexibility of the system, and provides strong support for the safe and stable operation of the power grid.

[0186] In an optional embodiment,

[0187] The method further comprises:

[0188] During the operation of the extraction steam storage system, the changes of the extraction steam parameters and the supply and return water parameters are monitored in real time, the actual values ​​are compared with the predicted values ​​of the optimal regulation strategy, and the tracking error is calculated;

[0189] When the tracking error exceeds the preset threshold, the feedback correction mechanism is triggered to adjust the control sequence of the optimal regulation strategy. At the same time, the operating status of the extraction steam storage system and the health status of key equipment are monitored, and potential fault hazards are diagnosed and handled in a timely manner to ensure that the extraction steam storage system always runs along the optimal control trajectory.

[0190] For example, in order to ensure that the extraction steam energy storage system always runs along the optimal control trajectory during actual operation, this paper proposes a strategy optimization method based on real-time monitoring and feedback correction. This method obtains system operation data in real time, dynamically evaluates the execution effect of the optimal regulation strategy, and adjusts the strategy and diagnoses faults based on the tracking error, thus realizing the intelligent operation and maintenance of the extraction steam energy storage system. The specific steps are as follows:

[0191] During the operation of the extraction steam energy storage system, key parameters are monitored in real time and data is collected. The main monitored parameters include extraction steam pressure, extraction steam temperature, extraction steam flow, supply water temperature, supply water pressure, return water temperature, return water pressure, etc. By installing sensors and instruments at key locations in the system, continuous measurement and recording of extraction steam parameters and supply and return water parameters can be achieved. Monitoring data is collected and transmitted at a certain sampling frequency and stored in the database to provide data support for subsequent strategy evaluation and adjustment.

[0192] For example, pressure sensors and temperature sensors can be installed on the steam extraction pipeline to measure the steam extraction pressure and temperature in real time; flow meters and temperature sensors can be installed on the water supply pipeline and the return water pipeline to measure the supply and return water flow and temperature in real time; pressure sensors can be installed at the outlets of the water supply pump and the return water pump to measure the supply and return water pressure in real time. These monitoring data are collected and transmitted with a sampling period of 1 second or 5 seconds and stored in the database.

[0193] According to the real-time monitoring data, the actual values ​​of the extraction parameters and the supply and return water parameters are compared with the predicted values ​​given by the optimal regulation strategy to calculate the tracking error. The tracking error indicates the degree of deviation between the actual operating state and the ideal optimization trajectory, and is an important indicator for evaluating the execution effect of the optimal regulation strategy.

[0194] For example, at a certain moment, the predicted value of the extraction steam pressure given by the optimal regulation strategy is 3.2MPa, while the actual value of the extraction steam pressure obtained by real-time monitoring is 3.1MPa, and the tracking error is 0.1MPa. Similarly, the tracking errors of parameters such as extraction steam temperature, extraction steam flow, supply water temperature, and return water temperature can be calculated.

[0195] In order to comprehensively evaluate the tracking effects of multiple parameters, indicators such as weighted average tracking error or root mean square tracking error can be introduced. The weighted average tracking error takes into account the impact of different parameters on system performance and calculates the comprehensive tracking error by setting weight coefficients. The root mean square tracking error reflects the average level and volatility of the tracking error.

[0196] For example, assuming that the tracking errors of the extraction pressure, extraction temperature, and water supply temperature are 0.1 MPa, 5°C, and 2°C, respectively, and the corresponding weight coefficients are 0.4, 0.3, and 0.3, respectively, the weighted average tracking error is 0.1×0.4+5×0.3+2×0.3=2.2. The root mean square tracking error is (0.1^2+5^2+2^2) / 3=3.4.

[0197] When the tracking error exceeds the preset threshold, the feedback correction mechanism is triggered to dynamically adjust the optimal regulation strategy. The preset threshold is set according to the actual working conditions and performance requirements of the system, indicating the tolerable tracking error range.

