Dynamic Scheduling Method and System for Advanced Adiabatic Compressed Air Energy Storage Power Station

Abstract

The present invention provides a dynamic scheduling method and system for an advanced adiabatic compressed air energy storage power station. The method includes: based on the thermodynamic model of advanced adiabatic compressed air energy storage, constructing an external characteristic model of combined heat and power supply of advanced adiabatic compressed air energy storage according to the ambient temperature and component off-design information; taking the minimization of the electricity purchase cost as the objective function, and constructing a dynamic scheduling model of the power station of advanced adiabatic compressed air energy storage according to the external characteristic model of combined heat and power supply, the operation constraints of the heat pump, and the energy balance constraints; and solving the dynamic scheduling model of the power station through an approximate dynamic programming algorithm to perform dynamic scheduling on the advanced adiabatic compressed air energy storage power station according to the solution result. The present invention can improve the flexibility and reliability of the operation of the AA-CAES system, improve the safety of the operation of the integrated energy system, and has a higher solution efficiency for the dynamic scheduling strategy.

Description

Technical Field

[0001] The present invention relates to the technical field of energy scheduling, and in particular, to a dynamic scheduling method and system for an advanced adiabatic compressed air energy storage power station. Background Art

[0002] Based on infrastructure such as power grids, gas grids, and heating grids, the regional integrated energy system integrates and uniformly schedules multi-energy supply, conversion, and storage devices within the system to achieve collaborative optimization of multi-energy flows within the system, which helps to improve energy utilization efficiency and reduce energy supply costs. Advanced adiabatic compressed air energy storage naturally has the characteristics of combined heat and power generation and storage, and can be connected to the integrated energy system as the core equipment of a multi-energy power station.

[0003] Regarding the self-scheduling problem of power stations containing advanced adiabatic compressed air energy storage (Advanced Adiabatic Compressed Air Energy Storage, abbreviated as AA-CAES), preliminary research has been carried out at home and abroad. However, for the modeling of the external characteristics of AA-CAES combined heat and power generation, an equivalent battery model is generally used, without considering the variable operating conditions of the system, ignoring the influence of environmental temperature on the operating characteristics of the system, and not considering the coordinated control of the internal air and heat transfer oil flow of the system; in addition, currently, most of the optimization scheduling problems of AA-CAES power stations are modeled as deterministic optimizations, without considering external uncertain factors such as electricity and heat loads, and most of the research focuses on the daily scheduling time scale, and the research on intraday scheduling is still blank.

[0004] Therefore, there is an urgent need for a dynamic scheduling method and system for an advanced adiabatic compressed air energy storage power station to solve the above problems. Summary of the Invention

[0005] In view of the problems existing in the prior art, the present invention provides a dynamic scheduling method and system for an advanced adiabatic compressed air energy storage power station.

[0006] The present invention provides a dynamic scheduling method for an advanced adiabatic compressed air energy storage power station, including:

[0007] Based on the thermodynamic model of advanced adiabatic compressed air energy storage, construct an external characteristic model of combined heat and power generation of advanced adiabatic compressed air energy storage according to the environmental temperature and component variable operating condition information;

[0008] Taking the minimization of the electricity purchase cost as the objective function, construct a dynamic scheduling model of the power station of advanced adiabatic compressed air energy storage according to the external characteristic model of combined heat and power generation, the operation constraints of the heat pump, and the energy balance constraints;

[0009] Solve the dynamic scheduling model of the energy station through an approximate dynamic programming algorithm, and perform dynamic scheduling on the advanced adiabatic compressed air energy storage energy station according to the solution results.

[0010] According to a dynamic scheduling method for an advanced adiabatic compressed air energy storage energy station provided by the present invention, the thermodynamic model includes a compressor thermodynamic model, a turbine thermodynamic model, and a heat exchanger thermodynamic model.

[0011] According to a dynamic scheduling method for an advanced adiabatic compressed air energy storage energy station provided by the present invention, based on the thermodynamic model of advanced adiabatic compressed air energy storage, a combined heat and power external characteristic model of advanced adiabatic compressed air energy storage is constructed according to the ambient temperature and component off-design information, including:

[0012] Based on the compressor thermodynamic model, the turbine thermodynamic model, and the heat exchanger thermodynamic model, simulations of the compression process, the expansion process, and the heat exchange process are respectively carried out;

[0013] According to the ambient temperature and component off-design information of advanced adiabatic compressed air energy storage, polynomial fitting is performed through the simulation results to obtain the combined heat and power external characteristic model of advanced adiabatic compressed air energy storage.

[0014] According to a dynamic scheduling method for an advanced adiabatic compressed air energy storage energy station provided by the present invention, the formula of the combined heat and power external characteristic model is:

[0015] p CAESc = Γ CAESc (m ac , m HTFc , T am );

[0016] p CAESd = Γ CAESd (m ad , m HTFd );

[0017] h CAES = Γ CAESh (m HTFh );

[0018] g CAESc (m ac , m HTFc , T am ) = 0;

[0019]

[0020]

[0021] g CAESd (m ad,m HTFd ) ≤ 0;

[0022]

[0023]

[0024] g CAESh (m HTFh ) ≤ 0;

[0025]

