Robust scheduling method, device and equipment for thermal power coupling compressed air energy storage system

Abstract

The invention relates to the technical field of compressed air energy storage, in particular to a thermal power coupling compressed air energy storage system robust scheduling method, device and equipment, and the method comprises the steps: constructing an operation model of each subsystem in a topological structure of the thermal power coupling compressed air energy storage system; a comprehensive operation model of the whole thermal power coupling compressed air energy storage system is established based on the operation model of each subsystem, thermal power resources can be utilized to the maximum extent, the energy storage efficiency is improved, the comprehensive model serves as a thermoelectric system hub, a power flow model on the power grid side and a heat supply network model on the heat supply network side are combined, an optimal scheduling model is established, and the energy storage efficiency is improved. A new energy output fuzzy set is obtained to cope with uncertain factors in actual operation, fuzzy set parameters are set according to actual data of a power grid in a target region, and the parameters are input into an optimal scheduling model to output an optimal scheduling strategy, so that the system operation cost is effectively reduced, and the system performance is improved. And the adaptability to new energy fluctuation and the risk management capability are enhanced.

Description

Technical Field

[0001] This application relates to the field of compressed air energy storage technology, and in particular to a robust scheduling method, apparatus and equipment for a thermal power coupled compressed air energy storage system. Background Technology

[0002] Vigorously promoting the development of green energy sources such as solar and wind power is a key approach to achieving energy structure transformation and strategic adjustment. However, the inherent intermittency and volatility of new energy sources, represented by wind and solar power, make their integration difficult. Furthermore, the large-scale grid connection of new energy sources necessitates more flexible adjustment resources in the power system to improve the utilization rate of new energy sources, thereby supporting the stable, efficient, and economical operation of the power grid. Since thermal power generation has long dominated my country's energy structure, it will inevitably play a crucial role in my country's energy structure transformation. Therefore, improving the operational flexibility of thermal power units is of great significance for adapting to the high proportion of new energy penetration and enhancing the flexible adjustment capabilities of the new power system. Summary of the Invention

[0003] This application provides a robust scheduling method, apparatus, and equipment for a thermal power coupled compressed air energy storage system, which can maximize the utilization of thermal power resources, improve energy storage efficiency, effectively reduce system operating costs, and enhance the adaptability of the energy storage system in new energy consumption and grid peak shaving.

[0004] The first aspect of this application provides a robust scheduling method for a thermal power coupled compressed air energy storage system, comprising the following steps: obtaining the topology of the thermal power coupled compressed air energy storage system, identifying the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system in the topology, and constructing operating models for each of the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system; constructing an operating model of the thermal power coupled compressed air energy storage system based on the operating models of the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system; constructing an optimized scheduling model of the thermal power coupled compressed air energy storage system using the operating model of the thermal power coupled compressed air energy storage system as the hub of the thermal power system, and combining the power flow model on the grid side and the heat network model on the heat network side; obtaining a fuzzy set of new energy output, setting the corresponding parameters of the fuzzy set according to the actual data of the power grid in the target area, inputting the set fuzzy set into the optimized scheduling model, and outputting a scheduling strategy for the thermal power coupled compressed air energy storage system from the optimized scheduling model.

[0005] Optionally, the operation process of the thermal power coupled compressed air energy storage system includes: In the energy storage stage, the compression subsystem is started, and the compressor is driven to work by absorbing excess power from the grid. The compressed high-temperature air exchanges heat with the low-temperature heat storage medium in the cold tank through the heat exchanger on the compression side. After heat exchange, the medium-temperature heat storage medium is fully heated to high temperature by the waste heat of the thermal power flue gas and stored in the hot tank. The multi-stage compressor compresses the air to high pressure step by step and stores it in the gas storage tank, thus completing the storage of electrical energy. In the power generation stage, the turbine subsystem is started. The high-pressure gas stored in the gas storage tank first exchanges heat with the high-temperature heat storage medium in the hot tank through the heat exchanger. The high-temperature and high-pressure air after heat exchange enters the turbine, drives the turbine to do work, drives the generator to work and outputs electricity to the grid, thus completing the release of electrical energy.

[0006] Optionally, the operating model of the thermal power coupled compressed air energy storage system consists of operating models for the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system, among which, The operating model of the compression subsystem is as follows:

[0007]

[0008] in, The compressor charging power at time t. Let be the mass flow rate of air flowing into the compressor at time t. This indicates the mass flow rate of air entering the compressor. The specific heat capacity of air. This refers to the air temperature at the compressor inlet. Indicates the lower / upper limit of compressed air mass flow rate. A binary variable representing the charging state. This refers to the compressor pressure ratio. The air insulation index. The compressor has isentropic efficiency; The operating model of the turbine subsystem is as follows:

[0009]

[0010] in, These represent the turbine's power generation at time t; Let be the mass flow rate of air flowing into the turbine at time t; The air temperature at the turbine inlet; This indicates the mass flow rate of air entering the turbine. Indicates the lower / upper limit of turbine air mass flow rate. A binary variable representing the control of the discharge state. The turbine ratio of the turbine. For turbine isentropic efficiency; The energy storage system includes a gas storage subsystem and a thermal storage tank system, among which... The operating model of the gas storage subsystem is as follows:

[0011]

[0012] in, Let t be the gas pressure in the gas storage tank. is the gas constant of air; Temperature of the gas storage facility; and These are the air mass flow rates on the compression side and turbine side of the gas storage facility, respectively. For gas storage capacity; The operating model of the gas storage subsystem is as follows:

[0013]

[0014] in, Let t be the flow rate of the high-temperature heat transfer fluid during the compression phase at time t; Specific heat capacity of the heat transfer fluid; Let be the flow rate of the high-temperature heat transfer fluid at time t; The operating model of the flue gas waste heat system is as follows:

[0015] in, The upper limit of heat supply for the flue gas waste heat system This is the lower limit for the heat supply of the flue gas waste heat system.

