Adiabatic compressed air energy storage scheduling method and device, electronic equipment and storage medium

Abstract

The application provides an adiabatic compressed air energy storage scheduling method and device, electronic equipment and storage medium. The method comprises the following steps: constructing a scheduling model of an adiabatic compressed air energy storage device, wherein the scheduling model comprises a scheduling target function and a target constraint, the scheduling target function is a function related to the economic income of the adiabatic compressed air energy storage device in the scheduling process; the target constraint is determined based on the state variable of the adiabatic compressed air energy storage device; the scheduling model is solved under the target constraint to obtain a target scheduling parameter so that the economic income of the scheduling target function is the highest, wherein the target scheduling parameter comprises a target first mass of air in the air storage, a target thermodynamic energy of air in the air storage, a target second mass of heat conducting oil in the high-temperature heat storage tank and a target enthalpy of the heat conducting oil in the high-temperature heat storage tank. The target scheduling parameter can be accurately and effectively obtained, so that the economic income of the adiabatic compressed air energy storage device under the scheduling according to the target scheduling parameter is the highest.

Description

Technical Field

[0001] The present invention relates to the technical field of compressed air energy storage, and in particular to an adiabatic compressed air energy storage scheduling method, device, electronic equipment and storage medium. Background Art

[0002] Adiabatic compressed air energy storage (A-CAES) is a new type of energy storage system with the characteristics of large construction scale, long energy storage time, long service life and short construction period. It can be used in scenarios such as peak shaving and valley filling, wind and solar power consumption, etc.

[0003] However, the currently established adiabatic compressed air energy storage scheduling models for trigeneration of electricity, heat and gas often have the problem of inaccurate modeling, which in turn makes it impossible to effectively guide the actual operation of adiabatic compressed air energy storage scheduling.

[0004] Therefore, finding an adiabatic compressed air energy storage scheduling method that can accurately and effectively schedule adiabatic compressed air energy storage has become a current research hotspot. Summary of the Invention

[0005] The present invention provides an adiabatic compressed air energy storage scheduling method, device, electronic device and storage medium, which realizes a scheduling model based on accurate construction and can accurately and effectively obtain target scheduling parameters to maximize the economic income of the adiabatic compressed air energy storage equipment when scheduled according to the target scheduling parameters.

[0006] The present invention provides an adiabatic compressed air energy storage scheduling method, which is applied to an adiabatic compressed air energy storage device, wherein the adiabatic compressed air energy storage device includes at least a gas storage reservoir and a high-temperature heat storage tank; the method includes: constructing a scheduling model for the adiabatic compressed air energy storage device, wherein the scheduling model includes a scheduling objective function and a target constraint, wherein the scheduling objective function is a function of the economic income of the adiabatic compressed air energy storage device during the scheduling process; the target constraint is determined based on state variables of the adiabatic compressed air energy storage device, wherein the state variables include a first mass of air in the gas storage reservoir, the thermodynamic energy of the air in the gas storage reservoir, a second mass of heat transfer oil in the high-temperature heat storage tank, and the enthalpy of the heat transfer oil in the high-temperature heat storage tank; and solving the scheduling model under the target constraint to obtain target scheduling parameters to maximize the economic income of the scheduling objective function, wherein the target scheduling parameters include a target first mass of air in the gas storage reservoir, a target thermodynamic energy of air in the gas storage reservoir, a target second mass of the heat transfer oil in the high-temperature heat storage tank, and a target enthalpy of the heat transfer oil in the high-temperature heat storage tank.

[0007] According to an adiabatic compressed air energy storage scheduling method provided by the present invention, the scheduling objective function is constructed in the following manner: determining the first income of the adiabatic compressed air energy storage device in the electricity supply mode, the second income in the heat supply mode, and the third income in the compressed air mode respectively; and determining the scheduling objective function based on the first income, the second income, and the third income.

[0008] According to an adiabatic compressed air energy storage scheduling method provided by the present invention, the scheduling objective function is determined based on the first income, the second income, and the third income, and is implemented using the following formula:

[0009]

[0010] in, ( - - ) represents the first income; represents the electricity price at time t; represents the discharge power of the adiabatic compressed air energy storage device at time t; represents the charging power of the adiabatic compressed air energy storage device at time t; represents the second income; represents the heating price at time t; represents the mass flow rate of the thermal oil supplied to the outside by the high-temperature heat storage tank of the adiabatic compressed air energy storage device at time t; represents said third income; represents the gas price at time t; represents the air mass flow rate of the gas storage reservoir of the adiabatic compressed air energy storage device supplying air to the outside at time t; T represents the set of scheduling time periods.

[0011] According to an adiabatic compressed air energy storage scheduling method provided by the present invention, the target constraints include state constraints of the state variables, system constraints of the adiabatic compressed air energy storage equipment, and operation constraints of the adiabatic compressed air energy storage equipment, wherein the state constraints include discrete state equations and switching signal equations; the system constraints include electric power constraints, gas storage constraints, and high-temperature heat storage tank constraints; the operation constraints include charging and discharging state constraints, upper and lower limit constraints of state variables, external gas supply operation constraints, and external heat supply operation constraints.

[0012] According to an adiabatic compressed air energy storage scheduling method provided by the present invention, the discrete state equation includes a first discrete state equation of the gas storage;

[0013] The first discrete state equation of the gas storage is implemented using the following formula:

[0014]

[0015]

[0016] in, represents the first mass of the air in the gas storage at time t+1; represents the first mass of air in the gas storage at time t; represents the mass flow rate of air entering the gas storage after being compressed by the compressor at time t; represents the mass flow rate of air leaving the gas storage reservoir at time t to drive the turbine to generate electricity; represents the air mass flow rate of the gas storage supplying air to the outside at time t; represents the thermodynamic energy of the air in the gas storage at time t+1; represents the thermodynamic energy of the air in the gas storage at time t; Indicates the pressure caused by air entering the gas storage at time t added value; represents the energy at time t+1 caused by air leaving the gas storage to drive the turbine to generate electricity the reduction in value; It represents the air leaving the gas storage at time t+1 to drive the turbine to supply air to the outside. the reduction in value; Indicates the heat transfer between the gas storage and the surrounding environment at time t+1. The change value of Indicates the duration of each scheduling period.

[0017] According to an adiabatic compressed air energy storage scheduling method provided by the present invention, the discrete state equation includes a second discrete state equation of the high-temperature heat storage tank;

[0018] The second discrete state equation of the high-temperature heat storage tank is implemented using the following formula:

[0019]

[0020]

[0021] in, represents the second mass of the thermal oil in the high-temperature heat storage tank at time t+1; represents the second mass of the thermal oil in the high-temperature heat storage tank at time t; represents the total heat transfer oil mass flow rate on the compression side at time t; represents the total heat transfer oil mass flow rate on the expansion side at time t; It represents the mass flow rate of the thermal oil entering the high-temperature heat storage tank after being heated by the electric heater at time t; represents the mass flow rate of the thermal oil supplied by the high-temperature heat storage tank to the outside at time t; represents the enthalpy of the heat transfer oil in the high-temperature heat storage tank at time t+1; represents the enthalpy of the heat transfer oil in the high-temperature heat storage tank at time t; It indicates that at time t, the heat transfer oil after heat exchange on the compression side enters the high-temperature heat storage tank. added value; It indicates that at time t, the heat transfer oil heated by the electric heater enters the high-temperature heat storage tank. added value; Indicates the heat transfer caused by the heat transfer oil leaving the high-temperature heat storage tank to exchange heat on the expansion side at time t. the reduction in value; It indicates the heat transfer oil leaving the high temperature heat storage tank and supplying heat to the outside through the expansion side at time t. The reduction value.

[0022] According to an adiabatic compressed air energy storage scheduling method provided by the present invention, the switching signal equation is used to define the subinterval of the state variable of the adiabatic compressed air energy storage device at different times, wherein the switching signal equation includes a first switching signal equation for the gas storage reservoir and a second switching signal equation for the high-temperature heat storage tank.