[0026] Wherein, p CAESc represents the analytical expression of the charging power, T am represents the ambient temperature, m ac represents the air mass flow rate on the compression side, m HTFc represents the heat transfer oil mass flow rate, Γ CAESc is a polynomial function based on the ambient temperature T am , the air mass flow rate m ac on the compression side, and the heat transfer oil mass flow rate m HTFc ; p CAESd represents the analytical expression of the power generation, m ad represents the air mass flow rate on the expansion side, m HTFd represents the heat transfer oil mass flow rate, Γ CAESd is a polynomial function based on the air mass flow rate m ad on the expansion side and the heat transfer oil mass flow rate m HTFd ; h CAES represents the analytical expression of the heating power, m HTFh represents the heat transfer oil mass flow rate on the primary side of the heat exchanger on the heating side, Γ CAESh is a polynomial function based on the heat transfer oil mass flow rate m HTFh on the primary side of the heat exchanger on the heating side; g CAESc is the feasible region for the regulation of the air and heat transfer oil mass flow rates during the compression process, representing the coordinated control strategy for maintaining the temperature of the high-temperature heat storage tank and the constant air and heat transfer oil mass flow rates on the compression side under different ambient temperatures and charging powers; represents the minimum value of the air mass flow rate on the compression side, represents the maximum value of the air mass flow rate on the compression side, represents the minimum value of the heat transfer oil mass flow rate on the compression side, represents the maximum value of the heat transfer oil mass flow rate on the compression side; g CAESd represents the feasible region for the regulation of the air and heat transfer oil mass flow rates during the expansion process, represents the minimum value of the air mass flow rate on the expansion side, represents the maximum value of the air mass flow rate on the expansion side, represents the minimum value of the heat transfer oil mass flow rate on the expansion side, Represents the maximum value of the mass flow rate of the heat transfer oil on the expansion side; g CAESh Represents the feasible region for adjusting the mass flow rate of the heat transfer oil during the heat supply process Represents the minimum value of the mass flow rate of the heat transfer oil on the primary side of the heat exchanger on the expansion side Represents the maximum value of the mass flow rate of the heat transfer oil on the primary side of the heat exchanger on the expansion side

[0027] According to a dynamic scheduling method for an advanced adiabatic compressed air energy storage power station provided by the present invention, minimizing the electricity purchase cost is used as the objective function. According to the external characteristic model of combined heat and power generation, the operation constraints of the heat pump, and the energy balance constraints, a dynamic scheduling model of the advanced adiabatic compressed air energy storage power station is constructed, including:

[0028] Minimizing the electricity purchase cost is used as the objective function, and the objective function is expressed as:

[0029]

[0030] Wherein, Represents the electricity price at time t; Represents the amount of electricity purchased by the advanced adiabatic compressed air energy storage power station from the power grid at time t;

[0031] According to the external characteristic model of combined heat and power generation, the actual charging power, the air mass flow rate during actual compression, the heat transfer oil mass flow rate during actual compression, the actual power generation power, the air mass flow rate on the actual expansion side, the heat transfer oil mass flow rate on the actual expansion side, the actual power generation and heat supply power, and the heat transfer oil mass flow rate on the actual heat supply side, a double energy storage state model of the advanced adiabatic compressed air energy storage is constructed;

[0032] Taking the double energy storage state model, the operation constraints of the heat pump, and the energy balance constraints as the constraint conditions of the objective function, a dynamic scheduling model of the advanced adiabatic compressed air energy storage power station is constructed.

[0033] According to a dynamic scheduling method for an advanced adiabatic compressed air energy storage power station provided by the present invention, the formula of the double energy storage state model is:

[0034] p CAESc = Γ CAESc (m ac , m HTFc , T am );

[0035] p CAESd = Γ CAESd (m ad , m HTFd );

[0036] h CAES = ΓCAESh (m HTFh );

[0037] g CAESc (m ac ,m HTFc ,T am )=0;

[0038]

[0039]

[0040] g CAESd (m ad ,m HTFd )≤0;

[0041]

[0042]

[0043] g CAESh (m HTFh )≤0;

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] Among them, represents the actual charging power, represents the air mass flow rate during actual compression, represents the heat transfer oil mass flow rate during actual compression, is a variable; represents the actual power generation, represents the air mass flow rate on the actual expansion side, represents the heat transfer oil mass flow rate on the actual expansion side, is a variable; represents the actual power generation and heat supply, represents the heat transfer oil mass flow rate on the actual heat supply side, is a variable; T Δ represents the unit dispatching period length, represents the mass of air in the gas storage chamber at the end of the t-th period, represents the mass of heat transfer oil in the high-temperature heat storage tank at the end of the t-th period, N c represents the number of compression stages, N e represents the number of expansion stages;

[0057] The formula for the heat pump operation constraint is:

[0058]

[0059]

[0060] where, COP(T t am ) represents the coefficient of performance of the heat pump, represents the heat supply power of the heat pump in the t-th period, represents the electric power input in the t-th period, represents the minimum value of the input electric power, represents the maximum value of the input electric power;

[0061] The formula for the energy balance constraint is:

[0062]

[0063]

[0064]

[0065] where, represents the maximum power purchase amount from the power grid by the energy station, represents the electric load in the t-th period, represents the load in the t-th period.

[0066] According to a dynamic dispatching method for an advanced adiabatic compressed air energy storage energy station provided by the present invention, the dynamic dispatching model of the energy station is solved by an approximate dynamic programming algorithm, and the advanced adiabatic compressed air energy storage energy station is dynamically dispatched according to the solution result, including:

[0067] By the Bellman optimality principle, the constraint conditions of the objective function are split to obtain the corresponding state space:

[0068]

[0069] wherein, denotes the state variable;

[0070] denotes the uncertainty;

[0071]

[0072] denotes the control variable;

[0073] V t (x t ,ζ t ) denotes the value function at the t-th time period, denotes the electricity purchase cost at the t-th time period; denotes the conditional expectation, p s,t (ξ t ) denotes the conditional probability, N s denotes the number of scenarios;

[0074] The state space is discretized, and based on historical data, the approximate values of the value function at each discrete state point are calculated sequentially from the last time period forward to obtain the approximate values of the value function at each discrete state point;

[0075] According to the actual observed value of the uncertainty ξ t , the actual observed value of the state variable x t and the approximate value of the value function, the dynamic scheduling strategy of the advanced adiabatic compressed air energy storage power station at the current time period is obtained.

[0076] The present invention also provides a dynamic scheduling system for an advanced adiabatic compressed air energy storage power station, including:

[0077] A combined heat and power external characteristic model construction module, configured to construct a combined heat and power external characteristic model of the advanced adiabatic compressed air energy storage based on the thermodynamic model of the advanced adiabatic compressed air energy storage according to the ambient temperature and component variable operating condition information;

[0078] A dynamic scheduling model construction module, configured to construct a dynamic scheduling model of the energy storage power station of the advanced adiabatic compressed air energy storage by taking the minimization of the electricity purchase cost as the objective function according to the combined heat and power external characteristic model, the heat pump operation constraint, and the energy balance constraint;

[0079] An energy storage power station self-scheduling module, configured to solve the dynamic scheduling model of the energy storage power station by an approximate dynamic programming algorithm, so as to perform dynamic scheduling on the advanced adiabatic compressed air energy storage power station according to the solution result.