[0016] Optionally, the operating model of the thermal power coupled compressed air energy storage system also includes: a model of the power plant's various rated values, which are as follows:

[0017] in, The upper limit of the rated compression power of the power plant This is the lower limit of the rated compression power of the power plant; Upper limit of rated turbine power of power plant This is the lower limit of the rated turbine power of the power plant; Upper limit of the volume of gas storage tank for power plants This is the lower limit of the gas storage volume of the power plant.

[0018] Optionally, the optimized scheduling model includes a first scheduling stage and a second scheduling stage. Before inputting the set fuzzy set into the thermoelectric system scheduling model, the model further includes: setting the objective function of the thermoelectric system scheduling model according to the objectives of the first scheduling stage and the objectives of the second scheduling stage; and optimizing the thermoelectric system scheduling model based on the objective function.

[0019] Optionally, the objective function for optimizing the scheduling model is:

[0020]

[0021] in, The objective function for the first scheduling phase is to minimize the system scheduling cost within the current time period. Let i be the system state transition probability; This represents the scheduling cost of the second scheduling phase given the current scheduling variables and the system state within the scheduling period; For the unit at time t The power generation capacity; and For the unit The power generation cost coefficient; To purchase electricity from the main power grid; The price of electricity purchased from the main grid; and These are the startup costs for the compression side and turbine side of compressed air energy storage, respectively. To generate heat for the heat pump, Heat generated for energy storage of compressed air; This is the heat production cost coefficient of the heat pump; This is the heat production cost coefficient for the compressed air energy storage station.

[0022] Optionally, before obtaining the fuzzy set of new energy output, the process also includes: constructing the fuzzy set of new energy output using infinite norm and 1-norm constraints.

[0023] A second aspect of this application provides a robust scheduling device for a thermal power coupled compressed air energy storage system, comprising: an acquisition module for acquiring the topology of the thermal power coupled compressed air energy storage system, identifying the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system in the topology, and constructing operating models for each of the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system; a first construction module for constructing an operating model of the thermal power coupled compressed air energy storage system based on the operating models of the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system; a second construction module for constructing an optimized scheduling model of the thermal power coupled compressed air energy storage system, using the operating model of the thermal power coupled compressed air energy storage system as the hub of the thermal power system, and combining the power flow model on the grid side and the heat network model on the heat network side; and a scheduling module for acquiring a fuzzy set of new energy output, setting corresponding parameters of the fuzzy set according to the actual data of the power grid in the target area, inputting the set fuzzy set into the optimized scheduling model, and outputting a scheduling strategy for the thermal power coupled compressed air energy storage system from the optimized scheduling model.

[0024] Optionally, the operation process of the thermal power coupled compressed air energy storage system includes: In the energy storage stage, the compression subsystem is started, and the compressor is driven to work by absorbing excess power from the grid. The compressed high-temperature air exchanges heat with the low-temperature heat storage medium in the cold tank through the heat exchanger on the compression side. After heat exchange, the medium-temperature heat storage medium is fully heated to high temperature by the waste heat of the thermal power flue gas and stored in the hot tank. The multi-stage compressor compresses the air to high pressure step by step and stores it in the gas storage tank, thus completing the storage of electrical energy. In the power generation stage, the turbine subsystem is started. The high-pressure gas stored in the gas storage tank first exchanges heat with the high-temperature heat storage medium in the hot tank through the heat exchanger. The high-temperature and high-pressure air after heat exchange enters the turbine, drives the turbine to do work, drives the generator to work and outputs electricity to the grid, thus completing the release of electrical energy.

[0025] Optionally, the operating model of the thermal power coupled compressed air energy storage system consists of operating models for the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system, among which, The operating model of the compression subsystem is as follows:

[0026]

[0027] in, The compressor charging power at time t. Let be the mass flow rate of air flowing into the compressor at time t. This indicates the mass flow rate of air entering the compressor. The specific heat capacity of air. This refers to the air temperature at the compressor inlet. Indicates the lower / upper limit of compressed air mass flow rate. A binary variable representing the charging state. This refers to the compressor pressure ratio. The air insulation index. The compressor has isentropic efficiency; The operating model of the turbine subsystem is as follows:

[0028]

[0029] in, These represent the turbine's power generation at time t; Let be the mass flow rate of air flowing into the turbine at time t; The air temperature at the turbine inlet; This indicates the mass flow rate of air entering the turbine. Indicates the lower / upper limit of turbine air mass flow rate. A binary variable representing the control of the discharge state. The turbine ratio of the turbine. For turbine isentropic efficiency; The energy storage system includes a gas storage subsystem and a thermal storage tank system, among which... The operating model of the gas storage subsystem is as follows:

[0030]

[0031] in, Let t be the gas pressure in the gas storage tank. is the gas constant of air; Temperature of the gas storage facility; and These are the air mass flow rates on the compression side and turbine side of the gas storage facility, respectively. For gas storage capacity; The operating model of the gas storage subsystem is as follows:

[0032]

[0033] in, Let t be the flow rate of the high-temperature heat transfer fluid during the compression phase at time t; Specific heat capacity of the heat transfer fluid; Let be the flow rate of the high-temperature heat transfer fluid at time t; The operating model of the flue gas waste heat system is as follows:

[0034] in, The upper limit of heat supply for the flue gas waste heat system This is the lower limit for the heat supply of the flue gas waste heat system.

