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

Abstract

The application provides a multi-stage heat release regulation optimization method and system based on steam extraction energy storage, relates to the technical field of heat regulation, and comprises the following steps: a dynamic thermodynamic model of a heat release process of a steam extraction energy storage system is established, which comprises multiple state variables of heat release power, heat release time, heat release efficiency and temperature change of a heat storage water tank; a multi-objective optimization model is established by taking the minimization of total energy consumption of the system and the minimization of entropy production of a heat exchanger as optimization objectives and combining 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, so as to determine an optimal regulation strategy set of the multi-stage heat release process of the steam extraction energy storage system; and the valve opening of a steam extraction motor-driven regulating valve and the feed water flow of a steam extraction temperature and pressure reducing device are controlled through a steam extraction energy storage control system, so as to realize the optimal regulation of steam extraction parameters, and the rotation speed of a cold source side water supply and return pump is controlled, so as to realize the optimal matching of water supply and return parameters.

Description

TECHNICAL FIELD

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

[0002] With the increasing proportion of new energy power generation, the grid puts forward higher requirements for the peak regulation and frequency modulation capability of conventional thermal power units. The steam extraction energy storage technology increases the heat storage device in the cogeneration unit, uses the surplus power during the low valley of the grid load for heat storage, and releases the heat storage during the peak load of the grid to improve the output of the steam turbine unit, which is an effective way to improve the flexibility and economy of the thermal power unit.

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

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

[0005] The embodiment of the present application provides 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] The first aspect of the embodiment of the present application is,

[0007] The multi-stage heat release regulation optimization method based on steam extraction energy storage comprises the following steps:

[0008] According to the heat transfer process between the heat source, the cold source and the wall surface of the heat exchanger, the convective heat transfer coefficient between the heat source, the cold source and the wall surface of the heat exchanger and the thermal conductivity coefficient of the wall surface of the heat exchanger are introduced to determine the convective heat transfer, thermal conduction and external heat loss inside the heat exchanger, and the heat storage water tank is simplified as a lumped parameter model, and a dynamic thermodynamic model of the heat release process of the steam extraction energy storage system is established, which includes multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the heat storage water tank;

[0009] According to the dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, a multi-objective optimization model is established by taking the minimum total energy consumption of the system and the minimum entropy production of the heat exchanger as the optimization objectives, taking the steam extraction pressure, the steam extraction temperature, the steam extraction flow rate and the heat release time as the optimization variables, and combining the constraint conditions corresponding to the optimization variables; the improved non-dominated sorting genetic algorithm is combined with the multi-objective particle swarm optimization algorithm 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;

[0010] According to the peak regulation and frequency modulation demand of the steam turbine unit, the best adjustment strategy is selected from the optimal adjustment strategy set, the valve opening degree of the steam extraction motor-driven regulating valve and the feed water flow rate of the steam extraction temperature and pressure reducing device are controlled through the steam extraction energy storage control system to realize the optimal adjustment of the steam extraction parameters, and the rotating speed of the water supply and return pump on the cold source side is controlled to realize the optimal matching of the water supply and return parameters, so that the steam extraction energy storage system always maintains the best working condition in the multi-stage heat release process.

[0011] In an alternative embodiment,

[0012] According to the heat transfer process between the heat source, the cold source and the wall surface of the heat exchanger of the steam turbine unit, the convective heat transfer coefficient between the heat source, the cold source and the wall surface of the heat exchanger and the thermal conductivity of the wall surface of the heat exchanger are introduced to determine the convective heat transfer, heat conduction and external heat loss inside the heat exchanger, and the heat storage water tank is simplified as a lumped parameter model, and a dynamic thermodynamic model of the heat release process of the steam extraction energy storage system is established, which includes multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the heat storage water tank.

[0013] Based on the finite time thermodynamics theory, the heat transfer process between the heat source, the cold source and the wall surface of the heat exchanger is considered, and an energy balance model and an 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 wall surface of the heat exchanger and the thermal conductivity of the wall surface of the heat exchanger are introduced to establish a heat transfer balance model describing the heat transfer process between the heat source, the cold source and the wall surface of the heat exchanger; the heat transfer balance model is combined with the energy balance model and the entropy balance model to construct a heat release comprehensive model reflecting the internal correlation among the heat release power, the heat release efficiency and the heat release time;

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

[0016] For the heat storage water tank, it is simplified as a lumped parameter model, and a water tank temperature variation model is established to describe the dynamic change of water temperature in the heat storage water tank by comprehensively considering the heat absorbed by the heat storage water tank from the heat exchanger, the heat loss of the heat storage water tank, and the heat supplied by the heat storage water tank to the load;

[0017] The model weights corresponding to the heat release comprehensive model, the temperature heat loss model and the water tank temperature variation model are respectively allocated, and a dynamic thermodynamic model of the heat release process of the steam extraction energy storage system is established according to a preset coupling condition.

[0018] In an alternative 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 production rate of the heat release process, and t is the heat release time;

[0024] A heat release comprehensive model reflecting the internal correlation among the heat release power, the heat release efficiency and the heat release time is constructed and shown in the following formula:

[0025]

[0026] A h and A c are the heat exchange areas of the heat source side and the cold source side respectively, 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 respectively, is the heat conduction rate of the heat exchanger wall;

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

[0028] A water tank temperature variation model describing the dynamic change of water temperature in the heat storage water tank is established and shown in the following formula:

[0029]

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

[0031] In an alternative embodiment,

[0032] According to a dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, a multi-objective optimization model is established with the optimization objectives of minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger, the optimization variables of the steam extraction pressure, the steam extraction temperature, the steam extraction flow rate and the heat release time, and the corresponding constraint conditions of 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 to determine the optimal adjustment strategy set of the multi-stage heat release process of the steam extraction energy storage system, including:

[0033] According to a dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, a multi-objective optimization model is established with the optimization objectives of minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger, the optimization variables of the steam extraction pressure, the steam extraction temperature, the steam extraction flow rate and the heat release time, and the corresponding constraint conditions of 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;

[0034] The improved non-dominated sorting genetic algorithm includes: randomly initializing a population containing multiple individuals, each individual representing a set of feasible combinations of optimization variables; performing fast non-dominated sorting and crowding distance calculation on the current population to obtain the non-dominated level and crowding distance of each individual; according to the non-dominated level and crowding distance, a binary tournament selection operator is used to select individuals from the current population as the parent population; simulating binary crossover and polynomial mutation on the parent population to generate new child individuals and construct a child population; merging the parent population and the child population, performing fast non-dominated sorting and crowding distance calculation on the merged population, and selecting excellent individuals as the new population; repeating the iteration until the 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 comprises: taking the first non-dominated solution set as an initial population, a position of each particle corresponding to a feasible combination of optimization variables; calculating a fitness value of each particle, i.e. a value of the multi-objective optimization model, and updating an individual optimal position and a global optimal position of each particle; updating a speed and a position of each particle according to the individual optimal position and the global optimal position of the particle by using a speed updating formula and a position updating formula; performing boundary processing on the updated particle position to ensure that each optimization variable is within its defined domain; repeating iteration until a termination condition is met, and outputting a second non-dominated solution set as an 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 merged to obtain a final non-dominated solution set as an optimal regulation strategy set of the multi-stage heat release process of the steam extraction energy storage system.