[0080] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of any of the above-mentioned dynamic scheduling methods for an advanced adiabatic compressed air energy storage power station are implemented.

[0081] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned dynamic scheduling methods for an advanced adiabatic compressed air energy storage power station are implemented.

[0082] Compared with the prior art, the dynamic scheduling method and system for an advanced adiabatic compressed air energy storage power station provided by the present invention can improve the flexibility and reliability of the operation of the AA-CAES system, enhance the safety of the operation of the integrated energy system, and have higher solution efficiency for the dynamic scheduling strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0084] Figure 1 It is a schematic flow chart of the dynamic scheduling method for an advanced adiabatic compressed air energy storage power station provided by the present invention;

[0085] Figure 2 It is a schematic structural diagram of the dynamic scheduling system for an advanced adiabatic compressed air energy storage power station provided by the present invention;

[0086] Figure 3 It is a schematic structural diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0087] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the accompanying drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0088] Figure 1 It is a schematic flow chart of the dynamic scheduling method for an advanced adiabatic compressed air energy storage power station provided by the present invention, as Figure 1As shown in the figure, the present invention provides a dynamic scheduling method for an advanced adiabatic compressed air energy storage power station, including:

[0089] Step 101, based on the thermodynamic model of advanced adiabatic compressed air energy storage, construct an external characteristic model of combined heat and power supply for advanced adiabatic compressed air energy storage according to the ambient temperature and component variable operating conditions information.

[0090] In the present invention, first, an external characteristic model of combined heat and power supply for AA-CAES is constructed. This model encapsulates the physical characteristics and coupling relationships of the internal components of the AA-CAES system, and considers the influence of ambient temperature on the system operation. According to the coordinated control strategy of maintaining the temperature of the high-temperature heat storage tank and maintaining a constant mass flow rate of compressed air and heat transfer oil on the compression side at different ambient temperatures and charging powers.

[0091] Step 102, taking the minimization of the electricity purchase cost as the objective function, construct a dynamic scheduling model of the power station for advanced adiabatic compressed air energy storage according to the external characteristic model of combined heat and power supply, the operation constraints of the heat pump, and the energy balance constraints.

[0092] In the present invention, a dynamic scheduling model of the energy station for AA-CAES is established. The constraints include a dual-SOC (Storage of charge) model of AA-CAES for combined heat and power supply scheduling, which is used for the operation constraints of AA-CAES; the operation constraints of the heat pump considering the influence of ambient temperature, and the system energy operation balance constraints.

[0093] Step 103, solve the dynamic scheduling model of the power station through an approximate dynamic programming algorithm, so as to perform dynamic scheduling on the advanced adiabatic compressed air energy storage power station according to the solution results.

[0094] In the present invention, based on data-driven stochastic dynamic programming, considering uncertain factors such as electric load, heat load, and ambient temperature, and obtaining their conditional probability distributions based on historical data, an approximate dynamic programming method is used to solve the dynamic scheduling model of the energy station, and according to the solution results, obtain the dynamic adjustment strategy of the energy station in the current period.

[0095] Compared with the prior art, the dynamic scheduling method for the advanced adiabatic compressed air energy storage power station provided by the present invention can improve the flexibility and reliability of the operation of the AA-CAES system, improve the safety of the operation of the integrated energy system, and the solution efficiency of the dynamic scheduling strategy is higher.

[0096] On the basis of the above embodiments, the thermodynamic model includes a compressor thermodynamic model, a turbine thermodynamic model, and a heat exchanger thermodynamic model.

[0097] In the present invention, the outlet temperature of the i-th stage compressor in the compressor thermodynamic model is expressed as:

[0098]

[0099] Among them, represents the inlet air temperature of the i-th stage compressor, represents the outlet air temperature of the i-th stage compressor, β c,i represents the pressure ratio of the i-th stage compressor, η c,i represents the isentropic efficiency of the i-th stage compressor, and k is the adiabatic index.

[0100] The actual outlet air pressure of the i-th stage compressor is:

[0101]

[0102] Among them, is the inlet air pressure of the i-th stage compressor, is the outlet air pressure of the i-th stage compressor.

[0103] The electric power consumed in the compression process (i.e., the charging power) is:

[0104]

[0105] Among them, p CAESc is the charging power, m ac is the mass flow rate of air on the compression side, is the specific heat capacity of air at constant pressure, η m is the mechanical efficiency of the motor on the compression side.

[0106] Under off-design operating conditions, the pressure ratio and isentropic efficiency of the compressor will deviate from the rated values, which are specifically expressed by the following formulas:

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] Among them, is the dimensionless mass flow rate, is the rotational speed, and the subscript 0 represents the rated value, a 1,i , a 2,i and a 3,i are intermediate variables, b 1 , b 2a and c are constants, and their specific values are related to the compressor model.

[0114] Furthermore, the outlet temperature of the i-th stage turbine in the turbine thermodynamic model satisfies:

[0115]

[0116] where is the inlet air temperature of the i-th stage turbine, is the outlet air temperature of the i-th stage turbine, β e,i is the turbine expansion ratio, η e,i is the turbine isentropic efficiency.

[0117] The outlet air pressure of the i-th stage turbine satisfies:

[0118]

[0119] where is the inlet air pressure of the i-th stage turbine, is the outlet air pressure of the i-th stage turbine.

[0120] The total power generation is:

[0121]

[0122] where p CAESd is the power generation, m ad is the air mass flow rate on the expansion side, η G is the generator efficiency.

[0123] Under off-design operation, the turbine isentropic efficiency and expansion ratio deviate from the rated values, and their specific values satisfy the following relationship:

[0124]

[0125]

[0126]

[0127]

[0128]

[0129] where b 0 is a constant, and its typical value in the present invention is 0.3, and are the dimensionless reduced mass flow rate and rotational speed respectively, n e,i is the rotational speed of the i-th stage turbine, α i is the influence factor of rotational speed on the expansion ratio.