[0023] According to an adiabatic compressed air energy storage scheduling method provided by the present invention, the adiabatic compressed air energy storage equipment also includes a compressor, an expansion turbine and a thermal oil electric heater; wherein the electric power constraint is used to stipulate that the power of the compressor remains within the rated operating range of the compressor, the power of the expansion turbine remains within the rated operating range of the expansion turbine, and the power of the thermal oil electric heater remains within the rated operating range of the thermal oil electric heater; the gas storage constraint is used to define the calculation formula of each physical quantity in the first discrete state equation of the gas storage; and the high-temperature heat storage tank constraint is used to define the calculation formula of each physical quantity in the second discrete state equation of the high-temperature heat storage tank.

[0024] According to an adiabatic compressed air energy storage scheduling method provided by the present invention, the charging and discharging state constraints are used to constrain the adiabatic compressed air energy storage device from operating in both the charging state and the discharging state at the same time; the upper and lower limit constraints of the state variables are used to constrain the state variables of the adiabatic compressed air energy storage device to remain within their respective rated operating ranges; the external air supply operation constraints are used to constrain the air mass flow rate of the external air supply of the adiabatic compressed air energy storage device to remain within the rated operating range; and the external heating operation constraints are used to constrain the adiabatic compressed air energy storage device to provide heating at a temperature of the thermal oil in the high-temperature heat storage tank within a rated heating temperature range, and the thermal oil mass flow rate of the external heating of the adiabatic compressed air energy storage device to remain within the rated operating range.

[0025] According to an adiabatic compressed air energy storage scheduling method provided by the present invention, the scheduling model is solved under the target constraints to obtain target scheduling parameters, specifically including: determining zero-setting auxiliary variables in the scheduling model; wherein the zero-setting auxiliary variables are used to characterize that the auxiliary variables can be set to zero under preset conditions; setting the zero-setting auxiliary variables in the scheduling model to zero to obtain a simplified scheduling model; and solving the simplified scheduling model under the target constraints to obtain target scheduling parameters.

[0026] The present invention also provides an adiabatic compressed air energy storage scheduling device, which is applied to an adiabatic compressed air energy storage device, wherein the adiabatic compressed air energy storage device includes at least a gas storage reservoir and a high-temperature heat storage tank; the device includes: a construction module for constructing a scheduling model for the adiabatic compressed air energy storage device, wherein the scheduling model includes a scheduling objective function and an objective constraint, the scheduling objective function is a function of the economic income of the adiabatic compressed air energy storage device during the scheduling process; the objective constraint is determined based on the state variables of the adiabatic compressed air energy storage device, and the state variables including a first mass of the air in the gas storage, the thermodynamic energy of the air in the gas storage, a second mass of the heat transfer oil in the high-temperature heat storage tank, and the enthalpy of the heat transfer oil in the high-temperature heat storage tank; a processing module, configured to solve the scheduling model under the target constraint to obtain target scheduling parameters so as to maximize the economic income of the scheduling objective function, wherein the target scheduling parameters include the target first mass of the air in the gas storage, the target thermodynamic energy of the air in the gas storage, the target second mass of the heat transfer oil in the high-temperature heat storage tank, and the target enthalpy of the heat transfer oil in the high-temperature heat storage tank.

[0027] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the adiabatic compressed air energy storage scheduling method as described above is implemented.

[0028] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described adiabatic compressed air energy storage scheduling methods.

[0029] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements any of the above-described adiabatic compressed air energy storage scheduling methods.

[0030] The adiabatic compressed air energy storage scheduling method, device, electronic device, and storage medium provided by the present invention are applied to an adiabatic compressed air energy storage device, wherein the adiabatic compressed air energy storage device includes at least a gas storage reservoir and a high-temperature heat storage tank; the method includes: constructing a scheduling model for the adiabatic compressed air energy storage device, wherein the scheduling model includes a scheduling objective function and a target constraint, the scheduling objective function being a function of the economic income of the adiabatic compressed air energy storage device during the scheduling process; the target constraint is determined based on state variables of the adiabatic compressed air energy storage device, the state variables including a first mass of air in the gas storage reservoir, the thermodynamic energy of the air in the gas storage reservoir, a second mass of heat transfer oil in the high-temperature heat storage tank, and the enthalpy of the heat transfer oil in the high-temperature heat storage tank; solving the scheduling model under the target constraint to obtain target scheduling parameters to maximize the economic income of the scheduling objective function, wherein the target scheduling parameters include a target first mass of air in the gas storage reservoir, a target thermodynamic energy of air in the gas storage reservoir, a target second mass of heat transfer oil in the high-temperature heat storage tank, and a target enthalpy of the heat transfer oil in the high-temperature heat storage tank. By defining state variables, a scheduling model that includes multi-energy coupling characteristics can be accurately constructed, thereby realizing a scheduling model based on accurate construction, which can accurately and effectively obtain the target scheduling parameters, so that the economic income of the adiabatic compressed air energy storage equipment under scheduling according to the target scheduling parameters is maximized. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction is given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 It is a flow chart of the adiabatic compressed air energy storage scheduling method provided by the present invention.

[0033] Figure 2 It is a flow chart of constructing a scheduling objective function provided by the present invention.

[0034] Figure 3It is a flow chart of solving the scheduling model under target constraints and obtaining target scheduling parameters provided by the present invention.

[0035] Figure 4 It is a structural schematic diagram of the adiabatic compressed air energy storage scheduling device provided by the present invention.

[0036] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0037] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0038] The adiabatic compressed air energy storage scheduling method provided by the present invention introduces accelerated calculation constraints on the basis of accurately characterizing the electric, thermal and gas multi-energy coupling characteristics of the adiabatic compressed air energy storage equipment. Its purpose is to provide an adiabatic compressed air energy storage scheduling method for the trigeneration of electric, thermal and gas that takes into account both accuracy and efficient solution.

[0039] Figure 1 It is a flow chart of the adiabatic compressed air energy storage scheduling method provided by the present invention.

[0040] The following will be combined Figure 1 The process of the adiabatic compressed air energy storage scheduling method provided by the present invention is described.

[0041] In an exemplary embodiment of the present invention, an adiabatic compressed air energy storage scheduling method can be applied to an adiabatic compressed air energy storage device, which is an energy storage device integrating an electric-thermal-mechanical multi-energy interface. The adiabatic compressed air energy storage device has the ability to provide electricity, heat, and compressed air trigeneration. Reasonable scheduling of the electricity, heat, and gas trigeneration of the adiabatic compressed air energy storage device can not only enhance the flexibility of the operation of the adiabatic compressed air energy storage device, but also help improve the comprehensive energy utilization efficiency of the adiabatic compressed air energy storage device. The adiabatic compressed air energy storage device may include at least a gas storage reservoir and a high-temperature heat storage tank. The high temperature in the high-temperature heat storage tank may refer to a temperature greater than a preset temperature.

[0042] Combine Figure 1 It can be seen that the adiabatic compressed air energy storage scheduling method may include step 110 and step 120, and each step will be introduced below.

[0043] In step 110, a scheduling model for the adiabatic compressed air energy storage device is constructed, wherein the scheduling model includes a scheduling objective function and an objective constraint. The scheduling objective function is a function of the economic income of the adiabatic compressed air energy storage device during the scheduling process; the objective constraint is determined based on the state variables of the adiabatic compressed air energy storage device.

[0044] In one embodiment, a scheduling model for an adiabatic compressed air energy storage device may be constructed, wherein the scheduling model may include a scheduling objective function and objective constraints. The scheduling objective function may be a function of the economic revenue of the adiabatic compressed air energy storage device during the scheduling process. The objective constraints may be determined based on state variables of the adiabatic compressed air energy storage device.

[0045] In another embodiment, the state variable may include a first mass of air in the gas storage tank , Thermodynamic energy of air in gas storage , the second quality of the thermal oil in the high temperature heat storage tank , and the enthalpy of the heat transfer oil in the high-temperature heat storage tank .