[0198] For example, the threshold of the extraction pressure tracking error can be set to ±0.2MPa, the threshold of the extraction temperature tracking error can be set to ±10℃, and the threshold of the supply and return water temperature tracking error can be set to ±5℃. When the actual tracking error exceeds these thresholds, it is considered that the execution effect of the optimal regulation strategy is not ideal and the strategy needs to be adjusted.

[0199] The main task of the feedback correction mechanism is to correct the control sequence of the optimal adjustment strategy so that the actual operating state of the extraction steam storage system returns to the optimal control trajectory. The adjustment method can be based on PID control, model predictive control, intelligent optimization algorithm, etc. By introducing feedback error terms, the adjustment amount of the control variable is calculated in real time, and execution instructions are issued to the corresponding actuators, such as extraction steam valves, water supply pumps, return water pumps, etc.

[0200] Taking PID control as an example, assume that at a certain moment, the extraction steam pressure tracking error is 0.3MPa, which exceeds the preset threshold. The feedback correction mechanism is based on the PID control algorithm to calculate the adjustment amount of the extraction steam valve opening. Assuming the adjustment amount is -5%, the extraction steam valve opening is reduced by 5%. The control command is sent to the extraction steam valve actuator to reduce the extraction steam flow by adjusting the valve opening, so that the extraction steam pressure gradually returns to the predicted value of the optimal regulation strategy. Similarly, other control variables are adjusted in real time to ensure that the extraction steam energy storage system operates along the optimal control trajectory.

[0201] In the process of real-time monitoring and feedback correction, fault diagnosis and health management of the extraction steam storage system are carried out simultaneously. By analyzing the abnormal conditions of the monitoring data, combined with the historical operation records and maintenance information of the equipment, potential fault hazards of the system can be discovered and located in a timely manner, and the health status of the equipment can be evaluated.

[0202] Common fault diagnosis methods include rule-based diagnosis, model-based diagnosis, data-driven diagnosis, etc. Rule-based diagnosis uses expert knowledge and experience to establish a correspondence between fault symptoms and fault causes, and achieves rapid fault judgment and location. Model-based diagnosis uses the physical model or data model of the system to identify the abnormal state of the system through the deviation between the measured data and the model prediction value. Data-driven diagnosis uses machine learning algorithms to mine the fault modes and laws in historical operation data to achieve intelligent diagnosis and prediction of faults.

[0203] For example, when the extraction temperature is monitored to be abnormally increased, and the increase exceeds the normal fluctuation range, combined with the change trends of parameters such as extraction temperature, extraction pressure, and turbine output, through the rule-based diagnosis method, it is judged that the extraction desuperheater may be faulty, resulting in abnormal extraction temperature control. Further, by analyzing the desuperheater's spray water volume, spray water temperature and other parameters, the cause of the fault is located, such as insufficient spray water volume, nozzle blockage, etc. Based on the diagnosis results, a fault alarm message is generated to notify the operation and maintenance personnel to conduct inspections and repairs.

[0204] Health management focuses on evaluating the long-term health status of equipment and predicting its remaining life and failure risk. By performing trend analysis and degradation modeling on the key performance indicators of the equipment, combining historical maintenance records and failure data, and using statistical methods or machine learning algorithms, a health degradation model and life prediction model for the equipment can be established. Based on these models, functions such as equipment health status assessment, remaining life prediction, and failure risk assessment can be realized, providing decision support for predictive maintenance and full life cycle management.

[0205] For example, for key equipment in the extraction steam storage system, such as steam turbines, heat accumulators, heat exchangers, etc., a health degradation model is established through long-term monitoring and analysis of performance parameters such as vibration, temperature, pressure, efficiency, etc. When the health index of the equipment drops to the warning threshold, its remaining life is predicted, the failure risk is assessed, and a health report and maintenance recommendations are generated, such as "the health index of the steam turbine is 75, the estimated remaining life is 2 years, and it is recommended to conduct a comprehensive inspection and maintenance of the steam turbine in the next maintenance cycle."