[0037] In an optional implementation,

[0038] According to a dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, a multi-objective optimization model is established by taking minimization of total energy consumption of the system and minimization of entropy production of the heat exchanger as optimization objectives, taking steam extraction pressure, steam extraction temperature, steam extraction flow rate and heat release time length of each stage as optimization variables, and combining constraint conditions corresponding to the optimization variables.

[0039] The multi-objective optimization model comprises:

[0040]

[0041] wherein f1 and f2 respectively represent total energy consumption of the system and total entropy production of the heat exchanger, N represents a number of stages of the heat release process, L i , h i , Δt i , respectively represent steam extraction flow rate, steam extraction enthalpy value, heat release time length and heat exchange amount on the cold source side of the i-th stage, represents an entropy production rate of the heat exchanger of the i-th stage;

[0042] The constraint conditions corresponding to the optimization variables comprise:

[0043]

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

[0045] In an optional implementation,

[0046] According to the peak shaving and frequency modulation demand of the steam turbine unit, an optimal regulation strategy is selected from the optimal regulation strategy set, the valve opening degree of the extraction motor-driven regulation valve and the feed water flow of the extraction desuperheating and pressure reducing device are controlled by the extraction energy storage control system to realize the optimal regulation of the extraction parameters, the rotation speed of the supply and return water pump on the cold source side is controlled to realize the optimal matching of the supply and return water parameters, and the extraction energy storage system always maintains the optimal working condition in the multi-stage heat release process, including:

[0047] The peak shaving and frequency modulation demand of the steam turbine unit is obtained in real time through the communication interface with the power grid dispatching center, the peak shaving and frequency modulation demand includes target output, output change rate and regulation duration information, and is used as the basis for selecting the optimal regulation strategy; the applicability of each regulation strategy in the optimal regulation strategy set is evaluated according to the peak shaving and frequency modulation demand, and a candidate regulation strategy that meets the peak shaving and frequency modulation demand is screened out; the regulation strategy with the optimal comprehensive performance in the candidate regulation strategy is selected as the optimal regulation strategy, and the comprehensive performance includes at least one of the heat release efficiency, the response speed and the stability.

[0048] According to the selected optimal regulation strategy, the time sequence of the control parameters of the valve opening degree of the extraction motor-driven regulation valve, the feed water flow of the extraction desuperheating and pressure reducing device and the rotation speed of the supply and return water pump on the cold source side is generated as the control instruction of the extraction energy storage control system.

[0049] The extraction energy storage control system adjusts the valve opening degree of the extraction motor-driven regulation valve through the PID control method based on adaptive gain, and adjusts the feed water flow of the extraction desuperheating and pressure reducing device through the fuzzy PID control method according to the received control instruction; the rotation speed of the supply and return water pump on the cold source side is adjusted in real time based on the model predictive control algorithm, the optimal rotation speed sequence in the future several control periods is obtained by solving the optimization problem containing the pump end flow and rotation speed dynamic model and rotation speed constraint, the optimal matching of the supply and return water parameters and the extraction parameters is realized, and the extraction energy storage system always maintains the optimal working condition in the multi-stage heat release process.

[0050] In an optional implementation,

[0051] The method further includes:

[0052] During the operation of the extraction energy storage system, the changes of the extraction 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, a feedback correction mechanism is triggered, the control sequence of the optimal regulation strategy is adjusted, the operation state of the extraction energy storage system and the health status of the key equipment are monitored, potential fault hidden dangers are diagnosed and processed in time, and it is ensured that the extraction energy storage system always operates along the optimal control trajectory.

[0054] In a second aspect of the embodiment of the present application,

[0055] A multi-stage heat release regulation optimization system based on steam extraction energy storage is provided, comprising:

[0056] A first unit is configured to introduce a convective heat transfer coefficient between a heat source, a cold source and a wall surface of a heat exchanger and a thermal conductivity of the wall surface of the heat exchanger according to a heat transfer process in which there is a limited temperature difference between the heat source, the cold source and the wall surface of the heat exchanger, determine convective heat transfer, thermal conduction inside the heat exchanger and external heat loss, simplify the heat storage tank into a lumped parameter model, and establish a dynamic thermodynamic model of a heat release process of the steam extraction energy storage system including multiple state variables of heat release power, heat release time, heat release efficiency and temperature change of the heat storage tank;

[0057] A second unit is configured to establish a multi-objective optimization model according to the dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, with the optimization objectives of minimizing total energy consumption of the system and minimizing entropy production of the heat exchanger, with the optimization variables of steam extraction pressure, steam extraction temperature, steam extraction flow and heat release time, and with the constraint conditions corresponding to the optimization variables, solve the multi-objective optimization model by using a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm, and determine an optimal regulation strategy set of the multi-stage heat release process of the steam extraction energy storage system;

[0058] A third unit is configured to select an optimal regulation strategy from the optimal regulation strategy set according to the peak shaving and frequency modulation demand of the steam turbine unit, control the valve opening degree of the steam extraction motorized regulating valve and the feed water flow of the steam extraction desuperheating and pressure reducing device through the steam extraction energy storage control system to realize optimal regulation of the steam extraction parameters, and control the rotating speed of the cold source side water supply and return pump to realize optimal matching of the water supply and return parameters, so that the steam extraction energy storage system always maintains an optimal working condition in the multi-stage heat release process.

[0059] A third aspect of the embodiment of the application,

[0060] An electronic device is provided, comprising:

[0061] A processor;

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

[0063] The processor is configured to invoke the instructions stored in the memory to perform the method described above.