[0130] Furthermore, in the present invention, counter-flow heat exchangers are used on the compression side, expansion side, and heat supply side. The following model is adopted. Specifically, the heat transfer amount of the heat exchanger is:

[0131] Φ = ε·C min (T h,in -T c,in ); (9)

[0132] where C min ≡(C c , C h ) min , is the minimum value of the cold fluid heat capacity C c and the hot fluid heat capacity C h flowing through the heat exchanger; Φ is the heat transfer amount, T c,in is the cold fluid inlet temperature of the heat exchanger, T h,in is the hot fluid inlet temperature of the heat exchanger, ε is the heat exchanger effectiveness, and the effectiveness calculation formula of the counter-flow heat exchanger at non-rated operating points is:

[0133]

[0134] where NTU is the number of heat transfer units of the heat exchanger, C ch is the heat capacity ratio of the heat exchanger, satisfying the following conditions:

[0135] NTU = UA / C min ; (11)

[0136] C ch = C min / C max ; (12)

[0137] where U is the heat transfer coefficient of the heat exchanger, A is the heat transfer area of the heat exchanger, and C max is the maximum heat capacity of the cold and hot fluids.

[0138] Based on the above embodiments, the thermodynamic model of advanced adiabatic compressed air energy storage constructs an external characteristic model of combined heat and power supply for advanced adiabatic compressed air energy storage according to the ambient temperature and component variable operating condition information, including:

[0139] Based on the compressor thermodynamic model, the turbine thermodynamic model, and the heat exchanger thermodynamic model, simulations of the compression process, expansion process, and heat exchange process are respectively carried out;

[0140] According to the ambient temperature and component variable operating condition information of advanced adiabatic compressed air energy storage, polynomial fitting is performed through the simulation results to obtain the external characteristic model of combined heat and power supply for advanced adiabatic compressed air energy storage.

[0141] In the present invention, based on the thermodynamic model of the AA-CAES system, the compression, expansion, and heat exchange processes are simulated respectively. According to the simulation results, an analytical AA-CAES combined heat and power external characteristic model is obtained through fitting.

[0142] Specifically, for the compression process, to meet the requirements of operation safety and economy, the following constraints are considered: 1) Compression surge and blockage margins; 2) The isentropic efficiency of each stage of compression is not lower than a certain set value; 3) The outlet pressure of the last stage of compression is not less than the maximum value of the air pressure in the gas storage chamber; 4) The inlet temperature of the high-temperature heat storage tank is equal to the design value. The charging power under different ambient temperatures, air and heat transfer oil mass flows, and the feasible region of air and heat transfer oil flow regulation are obtained through simulation.

[0143] Based on the simulation results, the relationship between the charging power and the ambient temperature, air mass flow, and heat transfer oil mass flow is obtained through numerical fitting, expressed as:

[0144] p CAESc =Γ CAESc (m ac ,m HTFc ,T am ); (13)

[0145] Where Γ CAESc is a polynomial function of the air mass flow m ac on the compression side, the heat transfer oil mass flow m HTFc and the ambient temperature T am . And the coordinated control strategy of air and heat transfer oil flow to ensure a constant inlet temperature of the high-temperature heat storage tank under different ambient temperatures and charging powers:

[0146] g CAESc (m ac ,m HTFc ,T am )=0; (14)

[0147] Where g CAESc is a polynomial function of m ac , m HTFc and T am .

[0148] In addition, the air and heat transfer oil flow regulation ranges respectively satisfy:

[0149]

[0150]

[0151] Where and are respectively the minimum and maximum values of the air mass flow on the compression side, and They are the minimum and maximum values of the mass flow rate of the heat transfer oil on the compression side, respectively.

[0152] Furthermore, for the expansion process, the following constraints are considered: 1) The isentropic efficiency of each stage of the turbine is not lower than a certain set value; (2) To prevent blade icing, the outlet temperature of the last stage of the turbine is not lower than 0 °C; 3) The inlet pressure of the first stage of the turbine is equal to the rated value; The output power under different air and heat transfer oil mass flow rates, as well as the feasible region of air and heat transfer oil flow regulation, are obtained through simulation. Based on the simulation results, the relationship between the power generation power and the air mass flow rate and the heat transfer oil mass flow rate is obtained through numerical fitting, expressed as:

[0153] p CAESd = Γ CAESd (m ad , m HTFd ); (17)

[0154] where Γ CAESd is a polynomial fitting function of the air mass flow rate m ad on the expansion side and the heat transfer oil mass flow rate m HTFd .

[0155] The feasible operating region of the heat transfer oil and air mass flow rates is expressed as:

[0156] g CAESd (m ad , m HTFd ) ≤ 0; (18)

[0157]

[0158]

[0159] where g CAESd is a polynomial fitting function of m ad and m HTFd , and are the minimum and maximum values of the air mass flow rate on the expansion side, respectively, and are the minimum and maximum values of the heat transfer oil mass flow rate on the expansion side, respectively.

[0160] Furthermore, for the heat supply process, the following constraints are considered: 1) The outlet temperature of the heating water is not lower than the set value, 2) The mass flow rate of the secondary side hot water supply is the rated value. The heat supply power under different heat transfer oil flow rates with the secondary side heating water flow rate remaining constant, as well as the adjustment range of the heat transfer oil flow rate, are obtained through simulation. Based on the simulation results, the expression of the relationship between the heat supply power and the heat transfer oil mass flow rate obtained through numerical fitting is:

[0161] h CAES = ΓCAESh (m HTFh ); (21)

[0162] where Γ CAESh is a polynomial function of the mass flow rate m HTFh of the primary side heat transfer oil in the heat supply side heat exchanger.

[0163] The adjustment range of the heat transfer oil flow rate on the heat supply side is:

[0164] g CAESh (m HTFh ) ≤ 0; (22)

[0165]

[0166] where g CAESh is a function of m HTFh , and and are respectively the minimum and maximum values of the mass flow rate of the primary side heat transfer oil in the expansion side heat exchanger.