[0206] Through real-time monitoring, feedback correction, fault diagnosis and health management, a closed-loop optimization control system for the steam extraction energy storage system has been built. This system can continuously track the operating status of the system, dynamically adjust the optimization strategy, and promptly detect and handle potential faults, ensuring that the steam extraction energy storage system maintains optimal operating performance throughout its life cycle, improving the system's energy efficiency and reliability, and reducing operation and maintenance costs.

[0207] The real-time monitoring and feedback correction mechanism proposed in this paper has the following characteristics: (1) Based on real-time monitoring data, the execution effect of the optimal adjustment strategy is dynamically evaluated, and the deficiencies and deviations of the strategy are discovered in time; (2) A feedback correction mechanism is introduced to adjust the control variables in real time through PID control, model predictive control and other methods to bring the system back to the optimal control trajectory; (3) Combined with fault diagnosis and health management technology, potential fault hazards of the system are discovered and located in time, the health status of the equipment is evaluated, and support is provided for predictive maintenance; (4) A closed-loop optimization control system for the extraction steam storage system is constructed, which realizes the intelligent operation and maintenance of the system and improves the operation efficiency and reliability of the system.

[0208] Figure 2 FIG. 1 is a schematic diagram of a multi-stage heat release regulation optimization system based on steam extraction energy storage according to an embodiment of the present invention. Figure 2 As shown, the system comprises:

[0209] The first unit is used to determine the convective heat transfer coefficient between the heat source, the cold source and the heat exchanger wall and the thermal conductivity of the heat exchanger wall according to the heat transfer process with a finite temperature difference between the heat source, the cold source and the heat exchanger wall of the steam turbine unit, and to simplify the heat storage tank into a lumped parameter model, and to establish a dynamic thermodynamic model of the heat release process of the extraction steam storage system including multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the heat storage tank;

[0210] The second unit is used to establish a multi-objective optimization model based on the dynamic thermodynamic model of the heat release process of the extraction steam storage system, with minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger as the optimization objectives, with the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage as the optimization variables, and combined with the constraint conditions corresponding to the optimization variables; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model, and determine the optimal adjustment strategy set for the multi-stage heat release process of the extraction steam storage system;

[0211] The third unit is used to select the best regulation strategy from the optimal regulation strategy set according to the peak-shaving and frequency-regulating requirements of the steam turbine set, and control the valve position opening of the steam extraction electric regulating valve and the feed water flow of the steam extraction temperature and pressure reduction device through the steam extraction energy storage control system to achieve optimal regulation of the steam extraction parameters. At the same time, the speed of the supply and return water pumps on the cold source side is controlled to achieve optimal matching of the supply and return water parameters, so that the steam extraction energy storage system always maintains the best operating condition during the multi-stage heat release process.

[0212] According to a third aspect of the embodiments of the present invention,

[0213] An electronic device is provided, comprising:

[0214] processor;

[0215] a memory for storing processor-executable instructions;

[0216] The processor is configured to call the instructions stored in the memory to execute the aforementioned method.

[0217] According to a fourth aspect of the embodiments of the present invention,

[0218] A computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the aforementioned method is implemented.

[0219] The present invention may be a method, an apparatus, a system and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for executing various aspects of the present invention.

