Economic evaluation method for participation of compressed air energy storage system in wind and light absorption

Abstract

The invention relates to the technical field of electric digital data processing, in particular to an economic evaluation method for a compressed air energy storage system participating in wind and light absorption, which comprises the following steps of: respectively constructing mathematical models, a wind power uncertainty model and a photovoltaic uncertainty model of a plurality of components in the compressed air energy storage system; the wind and light uncertainty of the compressed air energy storage system is obtained, and compressed air energy storage and the wind and light uncertainty in the compressed air energy storage system are regarded as game subjects to obtain an optimal scheduling strategy of the compressed air energy storage system. And performing economic evaluation on the compressed air energy storage system in combination with the optimal scheduling strategy, the mathematical model, the plurality of constraint terms and the robust optimization cost to obtain an economic evaluation result of the compressed air energy storage system participating in wind and light absorption. Therefore, the technical problem that the compressed air energy storage system is difficult to effectively assess from the perspective of wind and light absorption due to the fact that an uncertainty model is rough and does not comprehensively consider the destructive effect of wind and light on a power grid is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and in particular to a method for evaluating the economic performance of a compressed air energy storage system participating in wind and solar power consumption. Background Art

[0002] Wind-photovoltaic power system refers to the conversion of wind and sunshine resources into high-quality electric energy. Since wind power and photovoltaic power generation are both clean energy and do not produce any pollutants during the power generation process, they can effectively reduce carbon emissions and improve environmental quality. In addition, wind power and photovoltaic power generation are both renewable energy sources, and there is no problem of resource depletion. They can provide a long-term and stable energy supply for the sustainable development of the earth. However, due to the random and unpredictable distribution of wind and light, a large number of wind and light abandonment phenomena have been caused. The inherent instability of wind and solar power is the biggest obstacle to the large-scale grid connection of renewable power. Energy storage technology is one of the best ways to solve the above problems.

[0003] Compressed air energy storage is a storage technology with broad development prospects. It has the advantages of low construction cost, high energy storage efficiency and large energy storage capacity. Its working principle is similar to pumped storage. When the power consumption of the power system is at a low point, the electric energy is consumed to drive the air compressor and store the energy in the form of compressed air in the gas storage device. When the power load of the power system reaches a peak, the gas storage device releases the stored compressed air, which expands in the turbine expander to do work and drive the generator to generate electricity. Therefore, the above principle can be used to convert the surplus electricity into internal energy through the compressor during the peak wind period and store it, and release it through the expander during the valley period to realize the consumption of wind and solar power.

[0004] At present, the research on the participation of compressed air energy storage in wind and solar power consumption is still mainly stagnant in terms of the impact on the stability of the power grid, and few related technologies involve the economic evaluation of compressed air energy storage power stations. In addition, the existing wind and solar uncertainty modeling is relatively rough and does not fully consider the destructive effects of wind and solar on the power grid, which needs to be improved. Summary of the invention

[0005] The present invention provides an economic evaluation method for a compressed air energy storage system participating in wind and solar power consumption, so as to solve the technical problem in the related technology that the uncertainty model is relatively rough and does not fully consider the destructive effects of wind and solar power on the power grid, making it difficult to effectively evaluate the compressed air energy storage system from the perspective of wind and solar power consumption.

[0006] The first aspect of the present invention provides an economic evaluation method for a compressed air energy storage system participating in wind and solar power consumption, comprising the following steps: respectively constructing mathematical models of multiple components in the compressed air energy storage system; respectively constructing a wind power uncertainty model for predicting the uncertainty between wind power and actual wind power and a photovoltaic uncertainty model for predicting the uncertainty between sunlight and actual sunlight, so as to obtain the wind and solar power uncertainty of the compressed air energy storage system by using the wind power uncertainty model and the photovoltaic uncertainty model; regarding the compressed air energy storage and the wind and solar power uncertainty in the compressed air energy storage system as game subjects, so as to obtain an optimized scheduling strategy for the compressed air energy storage system; and performing an economic evaluation on the compressed air energy storage system in combination with the optimized scheduling strategy, the mathematical model, multiple constraints and the robust optimization cost, so as to obtain an economic evaluation result of the compressed air energy storage system participating in wind and solar power consumption.