[0167] Based on the above embodiments, the formula of the combined heat and power external characteristic model is:

[0168] p CAESc = Γ CAESc (m ac , m HTFc , T am );

[0169] p CAESd = Γ CAESd (m ad , m HTFd );

[0170] h CAES = Γ CAESh (m HTFh );

[0171] g CAESc (m ac , m HTFc , T am ) = 0;

[0172]

[0173]

[0174] g CAESd (m ad , m HTFd ) ≤ 0;

[0175]

[0176]

[0177] g CAESh (m HTFh ) ≤ 0;

[0178]

[0179] Among them, p CAESc represents the analytical expression of the charging power, T am represents the ambient temperature, m ac represents the air mass flow rate on the compression side, m HTFc represents the heat transfer oil mass flow rate, Γ CAESc is a polynomial function based on the ambient temperature T am , the air mass flow rate m ac on the compression side, and the heat transfer oil mass flow rate m HTFc ; p CAESd represents the analytical expression of the power generation power, m ad represents the air mass flow rate on the expansion side, m HTFd represents the heat transfer oil mass flow rate, Γ CAESd is a polynomial function based on the air mass flow rate m ad on the expansion side and the heat transfer oil mass flow rate m HTFd ; h CAES represents the analytical expression of the heating power, m HTFh represents the heat transfer oil mass flow rate on the primary side of the heat exchanger on the heating side, Γ CAESh is a polynomial function based on the heat transfer oil mass flow rate m HTFh on the primary side of the heat exchanger on the heating side; g CAESc is the feasible region for regulating the air and heat transfer oil mass flow rates during the compression process, representing the coordinated control strategy for maintaining the temperature of the high-temperature heat storage tank and the constant air and heat transfer oil mass flow rates on the compression side under different ambient temperatures and charging powers; represents the minimum value of the air mass flow rate on the compression side, represents the maximum value of the air mass flow rate on the compression side, represents the minimum value of the heat transfer oil mass flow rate on the compression side, represents the maximum value of the heat transfer oil mass flow rate on the compression side; g CAESd represents the feasible region for regulating the air and heat transfer oil mass flow rates during the expansion process, represents the minimum value of the air mass flow rate on the expansion side, represents the maximum value of the air mass flow rate on the expansion side, represents the minimum value of the heat transfer oil mass flow rate on the expansion side, represents the maximum value of the heat transfer oil mass flow rate on the expansion side; g CAESh represents the feasible region for regulating the heat transfer oil mass flow rate during the heating process, represents the minimum mass flow rate of the heat transfer oil on the primary side of the expansion-side heat exchanger. represents the maximum mass flow rate of the heat transfer oil on the primary side of the expansion-side heat exchanger.

[0180] Based on the above embodiments, the formula of the dual energy storage state model is:

[0181] p CAESc = Γ CAESc (m ac , m HTFc , T am );

[0182] p CAESd = Γ CAESd (m ad , m HTFd );

[0183] h CAES = Γ CAESh (m HTFh );

[0184] g CAESc (m ac , m HTFc , T am ) = 0;

[0185]

[0186]

[0187] g CAESd (m ad , m HTFd ) ≤ 0;

[0188]

[0189]

[0190] g CAESh (m HTFh ) ≤ 0;

[0191]

[0192]

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203] Among them, represents the actual charging power, represents the air mass flow rate during actual compression, represents the heat transfer oil mass flow rate during actual compression, is a variable, characterized by 01; represents the actual power generation, represents the air mass flow rate on the actual expansion side, represents the heat transfer oil mass flow rate on the actual expansion side, is a variable, characterized by 01; represents the actual power generation and heating power, represents the heat transfer oil mass flow rate on the actual heating side, is a variable, characterized by 01; T Δ represents the unit dispatching period length, represents the mass of air in the gas storage chamber at the end of the t-th period, represents the mass of heat transfer oil in the high-temperature heat storage tank at the end of the t-th period, N c represents the number of compression stages, N e represents the number of expansion stages. Among them, formula (24j) gives the constraint that charging and discharging do not occur simultaneously; the energy storage states of the two energy storage elements are described by the compressed air mass in the gas storage chamber and the high-temperature heat transfer oil mass in the heat storage tank. Formula (24k) describes the change in the air mass in the gas storage tank, and formula (24l) describes the change in the heat transfer oil mass in the high-temperature heat storage tank.

[0204] Based on the above embodiments, taking the minimization of the electricity purchase cost as the objective function, according to the external characteristics model of the combined heat and power supply, the operation constraints of the heat pump, and the energy balance constraints, a dynamic dispatching model of the energy station for advanced adiabatic compressed air energy storage is constructed, including:

[0205] Taking the minimization of the electricity purchase cost as the objective function, the objective function is expressed as:

[0206]

[0207] Among them, Represents the electricity price at time period t; Represents the electricity quantity purchased by the advanced adiabatic compressed air energy storage power station from the power grid at time period t;

[0208] Construct a dual energy storage state model of advanced adiabatic compressed air energy storage according to the cogeneration external characteristic model, actual charging power, air mass flow rate during actual compression, heat transfer oil mass flow rate during actual compression, actual power generation power, air mass flow rate on the actual expansion side, heat transfer oil mass flow rate on the actual expansion side, actual power generation and heating power, and heat transfer oil mass flow rate on the actual heating side;

[0209] Take the dual energy storage state model, heat pump operation constraints, and energy balance constraints as the constraint conditions of the objective function to construct an energy station dynamic scheduling model of advanced adiabatic compressed air energy storage.

[0210] Specifically, the formula for the heat pump operation constraints is:

[0211]

[0212]

[0213] Where COP(T t am ) represents the energy efficiency ratio of the heat pump, and the specific value is related to the ambient temperature; Represents the heating power of the heat pump at time period t, Represents the electric power input at time period t, Represents the minimum value of the input electric power, Represents the maximum value of the input electric power;

[0214] The formula for the energy balance constraints is:

[0215]

[0216]

[0217]

[0218] Where, Represents the maximum electricity purchase quantity of the energy station from the power grid, Represents the electrical load at time period t, Represents the load at time period t.

[0219] In the present invention, formulas (13) to (23) are used as part of the constraint conditions of the AA-CAES operation constraints (denoted as 24a).