[0220] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-stage heat release regulation optimization method based on extraction steam energy storage, characterized in that: include: According to the heat transfer process with a finite temperature difference between the heat source, cold source and the heat exchanger wall of the steam turbine unit, the convective heat transfer coefficient between the heat source, cold source and the heat exchanger wall and the thermal conductivity of the heat exchanger wall are introduced to determine the convective heat transfer, heat conduction and external heat dissipation losses inside the heat exchanger, and the hot water storage tank is simplified into a lumped parameter model to establish a dynamic thermodynamic model of the heat release process of the extraction steam storage system including multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the hot water storage tank; According to the dynamic thermodynamic model of the heat release process of the extraction steam storage system, minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger are taken as optimization objectives, the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage are taken as optimization variables, and combined with the constraint conditions corresponding to the optimization variables, a multi-objective optimization model is established; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the optimal regulation strategy set for the multi-stage heat release process of the extraction steam storage system is determined; According to the peak-shaving and frequency-regulating requirements of the steam turbine unit, the best regulation strategy is selected from the optimal regulation strategy set, and the valve position opening of the steam extraction electric regulating valve and the feed water flow of the steam extraction temperature and pressure reduction device are controlled through the steam extraction energy storage control system to achieve optimal regulation of the steam extraction parameters. At the same time, the speed of the supply and return water pumps on the cold source side is controlled to achieve optimal matching of the supply and return water parameters, so that the steam extraction energy storage system always maintains the best operating condition during the multi-stage heat release process.

2. The method according to claim 1, characterized in that According to the heat transfer process with a finite temperature difference between the heat source, cold source and the wall of the heat exchanger of the steam turbine unit, the convective heat transfer coefficient between the heat source, cold source and the wall of the heat exchanger and the thermal conductivity of the wall of the heat exchanger are introduced to determine the convective heat transfer, heat conduction and external heat dissipation losses inside the heat exchanger, and simplify the hot water storage tank into a lumped parameter model. The dynamic thermodynamic model of the heat release process of the extraction steam storage system including multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the hot water storage tank is established, including: Based on the finite-time thermodynamics theory, the heat transfer process with a finite temperature difference between the heat source, the cold source and the heat exchanger wall is considered, and the energy balance model and entropy balance model describing the heat release process are established; The convective heat transfer coefficient between the heat source, the cold source and the heat exchanger wall and the thermal conductivity of the heat exchanger wall are introduced to establish a heat transfer balance model that describes the heat transfer process between the heat source, the cold source and the heat exchanger wall; the above heat transfer balance model is combined with the energy balance model and the entropy balance model to construct a comprehensive heat release model that reflects the inherent relationship between heat release power, heat release efficiency and heat release time; Based on the thermal resistance theory, the various heat transfer forms of convection heat transfer, heat conduction and external heat dissipation loss in the heat exchanger are comprehensively considered, and a non-steady-state heat transfer partial differential equation describing the three-dimensional temperature distribution inside the heat exchanger is established; and combined with the preset initial conditions and boundary conditions, a temperature heat loss model describing the dynamic temperature distribution and heat dissipation loss inside the heat exchanger is constructed; The hot water storage tank is simplified into a lumped parameter model. The heat absorbed by the hot water storage tank from the heat exchanger, the heat loss of the hot water storage tank, and the heat supplied by the hot water storage tank to the load are comprehensively considered to establish a water tank temperature change model that describes the dynamic change of the water temperature in the hot water storage tank. Corresponding model weights are assigned to the heat release comprehensive model, the temperature heat loss model and the water tank temperature change model, respectively, and a dynamic thermodynamic model of the heat release process of the extraction steam storage system is established according to preset coupling conditions.

3. The method according to claim 2, characterized in that The energy balance model is shown in the following formula: The entropy balance model is shown in the following formula: In the formula, and are the heat transfer rates of the heat source and the cold source, respectively, is the output power of the heat release process, E is the total energy inside the heat exchanger, S is the total entropy inside the heat exchanger, T h and T c are the temperatures of the heat source and the cold source, respectively. is the entropy generation rate of the exothermic process, and t is the exothermic time; The heat release comprehensive model reflecting the intrinsic relationship between heat release power, heat release efficiency and heat release time is constructed as shown in the following formula: A h and A c are the heat exchange areas on the heat source side and the cold source side, A w is the cross-sectional area of ​​the heat exchanger wall, δ is the thickness of the heat exchanger wall, T w,h and T w,c are the temperatures of the heat source side and the cold source side of the heat exchanger wall, is the heat transfer rate of the heat exchanger wall; m is the mass of the working fluid in the heat exchanger, c p is the specific heat capacity of the working fluid; The water tank temperature change model describing the dynamic change of water temperature in the hot water storage tank is established as shown in the following formula: in, Indicates the amount of heat absorbed by the water tank from the heat exchanger. Indicates the heat loss of the water tank and Indicates the heat supplied by the water tank to the load, T tank It represents the water temperature in the water tank, and M is the mass of the water in the water tank.