[0046] It should be noted that when the adiabatic compressed air energy storage device (hereinafter referred to as A-CAES) performs trigeneration of electricity, heat and gas, the state of the gas storage reservoir and the high-temperature heat storage tank will affect its working characteristics, that is, the A-CAES has variable working characteristics. Specifically, the state of the A-CAES gas storage reservoir mainly affects the charging characteristics and external gas supply characteristics of the A-CAES, and the state of the A-CAES high-temperature heat storage tank mainly affects the discharge characteristics and external heat supply characteristics of the A-CAES. When the state of the A-CAES gas storage reservoir or the high-temperature heat storage tank changes, the working characteristics of different working behaviors will also change accordingly. Therefore, in order to fully characterize the variable working characteristics of A-CAES, the state variables of the A-CAES gas storage reservoir and the high-temperature heat storage tank can be selected as the state variables of A-CAES. In order to ensure that the entire model can be established in the framework of mixed integer linearity, the first mass of the air in the gas storage reservoir can be selected. , Thermodynamic energy of air in gas storage , the second quality of the thermal oil in the high temperature heat storage tank , and the enthalpy of the heat transfer oil in the high-temperature heat storage tank as a state variable.

[0047] During application, the target constraints can be determined based on the state variables of the adiabatic compressed air energy storage device.

[0048] In step 120, the scheduling model is solved under the target constraints to obtain target scheduling parameters to maximize the economic income of the scheduling objective function, wherein the target scheduling parameters include the target first mass of the air in the gas storage, the target thermodynamic energy of the air in the gas storage, the target second mass of the heat transfer oil in the high-temperature heat storage tank, and the target enthalpy of the heat transfer oil in the high-temperature heat storage tank.

[0049] In another embodiment, the scheduling model can be solved under the target constraints to obtain the target scheduling parameters so as to maximize the economic income of the scheduling objective function. The target scheduling parameters can be the target first mass of the air in the gas storage, the target thermodynamic energy of the air in the gas storage, the target second mass of the heat transfer oil in the high-temperature heat storage tank, and the target enthalpy of the heat transfer oil in the high-temperature heat storage tank. It should be noted that the first mass of the air in the gas storage is , Thermodynamic energy of air in gas storage , the second quality of the thermal oil in the high temperature heat storage tank , and the enthalpy of the heat transfer oil in the high-temperature heat storage tank It can be regarded as a variable, and the corresponding target scheduling parameters (including target first mass, target thermodynamic energy, target second mass and target enthalpy) are specific parameter values.

[0050] In this embodiment, by defining state variables, a scheduling model including multi-energy coupling characteristics can be accurately constructed, thereby realizing a scheduling model based on the accurate construction, and being able to accurately and effectively obtain the target scheduling parameters, so as to maximize the economic income of the adiabatic compressed air energy storage device when scheduled according to the target scheduling parameters.

[0051] The adiabatic compressed air energy storage scheduling method provided by the present invention is applied to an adiabatic compressed air energy storage device, wherein the adiabatic compressed air energy storage device includes at least a gas storage reservoir and a high-temperature heat storage tank; the method includes: constructing a scheduling model for the adiabatic compressed air energy storage device, wherein the scheduling model includes a scheduling objective function and a target constraint, the scheduling objective function being a function of the economic income of the adiabatic compressed air energy storage device during the scheduling process; the target constraint is determined based on state variables of the adiabatic compressed air energy storage device, the state variables including a first mass of air in the gas storage reservoir, the thermodynamic energy of the air in the gas storage reservoir, a second mass of heat transfer oil in the high-temperature heat storage tank, and the enthalpy of the heat transfer oil in the high-temperature heat storage tank; solving the scheduling model under the target constraint to obtain target scheduling parameters so as to maximize the economic income of the scheduling objective function, wherein the target scheduling parameters include a target first mass of air in the gas storage reservoir, a target thermodynamic energy of air in the gas storage reservoir, a target second mass of heat transfer oil in the high-temperature heat storage tank, and a target enthalpy of the heat transfer oil in the high-temperature heat storage tank. By defining state variables, a scheduling model that includes multi-energy coupling characteristics can be accurately constructed, thereby realizing a scheduling model based on accurate construction, which can accurately and effectively obtain the target scheduling parameters, so that the economic income of the adiabatic compressed air energy storage equipment under scheduling according to the target scheduling parameters is maximized.

[0052] Figure 2 It is a flow chart of constructing a scheduling objective function provided by the present invention.

[0053] The following will be combined Figure 2 The process of constructing the scheduling objective function provided by the present invention is described.

[0054] In an exemplary embodiment of the present invention, Figure 2 It can be seen that constructing the scheduling objective function may include step 210 and step 220, and each step will be introduced below.

[0055] In step 210, a first income of the adiabatic compressed air energy storage device in the electricity supply mode, a second income in the heat supply mode, and a third income in the compressed air mode are determined respectively;

[0056] In step 220 , a scheduling objective function is determined based on the first income, the second income, and the third income.

[0057] In one embodiment, the goal is to maximize the economic efficiency of the scheduling results, wherein the scheduling objective function can be divided into three parts, namely, the income obtained by A-CAES from supplying electricity, supplying heat, and compressing air, that is, determining the first income, the second income, and the third income respectively, and determining the scheduling objective function based on the first income, the second income, and the third income.

[0058] In another exemplary embodiment of the present invention, the scheduling objective function is determined based on the first income, the second income, and the third income, which can be implemented using the following formula (1):

[0059] (1)

[0060] in, ( - - ) represents the first income; represents the electricity price at time t; represents the discharge power of the adiabatic compressed air energy storage device at time t; represents the charging power of the adiabatic compressed air energy storage device at time t; Indicates second income; represents the heating price at time t; represents the mass flow rate of thermal oil supplied by the high-temperature heat storage tank of the adiabatic compressed air energy storage device at time t; Indicates the third income; represents the gas price at time t; It represents the air mass flow rate of the gas storage of the adiabatic compressed air energy storage device supplied to the outside at time t; T represents the set of scheduling periods.

[0061] In another exemplary embodiment of the present invention, the target constraints may include state constraints of state variables, system constraints of the adiabatic compressed air energy storage device, and operation constraints of the adiabatic compressed air energy storage device, wherein,

[0062] State constraints can include discrete state equations and switching signal equations;

[0063] System constraints may include electric power constraints, gas storage constraints, and high-temperature thermal storage tank constraints;

[0064] Operation constraints may include charge and discharge state constraints, upper and lower limit constraints of state variables, external gas supply operation constraints, and external heat supply operation constraints.

[0065] In another exemplary embodiment of the present invention, continuing with the above-mentioned embodiment as an example, a discrete state equation of an A-CAES for combined power, heat and gas generation can be established based on the selected state variables. The discrete state equation may include a first discrete state equation of a gas storage reservoir;

[0066] The first discrete state equation of the gas storage reservoir can be implemented using the following formulas (2)-(3):

[0067] (2)

[0068] (3)

[0069] in, represents the first mass of air in the gas storage at time t+1; represents the first mass of air in the gas storage at time t; represents the mass flow rate of air entering the gas storage after being compressed by the compressor at time t; represents the mass flow rate of air leaving the gas storage at time t to drive the turbine to generate electricity; represents the air mass flow rate of the gas storage supplying the outside at time t; represents the thermodynamic energy of the air in the gas storage at time t+1; represents the thermodynamic energy of the air in the gas storage at time t; Indicates the pressure caused by air entering the gas storage at time t added value; It represents the pressure at time t+1 caused by air leaving the gas storage to drive the turbine to generate electricity. the reduction in value; It represents the air leaving the gas storage at time t+1 to drive the turbine to supply air to the outside. the reduction in value; Indicates the heat transfer between the gas storage and the surrounding environment at time t+1. The change value of Indicates the duration of each scheduling period.

[0070] Based on formulas (2)-(3), the first discrete state equation for the gas storage is established.

[0071] In another exemplary embodiment of the present invention, continuing with the above-mentioned embodiment as an example, a discrete state equation of an A-CAES system for combined power, heat and gas generation can be established based on the selected state variables. The discrete state equation may include a second discrete state equation for the high-temperature heat storage tank;

[0072] Among them, the second discrete state equation of the high-temperature heat storage tank can be realized using the following formulas (4)-(5):

[0073] (4)

[0074] (5)

[0075] in, represents the second mass of the thermal oil in the high-temperature heat storage tank at time t+1; represents the second mass of the thermal oil in the high-temperature heat storage tank at time t; represents the total heat transfer oil mass flow rate on the compression side at time t; represents the total heat transfer oil mass flow rate on the expansion side at time t; It represents the mass flow rate of the thermal oil entering the high-temperature heat storage tank after being heated by the electric heater at time t; It represents the mass flow rate of thermal oil supplied by the high-temperature heat storage tank to the outside at time t; represents the enthalpy of the heat transfer oil in the high-temperature heat storage tank at time t+1; represents the enthalpy of the heat transfer oil in the high-temperature heat storage tank at time t; It indicates that at time t, the heat transfer oil after heat exchange on the compression side enters the high-temperature heat storage tank. added value; It indicates that at time t, the heat transfer oil heated by the electric heater enters the high-temperature heat storage tank. added value; Indicates the heat transfer caused by the heat transfer oil leaving the high-temperature heat storage tank to exchange heat on the expansion side at time t. the reduction in value; It indicates the heat transfer oil leaving the high temperature heat storage tank and supplying heat to the outside through the expansion side at time t. The reduction value.