[0064] A fourth aspect of the embodiment of the application,

[0065] A computer readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0066] The application can accurately reflect the dynamic heat release characteristics of the steam extraction energy storage system under the conditions of variable working conditions and variable parameters by constructing a dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, comprehensively considering the thermodynamic characteristics and mutual coupling relationship of each component of the system, and providing reliable theoretical guidance for the optimal design and operation control of the system.

[0067] The optimal regulation strategy generation method proposed in the present application has the following advantages: (1) a multi-objective optimization model is adopted, the system efficiency and thermodynamic performance are comprehensively considered, so that the optimization result is more comprehensive and reliable; (2) a hybrid optimization strategy is introduced, the advantages of the improved non-dominated sorting genetic algorithm and the multi-objective particle swarm optimization algorithm are utilized, and the optimization efficiency and solution quality are improved; (3) the optimal regulation strategy set generated provides flexible and optional strategy combinations for actual engineering applications, and has strong applicability. BRIEF DESCRIPTION OF DRAWINGS

[0068] Figure 1 A flowchart of the multi-stage heat release regulation optimization method based on steam extraction energy storage of the embodiment of the present application is shown in

[0069] Figure 2 A structural diagram of the multi-stage heat release regulation optimization system based on steam extraction energy storage of the embodiment of the present application is shown in DETAILED DESCRIPTION

[0070] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

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

[0072] Figure 1 A flowchart of the multi-stage heat release regulation optimization method based on steam extraction energy storage of the embodiment of the present application is shown in Figure 1 The method comprises:

[0073] S101. According to the heat transfer process between the heat source, the cold source and the wall surface of the heat exchanger of the steam turbine unit, the convective heat transfer coefficient between the heat source, the cold source and the wall surface of the heat exchanger and the thermal conductivity of the wall surface of the heat exchanger are introduced to determine the convective heat transfer, heat conduction and external heat loss inside the heat exchanger, and the heat storage water tank is simplified as a lumped parameter model, and a dynamic thermodynamic model of the heat release process of the steam storage system is established, which includes multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the heat storage water tank;

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

[0075] S103. According to the peak regulation and frequency modulation demand of the steam turbine unit, the best adjustment strategy is selected from the optimal adjustment strategy set, the valve opening degree of the steam extraction motor-driven regulating valve and the feed water flow of the steam extraction desuperheating and pressure reducing device are controlled through the steam extraction control system to realize the optimal adjustment of the steam extraction parameters, and the speed of the cold source side water supply and return pump is controlled to realize the optimal matching of the water supply and return parameters, so that the steam storage system always maintains the best working condition in the multi-stage heat release process.

[0076] In an alternative embodiment,

[0077] According to the heat transfer process between the heat source, the cold source and the wall surface of the heat exchanger of the steam turbine unit, the convective heat transfer coefficient between the heat source, the cold source and the wall surface of the heat exchanger and the thermal conductivity of the wall surface of the heat exchanger are introduced to determine the convective heat transfer, heat conduction and external heat loss inside the heat exchanger, and the heat storage water tank is simplified as a lumped parameter model, and a dynamic thermodynamic model of the heat release process of the steam storage system is established, which includes multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the heat storage water tank includes:

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

[0079] The heat transfer coefficient between the heat source, the cold source and the wall surface of the heat exchanger and the thermal conductivity of the wall surface of the heat exchanger are introduced to establish a heat transfer balance model for describing the heat transfer process between the heat source, the cold source and the wall surface of the heat exchanger; the heat transfer balance model is combined with an energy balance model and an entropy balance model to build a comprehensive heat release model reflecting the internal correlation among the heat release power, the heat release efficiency and the heat release time;

[0080] Based on the thermal resistance theory, various heat transfer forms such as the convective heat transfer, the conductive heat transfer in the heat exchanger and the external heat loss are comprehensively considered to establish a non-steady heat transfer partial differential equation for describing the three-dimensional temperature distribution in the heat exchanger; and combined with the preset initial condition and the boundary condition, a temperature heat loss model for describing the dynamic temperature distribution in the heat exchanger and the heat loss is built;

[0081] For the heat storage water tank, the heat storage water tank is simplified as a lumped parameter model, and the heat absorbed by the heat storage water tank from the heat exchanger, the heat loss of the heat storage water tank and the heat supplied by the heat storage water tank to the load are comprehensively considered to build a water tank temperature change model for describing the dynamic change of the water temperature in the heat storage water tank;

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

[0083] In an alternative 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 in the heat exchanger, S is the total entropy in the heat exchanger, T h and T c are the temperatures of the heat source and the cold source respectively, is the entropy production rate of the heat release process, and t is the heat release time;

[0089] The comprehensive heat release model reflecting the internal correlation among the heat release power, the heat release efficiency and the heat release time is shown in the following formula:

[0090]

[0091] A h and Ac A and A are the heat transfer areas of the heat source side and the cold source side, respectively w A and A are the heat transfer areas of the heat source side and the cold source side, respectively w,h A and A are the heat transfer areas of the heat source side and the cold source side, respectively w,c A and A are the heat transfer areas of the heat source side and the cold source side, respectively A and A are the heat transfer areas of the heat source side and the cold source side, respectively

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

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

[0094]

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

[0096] Exemplarily, after determining the optimal extraction steam position of the heat source side, the extraction steam parameters, and the supply and return water temperature and pressure of the cold source side, etc., a dynamic thermodynamic model of the extraction steam heat storage system heat release process is constructed in this paper based on the finite time thermodynamics theory and the thermal resistance theory, which comprehensively considers the heat release power, the heat release efficiency, the heat release time, the temperature distribution of the heat exchanger, the heat loss, and the temperature change of the heat storage water tank.

[0097] Firstly, considering the heat transfer process between the heat source (extraction steam), the cold source (heating water) and the heat exchanger wall with a finite temperature difference, according to the law of conservation of energy and the second law of thermodynamics, an energy balance model and an entropy balance model describing the heat release process are established as shown below:

[0098] Energy balance model:

[0099]

[0100] Entropy balance model:

[0101]

[0102] wherein, and Q and Q are the heat transfer rates of the heat source and the cold source, respectively, E 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 Q and Q are the temperatures of the heat source and the cold source, respectively, is the rate of entropy production for the heat-releasing process, t is the heat-releasing time.

[0103] Based on the energy balance model and the entropy balance model, the convective heat transfer coefficients h h and h c between the heat source and the heat exchanger wall, and between the cold source and the heat exchanger wall, and the thermal conductivity λ of the heat exchanger wall are introduced to establish a heat transfer balance model describing the heat transfer process between the heat source, the cold source and the heat exchanger wall, as follows:

[0104]

[0105] In the formula, A h and A c are the heat transfer areas on the heat source side and the cold source side, respectively, 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 on the heat source side and the cold source side of the heat exchanger wall, respectively, is the heat transfer rate of the heat exchanger wall.