[0035] Optionally, the operating model of the thermal power coupled compressed air energy storage system also includes: a model of the power plant's various rated values, which are as follows:

[0036] in, The upper limit of the rated compression power of the power plant This is the lower limit of the rated compression power of the power plant; Upper limit of rated turbine power of power plant This is the lower limit of the rated turbine power of the power plant; Upper limit of the volume of gas storage tank for power plants This is the lower limit of the gas storage volume of the power plant.

[0037] Optionally, the optimized scheduling model includes a first scheduling stage and a second scheduling stage. The robust scheduling device for the thermal power coupled compressed air energy storage system also includes: an optimization module, used to set the objective function of the optimized scheduling model according to the objectives of the first scheduling stage and the second scheduling stage before inputting the set fuzzy set into the optimized scheduling model; and to optimize the optimized scheduling model based on the objective function.

[0038] Optionally, the objective function for optimizing the scheduling model is:

[0039]

[0040] in, The objective function for the first scheduling phase is to minimize the system scheduling cost within the current time period. Let i be the system state transition probability; This represents the scheduling cost of the second scheduling phase given the current scheduling variables and the system state within the scheduling period; For the unit at time t The power generation capacity; and For the unit The power generation cost coefficient; To purchase electricity from the main power grid; The price of electricity purchased from the main grid; and These are the startup costs for the compression side and turbine side of compressed air energy storage, respectively. To generate heat for the heat pump, Heat generated for energy storage of compressed air; This is the heat production cost coefficient of the heat pump; This is the heat production cost coefficient for the compressed air energy storage station.

[0041] Optionally, the robust scheduling device for the thermal power coupled compressed air energy storage system further includes: a third construction module, which is used to construct a fuzzy set of new energy output by adopting infinite norm and 1-norm constraints before obtaining the fuzzy set of new energy output.

[0042] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the robust scheduling method for a thermal power coupled compressed air energy storage system as described in the above embodiments.

[0043] A fourth aspect of this application provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed, are used to implement the robust scheduling method for a thermal-electric coupled compressed air energy storage system as described in the above embodiments.

[0044] Therefore, this application has at least the following beneficial effects: This application's embodiments identify the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system in the topology of a thermal power coupled compressed air energy storage system, construct operation models for each subsystem, and establish a comprehensive operation model for the entire thermal power coupled compressed air energy storage system based on these models. This maximizes the utilization of thermal power resources, improves energy storage efficiency, and effectively reduces system operating costs. Using the comprehensive model as the hub of the thermal power system, combined with the power flow model on the grid side and the heat network model on the heat network side, an optimized scheduling model is constructed. Fuzzy sets of renewable energy output are obtained to address uncertainties in actual operation, and fuzzy set parameters are set according to the actual data of the target region's power grid. Finally, these parameters are input into the optimized scheduling model to output the optimal scheduling strategy. This not only improves the system's flexibility and energy utilization efficiency but also enhances its adaptability to renewable energy fluctuations and risk management capabilities, providing strong support for achieving efficient and stable power supply.

[0045] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0046] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart illustrating a robust scheduling method for a thermal power coupled compressed air energy storage system according to an embodiment of this application. Figure 2 This is a schematic diagram of the operating principle of a thermal power coupled compressed air energy storage system according to an embodiment of this application; Figure 3 This is an example diagram illustrating robust scheduling of a thermal power coupled compressed air energy storage system according to an embodiment of this application; Figure 4 This is a block diagram of a robust dispatching device for a thermal power coupled compressed air energy storage system according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0047] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0048] The following description, with reference to the accompanying drawings, outlines a robust scheduling method, apparatus, electronic device, and storage medium for a thermal power coupled compressed air energy storage system according to embodiments of this application. Addressing the problems mentioned in the background section, this application provides a robust scheduling method for a thermal power coupled compressed air energy storage system. This method identifies the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system within the topology of the thermal power coupled compressed air energy storage system, constructs operational models for each subsystem, and establishes a comprehensive operational model for the entire thermal power coupled compressed air energy storage system based on these models. This maximizes the utilization of thermal power resources, improves energy storage efficiency, and effectively reduces system operating costs. Using the comprehensive model as the hub of the thermal power system, and combining the power flow model on the grid side and the heat network model on the heat network side, an optimized scheduling model is constructed. A fuzzy set of renewable energy output is obtained to address uncertainties in actual operation. Fuzzy set parameters are set based on actual data from the target region's power grid, and these parameters are ultimately input into the optimized scheduling model to output the optimal scheduling strategy. This not only improves the system's flexibility and energy utilization efficiency but also enhances its adaptability to renewable energy fluctuations and risk management capabilities, providing strong support for achieving efficient and stable power supply.

[0049] Specifically, Figure 1 This is a flowchart illustrating a robust scheduling method for a thermal power coupled compressed air energy storage system provided in an embodiment of this application.

[0050] like Figure 1 As shown, the robust scheduling method for the thermal power coupled compressed air energy storage system includes the following steps: In step S101, the topology of the thermal power coupled compressed air energy storage system is obtained, the compression subsystem, turbine subsystem, energy storage system and flue gas waste heat system in the topology are identified, and the operation models of the compression subsystem, turbine subsystem, energy storage system and flue gas waste heat system are constructed respectively.