[0220] Based on the above embodiments, solving the dynamic scheduling model of the energy station through the approximate dynamic programming algorithm, and dynamically scheduling the advanced adiabatic compressed air energy storage energy station according to the solution results, including:

[0221] By the Bellman optimality principle, splitting the constraint conditions of the objective function to obtain the corresponding state space, that is, equivalently splitting the formulas (24) to (30) into the following sub-problems:

[0222]

[0223] Among them, represents the state variable;

[0224] represents the uncertain quantity;

[0225]

[0226] represents the control variable;

[0227] V t (x t ,ζ t ) represents the value function at the t-th time period, represents the electricity purchase cost at the t-th time period; represents the conditional expectation, p s,t (ξ t ) represents the conditional probability, N s represents the number of scenarios.

[0228] In the present invention, the conditional probability distribution can be approximated by performing NW kernel regression on the historical data of the uncertain quantity. The specific method is as follows:

[0229]

[0230] Among them, is the Gaussian kernel function.

[0231] Discretize the state space, and based on the historical data, calculate the approximate values of the value functions at each state discrete point sequentially from the last time period to obtain the approximate values of the value functions at each state discrete point.

[0232] In the present invention, first, divide the two-dimensional state space into (M - 1)×(N - 1) rectangles with M g grid points, and the vertices of each rectangle are represented as i = Mn + m - M;

[0233] Then, train the value functions at each state point according to the historical data. The specific algorithm is:

[0234] 1. Initialization Input M g lattice point coordinates x [i] and N s historical observation trajectories of N uncertainties

[0235] 2. For t = T,..., 2

[0236] For j = 0,..., M g

[0237] For k = 0,..., N s

[0238] For the given use the baron solver to solve the following mixed-integer non-linear programming (MINLP) problem:

[0239]

[0240] where is obtained from the value iteration process at the previous moment.

[0241] End

[0242] End

[0243] End

[0244] 3. Output: The approximate value function at each state point

[0245] According to the actual observed value of the uncertainty ξ t , the actual observed value of the state variable x t and the approximate value of the value function, obtain the dynamic scheduling strategy of the advanced adiabatic compressed air energy storage power station for the current period.

[0246] In the present invention, during actual scheduling, according to the uncertainty ξ t observed in real time at the beginning of the t-th period and the actual observed value of the state variable x t , and the value function obtained by training in the above embodiments, solve formula (33) to obtain the dynamic scheduling strategy for this period.

[0247] Figure 2 is the structural schematic diagram of the dynamic scheduling system for the advanced adiabatic compressed air energy storage power station provided by the present invention, as shown in Figure 2As shown in the figure, the present invention provides a dynamic scheduling system for an advanced adiabatic compressed air energy storage power station, including a cogeneration external characteristic model construction module 201, a dynamic scheduling model construction module 202, and an energy station self-scheduling module 203. Among them, the cogeneration external characteristic model construction module 201 is used to construct a cogeneration external characteristic model of the advanced adiabatic compressed air energy storage based on the thermodynamic model of the advanced adiabatic compressed air energy storage and according to the ambient temperature and component variable operating condition information; the dynamic scheduling model construction module 202 is used to take minimizing the electricity purchase cost as the objective function and construct a dynamic scheduling model of the energy station of the advanced adiabatic compressed air energy storage according to the cogeneration external characteristic model, the heat pump operation constraint, and the energy balance constraint; the energy station self-scheduling module 203 is used to solve the dynamic scheduling model of the energy station through an approximate dynamic programming algorithm so as to perform dynamic scheduling on the advanced adiabatic compressed air energy storage power station according to the solution result.

[0248] Compared with the prior art, the dynamic scheduling system for the advanced adiabatic compressed air energy storage power station provided by the present invention can improve the flexibility and reliability of the operation of the AA-CAES system, improve the safety of the operation of the integrated energy system, and has a higher solution efficiency for the dynamic scheduling strategy.

[0249] The system provided by the embodiments of the present invention is used to execute the above-mentioned method embodiments. For the specific process and detailed content, please refer to the above embodiments and will not be elaborated here.

[0250] Figure 3 The structural schematic diagram of the electronic device provided by the present invention is as Figure 3 shown. The electronic device may include: a processor 301, a communication interface 302, a memory 303, and a communication bus 304. Among them, the processor 301, the communication interface 302, and the memory 303 complete mutual communication through the communication bus 304. The processor 301 can call the logical instructions in the memory 303 to execute a dynamic scheduling method for an advanced adiabatic compressed air energy storage power station. The method includes: constructing a cogeneration external characteristic model of the advanced adiabatic compressed air energy storage based on the thermodynamic model of the advanced adiabatic compressed air energy storage and according to the ambient temperature and component variable operating condition information; taking minimizing the electricity purchase cost as the objective function and constructing a dynamic scheduling model of the energy station of the advanced adiabatic compressed air energy storage according to the cogeneration external characteristic model, the heat pump operation constraint, and the energy balance constraint; solving the dynamic scheduling model of the energy station through an approximate dynamic programming algorithm so as to perform dynamic scheduling on the advanced adiabatic compressed air energy storage power station according to the solution result.

[0251] In addition, when the logical instructions in the above-mentioned memory 303 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this 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 may be a personal computer, a server, or a 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 such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0252] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the dynamic scheduling method for an advanced adiabatic compressed air energy storage power station provided by the above-mentioned various methods. The method includes: based on the thermodynamic model of advanced adiabatic compressed air energy storage, constructing an external characteristic model of combined heat and power supply for advanced adiabatic compressed air energy storage according to the ambient temperature and component variable operating condition information; taking minimizing the electricity purchase cost as the objective function, constructing a dynamic scheduling model of the energy storage power station for advanced adiabatic compressed air energy storage according to the external characteristic model of combined heat and power supply, the heat pump operation constraints, and the energy balance constraints; and solving the dynamic scheduling model of the energy storage power station through an approximate dynamic programming algorithm to perform dynamic scheduling on the advanced adiabatic compressed air energy storage power station according to the solution result.

[0253] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the dynamic scheduling method for an advanced adiabatic compressed air energy storage power station provided by the above-mentioned various embodiments. The method includes: based on the thermodynamic model of advanced adiabatic compressed air energy storage, constructing an external characteristic model of combined heat and power supply for advanced adiabatic compressed air energy storage according to the ambient temperature and component variable operating condition information; taking minimizing the electricity purchase cost as the objective function, constructing a dynamic scheduling model of the energy storage power station for advanced adiabatic compressed air energy storage according to the external characteristic model of combined heat and power supply, the heat pump operation constraints, and the energy balance constraints; and solving the dynamic scheduling model of the energy storage power station through an approximate dynamic programming algorithm to perform dynamic scheduling on the advanced adiabatic compressed air energy storage power station according to the solution result.