[0106] The above heat transfer balance model is combined with the energy balance model and the entropy balance model, and appropriate mathematical transformations and simplifications are performed, so that a comprehensive heat-releasing model reflecting the internal relationship between the heat-releasing power and the heat-releasing efficiency η and the heat-releasing time t is obtained, as follows:

[0107]

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

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

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

[0111]

[0112] where p is the density of the heat exchanger wall, is the internal heat source term of the heat exchanger wall, which reflects the heat loss of the heat exchanger with the outside world, and (x, y, z) represents the three-dimensional coordinates of any point inside the heat exchanger.

[0113] For the heat loss Newton's cooling law can be used to describe it:

[0114]

[0115] where 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, and T a is the ambient temperature.

[0116] The heat loss is introduced as an internal heat source term into the heat transfer equation of the heat exchanger, and combined with appropriate initial conditions and boundary conditions, a temperature heat loss model is established to describe the dynamic distribution of the internal temperature of the heat exchanger and the heat loss. This model needs to be solved by numerical method, and the three-dimensional temperature distribution inside the heat exchanger and the variation of heat loss with time can be obtained, which provides more detailed and accurate basis for the design and optimization of the steam extraction and energy storage system.

[0117] Finally, for the heat storage water tank, it can be simplified as a lumped parameter model, which comprehensively considers the heat absorbed by the water tank from the heat exchanger the heat loss of the water tank and the heat supplied by the water tank to the load A water tank temperature change model is established to describe the dynamic change of the water temperature T tank in the tank, as follows:

[0118]

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

[0120] In summary, the dynamic thermodynamic model of the heat release process of the steam extraction and energy storage system consists of three sub-models: the heat release synthesis 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 method of weighted coupling, i.e. assigning corresponding model weights to the three sub-models, and according to the pre-set coupling conditions, a complete dynamic thermodynamic model of the heat release process of the steam extraction and energy storage system is established, as follows:

[0121] min J = w1J1 + w2J2 + w3J3;

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

[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 energy storage system can be obtained. The 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 energy storage system under variable working conditions and variable parameters, thereby providing reliable theoretical guidance for the optimized design and operation control of the system.

[0124] In an alternative embodiment,

[0125] According to the dynamic thermodynamic model of the heat release process of the extraction energy storage system, a multi-objective optimization model is established by taking the minimization of the total energy consumption of the system and the minimization of the entropy production of the heat exchanger as the optimization objectives, taking the extraction pressure, the extraction temperature, the extraction flow rate and the heat release time length of each stage as the optimization variables, and combining the constraint conditions corresponding to the optimization variables; the improved non-dominated sorting genetic algorithm is combined with the multi-objective particle swarm optimization algorithm to solve the multi-objective optimization model by using a hybrid optimization strategy, and an optimal adjustment strategy set of the multi-stage heat release process of the extraction energy storage system is determined.

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

[0127] The improved non-dominated sorting genetic algorithm comprises: randomly initializing a population containing a plurality of individuals, each individual representing a set of feasible optimization variable combinations; performing fast non-dominated sorting and congestion calculation on the current population to obtain the non-dominated level and congestion distance of each individual; according to the non-dominated level and congestion distance, using a binary tournament selection operator to select individuals from the current population as a parent population; performing simulated binary crossover and polynomial mutation on the parent population to generate new offspring individuals and construct an offspring population; merging the parent population and the offspring population, and performing fast non-dominated sorting and congestion calculation on the merged population to select excellent individuals as a new population; repeating iteration until a termination condition is met, and outputting a 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 comprises: taking the first non-dominated solution set as an initial population, and the position of each particle corresponding 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 global optimal position of each particle; according to the individual optimal position and global optimal position of the particle, using a speed update formula and a position update formula to update the speed and position of each particle; performing boundary processing on the updated particle position to ensure that each optimization variable is within its defined domain; repeating iteration until a termination condition is met, and outputting a 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 merged to obtain a final non-dominated solution set as the optimal regulation strategy set of the multi-stage heat release process of the steam extraction energy storage system.

[0130] In an optional embodiment,

[0131] According to a dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, taking minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger as optimization objectives, taking the steam extraction pressure, the steam extraction temperature, the steam extraction flow rate and the heat release time length of each stage as optimization variables, and combining the constraint conditions corresponding to the optimization variables, a multi-objective optimization model is established, which comprises:

[0132] The multi-objective optimization model comprises:

[0133]

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

[0135] The constraint conditions corresponding to the optimization variables include:

[0136]

[0137] wherein, respectively represent the upper and lower limits of the extraction steam enthalpy value, respectively represent the upper and lower limits of the extraction steam flow rate, respectively represent the upper and lower limits of the heat release duration.

[0138] Exemplarily, in order to realize the optimal regulation of the multi-stage heat release process of the extraction energy storage system, an optimal regulation strategy generation method based on a multi-objective optimization model and a hybrid optimization algorithm is proposed herein. The specific steps are as follows:

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

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

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

[0142] In order to efficiently solve the established multi-objective optimization model, a hybrid optimization strategy combining the improved non-dominated sorting genetic algorithm with the multi-objective particle swarm optimization algorithm is adopted herein. This strategy fully utilizes the advantages of the two algorithms, which can ensure the diversity of solutions while accelerating the optimization speed and improving the quality of solutions.

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

[0144] Randomly initialize the population. Generate an initial population containing multiple individuals, each representing a feasible combination of optimization variables, i.e., the values of extraction pressure, extraction temperature, extraction flow rate, and heat release duration. For example, the initial population can contain 100 individuals, each composed of 20 optimization variables.

[0145] Fast non-dominated sorting and crowding distance calculation. Perform fast non-dominated sorting on the current population, dividing the population into multiple non-dominated levels based on the dominance relationship between individuals. Individuals in the first non-dominated level are not dominated by any other individual, and so on. Then, calculate the crowding distance of each individual, representing the distribution density of the individual 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 levels and crowding distances. Prefer the individual with a smaller non-dominated level, and if the non-dominated levels are the same, select the individual with a larger crowding distance. Repeat the selection operation until a parent population of 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 the 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 to decide whether to perform the mutation operation based on the mutation probability and perturb each optimization variable of the individual. The mutation probability can be set to 0.1.