[0051] The operating model of the compression subsystem is as follows:

[0052]

[0053] in, The compressor charging power at time t. Let be the mass flow rate of air flowing into the compressor at time t. This indicates the mass flow rate of air entering the compressor. The specific heat capacity of air. This refers to the air temperature at the compressor inlet. Indicates the lower / upper limit of compressed air mass flow rate. A binary variable representing the charging state. This refers to the compressor pressure ratio. The air insulation index. The compressor has isentropic efficiency; The operating model of the turbine subsystem is as follows:

[0054]

[0055] in, These represent the turbine's power generation at time t; Let be the mass flow rate of air flowing into the turbine at time t; The air temperature at the turbine inlet; This indicates the mass flow rate of air entering the turbine. Indicates the lower / upper limit of turbine air mass flow rate. A binary variable representing the control of the discharge state. The turbine ratio of the turbine. This refers to the isentropic efficiency of the turbine.

[0056] The energy storage system includes a gas storage subsystem and a thermal storage tank system, among which... The operating model of the gas storage subsystem is as follows:

[0057]

[0058] in, Let t be the gas pressure in the gas storage tank. is the gas constant of air; Temperature of the gas storage facility; and These are the air mass flow rates on the compression side and turbine side of the gas storage facility, respectively. For gas storage capacity; The operating model of the gas storage subsystem is as follows:

[0059]

[0060] in, Let t be the flow rate of the high-temperature heat transfer fluid during the compression phase at time t; Specific heat capacity of the heat transfer fluid; Let be the flow rate of the high-temperature heat transfer fluid at time t; The operating model of the flue gas waste heat system is as follows:

[0061] in, The upper limit of heat supply for the flue gas waste heat system This is the lower limit for the heat supply of the flue gas waste heat system.

[0062] In step S102, an operating model for a thermal power coupled compressed air energy storage system is constructed based on the operating models of the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system.

[0063] It is understandable that the embodiments of this application first need to clarify the operating models of each component of the system. Each component, including the compressor, expander (turbine), gas storage tank, thermal storage tank, and flue gas waste heat system, has a detailed mathematical model to support its operation, ensuring the efficient operation of the entire system. These models consider various losses and efficiencies in the energy conversion process, as well as the optimal operating points under different operating conditions, aiming to maximize energy utilization and system economic benefits. Advanced adiabatic compressed air energy storage, with its advantages of large capacity, long lifespan, and combined heat and power (CHP) technology, exhibits excellent thermodynamic interface characteristics and can be coupled with external heat sources, thus providing an effective path for the flexible transformation of thermal power plants. Therefore, the embodiments of this application construct an operating model for a thermal power coupled compressed air energy storage system based on the operating models of the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system, to improve the flexibility of thermal power units, effectively utilize thermal energy, optimize scheduling strategies, and enhance peak-shaving capabilities, thereby maximizing the utilization of thermal power resources. It can not only maximize the use of thermal power resources and improve energy storage efficiency, but also effectively reduce system operating costs and enhance the adaptability of energy storage systems in new energy consumption and grid peak shaving.

[0064] The operation process of the thermal power coupled compressed air energy storage system includes: In the energy storage stage, the compression subsystem is started, and the compressor is driven by absorbing excess power from the grid. The compressed high-temperature air exchanges heat with the low-temperature heat storage medium in the cold tank through the heat exchanger on the compression side. After heat exchange, the medium-temperature heat storage medium is further heated to high temperature by the waste heat of the flue gas from the thermal power plant and stored in the hot tank. The multi-stage compressor compresses the air to high pressure step by step and stores it in the gas storage tank, thus completing the storage of electrical energy. In the power generation stage, the turbine subsystem is started. The high-pressure gas stored in the gas storage tank first exchanges heat with the high-temperature heat storage medium in the hot tank through the heat exchanger. The high-temperature and high-pressure air after heat exchange enters the turbine, drives the turbine to do work, drives the generator to work and outputs electricity to the grid, thus completing the release of electrical energy.

[0065] The operating model of the thermal power coupled compressed air energy storage system in this application embodiment is as follows: Figure 2 As shown, it includes: a compression subsystem, a turbine subsystem, an energy storage system, and a flue gas waste heat system.

[0066] The compression subsystem activates when electricity demand is low or when there is a surplus of renewable energy (such as wind and solar power). It draws excess electricity from the grid to drive the compressor, compressing air to high pressure and storing it in a storage tank. Simultaneously, the heat generated during compression is not wasted; it is exchanged with a low-temperature heat storage medium in a cold tank via a heat exchanger, and then further heated to a high temperature using waste heat from the power plant's flue gas before finally being stored in a hot tank.

[0067] The turbine subsystem starts up during peak electricity demand or when the system's power generation is insufficient. This process involves releasing high-pressure gas from the gas storage tank, exchanging heat with the high-temperature heat storage medium in the hot tank, and then entering the turbine to do work, driving the generator to produce electricity and output it to the grid.

[0068] Energy storage systems mainly consist of gas storage tanks and thermal storage tanks. Gas storage tanks are used to store high-pressure gas supplied by the compression subsystem, while thermal storage tanks are used to store the heat storage medium after it has been heated from medium to high temperature for later use.

[0069] The flue gas waste heat system is an important component of thermal power units. By recovering the waste heat from the flue gas discharged from the power plant, it provides an additional heat source for the compression subsystem, thereby improving the energy utilization rate of the entire system. This paper identifies the key components of the system and constructs operating models for each of these components.

[0070] In one embodiment of this application, the operating model of the thermal power coupled compressed air energy storage system further includes: a power plant rating model, which is as follows:

[0071] in, The upper limit of the rated compression power of the power plant This is the lower limit of the rated compression power of the power plant; Upper limit of rated turbine power of power plant This is the lower limit of the rated turbine power of the power plant; Upper limit of the volume of gas storage tank for power plants This is the lower limit of the gas storage volume of the power plant.

[0072] In step S103, the operation model of the thermal power coupled compressed air energy storage system is used as the hub of the thermal power system, and an optimized scheduling model of the thermal power coupled compressed air energy storage system is constructed by combining the power flow model on the grid side and the heat network model on the heat network side.