[0076] Based on formulas (4)-(5), the second discrete state equation for the high-temperature heat storage tank is established.

[0077] In another example embodiment of the present invention, the switching signal equation can be used to define the subinterval of the state variable of the adiabatic compressed air energy storage device at different times, wherein the switching signal equation may include a first switching signal equation of the gas storage reservoir and a second switching signal equation of the high-temperature heat storage tank.

[0078] In another exemplary embodiment of the present invention, the first switching signal equation of the gas storage reservoir can be implemented using the following formulas (6)-(7):

[0079] (6)

[0080] (7)

[0081] In formula (6), is an ascending sequence, the total number of elements, each represents a value that the mass of the air in the gas storage can take, and, , , and Respectively represent the upper and lower limits of the air quality in the gas storage. indivual After, interval is divided into N subintervals, and the i-th subinterval can be expressed as , , Representing a collection . It means that at time t A Boolean variable indicating whether the value falls within the i-th subinterval. and At time t The left and right endpoints of the subinterval in which the value falls.

[0082] Under the constraint of formula (6), if Moment Falling in subintervals, then ,and , .at this time, , .

[0083] In formula (7), is an ascending sequence, the total number of elements, each represents a value that the thermodynamic energy of the air in the gas storage can take, and, , , is the specific heat capacity of air at constant volume, and Respectively represent the upper and lower limits of the air temperature in the gas storage. indivual After, interval Divided into subintervals, The subintervals can be expressed as , , Representing a collection . Yes Moment Whether it falls in the first A Boolean variable in a subinterval. and They are Moment The left and right endpoints of the subinterval in which the value falls.

[0084] Under the constraint of formula (7), if Moment Falling in subintervals, then ,and , .at this time, , .

[0085] In another exemplary embodiment of the present invention, the second switching signal equation of the high-temperature heat storage tank can be implemented using the following formulas (8)-(9):

[0086] (8)

[0087] (9)

[0088] In formula (8), is an ascending sequence, the total number of elements, each It represents a value that the mass of the thermal oil in the high-temperature heat storage tank can take, and, , , and Respectively represent the upper and lower limits of the quality of the thermal oil in the high-temperature heat storage tank. indivual After, interval Divided into subintervals, The subintervals can be expressed as , , Representing a collection . Yes Moment Whether it falls in the first A Boolean variable in a subinterval. and They are The left and right endpoints of the subinterval where the moment falls.

[0089] In the constraints of formula (8), if Moment Falling in subintervals, then ,and , .at this time, , .

[0090] In formula (9), is an ascending sequence, the total number of elements, each represents a value that the enthalpy of the heat transfer oil in the high-temperature heat storage tank can take, and, , , is the constant pressure specific heat capacity of the thermal oil, and Respectively represent the upper and lower limits of the temperature of the thermal oil in the high-temperature heat storage tank. indivual After, interval Divided into subintervals, The subintervals can be expressed as , , Representing a collection . Yes Moment Whether it falls in the first A Boolean variable in a subinterval. and They are Moment The left and right endpoints of the subinterval in which the value falls.

[0091] Under the constraints of formula (9), if Moment Falling in subintervals, then ,and , .at this time, , .

[0092] According to the above-mentioned formula (6)-formula (9), it can be determined at different times 、 、 and In the subsequent modeling process, the operating characteristics of the A-CAES will be independently characterized in each subinterval, so that when the state variables of the A-CAES move to different subintervals, the operating characteristics of the A-CAES described in the model will also change. Ultimately, the variable operating characteristics of the A-CAES will be simulated as a whole, and the electric, thermal, and gas multi-energy coupling characteristics of the A-CAES will be accurately characterized.

[0093] In another exemplary embodiment of the present invention, the adiabatic compressed air energy storage device may further include a compressor, an expansion turbine and a thermal oil electric heater; wherein,

[0094] The electric power constraint is used to stipulate that the power of the compressor is maintained within the rated operating range of the compressor, the power of the expansion turbine is maintained within the rated operating range of the expansion turbine, and the power of the thermal oil electric heater is maintained within the rated operating range of the thermal oil electric heater;

[0095] The gas storage reservoir constraint is used to define the calculation formula of each physical quantity in the first discrete state equation of the gas storage reservoir;

[0096] The high-temperature heat storage tank constraint is used to define the calculation formulas of various physical quantities in the second discrete state equation of the high-temperature heat storage tank.

[0097] The following will introduce the electric power constraints, gas storage constraints and high-temperature heat storage tank constraints respectively.

[0098] In one embodiment, the electric power constraint may include a compressor electric power constraint, an expansion turbine electric power constraint, and a thermal oil heater electric power constraint.

[0099] In yet another embodiment, the compressor electrical power constraint may be established using the following formulas (10)-(11):

[0100] (10)

[0101] (11)

[0102] in, Yes A Boolean variable indicating whether the A-CAES is working in the charging state at the moment; is an auxiliary Boolean variable introduced, which indicates that when and Falling in the 、 In the sub-interval, whether A-CAES is working in the charging state; is an auxiliary continuous variable introduced, which indicates that and Falling in the 、 The charging power of A-CAES in the sub-interval; and Respectively indicate when and Falling in the 、 The upper and lower limits of A-CAES charging power in the sub-intervals.

[0103] Based on formulas (10) and (11), the charging power constraint of A-CAES can be established. This charging power constraint can take into account the impact of the gas storage reservoir status on the upper and lower limits of the charging power, thereby characterizing the variable upper and lower limits of the A-CAES charging power.

[0104] In yet another embodiment, the expansion turbine electrical power constraint is established using equations (12)-(13):

[0105] (12)

[0106] (13)

[0107] in, Yes A Boolean variable indicating whether the A-CAES is operating in the discharge state at the moment; is an auxiliary Boolean variable introduced, which indicates that when and Falling in the 、 In the sub-interval, whether A-CAES is working in the discharge state; is an auxiliary continuous variable introduced, which indicates that and Falling in the 、 The discharge power of A-CAES in the sub-interval; and Respectively indicate when and Falling in the 、 The upper and lower limits of A-CAES discharge power in the sub-intervals.

[0108] Based on formulas (12) and (13), the discharge power constraint of A-CAES can be established. This discharge power constraint can take into account the impact of the high-temperature heat storage tank state on the upper and lower limits of the discharge power, thereby characterizing the variable upper and lower limit characteristics of the A-CAES discharge power.

[0109] In another embodiment, the following formula (14) can be used to establish the electric power constraint of the thermal oil electric heater:

[0110] (14)

[0111] in, Yes A Boolean variable indicating whether the thermal oil heater is working at the current moment; and They respectively represent the upper and lower power limits of the thermal oil electric heater.

[0112] Based on formula (14), the power of the thermal oil electric heater can be constrained to be kept within its normal operating range.

[0113] In another embodiment, the gas storage constraint may provide the physical quantities ( 、 、 、 、 and ) calculation formula.

[0114] In one embodiment, the gas storage constraint can be implemented according to formula (15):

[0115] (15)

[0116] in, is an auxiliary continuous variable introduced, which indicates that and Falling in the 、 The mass flow rate of air entering the gas storage after being compressed by the compressor in the sub-interval; and are fitting coefficients, which are used to fit the and Falling in the 、 When the subinterval and The functional relationship between them.

[0117] Based on formula (15), the compressed air intake constraint of A-CAES can be established. This compressed air intake constraint can take into account the effect of the gas storage state on the and The influence of the functional relationship between , thus being able to characterize the variable working characteristics of the A-CAES compressor. That is, formula (15) gives the first discrete state equation of the gas storage The calculation formula of .