[0148] Merge the population and elite preservation strategy. Merge the parent population and the offspring population to obtain a merged population containing individuals of two generations. Perform fast non-dominated sorting and crowding distance calculation on the merged population, and select the individuals with high ranking as the new generation population based on the elite preservation strategy. The elite preservation strategy ensures that excellent individuals are not lost during the evolution process. The size of the new generation population is the same as that of the initial population.

[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, denoted 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 combinations of optimization variables. 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 global optimal position. For each particle, calculate the objective function value of the multi-objective optimization model corresponding to its position, i.e., the total energy consumption and heat exchanger entropy production of the system, 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] Velocity and position update. According to the individual optimal position and global optimal position of the particle, the velocity and position of each particle in the search space are updated using the velocity update formula and position update formula. The inertia weight, individual learning factor and social learning factor are introduced in the velocity update formula, which represent the degree of particle maintaining the original motion trend, learning from the individual optimal position and learning from the global optimal position. The position update formula calculates the new position of the particle at the next time according to the current position and updated velocity of the particle.

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

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

[0156] Merge 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, and remove the duplicate solutions to obtain the final non-dominated solution set. This solution set contains multiple non-dominated optimization solutions, each corresponding to a set of optimal combinations of extraction pressure, extraction temperature, extraction flow rate and heat release duration, forming an optimal regulation strategy set for the multi-stage heat release process of the extraction energy storage system. The optimal regulation strategy set provides a basis for subsequent strategy selection and implementation.

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

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

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

[0160] In an alternative embodiment,

[0161] According to the peak shaving and frequency modulation demand of the steam turbine unit, the best regulation strategy is selected from the optimal regulation strategy set, the valve opening degree of the steam extraction motor regulating valve and the feed water flow of the steam extraction desuperheating and pressure reducing device are controlled by the steam extraction storage control system to realize the optimal regulation of the steam extraction parameters, and the speed of the cold source side supply and return water pump is controlled to realize the optimal matching of the supply and return water parameters, so that the steam extraction storage system always maintains the best working condition in the multi-stage heat release process, including:

[0162] Through the communication interface with the power grid dispatching center, the peak shaving and frequency modulation demand of the steam turbine unit is obtained in real time, including target output, output change rate and regulation duration information, which serves as the basis for selecting the best regulation strategy; the applicability of each regulation strategy in the optimal regulation strategy set is evaluated according to the peak shaving and frequency modulation demand, and candidate regulation strategies that meet the peak shaving and frequency modulation demand are selected; the regulation strategy with the best comprehensive performance in the candidate regulation strategies is selected as the best regulation strategy, including at least one of the performance indicators of heat release efficiency, response speed and stability;

[0163] According to the selected best regulation strategy, the time series of the valve opening degree of the steam extraction motor regulating valve, the feed water flow of the steam extraction desuperheating and pressure reducing device, and the control parameters of the speed of the cold source side supply and return water pump are generated as the control instructions of the steam extraction storage control system;

[0164] The steam extraction energy storage control system adjusts the valve opening of the steam extraction motorized regulating valve based on the received control instructions through a PID control method based on adaptive gain, and adjusts the feedwater flow of the steam extraction desuperheating and pressure reducing device through a fuzzy PID control method; a model predictive control algorithm is used to adjust the rotational speed of the cold source side feed and return water pump in real time, an optimal rotational speed sequence in future control periods is obtained by solving an optimization problem containing a pump end flow and rotational speed dynamic model and rotational speed constraints, and optimal matching of the feed and return water parameters and the steam extraction parameters is achieved, so that the steam extraction energy storage system always maintains the best working condition during the multi-stage heat release process.

[0165] Exemplarily, in the multi-stage heat release process of the steam extraction energy storage system, in order to adapt to the peak regulation and frequency regulation demand of the power grid, the best adjustment strategy needs to be selected from the optimal adjustment strategy set, and the optimal adjustment of the steam extraction parameters and the feed and return water parameters is realized through the steam extraction energy storage control system, so that the system always maintains the best working condition. The specific implementation steps are as follows:

[0166] Firstly, through the communication interface with the power grid dispatching center, the peak regulation and frequency regulation demand of the steam turbine unit is obtained in real time, including target output, output change rate, adjustment duration and other information. These information will be used as an important basis for selecting the best adjustment strategy.

[0167] According to the obtained peak regulation and frequency regulation demand, the applicability of each strategy in the optimal adjustment strategy set is evaluated. For each adjustment strategy, the following factors are considered:

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

[0169] Through comprehensive evaluation of the above factors, the candidate adjustment strategies that meet the peak regulation and frequency regulation demand are selected.

[0170] Among the candidate adjustment strategies that meet the peak regulation and frequency regulation demand, 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 best adjustment strategy.

[0171] According to the selected best adjustment strategy, the time sequence of the control parameters such as the valve opening of the steam extraction motorized regulating valve, the feedwater flow of the steam extraction desuperheating and pressure reducing device, and the rotational speed of the cold source side feed and return water pump is generated as the control instruction of the steam extraction energy storage control system.

[0172] The steam extraction energy storage control system adjusts the valve opening of the steam extraction motorized regulating valve and the feedwater flow of the steam extraction desuperheating and pressure reducing device according to the received control instructions, realizes the optimal adjustment of the steam extraction pressure, temperature and other parameters, and ensures that the steam extraction state always changes along the optimal trajectory.

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

[0174]

[0175] wherein 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 the 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 and pressure reducing device adopts a control method based on fuzzy PID, and according to the deviation of the feedwater flow and the extraction steam temperature, the parameters of the PID controller are dynamically adjusted through fuzzy reasoning to realize accurate control of the extraction steam temperature.

[0177] In order to ensure the efficient operation of the extraction steam energy storage system, the speed of the cold source side feed and return water pump needs to be adjusted in real time according to the changes of the extraction steam parameters to realize the optimal matching of the feed and return water parameters and the extraction steam parameters.

[0178] The speed regulation of the feed and return water pump adopts a method based on model predictive control, and a dynamic model of the pump end flow and the speed is established:

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

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

[0181] In each control period, 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 periods:

[0182]

[0183] wherein Q ref (k+i) is the predicted value of the extraction steam flow 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 feed and return water pump to realize the dynamic matching of the feed and return water flow and the extraction steam flow.

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

[0186] In an alternative embodiment,

[0187] The method further comprises:

[0188] During the operation of the extraction storage system, the changes in the extraction 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, a feedback correction mechanism is triggered to adjust the control sequence of the optimal regulation strategy, while the operating state of the extraction storage system and the health status of the key equipment are monitored to diagnose and handle potential fault risks in a timely manner, ensuring that the extraction storage system always operates along the optimal control trajectory.