4. The method according to claim 1, characterized in that According to the dynamic thermodynamic model of the heat release process of the extraction steam storage system, minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger are taken as the optimization objectives, and the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage are taken as optimization variables, and combined with the constraint conditions corresponding to the optimization variables, a multi-objective optimization model is established; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model, and the optimal regulation strategy set for the multi-stage heat release process of the extraction steam storage system is determined to include: According to the dynamic thermodynamic model of the heat release process of the extraction steam energy storage system, minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger are taken as optimization objectives, and the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage are taken as optimization variables, and combined with the constraint conditions corresponding to the optimization variables, a multi-objective optimization model is established; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model; The improved non-dominated sorting genetic algorithm comprises: randomly initializing a population including a plurality of individuals, each individual representing a set of feasible optimization variable combinations; performing fast non-dominated sorting and crowding calculation on the current population to obtain the non-dominated level and crowding distance of each individual; selecting individuals from the current population as the parent population according to the non-dominated level and crowding distance by using a binary tournament selection operator; performing simulated binary crossover and polynomial mutation on the parent population to generate new offspring individuals and construct a offspring population; merging the parent population and the offspring population, performing fast non-dominated sorting and crowding calculation on the merged population, and selecting excellent individuals as the new population; repeating iterations until a termination condition is met, and outputting the first non-dominated solution set as the optimization result of the improved non-dominated sorting genetic algorithm; The multi-objective particle swarm optimization algorithm includes: taking the first non-dominated solution set as the initial population, and the position of each particle corresponds to a set of feasible optimization variable combinations; calculating the fitness value of each particle, that is, the value of the multi-objective optimization model, and updating the individual optimal position and the global optimal position of each particle; according to the individual optimal position and the global optimal position of the particle, using the speed update formula and the position update formula, updating the speed and position of each particle; performing boundary processing on the updated particle position to ensure that each optimization variable is within its definition domain; repeating the iteration until the termination condition is met, and outputting the second non-dominated solution set as the optimization result of the multi-objective particle swarm optimization algorithm; The first non-dominated solution set and the second non-dominated solution set are combined to obtain a final non-dominated solution set as an optimal regulation strategy set for the multi-stage heat release process of the extraction steam storage system.

5. The method according to claim 4, characterized in that According to the dynamic thermodynamic model of the heat release process of the extraction steam energy storage system, with minimizing the total energy consumption of the system and minimizing the entropy generation of the heat exchanger as the optimization objectives, the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage as the optimization variables, and combined with the constraints corresponding to the optimization variables, a multi-objective optimization model is established, including: The multi-objective optimization model includes: Among them, f1 and f2 are the total energy consumption of the system and the total entropy generation of the heat exchanger, respectively, N represents the number of stages of the heat release process, and L i 、h i , Δt i , They represent the steam extraction flow rate, steam extraction enthalpy, heat release time and heat exchange amount on the cold source side in the i-th stage respectively. represents the entropy generation rate of the heat exchanger in stage i; The constraints corresponding to the optimization variables include: in, Respectively represent the upper and lower limits of the extraction enthalpy value, Respectively represent the upper and lower limits of the extraction steam flow rate, They represent the upper and lower limits of the heat release time respectively.