[0118] In one embodiment, the gas storage constraint can also be implemented according to formula (16):

[0119] (16)

[0120] in, is an auxiliary continuous variable introduced, which indicates that and Falling in the 、 The mass flow rate of air leaving the gas storage reservoir to drive the turbine to generate electricity during the subinterval; and are fitting coefficients, which are used to fit the and Falling in the 、 When the subinterval and The functional relationship between them.

[0121] Based on formula (16), the expansion and deflation constraints of A-CAES can be established. This expansion and deflation constraint can take into account the effect of gas storage state on the and The influence of the functional relationship between and can be used to characterize the variable working characteristics of the A-CAES expansion turbine. That is, formula (16) gives the first discrete state equation of the gas storage reservoir: The calculation formula of .

[0122] In one embodiment, the gas storage constraint can also be implemented according to formula (17):

[0123] (17)

[0124] in, is an auxiliary continuous variable introduced, which indicates that and Falling in the 、 When the sub-interval is reached, the air enters the gas storage tank. added value; and are fitting coefficients, which are used to fit the and Falling in the 、 When the subinterval and That is, formula (17) gives the first discrete state equation of the gas storage: The calculation formula of .

[0125] In one embodiment, the gas storage constraint may also be implemented according to formula (18):

[0126] (18)

[0127] in, for The temperature of the air in the gas storage at the moment; is a constant representing the air temperature in the gas storage tank and satisfies ; is an auxiliary Boolean variable introduced.

[0128] After introducing formula (18), At this moment, when and Falling in the 、 When the air temperature in the gas storage is Can be approximately expressed as a constant , and then calculate 、 and .

[0129] Furthermore, the first discrete state equation of the gas storage can be obtained in sequence according to formulas (19)-(21): 、 and The calculation formula of .

[0130] (19)

[0131] (20)

[0132] (twenty one)

[0133] in, is the heat transfer coefficient between the gas storage and the surrounding environment; is the effective heat exchange area of ​​the gas storage relative to the surrounding environment; is the temperature of the environment surrounding the gas storage facility.

[0134] In another embodiment, the high temperature heat storage tank constraint can provide the physical quantities ( 、 、 、 、 、 and ) calculation formula.

[0135] In one embodiment, the high temperature heat storage tank constraint can be implemented according to formula (22):

[0136] (twenty two)

[0137] in, is an auxiliary continuous variable introduced, which indicates that and Falling in the 、 The total mass flow rate of heat transfer oil for heat exchange on the compression side in the sub-interval; and are fitting coefficients, which are used to fit the and Falling in the 、 When the subinterval and That is, formula (22) gives the second discrete state equation of the high-temperature heat storage tank. The calculation formula of .

[0138] In one embodiment, the high temperature heat storage tank constraint can be implemented according to formula (23):

[0139] (twenty three)

[0140] in, is an auxiliary continuous variable introduced, which indicates that and Falling in the 、 The total mass flow rate of the heat transfer oil in the expansion side during heat exchange in the sub-interval; and are fitting coefficients, which are used to fit the and Falling in the 、 When the subinterval and That is, formula (23) gives the second discrete state equation of the high-temperature heat storage tank. The calculation formula of .

[0141] In one embodiment, the high temperature heat storage tank constraint can be implemented according to formula (24):

[0142] (twenty four)

[0143] in, and are fitting coefficients, which are used to fit and That is, formula (24) gives the second discrete state equation of the high-temperature heat storage tank. The calculation formula of .

[0144] In one embodiment, the high temperature heat storage tank constraint can be implemented according to formula (25):

[0145] (25)

[0146] in, is an auxiliary continuous variable introduced, which indicates that and Falling in the 、 When the heat transfer oil enters the high-temperature heat storage tank after the heat exchange on the compression side, added value; and are fitting parameters, which are used to fit the and Falling in the 、 When the subinterval and That is, formula (25) gives the second discrete state equation of the high-temperature heat storage tank. The calculation formula of .

[0147] In one embodiment, the high temperature heat storage tank constraint can be implemented according to formula (26):

[0148] (26)

[0149] in, and are fitting coefficients, which are used to fit and That is, formula (26) gives the second discrete state equation of the high-temperature heat storage tank. The calculation formula of .

[0150] In one embodiment, the high-temperature heat storage tank constraint can be implemented according to formula (27):

[0151] (27)

[0152] in, for The temperature of the heat transfer oil in the high-temperature heat storage tank at the moment; is a constant that represents the temperature of the thermal oil in the high-temperature heat storage tank and satisfies ; is an auxiliary Boolean variable introduced. In the application process, the temperature of the heat transfer oil in the high-temperature heat storage tank at each moment can be approximately determined based on formula (27): .

[0153] After introducing formula (27), At this moment, when and Falling in the 、 When the sub-interval is reached, the temperature of the heat transfer oil in the high-temperature heat storage tank Can be approximately expressed as a constant , and then calculate and .

[0154] In one embodiment, the high-temperature heat storage tank constraint can be implemented according to formulas (28)-(29):

[0155] (28)

[0156] (29)

[0157] Among them, based on formulas (28)-(29), the second discrete state equation of the high-temperature heat storage tank can be given respectively: and The calculation formula of .

[0158] In another exemplary embodiment of the present invention, the charge and discharge state constraint is used to constrain the adiabatic compressed air energy storage device from operating in the charge state and the discharge state at the same time;

[0159] The upper and lower limit constraints of the state variables are used to constrain the state variables of the adiabatic compressed air energy storage device to remain within their respective rated working ranges;

[0160] The external air supply operation constraint is used to constrain the air mass flow rate of the external air supply of the adiabatic compressed air energy storage device to remain within the rated operating range;

[0161] The external heating operation constraint is used to constrain the adiabatic compressed air energy storage device to provide heat in the high-temperature heat storage tank at a temperature within the rated heating temperature range, and the mass flow rate of the thermal oil supplied by the adiabatic compressed air energy storage device to the outside is maintained within the rated operating range.

[0162] In one embodiment, the charge and discharge state constraint can be established using formula (30):

[0163] (30)

[0164] After introducing equation (30), the A-CAES is constrained to be unable to work in the charging and discharging states at the same time.

[0165] In one embodiment, the upper and lower bounds of the state variables can be established using formulas (31)-(32):

[0166] (31)

[0167] (32)

[0168] After introducing equations (31)-(32), the mass and thermodynamic energy of the air in the gas storage reservoir can be constrained to be maintained within their normal operating range, that is, the upper and lower limit constraints of the gas storage reservoir state variables are established.

[0169] In one embodiment, the upper and lower bounds of the state variables can be established using formulas (33)-(34):

[0170] (33)

[0171] (34)

[0172] After introducing equations (33)-(34), the mass and enthalpy of the heat transfer oil in the high-temperature heat storage tank can be constrained to be maintained within their normal operating range, that is, the upper and lower limit constraints of the state variables of the high-temperature heat storage tank are established.

[0173] In one embodiment, the external gas supply operation constraint can be established using formula (35):

[0174] (35)

[0175] in, Yes A Boolean variable indicating whether A-CAES is supplying gas to the outside at the current moment; and They represent the lower limit and upper limit of the air mass flow rate supplied by A-CAES to the outside respectively.

[0176] After introducing equation (35), the air mass flow rate of the A-CAES supplying air to the outside must be kept within its normal operating range.

[0177] In one embodiment, the external heat supply operation constraint can be established using formula (36):

[0178] (36)

[0179] in, Yes A Boolean variable indicating whether A-CAES is supplying heat to the outside at the current moment; and They represent the upper and lower limits of the thermal oil temperature when A-CAES supplies heat to the outside; and They represent the upper and lower limits of the thermal oil mass flow rate when A-CAES supplies heat to the outside; and These are all auxiliary Boolean variables introduced; is a very large positive constant.

[0180] Under the constraint of formula (36), At this moment, if the temperature of the heat transfer oil in the high temperature heat storage tank is lower than the minimum heating temperature ,but Can only be equal to 0, and then Can only be equal to 0, at this time A-CAES cannot supply heat to the outside; similarly, At this moment, if the temperature of the heat transfer oil in the high temperature heat storage tank is higher than the maximum heating temperature ,but Can only be equal to 0, and then It can only be equal to 0, at which time A-CAES cannot supply heat to the outside.