[0073] It is understood that this application proposes using the operating model of a thermal power-coupled compressed air energy storage system as the hub of the thermal power system, and combining the power flow model on the grid side and the heat network model on the heat network side to construct an optimized scheduling model for the thermal power-coupled compressed air energy storage system. This process aims to optimize the operating efficiency and economy of the entire system by integrating the models of the power system and the heating system.

[0074] Specifically, the operating model of a thermal power coupled compressed air energy storage system serves as the hub of the thermoelectric system. This system not only generates electricity but also provides heat, thus acting as a bridge between electricity and heat supply. For example, during off-peak electricity demand, it can utilize surplus electricity for compressed air energy storage while recovering the heat generated during compression for heating; during peak hours, it can release the stored energy to meet electricity demand and may continue to provide heat. The power flow model on the grid side can be a Dist-Flow model, a method for simulating power flow in the distribution network. It considers the impact of resistance on voltage drop and is more suitable for the analysis of low-voltage distribution networks. Through this model, the voltage, current, and power distribution of each node in the grid can be accurately calculated, thereby ensuring the safe and stable operation of the grid. The heating network model takes into account the relatively fixed water flow rate in the heating network, while the temperature changes according to user demand and external conditions. This model helps to better understand and predict the behavior of the heating network, thereby optimizing heat energy distribution. It mainly consists of heat sources, transmission and distribution networks, heat exchange stations, and heat loads. The heat sources mainly include compressed air energy storage stations and heat pumps. The heating system network consists of a water supply network and a return network with identical topologies. The heat generated by the heat source is transferred to heat exchange stations at all levels through the heat carriers in the network to provide heat to users with heat loads.

[0075] This application embodiment integrates models of both the power grid and the heating network, and takes the thermal power coupled compressed air energy storage system as the center to create a comprehensive optimization scheduling framework, forming a comprehensive optimization scheduling model that can achieve the best allocation of power and heat resources, reduce energy waste, and improve overall energy utilization efficiency.

[0076] In step S104, a fuzzy set of new energy output is obtained, and the corresponding parameters of the fuzzy set are set according to the actual data of the power grid in the target area. The set fuzzy set is then input into the optimization scheduling model, and the optimization scheduling model outputs the scheduling strategy of the thermal power coupled compressed air energy storage system.

[0077] It is understandable that before obtaining the fuzzy set of renewable energy output, robust optimization algorithms (such as infinity norm and 1-norm constraints) are used to establish the fuzzy set of renewable energy output in order to cope with uncertainties in actual operation and improve the system's adaptability and risk management capabilities. In the embodiments of this application, historical operating data of the power grid in the target area are collected, including but not limited to the actual output of renewable energy, load demand, weather conditions, etc.

[0078] By setting the corresponding parameters of the fuzzy set based on the actual data of the power grid in the target area, the risks associated with the uncertainty of new energy output can be effectively managed, ensuring that the system can operate stably under various conditions.

[0079] Furthermore, in this embodiment, the fuzzy set with adjusted parameters can be incorporated into the established optimization scheduling model of the thermal power coupled compressed air energy storage system. The optimization scheduling model uses a robust optimization algorithm to consider the uncertainty of new energy output and other related factors (such as load demand, equipment technical limitations, etc.) to perform complex mathematical calculations and find the optimal scheduling strategy, thereby improving the flexibility, economy and adaptability of the entire thermal power coupled compressed air energy storage system.

[0080] In one embodiment of this application, the optimized scheduling model includes a first scheduling stage and a second scheduling stage. Before inputting the set fuzzy set into the thermoelectric system scheduling model, the method further includes: setting an objective function for the thermoelectric system scheduling model according to the objectives of the first scheduling stage and the second scheduling stage; and optimizing the thermoelectric system scheduling model based on the objective function.

[0081] In this embodiment, the aforementioned optimization scheduling problem can be divided into power grid scheduling costs. and heating network dispatch costs The objective function for optimizing the scheduling model consists of two parts:

[0082]

[0083] in, The objective function for the first scheduling phase is to minimize the system scheduling cost within the current time period. Let i be the system state transition probability; This represents the scheduling cost of the second scheduling phase given the current scheduling variables and the system state within the scheduling period; For the unit at time t The power generation capacity; and For the unit The power generation cost coefficient; To purchase electricity from the main power grid; The price of electricity purchased from the main grid; and These are the startup costs for the compression side and turbine side of compressed air energy storage, respectively. To generate heat for the heat pump, Heat generated for energy storage of compressed air; This is the heat production cost coefficient of the heat pump; This is the heat production cost coefficient for the compressed air energy storage station.

[0084] In summary, the robust scheduling method for thermal power coupled compressed air energy storage in this application adopts infinite norm and 1-norm constraints to construct a fuzzy set of new energy output. By establishing an optimized scheduling model for the thermal power coupled compressed air energy storage system, factors such as actual wind and solar power output and load demand are considered to improve the accuracy of the model. It has good risk management capabilities and can reasonably set fuzzy set parameters according to the actual data statistics of the power grid in the target area to improve the economic efficiency of planning and construction.

[0085] lower combination Figure 3 The scheduling optimization flowchart of the robust scheduling method for thermal power coupled compressed air energy storage described in this application seeks the optimal scheduling strategy. In this embodiment, the discrete scenario probability values ​​and the second-stage variables in the subproblem are independent of each other; therefore, the solution to the subproblem can be divided into two steps. First, the inner-layer min problem in the subproblem is solved, and then the outer-layer max problem is processed. The specific solution process is as follows: 1) Initialize the solution parameters, set the lower bound LB and upper bound UB, and set the number of iterations. With convergence value .