[0254] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative effort.

[0255] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0256] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or equivalently replace some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic scheduling method for an advanced adiabatic compressed air energy storage power station, characterized in that, it includes: Based on the thermodynamic model of advanced adiabatic compressed air energy storage, according to the ambient temperature and component variable operating condition information, construct the external characteristics model of combined heat and power supply of advanced adiabatic compressed air energy storage; Taking the minimization of electricity purchase cost as the objective function, according to the combined heat and power supply external characteristics model, heat pump operation constraints and energy balance constraints, construct the dynamic scheduling model of the advanced adiabatic compressed air energy storage power station; Through the approximate dynamic programming algorithm, solve the dynamic scheduling model of the power station to dynamically schedule the advanced adiabatic compressed air energy storage power station according to the solution results; The thermodynamic model includes a compressor thermodynamic model, a turbine thermodynamic model and a heat exchanger thermodynamic model; Based on the thermodynamic model of advanced adiabatic compressed air energy storage, according to the ambient temperature and component variable operating condition information, constructing the external characteristics model of combined heat and power supply of advanced adiabatic compressed air energy storage includes: Based on the compressor thermodynamic model, the turbine thermodynamic model and the heat exchanger thermodynamic model, respectively conduct simulations of the compression process, expansion process and heat exchange process; According to the ambient temperature and component variable operating condition information of advanced adiabatic compressed air energy storage, perform polynomial fitting through the simulation results to obtain the external characteristics model of combined heat and power supply of advanced adiabatic compressed air energy storage; The formula of the combined heat and power supply external characteristics model is: p CAESc = Γ CAESc (m ac , m HTFc , T am ); p CAESd = Γ CAESd (m ad , m HTFd ); h CAES = Γ CAESh (m HTFh ); g CAESc (m ac ,m HTFc ,T am ) = 0; g CAESd (m ad ,m HTFd )≤0; g CAESh (m HTFh )≤0; Among them, p CAESc represents the analytical expression of the charging power, T am represents the ambient temperature, m ac represents the air mass flow rate on the compression side, m HTFc represents the heat transfer oil mass flow rate, Γ CAESc is a polynomial function based on the ambient temperature T am , the air mass flow rate m ac on the compression side, and the heat transfer oil mass flow rate m HTFc ; p CAESd represents the analytical expression of the power generation power, m ad represents the air mass flow rate on the expansion side, m HTFd represents the heat transfer oil mass flow rate, Γ CAESd is a polynomial function based on the air mass flow rate m ad on the expansion side and the heat transfer oil mass flow rate m HTFd ; h CAES represents the analytical expression of the heating power, m HTFh represents the heat transfer oil mass flow rate on the primary side of the heat exchanger on the heating side, Γ CAESh is a polynomial function based on the heat transfer oil mass flow rate m HTFh on the primary side of the heat exchanger on the heating side; g CAESc is the feasible region for regulating the air and heat transfer oil mass flow rates during the compression process, indicating the coordinated control strategy for maintaining the temperature of the high-temperature heat storage tank and the constant air and heat transfer oil mass flow rates on the compression side under different ambient temperatures and charging powers; represents the minimum value of the air mass flow rate on the compression side, represents the maximum value of the air mass flow rate on the compression side, represents the minimum value of the heat transfer oil mass flow rate on the compression side, represents the maximum value of the heat transfer oil mass flow rate on the compression side; g CAESd represents the feasible region for regulating the air and heat transfer oil mass flow rates during the expansion process, represents the minimum value of the air mass flow rate on the expansion side, represents the maximum value of the air mass flow rate on the expansion side, represents the minimum value of the heat transfer oil mass flow rate on the expansion side, represents the maximum value of the heat transfer oil mass flow rate on the expansion side; g CAESh represents the feasible region for regulating the heat transfer oil mass flow rate during the heating process, represents the minimum value of the heat transfer oil mass flow rate on the primary side of the heat exchanger on the expansion side, represents the maximum value of the heat transfer oil mass flow rate on the primary side of the heat exchanger on the expansion side.

2. The dynamic scheduling method for an advanced adiabatic compressed air energy storage power station according to claim 1, characterized in that, Taking the minimization of electricity purchase cost as the objective function, according to the combined heat and power supply external characteristics model, heat pump operation constraints and energy balance constraints, constructing the dynamic scheduling model of the advanced adiabatic compressed air energy storage power station includes: Taking the minimization of electricity purchase cost as the objective function, and the objective function is expressed as: Among them, π t e represents the electricity price at time period t; represents the electricity quantity purchased by the advanced adiabatic compressed air energy storage power station from the power grid at time period t; According to the combined heat and power supply external characteristics model, actual charging power, air mass flow rate during actual compression, heat transfer oil mass flow rate during actual compression, actual power generation power, air mass flow rate on the actual expansion side, heat transfer oil mass flow rate on the actual expansion side, actual power generation and heat supply power, heat transfer oil mass flow rate on the actual heat supply side, construct the dual energy storage state model of advanced adiabatic compressed air energy storage; Taking the dual energy storage state model, heat pump operation constraints and energy balance constraints as the constraint conditions of the objective function, construct the dynamic scheduling model of the advanced adiabatic compressed air energy storage power station.

3. The dynamic scheduling method for an advanced adiabatic compressed air energy storage power station according to claim 2, characterized in that, The formula of the dual energy storage state model is: p CAESc = Γ CAESc (m ac , m HTFc , T am ); p CAESd = Γ CAESd (m ad , m HTFd ); h CAES = Γ CAESh (m HTFh ); g CAESc (m ac ,m HTFc ,T am ) = 0; g CAESd (m ad ,m HTFd ) ≤ 0; g CAESh (m HTFh ) ≤ 0; Among them, represents the actual charging power, represents the air mass flow rate during actual compression, represents the heat transfer oil mass flow rate during actual compression, is a variable; represents the actual power generation, represents the air mass flow rate on the actual expansion side, represents the heat transfer oil mass flow rate on the actual expansion side, is a variable; represents the actual power generation and heat supply, represents the heat transfer oil mass flow rate on the actual heat supply side, is a variable; T Δ represents the unit dispatch period length, represents the mass of air in the gas storage chamber at the end of the t-th period, represents the mass of heat transfer oil in the high-temperature heat storage tank at the end of the t-th period, N c represents the number of compression stages, N e represents the number of expansion stages; The formula of the heat pump operation constraints is: Among them, COP(T t am ) represents the coefficient of performance of the heat pump, represents the heating power of the heat pump at time period t, represents the electric power input at time period t, represents the minimum value of the input electric power, represents the maximum value of the input electric power; The formula of the energy balance constraints is: Among them, represents the maximum power purchase amount of the energy station from the power grid, represents the electrical load in period t, represents the load in period t.