[0181] After introducing equation (36), A-CAES is constrained to only operate when the temperature of the heat transfer oil in the high-temperature heat storage tank is within the heating temperature range ( arrive ) can only supply heat to the outside, and the mass flow rate of the heat transfer oil for external heat supply must be kept within its normal working range.

[0182] Figure 3 It is a flow chart of solving the scheduling model under target constraints and obtaining target scheduling parameters provided by the present invention.

[0183] The following will be combined Figure 3 The process of solving the scheduling model under target constraints and obtaining the target scheduling parameters is explained.

[0184] In an exemplary embodiment of the present invention, Figure 3 It can be seen that solving the scheduling model under the target constraints to obtain the target scheduling parameters may include steps 310 to 330, and each step will be introduced below.

[0185] In step 310 , a zero-setting auxiliary variable in the scheduling model is determined; wherein the zero-setting auxiliary variable is used to indicate that the auxiliary variable can be set to zero when a preset condition is met.

[0186] In step 320, the zero-setting auxiliary variables in the scheduling model are reset to zero to obtain a simplified scheduling model.

[0187] In step 330, the simplified scheduling model is solved under the target constraints to obtain the target scheduling parameters.

[0188] It should be noted that in the process of modeling, a large number of auxiliary variables were introduced, such as 、 However, due to the particularity of this model, some auxiliary variables have no practical significance, such as , which means and Falling in the , 1 sub-interval, whether A-CAES is working in the charging state. At this time, Between subintervals ,and Between subintervals , at this time calculate the air temperature in the gas storage room , at the lowest temperature, , at the highest temperature, .like Still less than , which means that when and Falling in the When the air temperature in the gas storage is always lower than the minimum value of the air temperature in the gas storage, it conflicts with the constraint of formula (32). Is an auxiliary Boolean variable with no practical meaning. Setting the value of to zero will not only not affect the solution of the entire model, but also reduce the amount of computation required to solve the mixed integer linear model. This type of constraint that sets the auxiliary variables that have no practical significance to zero is summarized as an accelerated computation constraint, which can be specifically expressed as formula (37) and formula (38):

[0189] (37)

[0190] (38)

[0191] in, is an auxiliary Boolean variable introduced, which indicates that when and Falling in the 、 During the sub-interval, whether the adiabatic compressed air energy storage is working in the charging state; is an auxiliary Boolean variable introduced, which indicates that when and Falling in the 、 In the sub-interval, whether the adiabatic compressed air energy storage is working in the discharge state; whether it meets the judgment conditions (corresponding to the preset conditions, where the preset conditions are the content corresponding to the if conditions) and are considered to be zero-set auxiliary variables; yes The left endpoint of the i-th subinterval; yes The right endpoint of the j-th subinterval; yes The left endpoint of the mth subinterval; yes The right endpoint of the nth subinterval; is the specific heat capacity of air at constant volume; is the constant pressure specific heat capacity of the thermal oil; Indicates the lower limit of the air temperature in the gas storage; Indicates the lower limit of the thermal oil temperature in the high-temperature heat storage tank.

[0192] It is understandable that those who meet the judgment conditions and It is considered as a zero-setting auxiliary variable. The zero-setting auxiliary variables in the scheduling model can be set to zero to obtain a simplified scheduling model. The simplified scheduling model is then solved under the target constraints to obtain the target scheduling parameters.

[0193] At this point, the scheduling model for adiabatic compressed air energy storage for combined power, heat, and gas has been fully established. The entire scheduling model is built within a mixed-integer linear framework, enabling efficient solver computation. Simply input the entire scheduling model into the solver to generate a scheduling plan.

[0194] The following describes the adiabatic compressed air energy storage scheduling device provided by the present invention. The adiabatic compressed air energy storage scheduling device described below and the adiabatic compressed air energy storage scheduling method described above can be referenced to each other.

[0195] Figure 4 It is a structural schematic diagram of the adiabatic compressed air energy storage scheduling device provided by the present invention.

[0196] The following will be combined Figure 4 The structure of the adiabatic compressed air energy storage scheduling device provided by the present invention is described.

[0197] In an exemplary embodiment of the present invention, the adiabatic compressed air energy storage scheduling device can be applied to an adiabatic compressed air energy storage device, wherein the adiabatic compressed air energy storage device includes at least an air storage reservoir and a high-temperature heat storage tank. Figure 4 It can be seen that the adiabatic compressed air energy storage scheduling device can include a construction module 410 and a processing module 420. Each module will be introduced below.

[0198] A construction module 410 may be configured to construct a scheduling model for the adiabatic compressed air energy storage device, wherein the scheduling model includes a scheduling objective function and an objective constraint, wherein the scheduling objective function is a function of the economic income of the adiabatic compressed air energy storage device during the scheduling process; the objective constraint is determined based on state variables of the adiabatic compressed air energy storage device, wherein the state variables include a first mass of air in the gas storage reservoir, the thermodynamic energy of the air in the gas storage reservoir, a second mass of the thermal oil in the high-temperature heat storage tank, and the enthalpy of the thermal oil in the high-temperature heat storage tank;

[0199] The processing module 420 can be configured to solve the scheduling model under the target constraints to obtain target scheduling parameters to maximize the economic income of the scheduling objective function, wherein the target scheduling parameters include a target first mass of the air in the gas storage, a target thermodynamic energy of the air in the gas storage, a target second mass of the thermal oil in the high-temperature heat storage tank, and a target enthalpy of the thermal oil in the high-temperature heat storage tank.

[0200] In an exemplary embodiment of the present invention, the construction module 410 may construct the scheduling objective function in the following manner:

[0201] respectively determining a first income of the adiabatic compressed air energy storage device in the electricity supply mode, a second income in the heat supply mode, and a third income in the compressed air mode;

[0202] The scheduling objective function is determined based on the first income, the second income, and the third income.

[0203] In an exemplary embodiment of the present invention, the construction module 410 may determine the scheduling objective function based on the first income, the second income, and the third income in the following manner:

[0204]

[0205] in, ( - - ) represents the first income; represents the electricity price at time t; represents the discharge power of the adiabatic compressed air energy storage device at time t; represents the charging power of the adiabatic compressed air energy storage device at time t; represents the second income; represents the heating price at time t; represents the mass flow rate of the thermal oil supplied to the outside by the high-temperature heat storage tank of the adiabatic compressed air energy storage device at time t; represents said third income; represents the gas price at time t; represents the air mass flow rate of the gas storage reservoir of the adiabatic compressed air energy storage device supplying air to the outside at time t; T represents the set of scheduling time periods.

[0206] In an exemplary embodiment of the present invention, the target constraints include state constraints of the state variables, system constraints of the adiabatic compressed air energy storage device, and operation constraints of the adiabatic compressed air energy storage device, wherein:

[0207] The state constraints include discrete state equations and switching signal equations;

[0208] The system constraints include electric power constraints, gas storage constraints and high-temperature heat storage tank constraints;

[0209] The operation constraints include charge and discharge state constraints, upper and lower limit constraints of state variables, external gas supply operation constraints and external heat supply operation constraints.

[0210] In an exemplary embodiment of the present invention, the discrete state equation includes a first discrete state equation of the gas storage;

[0211] The construction module 410 may construct the first discrete state equation of the gas storage in the following manner:

[0212]

[0213]

[0214] in, represents the first mass of the air in the gas storage at time t+1; represents the first mass of air in the gas storage at time t; represents the mass flow rate of air entering the gas storage after being compressed by the compressor at time t; represents the mass flow rate of air leaving the gas storage reservoir at time t to drive the turbine to generate electricity; represents the air mass flow rate of the gas storage supplying air to the outside at time t; represents the thermodynamic energy of the air in the gas storage at time t+1; represents the thermodynamic energy of the air in the gas storage at time t; Indicates the pressure caused by air entering the gas storage at time t added value; represents the energy at time t+1 caused by air leaving the gas storage to drive the turbine to generate electricity the reduction in value; It represents the air leaving the gas storage at time t+1 to drive the turbine to supply air to the outside. the reduction in value; Indicates the heat transfer between the gas storage and the surrounding environment at time t+1. The change value of Indicates the duration of each scheduling period.