[0086] 2) Solve the main problem part to obtain a set of intermediate solutions. and And update the lower bound value. .

[0087] 3) The solution obtained from solving the main problem in the previous step and Based on this, the subproblems are solved to obtain the objective function value of the subproblems and the probability value considering the worst-case scenario.

[0088] 4) Update the upper bound value .

[0089] 5) If Stop iterating and obtain the optimal solution. Conversely, update the worst-case probability distribution of the main problem by adding new variables to the main problem. The constraints in the main problem that include new variables.

[0090] 6) Update Return to step 2), and repeat the subsequent steps until convergence.

[0091] The robust scheduling method for thermal power coupled compressed air energy storage systems proposed in this application identifies the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system in the topology of the thermal power coupled compressed air energy storage system, constructs the operation model of each subsystem, and establishes a comprehensive operation model of the entire thermal power coupled compressed air energy storage system based on these models. This method maximizes the utilization of thermal power resources, improves energy storage efficiency, and effectively reduces system operating costs. Using the comprehensive model as the hub of the thermal power system, and combining the power flow model on the grid side and the heat network model on the heat network side, an optimized scheduling model is constructed. Fuzzy sets of renewable energy output are obtained to cope with uncertainties in actual operation. Fuzzy set parameters are set according to the actual data of the power grid in the target area, and finally these parameters are input into the optimized scheduling model to output the best scheduling strategy. This not only improves the system's flexibility and energy utilization efficiency, but also enhances its adaptability to renewable energy fluctuations and risk management capabilities, providing strong support for achieving efficient and stable power supply.

[0092] Next, referring to the accompanying drawings, a robust dispatching device for a thermal power coupled compressed air energy storage system according to an embodiment of this application is described.

[0093] Figure 4 This is a block diagram of a robust scheduling device for a thermal power coupled compressed air energy storage system according to an embodiment of this application.

[0094] like Figure 4 As shown, the robust scheduling device 10 of the thermal power coupled compressed air energy storage system includes: an acquisition module 100, a first construction module 200, a second construction module 300, and a scheduling module 400.

[0095] The acquisition module 100 is used to acquire the topology of the thermal power coupled compressed air energy storage system, identify the compression subsystem, turbine subsystem, energy storage system and flue gas waste heat system in the topology, and construct the operation models of each of the compression subsystem, turbine subsystem, energy storage system and flue gas waste heat system. The first construction module 200 is used to construct the operation model of the thermal power coupled compressed air energy storage system based on the operation models of each of the compression subsystem, turbine subsystem, energy storage system and flue gas waste heat system. The second construction module 300 is used to construct the optimized scheduling model of the thermal power coupled compressed air energy storage system, taking the operation model of the thermal power coupled compressed air energy storage system as the hub of the thermal power system, and combining the power flow model on the grid side and the heat network model on the heat network side. The scheduling module 400 is used to acquire the fuzzy set of new energy output, set the corresponding parameters of the fuzzy set according to the actual data of the power grid in the target area, input the set fuzzy set into the optimized scheduling model, and output the scheduling strategy of the thermal power coupled compressed air energy storage system.

[0096] In one embodiment of this application, the operation process of the thermal power coupled compressed air energy storage system includes: in the energy storage stage, the compression subsystem is started, and the compressor is driven to work by absorbing excess electrical energy from the grid. The compressed high-temperature air exchanges heat with the low-temperature heat storage medium in the cold tank through the heat exchanger on the compression side. After heat exchange, the medium-temperature heat storage medium is fully heated to high temperature by the waste heat of the thermal power flue gas and stored in the hot tank. The multi-stage compressor compresses the air to high pressure step by step and stores it in the gas storage tank, thus completing the storage of electrical energy. In the power generation stage, the turbine subsystem is started. The high-pressure gas stored in the gas storage tank first exchanges heat with the high-temperature heat storage medium in the hot tank through the heat exchanger. The high-temperature and high-pressure air after heat exchange enters the turbine, drives the turbine to do work, drives the generator to work and outputs electricity to the grid, thus completing the release of electrical energy.

[0097] In one embodiment of this application, the operating model of the thermal power coupled compressed air energy storage system consists of operating models of the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system, wherein, The operating model of the compression subsystem is as follows:

[0098]

[0099] in, The compressor charging power at time t. Let be the mass flow rate of air flowing into the compressor at time t. This indicates the mass flow rate of air entering the compressor. The specific heat capacity of air. This refers to the air temperature at the compressor inlet. Indicates the lower / upper limit of compressed air mass flow rate. A binary variable representing the charging state. This refers to the compressor pressure ratio. The air insulation index. The compressor has isentropic efficiency; The operating model of the turbine subsystem is as follows:

[0100]

[0101] in, These represent the turbine's power generation at time t; Let be the mass flow rate of air flowing into the turbine at time t; The air temperature at the turbine inlet; This indicates the mass flow rate of air entering the turbine. Indicates the lower / upper limit of turbine air mass flow rate. A binary variable representing the control of the discharge state. The turbine ratio of the turbine. For turbine isentropic efficiency; The energy storage system includes a gas storage subsystem and a thermal storage tank system, among which... The operating model of the gas storage subsystem is as follows:

[0102]

[0103] in, Let t be the gas pressure in the gas storage tank. is the gas constant of air; Temperature of the gas storage facility; and These are the air mass flow rates on the compression side and turbine side of the gas storage facility, respectively. For gas storage capacity; The operating model of the gas storage subsystem is as follows:

[0104]

[0105] in, Let t be the flow rate of the high-temperature heat transfer fluid during the compression phase at time t; Specific heat capacity of the heat transfer fluid; Let be the flow rate of the high-temperature heat transfer fluid at time t; The operating model of the flue gas waste heat system is as follows:

[0106] in, The upper limit of heat supply for the flue gas waste heat system This is the lower limit for the heat supply of the flue gas waste heat system.