[0190] Illustratively, in order to ensure that the extraction storage system always operates along the optimal control trajectory during actual operation, a strategy optimization method based on real-time monitoring and feedback correction is proposed herein. This method dynamically assesses the execution effect of the optimal regulation strategy by obtaining system operation data in real time, and adjusts the strategy and diagnoses faults according to the tracking error, achieving intelligent operation and maintenance of the extraction storage system. The specific steps are as follows:

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

[0192] For example, pressure sensors and temperature sensors can be installed on the extraction pipeline to measure extraction pressure and temperature in real time; flow meters and temperature sensors can be installed on the supply and return water pipelines to measure supply and return water flow and temperature in real time; pressure sensors can be installed at the outlets of the supply and return water pumps to measure supply and return water pressure in real time. These monitoring data are collected and transmitted at a sampling period of 1 second or 5 seconds and stored in a 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, and the tracking error is calculated. The tracking error represents the degree of deviation between the actual operating state and the ideal optimal 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 pressure given by the optimal regulation strategy is 3.2 MPa, while the actual value of the extraction pressure obtained by real-time monitoring is 3.1 MPa, and the tracking error is 0.1 MPa. Similarly, the tracking errors of the extraction temperature, extraction flow, supply water temperature, and return water temperature can be calculated.

[0195] To comprehensively evaluate the tracking effect of multiple parameters, weighted average tracking error or root mean square tracking error can be introduced. The weighted average tracking error considers the influence 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 fluctuation degree of the tracking error.

[0196] For example, assuming that the tracking errors of the extraction pressure, extraction temperature, and supply water 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, then the weighted average tracking error is 0.1 x 0.4 + 5 x 0.3 + 2 x 0.3 = 2.2. The root mean square tracking error is sqrt((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 condition and performance requirements of the system, representing the tolerable range of tracking error.

[0198] For example, the threshold of the extraction pressure tracking error can be set to ±0.2 MPa, the threshold of the extraction temperature tracking error can be set to ±10°C, and the threshold of the supply and return water temperature tracking error can be set to ±5°C. 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 regulation strategy, so that the actual operating state of the extraction energy 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 a feedback error term, the adjustment amount of the control variable is calculated in real time, and the execution instruction is issued to the corresponding execution mechanism, such as extraction valve, water supply pump, return water pump, etc.

[0200] Taking PID control as an example, assuming that at a certain moment, the extraction pressure tracking error is 0.3 MPa, which exceeds the preset threshold. The feedback correction mechanism is based on the PID control algorithm to calculate the adjustment amount of the extraction valve opening, assuming that the adjustment amount is -5%, i.e. the extraction valve opening is reduced by 5%. The control instruction is issued to the extraction valve execution mechanism, and by adjusting the valve opening, the extraction flow is reduced, so that the extraction 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 energy storage system runs along the optimal control trajectory.

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

[0202] Common fault diagnosis methods include rule-based diagnosis, model-based diagnosis, and data-driven diagnosis. Rule-based diagnosis establishes the correspondence between fault symptoms and fault causes through expert knowledge and experience, achieving rapid judgment and positioning of faults. Model-based diagnosis uses the physical model or data model of the system to identify abnormal states of the system by the deviation between the measured data and the model prediction value. Data-driven diagnosis uses machine learning algorithms to mine fault patterns and rules from historical operation data, achieving intelligent diagnosis and prediction of faults.

[0203] For example, when an abnormal rise in extraction temperature is monitored, and the rise exceeds the normal fluctuation range, combined with the change trend of extraction temperature, extraction pressure, turbine output and other parameters, the rule-based diagnosis method is used to determine that the extraction temperature control is abnormal due to the failure of the extraction temperature reducer. Further analysis of the water injection amount, water injection temperature and other parameters of the temperature reducer locates the fault causes such as insufficient water injection, nozzle blockage, etc. According to the diagnosis results, fault alarm information is generated to notify the operation and maintenance personnel for inspection and repair.

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

[0205] For example, for key equipment in the extraction energy storage system such as steam turbines, accumulators, heat exchangers, etc., long-term monitoring and analysis of performance parameters such as vibration, temperature, pressure, efficiency, etc. are carried out to establish health degradation models. When the health index of the equipment decreases to the warning threshold, the remaining life is predicted, the failure risk is evaluated, and a health report and maintenance suggestion are generated, such as "the health index of the steam turbine is 75, the remaining life is predicted to be 2 years, and it is recommended to carry out a comprehensive inspection and maintenance of the steam turbine in the next maintenance cycle".

[0206] By real-time monitoring, feedback correction, fault diagnosis and health management, a closed-loop optimization control system of the steam extraction and energy storage system is constructed. This system can continuously track the running state of the system, dynamically adjust the optimization strategy, timely discover and handle potential faults, ensure the optimal operation performance of the steam extraction and energy storage system in the whole life cycle, improve the energy efficiency and reliability of the system, and reduce the operation and maintenance cost.

[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 timely discovered; (2) The feedback correction mechanism is introduced, and the control variables are adjusted in real time through PID control, model predictive control and other methods, so that the system returns to the optimal control trajectory again; (3) Combined with fault diagnosis and health management technology, potential fault hidden dangers of the system are timely discovered and located, and the health status of the equipment is evaluated to provide support for predictive maintenance; (4) A closed-loop optimization control system of the steam extraction and energy storage system is constructed, intelligent operation and maintenance of the system are realized, and the operation efficiency and reliability of the system are improved.

[0208] Figure 2 The structure diagram of the multi-stage heat release regulation and optimization system based on steam extraction and energy storage of the embodiment of the present application is shown as Figure 2 The system comprises:

[0209] The first unit is used for introducing the convective heat transfer coefficient between the heat source, the cold source and the wall surface of the heat exchanger and the thermal conductivity of the wall surface of the heat exchanger according to the heat transfer process with limited temperature difference between the heat source, the cold source and the wall surface of the steam turbine unit, determining the convective heat transfer, heat conduction and external heat loss inside the heat exchanger, simplifying the heat storage water tank into a lumped parameter model, and establishing a dynamic thermodynamic model of the heat release process of the steam extraction and energy storage system containing multiple state variables of heat release power, heat release time, heat release efficiency and temperature change of the heat storage water tank;

[0210] The second unit is used for establishing a multi-objective optimization model according to the dynamic thermodynamic model of the heat release process of the steam extraction and energy storage system, taking the minimization of the total energy consumption of the system and the minimization of the entropy production of the heat exchanger as the optimization objectives, taking the steam extraction pressure, the steam extraction temperature, the steam extraction flow and the heat release time as the optimization variables, and combining the constraint conditions corresponding to the optimization variables; a hybrid optimization strategy combining the improved non-dominated sorting genetic algorithm and the 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 and energy storage system is determined;

[0211] The third unit is configured to select an optimal regulation strategy from the optimal regulation strategy set according to the peak regulation and frequency modulation requirement of the steam turbine unit, control the valve opening degree of the extraction motor-driven regulation valve and the feed water flow of the extraction desuperheating and pressure reducing device through the extraction energy storage control system, realize the optimal regulation of the extraction parameters, control the rotating speed of the water supply and return pump on the cold source side, realize the optimal matching of the water supply and return parameters, and keep the optimal working condition of the extraction energy storage system in the multi-stage heat release process.