4. The dynamic scheduling method for an advanced adiabatic compressed air energy storage power station according to claim 3, characterized in that, Through the approximate dynamic programming algorithm, solving the dynamic scheduling model of the power station to dynamically schedule the advanced adiabatic compressed air energy storage power station according to the solution results includes: Through the Bellman optimality principle, split the constraint conditions of the objective function to obtain the corresponding state space: Among them, represents a state variable; Denote an uncertain quantity; Denote the control variable; V t (x t ,ζ t ) represents the value function at the t-th period, represents the electricity purchase cost at the t-th period; represents the conditional expectation, p s,t (ξ t ) represents the conditional probability, N s represents the number of scenarios; Discretize the state space, and based on historical data, calculate the approximate value function of each state discrete point sequentially forward from the last time period to obtain the approximate value function of each state discrete point; According to the actual observation value of the uncertainty ξ t , the actual observation value of the state variable x t , and the approximate value of the value function, a dynamic scheduling strategy for the advanced adiabatic compressed air energy storage power station in the current period is obtained.

5. A dynamic scheduling system for an advanced adiabatic compressed air energy storage power station, Characterized in that, Comprising: A combined heat and power external characteristic model construction module, configured to construct a combined heat and power external characteristic model of the advanced adiabatic compressed air energy storage based on the thermodynamic model of the advanced adiabatic compressed air energy storage and according to the ambient temperature and component variable operating condition information; A dynamic scheduling model construction module, configured to use minimizing the electricity purchase cost as the objective function, and construct a dynamic scheduling model of the energy storage power station of the advanced adiabatic compressed air energy storage according to the combined heat and power external characteristic model, the heat pump operation constraint and the energy balance constraint; An energy storage power station self-scheduling module, configured to solve the dynamic scheduling model of the energy storage power station through an approximate dynamic programming algorithm, so as to perform dynamic scheduling on the advanced adiabatic compressed air energy storage power station according to the solution result; The thermodynamic model includes a compressor thermodynamic model, a turbine thermodynamic model and a heat exchanger thermodynamic model; The combined heat and power external characteristic model construction module is specifically configured to: Based on the compressor thermodynamic model, the turbine thermodynamic model and the heat exchanger thermodynamic model, respectively perform simulations of the compression process, the expansion process and the heat exchange process; According to the ambient temperature and component variable operating condition information of the advanced adiabatic compressed air energy storage, perform polynomial fitting through the simulation results to obtain the combined heat and power external characteristic model of the advanced adiabatic compressed air energy storage; The formula of the combined heat and power external characteristic model is: p CAESc = Γ CAESc (m ac , m HTFc , T am ); p CAESd = Γ CAESd (m ad , m HTFd ); h CAES = Γ CAESh (m HTFh ); g CAESc (m ac ,m HTFc ,T am ) = 0; g CAESd (m ad ,m HTFd )≤0; g CAESh (m HTFh )≤0; Among them, p CAESc represents the analytical expression of the charging power, T am represents the ambient temperature, m ac represents the air mass flow rate on the compression side, m HTFc represents the heat transfer oil mass flow rate, Γ CAESc is a polynomial function based on the ambient temperature T am , the air mass flow rate m ac on the compression side, and the heat transfer oil mass flow rate m HTFc ; p CAESd represents the analytical expression of the power generation power, m ad represents the air mass flow rate on the expansion side, m HTFd represents the heat transfer oil mass flow rate, Γ CAESd is a polynomial function based on the air mass flow rate m ad on the expansion side and the heat transfer oil mass flow rate m HTFd ; h CAES represents the analytical expression of the heating power, m HTFh represents the heat transfer oil mass flow rate on the primary side of the heat exchanger on the heating side, Γ CAESh is a polynomial function based on the heat transfer oil mass flow rate m HTFh on the primary side of the heat exchanger on the heating side; g CAESc is the feasible region for regulating the air and heat transfer oil mass flow rates during the compression process, representing the coordinated control strategy for maintaining the temperature of the high-temperature heat storage tank and the constant air and heat transfer oil mass flow rates on the compression side under different ambient temperatures and charging powers; represents the minimum value of the air mass flow rate on the compression side, represents the maximum value of the air mass flow rate on the compression side, represents the minimum value of the heat transfer oil mass flow rate on the compression side, represents the maximum value of the heat transfer oil mass flow rate on the compression side; g CAESd represents the feasible region for regulating the air and heat transfer oil mass flow rates during the expansion process, represents the minimum value of the air mass flow rate on the expansion side, represents the maximum value of the air mass flow rate on the expansion side, represents the minimum value of the heat transfer oil mass flow rate on the expansion side, represents the maximum value of the heat transfer oil mass flow rate on the expansion side; g CAESh represents the feasible region for regulating the heat transfer oil mass flow rate during the heating process, represents the minimum value of the heat transfer oil mass flow rate on the primary side of the heat exchanger on the expansion side, represents the maximum value of the heat transfer oil mass flow rate on the primary side of the heat exchanger on the expansion side.

6. An electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, Characterized in that, When the processor executes the computer program, the steps of the dynamic scheduling method for the advanced adiabatic compressed air energy storage power station according to any one of claims 1 to 4 are implemented.

7. A non-transitory computer-readable storage medium, on which a computer program is stored, Characterized in that, When the computer program is executed by a processor, the steps of the dynamic scheduling method for the advanced adiabatic compressed air energy storage power station according to any one of claims 1 to 4 are implemented.

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