[0107] In one embodiment of this application, the operating model of the thermal power coupled compressed air energy storage system further includes: a power plant rating model, which is as follows:

[0108] in, The upper limit of the rated compression power of the power plant This is the lower limit of the rated compression power of the power plant; Upper limit of rated turbine power of power plant This is the lower limit of the rated turbine power of the power plant; Upper limit of the volume of gas storage tank for power plants This is the lower limit of the gas storage volume of the power plant.

[0109] In one embodiment of this application, the optimized scheduling model includes a first scheduling stage and a second scheduling stage. The robust scheduling device 10 for a thermal power coupled compressed air energy storage system further includes: an optimization module, used to set the objective function of the optimized scheduling model according to the objective of the first scheduling stage and the objective of the second scheduling stage before inputting the set fuzzy set into the optimized scheduling model; and to optimize the optimized scheduling model based on the objective function.

[0110] In one embodiment of this application, the objective function for optimizing the scheduling model is:

[0111]

[0112] in, The objective function for the first scheduling phase is to minimize the system scheduling cost within the current time period. Let i be the system state transition probability; This represents the scheduling cost of the second scheduling phase given the current scheduling variables and the system state within the scheduling period; For the unit at time t The power generation capacity; and For the unit The power generation cost coefficient; To purchase electricity from the main power grid; The price of electricity purchased from the main grid; and These are the startup costs for the compression side and turbine side of compressed air energy storage, respectively. To generate heat for the heat pump, Heat generated for energy storage of compressed air; This is the heat production cost coefficient of the heat pump; This is the heat production cost coefficient for the compressed air energy storage station.

[0113] In one embodiment of this application, the robust scheduling device 10 for a thermal power coupled compressed air energy storage system further includes: a third construction module, used to construct a fuzzy set of new energy output by employing infinite norm and 1-norm constraints before obtaining the fuzzy set of new energy output.

[0114] It should be noted that the foregoing explanation of the robust scheduling method embodiment for thermal power coupled compressed air energy storage system also applies to the robust scheduling device of thermal power coupled compressed air energy storage system in this embodiment, and will not be repeated here.

[0115] The robust dispatching device for a thermal power coupled compressed air energy storage system proposed in this application identifies the compression subsystem, turbine subsystem, energy storage system, and flue gas waste heat system in the topology of the thermal power coupled compressed air energy storage system, constructs the operation model of each subsystem, and establishes a comprehensive operation model of the entire thermal power coupled compressed air energy storage system based on these models. This maximizes the utilization of thermal power resources, improves energy storage efficiency, and effectively reduces system operating costs. Using the comprehensive model as the hub of the thermal power system, and combining the power flow model on the grid side and the heat network model on the heat network side, an optimized dispatching model is constructed. Fuzzy sets of new energy output are obtained to cope with uncertainties in actual operation, and fuzzy set parameters are set according to the actual data of the power grid in the target area. Finally, these parameters are input into the optimized dispatching model to output the best dispatching strategy. This not only improves the system's flexibility and energy utilization efficiency, but also enhances the adaptability to new energy fluctuations and risk management capabilities, providing strong support for achieving efficient and stable power supply.

[0116] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0117] When processor 502 executes the program, it implements the robust scheduling method for the thermal power coupled compressed air energy storage system provided in the above embodiments.

[0118] Furthermore, the electronic device also includes a communication interface 503 for communication between the memory 501 and the processor 502.

[0119] The memory 501 is used to store computer programs that can run on the processor 502.

[0120] The memory 501 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0121] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0122] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0123] The processor 502 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0124] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the robust scheduling method for a thermal-electric coupled compressed air energy storage system as described above.

[0125] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0126] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0127] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0128] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc. Those skilled in the art will understand that all or part of the steps carried out by the methods of the above embodiments can be implemented by a program instructing related hardware, the program being stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiments. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A robust scheduling method for a thermal power coupled compressed air energy storage system, characterized in that, Includes the following steps: Obtain the topology of the thermal power coupled compressed air energy storage system, identify the compression subsystem, turbine subsystem, energy storage system and flue gas waste heat system in the topology, and construct the operation models of the compression subsystem, turbine subsystem, energy storage system and flue gas waste heat system respectively; Based on the respective operating models of the compression subsystem, the turbine subsystem, the energy storage system, and the flue gas waste heat system, an operating model for the thermal power coupled compressed air energy storage system is constructed. Using the operation model of the thermal power coupled compressed air energy storage system as the hub of the thermal power system, and combining the power flow model on the grid side and the heat network model on the heat network side, an optimized scheduling model of the thermal power coupled compressed air energy storage system is constructed. A fuzzy set of new energy output is obtained, and the corresponding parameters of the fuzzy set are set according to the actual data of the power grid in the target area. The set fuzzy set is then input into the optimized scheduling model, and the optimized scheduling model outputs the scheduling strategy of the thermal power coupled compressed air energy storage system.

2. The robust scheduling method for a thermal power coupled compressed air energy storage system according to claim 1, characterized in that, The operation process of the thermal power coupled compressed air energy storage system includes: During the energy storage phase, the compression subsystem is started, and the compressor is driven by absorbing excess power from the grid. The compressed high-temperature air exchanges heat with the low-temperature heat storage medium in the cold tank through the heat exchanger on the compression side. After the heat exchange, the medium-temperature heat storage medium is fully heated to a high temperature by the waste heat of the flue gas from the thermal power plant and stored in the hot tank. The multi-stage compressor compresses the air to high pressure step by step and stores it in the gas storage tank, thus completing the storage of electrical energy. During the power generation phase, the turbine subsystem is started. The high-pressure gas stored in the gas storage tank first exchanges heat with the high-temperature heat storage medium in the hot tank through a heat exchanger. The high-temperature and high-pressure air after heat exchange enters the turbine, drives the turbine to do work, drives the generator to work and output electricity to the grid, thus completing the release of electrical energy.