[0212] A third aspect of the embodiments of the present application,

[0213] An electronic device is provided, comprising:

[0214] A processor;

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

[0216] The processor is configured to invoke the instructions stored in the memory to execute the method described above.

[0217] A fourth aspect of the embodiments of the present application,

[0218] A computer readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the method described above.

[0219] The present application can be a method, device, system and / or computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions stored therein, which are used to perform various aspects of the present application.

[0220] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-stage heat release regulation optimization method based on steam extraction storage, characterized in that, include: Based on the heat transfer process with a finite temperature difference between the heat source, cold source and heat exchanger wall of the steam turbine unit, the convective heat transfer coefficient between the heat source, cold source and 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. 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, which includes multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the hot water storage tank. Based on the dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, a multi-objective optimization model is established with the optimization objectives of minimizing the total system energy consumption and minimizing the heat exchanger entropy production. The optimization variables are the steam extraction pressure, steam extraction temperature, steam extraction flow rate, and heat release duration at each stage. Combined with the constraints corresponding to the optimization variables, a multi-objective optimization model is established. The multi-objective optimization model is solved by a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm, thereby determining the optimal set of regulation strategies for the multi-stage heat release process of the steam extraction energy storage system. Based on the peak shaving and frequency regulation requirements of the steam turbine unit, the optimal regulation strategy is selected from the set of optimal regulation strategies. The valve opening of the extraction steam electric regulating valve and the feedwater flow of the extraction steam desuperheating and pressure reducing device are controlled by the extraction steam energy storage control system to achieve optimized regulation of extraction steam parameters. At the same time, the speed of the cold source side supply and return water pumps is controlled to achieve optimized matching of supply and return water parameters, so that the extraction steam energy storage system always maintains the best operating conditions during the multi-stage heat release process.

2. The method according to claim 1, characterized in that, Based on the heat transfer process with a finite temperature difference between the heat source, cold source, and heat exchanger wall of the steam turbine unit, the convective heat transfer coefficient between the heat source, cold source, and heat exchanger wall, as well as the thermal conductivity of the heat exchanger wall, are introduced to determine the convective heat transfer, conduction, and external heat dissipation losses inside the heat exchanger. The hot water storage tank is simplified into a lumped parameter model, and a dynamic thermodynamic model of the heat release process of the extraction steam storage system is established, including multiple state variables such as heat release power, heat release time, heat release efficiency, and temperature change of the hot water storage tank. Based on the finite-time thermodynamics theory, considering the heat transfer process with a finite temperature difference between the heat source, cold source and the heat exchanger wall, an energy balance model and an entropy balance model describing the heat release process are established. By introducing the convective heat transfer coefficient between the heat source, cold source and heat exchanger wall, as well as the thermal conductivity of the heat exchanger wall, a heat transfer balance model describing the heat transfer process between the heat source, cold source and heat exchanger wall is established. 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 intrinsic relationship between heat release power, heat release efficiency and heat release time. Based on thermal resistance theory, considering various heat transfer forms such as convective heat transfer, conduction heat transfer and external heat dissipation loss inside the heat exchanger, an unsteady heat transfer partial differential equation describing the three-dimensional temperature distribution inside the heat exchanger is established; and combined with 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. For hot water storage tanks, a lumped parameter model is simplified to comprehensively consider 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, and a water tank temperature change model is established to describe the dynamic changes in water temperature inside the hot water storage tank. The heat release model, the temperature heat loss model, and the water tank temperature change model are assigned corresponding model weights, and a dynamic thermodynamic model of the heat release process of the steam extraction energy storage system is established according to the 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 These are the heat transfer rates of the heat source and the cold source, respectively. Let E be the output power of the exothermic process, E be the total energy inside the heat exchanger, S be the total entropy inside the heat exchanger, and T be the total energy output power. h and T c The temperatures of the heat source and the cold source are respectively. Let t be the entropy yield of the exothermic process, and t be the exothermic time. The comprehensive heat release model reflecting the intrinsic relationship between heat release power, heat release efficiency, and heat release time is shown in the following formula: A h and A c The heat exchange areas are the heat source side and the cold source side, respectively. w T is the cross-sectional area of ​​the heat exchanger wall, δ is the thickness of the heat exchanger wall, and T is the cross-sectional area of ​​the heat exchanger wall. w,h and T w,c These are the temperatures on the heat source side and the cold source side of the heat exchanger wall, respectively. The thermal conductivity of the heat exchanger wall surface; m is the mass of the working fluid inside the heat exchanger, and c is the mass of the working fluid inside the heat exchanger. p Specific heat capacity of the working fluid; The water temperature change model describing the dynamic changes in water temperature within the hot water storage tank is shown in the following formula: in, This indicates the amount of heat absorbed by the water tank from the heat exchanger. Indicates the heat loss of the water tank and T represents the amount of heat supplied by the water tank to the load. tank This indicates the water temperature inside the tank, and M is the mass of the water inside the tank.