[0215] In an exemplary embodiment of the present invention, the discrete state equation includes a second discrete state equation of the high-temperature heat storage tank;

[0216] The construction module 410 can construct the second discrete state equation of the high-temperature heat storage tank in the following manner:

[0217]

[0218]

[0219] in, represents the second mass of the thermal oil in the high-temperature heat storage tank at time t+1; represents the second mass of the thermal oil in the high-temperature heat storage tank at time t; represents the total heat transfer oil mass flow rate on the compression side at time t; represents the total heat transfer oil mass flow rate on the expansion side at time t; It represents the mass flow rate of the thermal oil entering the high-temperature heat storage tank after being heated by the electric heater at time t; represents the mass flow rate of the thermal oil supplied by the high-temperature heat storage tank to the outside at time t; represents the enthalpy of the heat transfer oil in the high-temperature heat storage tank at time t+1; represents the enthalpy of the heat transfer oil in the high-temperature heat storage tank at time t; It indicates that at time t, the heat transfer oil after heat exchange on the compression side enters the high-temperature heat storage tank. added value; It indicates that at time t, the heat transfer oil heated by the electric heater enters the high-temperature heat storage tank. added value; Indicates the heat transfer caused by the heat transfer oil leaving the high-temperature heat storage tank to exchange heat on the expansion side at time t. the reduction in value; It indicates the heat transfer oil leaving the high temperature heat storage tank and supplying heat to the outside through the expansion side at time t. The reduction value.

[0220] In an exemplary embodiment of the present invention, the switching signal equation is used to define the subinterval of the state variable of the adiabatic compressed air energy storage device at different times, wherein the switching signal equation includes a first switching signal equation of the gas storage reservoir and a second switching signal equation of the high-temperature heat storage tank.

[0221] In an exemplary embodiment of the present invention, the adiabatic compressed air energy storage device further includes a compressor, an expansion turbine and a thermal oil electric heater; wherein,

[0222] The electric power constraint is used to stipulate that the power of the compressor is maintained within the rated operating range of the compressor, the power of the expansion turbine is maintained within the rated operating range of the expansion turbine, and the power of the thermal oil electric heater is maintained within the rated operating range of the thermal oil electric heater;

[0223] The gas storage constraint is used to define the calculation formula of each physical quantity in the first discrete state equation of the gas storage;

[0224] The high-temperature heat storage tank constraint is used to define calculation formulas for various physical quantities in the second discrete state equation of the high-temperature heat storage tank.

[0225] In an exemplary embodiment of the present invention, the charge and discharge state constraint is used to constrain the adiabatic compressed air energy storage device from operating in the charging state and the discharging state at the same time; the upper and lower limit constraints of the state variables are used to constrain the state variables of the adiabatic compressed air energy storage device to remain within their respective rated operating ranges; the external air supply operation constraint is used to constrain the air mass flow rate of the external air supply of the adiabatic compressed air energy storage device to remain within the rated operating range; the external heating operation constraint is used to constrain the adiabatic compressed air energy storage device to provide heating at a temperature of the thermal oil in the high-temperature heat storage tank within the rated heating temperature range, and the thermal oil mass flow rate of the external heating of the adiabatic compressed air energy storage device to remain within the rated operating range.

[0226] In an exemplary embodiment of the present invention, the processing module 420 may solve the scheduling model under the target constraints to obtain the target scheduling parameters in the following manner:

[0227] Determining a zero-setting auxiliary variable in the scheduling model; wherein the zero-setting auxiliary variable is used to indicate that the auxiliary variable can be set to zero under a preset condition;

[0228] performing zero-setting processing on the zero-setting auxiliary variables in the scheduling model to obtain a simplified scheduling model;

[0229] The simplified scheduling model is solved under the target constraints to obtain target scheduling parameters.

[0230] Figure 5 An example of a physical structure diagram of an electronic device is shown below. Figure 5As shown, the electronic device may include: a processor 510, a communications interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communications interface 520 and the memory 530 communicate with each other via the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the adiabatic compressed air energy storage scheduling method, which is applied to an adiabatic compressed air energy storage device, wherein the adiabatic compressed air energy storage device includes at least a gas storage reservoir and a high-temperature heat storage tank; the method includes: constructing a scheduling model for the adiabatic compressed air energy storage device, wherein the scheduling model includes a scheduling objective function and an objective constraint, wherein the scheduling objective function is a function of the economic income of the adiabatic compressed air energy storage device during the scheduling process; the objective constraint is based on the state variables of the adiabatic compressed air energy storage device. Determine, the state variables include a first mass of the air in the gas storage, the thermodynamic energy of the air in the gas storage, a second mass of the heat transfer oil in the high-temperature heat storage tank, and the enthalpy of the heat transfer oil in the high-temperature heat storage tank; solve the scheduling model under the target constraint to obtain target scheduling parameters so as to maximize the economic income of the scheduling objective function, wherein the target scheduling parameters include a target first mass of the air in the gas storage, a target thermodynamic energy of the air in the gas storage, a target second mass of the heat transfer oil in the high-temperature heat storage tank, and a target enthalpy of the heat transfer oil in the high-temperature heat storage tank.

[0231] Furthermore, the logic instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0232] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the adiabatic compressed air energy storage scheduling method provided by the above methods. The method is applied to an adiabatic compressed air energy storage device, wherein the adiabatic compressed air energy storage device includes at least a gas storage reservoir and a high-temperature heat storage tank; the method includes: constructing a scheduling model for the adiabatic compressed air energy storage device, wherein the scheduling model includes a scheduling objective function and an objective constraint, and the scheduling objective function is about the adiabatic compressed air energy storage device during the scheduling process. The objective constraint is determined based on state variables of the adiabatic compressed air energy storage device, wherein the state variables include a first mass of air in the gas storage reservoir, the thermodynamic energy of the air in the gas storage reservoir, a second mass of the heat transfer oil in the high-temperature heat storage tank, and the enthalpy of the heat transfer oil in the high-temperature heat storage tank. The scheduling model is solved under the objective constraint to obtain target scheduling parameters so as to maximize the economic income of the scheduling objective function, wherein the target scheduling parameters include a target first mass of air in the gas storage reservoir, a target thermodynamic energy of air in the gas storage reservoir, a target second mass of the heat transfer oil in the high-temperature heat storage tank, and a target enthalpy of the heat transfer oil in the high-temperature heat storage tank.

[0233] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the adiabatic compressed air energy storage scheduling method provided by the above methods, the method being applied to an adiabatic compressed air energy storage device, wherein the adiabatic compressed air energy storage device comprises at least a gas storage reservoir and a high-temperature heat storage tank; the method comprising: constructing a scheduling model for the adiabatic compressed air energy storage device, wherein the scheduling model comprises a scheduling objective function and an objective constraint, the scheduling objective function being a function of the economic income of the adiabatic compressed air energy storage device during the scheduling process; the objective constraint ... The method comprises the steps of: determining the state variables of the adiabatic compressed air energy storage device, the state variables including a first mass of air in the gas storage reservoir, the thermodynamic energy of the air in the gas storage reservoir, a second mass of the heat transfer oil in the high-temperature heat storage tank, and the enthalpy of the heat transfer oil in the high-temperature heat storage tank; solving the scheduling model under the target constraint to obtain target scheduling parameters so as to maximize the economic income of the scheduling objective function, wherein the target scheduling parameters include a target first mass of air in the gas storage reservoir, a target thermodynamic energy of air in the gas storage reservoir, a target second mass of the heat transfer oil in the high-temperature heat storage tank, and a target enthalpy of the heat transfer oil in the high-temperature heat storage tank.