6. The method according to claim 1, characterized in that According to the peak and frequency regulation requirements of the steam turbine unit, the best regulation strategy is selected from the optimal regulation strategy set, and the valve position opening of the steam extraction electric regulating valve and the feed water flow of the steam extraction temperature and pressure reduction device are controlled through the steam extraction energy storage control system to achieve optimal regulation of the steam extraction parameters. At the same time, the speed of the supply and return water pumps on the cold source side is controlled to achieve optimal matching of the supply and return water parameters, so that the steam extraction energy storage system always maintains the best working condition during the multi-stage heat release process, including: Through the communication interface with the power grid dispatching center, the peak-shaving and frequency-regulating requirements of the steam turbine unit are obtained in real time, and the peak-shaving and frequency-regulating requirements include target output, output change rate and regulation duration information, which are used as the basis for selecting the best regulation strategy; the applicability of each regulation strategy in the best regulation strategy set is evaluated according to the peak-shaving and frequency-regulating requirements, and the candidate regulation strategies that meet the peak-shaving and frequency-regulating requirements are screened out; the regulation strategy with the best comprehensive performance among the candidate regulation strategies is used as the best regulation strategy, and the comprehensive performance includes at least one performance indicator among heat release efficiency, response speed and stability; According to the selected optimal regulation strategy, the time series of the valve position opening of the extraction steam electric regulating valve, the feed water flow of the extraction steam temperature and pressure reduction device, and the speed of the supply and return water pump on the cold source side are generated as the control instructions of the extraction steam energy storage control system; The extraction steam energy storage control system adjusts the valve position opening of the extraction steam electric regulating valve through the PID control method based on adaptive gain according to the control instructions received, and adjusts the feed water flow of the extraction steam cooling and pressure reducing device through the fuzzy PID control method; the speed of the supply and return water pump on the cold source side is adjusted in real time by using the model predictive control algorithm, and the optimal speed sequence in the future several control cycles is obtained by solving the optimization problem including the dynamic model of pump end flow and speed and speed constraint, so as to achieve the optimal matching of supply and return water parameters with extraction steam parameters, so that the extraction steam energy storage system always maintains the best working condition during the multi-stage heat release process.

7. The method according to claim 6, characterized in that The method further comprises: During the operation of the extraction steam storage system, the changes of the extraction steam parameters and the supply and return water parameters are monitored in real time, the actual values ​​are compared with the predicted values ​​of the optimal regulation strategy, and the tracking error is calculated; When the tracking error exceeds the preset threshold, the feedback correction mechanism is triggered to adjust the control sequence of the optimal regulation strategy. At the same time, the operating status of the extraction steam storage system and the health status of key equipment are monitored, and potential fault hazards are diagnosed and handled in a timely manner to ensure that the extraction steam storage system always runs along the optimal control trajectory.

8. A multi-stage heat release regulation optimization system based on extraction steam energy storage, used to implement the method described in any one of claims 1 to 7, characterized in that: include: The first unit is used to determine the convective heat transfer coefficient between the heat source, the cold source and the heat exchanger wall and the thermal conductivity of the heat exchanger wall according to the heat transfer process with a finite temperature difference between the heat source, the cold source and the heat exchanger wall of the steam turbine unit, and to simplify the heat storage tank into a lumped parameter model, and to establish a dynamic thermodynamic model of the heat release process of the extraction steam storage system including multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the heat storage tank; The second unit is used to establish a multi-objective optimization model based on the dynamic thermodynamic model of the heat release process of the extraction steam storage system, with minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger as the optimization objectives, with the extraction steam pressure, extraction steam temperature, extraction steam flow rate and heat release time of each stage as the optimization variables, and combined with the constraint conditions corresponding to the optimization variables; a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization model, and determine the optimal adjustment strategy set for the multi-stage heat release process of the extraction steam storage system; The third unit is used to select the best regulation strategy from the optimal regulation strategy set according to the peak-shaving and frequency-regulating requirements of the steam turbine set, and control the valve position opening of the steam extraction electric regulating valve and the feed water flow of the steam extraction temperature and pressure reduction device through the steam extraction energy storage control system to achieve optimal regulation of the steam extraction parameters. At the same time, the speed of the supply and return water pumps on the cold source side is controlled to achieve optimal matching of the supply and return water parameters, so that the steam extraction energy storage system always maintains the best operating condition during the multi-stage heat release process.

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

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

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