4. The method according to claim 1, characterized in that, Based on the dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, a multi-objective optimization model is established with the optimization objectives of minimizing the total system energy consumption and minimizing the heat exchanger entropy production. The optimization variables are the extraction steam pressure, extraction steam temperature, extraction steam flow rate, and heat release duration at each stage. A hybrid optimization strategy combining the constraints corresponding to these optimization variables is then employed to solve the multi-objective optimization model. The optimal set of regulation strategies for the multi-stage heat release process of the steam extraction energy storage system is determined as follows: Based on the dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, a multi-objective optimization model is established with the optimization objectives of minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger. The optimization variables are the steam extraction pressure, steam extraction temperature, steam extraction flow rate, and heat release duration at each stage. The model is then solved using a hybrid optimization strategy that combines an improved non-dominated sorting genetic algorithm with a multi-objective particle swarm optimization algorithm. The improved non-dominated sorting genetic algorithm includes: randomly initializing a population containing multiple individuals, each individual representing a set of feasible combinations of optimization variables; 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 based on the non-dominated level and crowding distance using a binary tournament selection operator; performing simulated binary crossover and polynomial mutation on the parent population to generate new offspring individuals and construct the offspring population; merging the parent and offspring populations, performing fast non-dominated sorting and crowding calculation on the merged population, and selecting superior individuals as the new population; repeating the iteration until the 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: using the first non-dominated solution set as the initial population, with each particle's position corresponding to a set of feasible optimization variable combinations; calculating the fitness value of each particle, i.e., the value of the multi-objective optimization model, and updating the individual optimal position and global optimal position of each particle; updating the velocity and position of each particle using velocity update formulas and position update formulas based on the individual optimal position and global optimal position of the particles; performing boundary processing on the updated particle positions to ensure that each optimization variable is within its 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 merged to obtain the final non-dominated solution set, which serves as the optimal regulation strategy set for the multi-stage heat release process of the steam extraction energy storage system.

5. The method according to claim 4, characterized in that, Based on the dynamic thermodynamic model of the heat release process of the steam extraction energy storage system, with the optimization objectives of minimizing the total system energy consumption and minimizing the heat exchanger entropy production, and with the extraction steam pressure, extraction steam temperature, extraction steam flow rate, and heat release duration at each stage as 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: Where f1 and f2 are the total system energy consumption and total heat exchanger entropy production, respectively, N represents the number of stages in the heat release process, and L... i h i Δt i , Let represent the extraction steam flow rate, extraction steam enthalpy, heat release duration, and heat exchange on the cold source side for the i-th stage, respectively. This represents the entropy production rate of the heat exchanger in the i-th stage; The constraints corresponding to the optimization variables include: in, These represent the upper and lower limits of the extraction enthalpy, respectively. These represent the upper and lower limits of the extraction steam flow rate, respectively. These represent the upper and lower limits of the heat release duration, respectively.

6. The method according to claim 1, characterized in that, Based on the peak-shaving and frequency regulation requirements of the steam turbine unit, the optimal regulation strategy is selected from the set of optimal regulation strategies. The extraction steam storage control system controls the valve opening of the extraction steam electric regulating valve and the feedwater flow rate of the extraction steam desuperheating and pressure-reducing device to achieve optimized regulation of extraction steam parameters. Simultaneously, the speed of the cold source-side supply and return water pumps is controlled to achieve optimized matching of supply and return water parameters, ensuring that the extraction steam storage system maintains optimal operating conditions throughout the multi-stage heat release process, including: Through the communication interface with the power grid dispatch center, the peak shaving and frequency regulation requirements of the steam turbine units are obtained in real time. The peak shaving and frequency regulation requirements include target output, output change rate, and regulation duration information, which serve as the basis for selecting the optimal regulation strategy. Based on the peak shaving and frequency regulation requirements, the applicability of each regulation strategy in the optimal regulation strategy set is evaluated, and candidate regulation strategies that meet the peak shaving and frequency regulation requirements are screened out. The regulation strategy with the best comprehensive performance among the candidate regulation strategies is selected as the optimal regulation strategy. The comprehensive performance includes at least one performance index among heat release efficiency, response speed, and stability. Based on the selected optimal regulation strategy, a time series of control parameters is generated for the valve opening of the extraction steam electric regulating valve, the feed water flow of the extraction steam desuperheating and pressure reducing device, and the speed of the cold source side supply and return water pumps, which serve as the control commands for the extraction steam energy storage control system. The extraction steam storage control system adjusts the valve opening of the extraction steam electric regulating valve through an adaptive gain-based PID control method and the feedwater flow of the extraction steam desuperheating and pressure reducing device through a fuzzy PID control method, based on the received control commands. It also adjusts the speed of the cold source side supply and return water pumps in real time using a model predictive control algorithm. By solving an optimization problem that includes the dynamic model of pump flow and speed and speed constraints, the optimal speed sequence for several future control cycles is obtained, achieving optimal matching between supply and return water parameters and extraction steam parameters. This ensures that the extraction steam storage system maintains optimal operating conditions throughout the multi-stage heat release process.

7. The method according to claim 6, characterized in that, The method further includes: During the operation of the extraction steam storage system, the changes in extraction steam parameters and 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 to diagnose and deal with potential faults in a timely manner, ensuring that the extraction steam storage system always operates along the optimal control trajectory.

8. A multi-stage heat release regulation and optimization system based on steam extraction energy storage, used to implement the method of any one of claims 1-7, characterized in that, include: The first unit is used to determine the convective heat transfer coefficient between the heat source, cold source and heat exchanger wall of the steam turbine unit, as well as the thermal conductivity of the heat exchanger wall, based on the heat transfer process with a finite temperature difference between the heat source, cold source and heat exchanger wall. It also simplifies the hot water storage tank into a lumped parameter model and establishes a dynamic thermodynamic model of the heat release process of the extraction steam energy storage system, which includes multiple state variables such as heat release power, heat release time, heat release efficiency and temperature change of the hot water 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 steam extraction energy storage system, with the optimization objectives of minimizing the total energy consumption of the system and minimizing the entropy production of the heat exchanger, and with the steam extraction pressure, steam extraction temperature, steam extraction flow rate and heat release duration at each stage as optimization variables, and in combination with the constraints corresponding to the optimization variables. The multi-objective optimization model is solved by a hybrid optimization strategy combining an improved non-dominated sorting genetic algorithm and a multi-objective particle swarm optimization algorithm, so as to determine the optimal set of regulation strategies for the multi-stage heat release process of the steam extraction energy storage system. The third unit is used to select the best regulation strategy from the set of optimal regulation strategies according to the peak shaving and frequency regulation requirements of the steam turbine unit. It controls the valve opening of the extraction steam electric regulating valve and the feed water flow of the extraction steam desuperheating and pressure reducing device through the extraction steam energy storage control system to achieve optimized regulation of extraction steam parameters. At the same time, it controls the speed of the cold source side supply and return water pumps to achieve optimized matching of supply and return water parameters, so that the extraction steam energy storage system always maintains the best operating conditions during the multi-stage heat release process.

9. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to 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 the processor, they implement the method described in any one of claims 1 to 7.

Images