[0234] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0235] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0236] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for scheduling adiabatic compressed air energy storage, characterized in that: The method is applied to an adiabatic compressed air energy storage device, wherein the adiabatic compressed air energy storage device includes at least a gas storage reservoir and a high-temperature heat storage tank; the method comprises: Constructing a scheduling model for the adiabatic compressed air energy storage device, wherein the scheduling model includes a scheduling objective function and an objective constraint, the scheduling objective function being a function of the economic income of the adiabatic compressed air energy storage device during the scheduling process; the objective constraint being determined based on state variables of the adiabatic compressed air energy storage device, the state variables including a first mass of air in the gas storage reservoir, the thermodynamic energy of the air in the gas storage reservoir, a second mass of the thermal oil in the high-temperature heat storage tank, and the enthalpy of the thermal oil in the high-temperature heat storage tank; The scheduling model is solved under the target constraints to obtain target scheduling parameters so as to maximize the economic income of the scheduling objective function, wherein the target scheduling parameters include a target first mass of air in the gas storage, a target thermodynamic energy of the air in the gas storage, a target second mass of the heat transfer oil in the high-temperature heat storage tank, and a target enthalpy of the heat transfer oil in the high-temperature heat storage tank. The scheduling objective function is constructed in the following manner: respectively determining a first income of the adiabatic compressed air energy storage device in the electricity supply mode, a second income in the heat supply mode, and a third income in the compressed air mode; The scheduling objective function is determined based on the first income, the second income, and the third income, wherein: The scheduling objective function is determined based on the first income, the second income, and the third income, and is implemented using the following formula: ； in, represents the first income; represents the electricity price at time t; represents the discharge power of the adiabatic compressed air energy storage device at time t; represents the charging power of the adiabatic compressed air energy storage device at time t; represents the second income; represents the heating price at time t; represents the mass flow rate of the thermal oil supplied to the outside by the high-temperature heat storage tank of the adiabatic compressed air energy storage device at time t; represents said third income; represents the gas price at time t; represents the air mass flow rate of the gas storage reservoir of the adiabatic compressed air energy storage device supplying air to the outside at time t; T represents the set of scheduling periods, where, The target constraints include the state constraints of the state variables, the system constraints of the adiabatic compressed air energy storage device and the operation constraints of the adiabatic compressed air energy storage device, wherein: The state constraints include discrete state equations and switching signal equations; The system constraints include electric power constraints, gas storage constraints and high-temperature heat storage tank constraints; The operation constraints include charge and discharge state constraints, upper and lower limit constraints of state variables, external gas supply operation constraints and external heat supply operation constraints.

2. The adiabatic compressed air energy storage scheduling method according to claim 1, characterized in that: The discrete state equation includes a first discrete state equation of the gas storage; The first discrete state equation of the gas storage is implemented using the following formula: ； in, represents the first mass of the air in the gas storage at time t+1; represents the first mass of air in the gas storage at time t; represents the mass flow rate of air entering the gas storage after being compressed by the compressor at time t; represents the mass flow rate of air leaving the gas storage reservoir at time t to drive the turbine to generate electricity; represents the air mass flow rate of the gas storage supplying air to the outside at time t; represents the thermodynamic energy of the air in the gas storage at time t+1; represents the thermodynamic energy of the air in the gas storage at time t; Indicates the pressure caused by air entering the gas storage at time t added value; represents the energy at time t+1 caused by air leaving the gas storage to drive the turbine to generate electricity the reduction in value; It represents the air leaving the gas storage at time t+1 to drive the turbine to supply air to the outside. the reduction in value; Indicates the heat transfer between the gas storage and the surrounding environment at time t+1. The change value of Indicates the duration of each scheduling period.

3. The adiabatic compressed air energy storage scheduling method according to claim 2, characterized in that: The discrete state equation includes a second discrete state equation of the high-temperature heat storage tank; The second discrete state equation of the high-temperature heat storage tank is implemented using the following formula: ； in, represents the second mass of the thermal oil in the high-temperature heat storage tank at time t+1; represents the second mass of the thermal oil in the high-temperature heat storage tank at time t; represents the total heat transfer oil mass flow rate on the compression side at time t; represents the total heat transfer oil mass flow rate on the expansion side at time t; It represents the mass flow rate of the thermal oil entering the high-temperature heat storage tank after being heated by the electric heater at time t; represents the mass flow rate of the thermal oil supplied by the high-temperature heat storage tank to the outside at time t; represents the enthalpy of the heat transfer oil in the high-temperature heat storage tank at time t+1; represents the enthalpy of the heat transfer oil in the high-temperature heat storage tank at time t; It indicates that at time t, the heat transfer oil after heat exchange on the compression side enters the high-temperature heat storage tank. added value; It indicates that at time t, the heat transfer oil heated by the electric heater enters the high-temperature heat storage tank. added value; Indicates the heat transfer caused by the heat transfer oil leaving the high-temperature heat storage tank to exchange heat on the expansion side at time t. the reduction in value; It indicates the heat transfer oil leaving the high temperature heat storage tank and supplying heat to the outside through the expansion side at time t. The reduction value.

4. The adiabatic compressed air energy storage scheduling method according to claim 1, characterized in that: The switching signal equation is used to define the subinterval of the state variable of the adiabatic compressed air energy storage device at different times, wherein the switching signal equation includes a first switching signal equation of the gas storage reservoir and a second switching signal equation of the high-temperature heat storage tank.

5. The adiabatic compressed air energy storage scheduling method according to any one of claims 1 to 4, characterized in that: The adiabatic compressed air energy storage device also includes a compressor, an expansion turbine and a thermal oil electric heater; wherein, The electric power constraint is used to stipulate that the power of the compressor is maintained within the rated operating range of the compressor, the power of the expansion turbine is maintained within the rated operating range of the expansion turbine, and the power of the thermal oil electric heater is maintained within the rated operating range of the thermal oil electric heater; The gas storage constraint is used to define the calculation formula of each physical quantity in the first discrete state equation of the gas storage; The high-temperature heat storage tank constraint is used to define calculation formulas for various physical quantities in the second discrete state equation of the high-temperature heat storage tank.

6. The adiabatic compressed air energy storage scheduling method according to any one of claims 1 to 4, characterized in that: The charge and discharge state constraint is used to constrain the adiabatic compressed air energy storage device from operating in the charging state and the discharging state at the same time; the upper and lower limit constraints of the state variables are used to constrain the state variables of the adiabatic compressed air energy storage device to remain within their respective rated operating ranges; the external air supply operation constraint is used to constrain the air mass flow rate of the adiabatic compressed air energy storage device supplied to the outside to remain within the rated operating range; the external heating operation constraint is used to constrain the adiabatic compressed air energy storage device to provide heating at the temperature of the thermal oil in the high-temperature heat storage tank within the rated heating temperature range, and the thermal oil mass flow rate of the adiabatic compressed air energy storage device supplied to the outside to remain within the rated operating range.

7. The adiabatic compressed air energy storage scheduling method according to claim 1, characterized in that: Solving the scheduling model under the target constraints to obtain target scheduling parameters specifically includes: Determining a zero-setting auxiliary variable in the scheduling model; wherein the zero-setting auxiliary variable is used to indicate that the auxiliary variable can be set to zero under a preset condition; performing zero-setting processing on the zero-setting auxiliary variables in the scheduling model to obtain a simplified scheduling model; The simplified scheduling model is solved under the target constraints to obtain target scheduling parameters.

8. An adiabatic compressed air energy storage scheduling device, characterized in that: The device is applied to an adiabatic compressed air energy storage device, wherein the adiabatic compressed air energy storage device includes at least a gas storage reservoir and a high-temperature heat storage tank; the device is used to implement the adiabatic compressed air energy storage scheduling method according to any one of claims 1 to 7, and the device includes: a construction module for constructing a scheduling model for the adiabatic compressed air energy storage device, wherein the scheduling model includes a scheduling objective function and an objective constraint, the scheduling objective function being a function of the economic income of the adiabatic compressed air energy storage device during the scheduling process; the objective constraint being determined based on state variables of the adiabatic compressed air energy storage device, the state variables including a first mass of air in the gas storage reservoir, the thermodynamic energy of the air in the gas storage reservoir, a second mass of the heat transfer oil in the high-temperature heat storage tank, and the enthalpy of the heat transfer oil in the high-temperature heat storage tank; a processing module, configured to solve the scheduling model under the target constraints to obtain target scheduling parameters so as to maximize the economic income of the scheduling objective function, wherein the target scheduling parameters include a target first mass of air in the gas storage reservoir, a target thermodynamic energy of the air in the gas storage reservoir, a target second mass of the heat transfer oil in the high-temperature heat storage tank, and a target enthalpy of the heat transfer oil in the high-temperature heat storage tank.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the adiabatic compressed air energy storage scheduling method as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the adiabatic compressed air energy storage scheduling method according to any one of claims 1 to 7 is implemented.

11. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the adiabatic compressed air energy storage scheduling method according to any one of claims 1 to 7 is implemented.

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