3. The robust scheduling method for a thermal power coupled compressed air energy storage system according to claim 1, characterized in that, The operating model of the thermal-electric coupled compressed air energy storage system consists of operating models for the compression subsystem, the turbine subsystem, the energy storage system, and the flue gas waste heat system, wherein... The operating model of the compression subsystem is as follows: in, The compressor charging power at time t. Let be the mass flow rate of air flowing into the compressor at time t. This indicates the mass flow rate of air entering the compressor. The specific heat capacity of air. This refers to the air temperature at the compressor inlet. Indicates the lower / upper limit of compressed air mass flow rate. A binary variable representing the charging state. This refers to the compressor pressure ratio. The air insulation index. The compressor has isentropic efficiency; The operating model of the turbine subsystem is as follows: in, These represent the turbine's power generation at time t; Let be the mass flow rate of air flowing into the turbine at time t; The air temperature at the turbine inlet; This indicates the mass flow rate of air entering the turbine. Indicates the lower / upper limit of turbine air mass flow rate. A binary variable representing the control of the discharge state. The turbine ratio of the turbine. For turbine isentropic efficiency; The energy storage system includes a gas storage subsystem and a thermal storage tank system, wherein... The operating model of the gas storage subsystem is as follows: in, Let t be the gas pressure in the gas storage tank. is the gas constant of air; Temperature of the gas storage facility; and These are the air mass flow rates on the compression side and turbine side of the gas storage facility, respectively. For gas storage capacity; The operational model of the gas storage subsystem is as follows: in, Let t be the flow rate of the high-temperature heat transfer fluid during the compression phase at time t; Specific heat capacity of the heat transfer fluid; Let be the flow rate of the high-temperature heat transfer fluid at time t; The operating model of the flue gas waste heat system is as follows: in, The upper limit of heat supply for the flue gas waste heat system This is the lower limit for the heat supply of the flue gas waste heat system.

4. The robust scheduling method for a thermal power coupled compressed air energy storage system according to claim 2, characterized in that, The operating model of the thermal power coupled compressed air energy storage system also includes: a model of various rated values ​​of the power plant, wherein the model of various rated values ​​of the power plant is as follows: in, The upper limit of the rated compression power of the power plant This is the lower limit of the rated compression power of the power plant; Upper limit of rated turbine power of power plant This is the lower limit of the rated turbine power of the power plant; Upper limit of the volume of gas storage tank for power plants This is the lower limit of the gas storage volume of the power plant.

5. The robust scheduling method for a thermal power coupled compressed air energy storage system according to claim 1, characterized in that, The optimized scheduling model includes a first scheduling phase and a second scheduling phase. Before inputting the configured fuzzy set into the optimized scheduling model, it also includes: The objective function of the optimized scheduling model is set according to the objectives of the first scheduling phase and the second scheduling phase. The optimized scheduling model is optimized based on the objective function.

6. The robust scheduling method for a thermal power coupled compressed air energy storage system according to claim 5, characterized in that, The objective function of the optimized scheduling model is: in, The objective function for the first scheduling phase is to minimize the system scheduling cost within the current time period. Let i be the system state transition probability; This represents the scheduling cost of the second scheduling phase given the current scheduling variables and the system state within the scheduling period; For the unit at time t The power generation capacity; and For the unit The power generation cost coefficient; To purchase electricity from the main power grid; The price of electricity purchased from the main grid; and These are the startup costs for the compression side and turbine side of compressed air energy storage, respectively. To generate heat for the heat pump, Heat generated for energy storage of compressed air; This is the heat production cost coefficient of the heat pump; This is the heat production cost coefficient for the compressed air energy storage station.

7. The robust scheduling method for a thermal power coupled compressed air energy storage system according to claim 1, characterized in that, Before obtaining the fuzzy set of new energy output, it also includes: A fuzzy set of new energy output is constructed by using infinite norm and 1-norm constraints.

8. A robust dispatching device for a thermal power coupled compressed air energy storage system, characterized in that, include: The acquisition module is used to acquire the topology of the thermal power coupled compressed air energy storage system, identify the compression subsystem, turbine subsystem, energy storage system and flue gas waste heat system in the topology, and construct the respective operation models of the compression subsystem, the turbine subsystem, the energy storage system and the flue gas waste heat system. The first construction module is used to construct the operation model of the thermal power coupled compressed air energy storage system based on the respective operation models of the compression subsystem, the turbine subsystem, the energy storage system and the flue gas waste heat system. The second construction module is used to construct an optimized scheduling model for the thermal power coupled compressed air energy storage system, taking the operation model of the thermal power coupled compressed air energy storage system as the hub of the thermal power system, and combining the power flow model on the grid side and the heat network model on the heat network side. The scheduling module is used to obtain the fuzzy set of new energy output, set the corresponding parameters of the fuzzy set according to the actual data of the power grid in the target area, input the set fuzzy set into the optimized scheduling model, and the optimized scheduling model outputs the scheduling strategy of the thermal power coupled compressed air energy storage system.

9. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the robust scheduling method for a thermal power coupled compressed air energy storage system as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they implement the robust scheduling method for the thermal power coupled compressed air energy storage system as described in any one of claims 1-7.

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