[0007] Optionally, in one embodiment of the present invention, respectively constructing mathematical models of multiple components in the compressed air energy storage system includes: constructing a first mathematical model of the compressor in the compressed air energy storage system, wherein the first mathematical model is a relationship model between the compression power and air flow rate of the compressor at any time; constructing a second mathematical model of the expander in the compressed air energy storage system, wherein the second mathematical model is a relationship model between the expansion power and air flow rate of the expander at any time; constructing a third mathematical model of the heat exchanger in the compressed air energy storage system, wherein the third mathematical model is a relationship model between the mass, temperature and flow rate of the air fluid entering and exiting the heat exchanger at the same heat exchange efficiency.

[0008] Optionally, in one embodiment of the present invention, the step of constructing mathematical models of multiple components in the compressed air energy storage system comprises:

[0009] The expression of the first mathematical model of the compressor is:

[0010]

[0011] Where κ represents the adiabatic index, R g is the gas constant of air, n c represents the number of compressor stages, η c Indicates the efficiency of the compressor, T c,k,in and T c,k,out Respectively represent the inlet temperature and outlet temperature of the air entering the kth stage compressor, P CAESC,t represents the compression power of the compressor at time t, represents the air flow at time t.

[0012] The expression of the second mathematical model of the expander is:

[0013]

[0014] Among them, n g represents the number of expander stages, T g,j,in and T g,j,aut denote the inlet air temperature and outlet air temperature of the j-th stage expander, respectively, and η g Represents the efficiency of the power generation process, P CAESG,t represents the expansion power at time t, represents the air flow rate at time t;

[0015] The expression of the third mathematical model of the heat exchanger is:

[0016]

[0017] Where m represents the mass of the fluid, T represents the temperature of the fluid, and c p,air It represents the constant pressure specific heat of air. The parameters with subscripts 1 and 2 represent the corresponding hot and cold fluids respectively. The parameters with subscripts in and out represent the air flow parameters corresponding to the air flow entering and leaving the heat exchanger respectively.

[0018] Optionally, in one embodiment of the present invention, the respectively constructing of a wind power uncertainty model for predicting the uncertainty between wind power and actual wind power and a photovoltaic uncertainty model for predicting the uncertainty between sunshine and actual sunshine comprises:

[0019] The expression of the wind power uncertainty model is:

[0020]

[0021] Among them, P wind,t and Represent the actual output and predicted output of wind power respectively, represents the wind power offset, and The upper and lower limits of wind power output, P wind represents wind power output, Γ tw represents the uncertainty of wind power output; the expression of the photovoltaic uncertainty model is

[0022]

[0023] Among them, P pv,t and They represent the actual output and predicted output of photovoltaic power, represents the photovoltaic offset, and Respectively represent the upper and lower limits of photovoltaic output, Γ tp Represents the uncertainty of photovoltaic output, Ppv Represents the photovoltaic output power.

[0024] Optionally, in one embodiment of the present invention, the compressed air energy storage and the wind-solar uncertainty in the compressed air energy storage system are regarded as game subjects to obtain an optimized scheduling strategy for the compressed air energy storage system, including: constructing a game model based on the compressed air energy storage and the wind-solar uncertainty to obtain the optimized scheduling strategy using the game model;

[0025] Among them, the expression of the game model is:

[0026]

[0027] Among them, U and W are the decision sets of the two parties in the game, x represents the constant parameter, G() represents all equality constraints and inequality constraints that the two parties in the game need to satisfy, u represents the dispatcher, w represents the uncertainty of wind and solar power, and J() represents the dispatch cost.

[0028] Optionally, in one embodiment of the present invention, the plurality of constraints include at least one of a compression power limit, an expansion power limit, an air pressure constraint, a heat storage constraint and a system power balance constraint of the power station.

[0029] Optionally, in one embodiment of the present invention, the robustness optimization cost includes operation cost and risk cost;

[0030] Among them, the expression of the robustness optimization cost is:

[0031]

[0032] Among them, F 1 represents the operating cost, F 11 represents the start-stop cost of compressed air energy storage, F 12 Represents the secondary operating cost of conventional units, N x Indicates the number of compressed air energy storage units, S n Indicates the power on. Indicates the power-on status variable, S Di represents the shutdown cost, Indicates the shutdown state variable, N g represents the number of conventional units, α i , b i , c i are constant parameters, P gi,t Indicates the output of conventional units, I i,t Indicates the main circuit current of the power plant;

[0033] The expression of risk cost is:

[0034]

[0035] in, represents the cost of wind curtailment, represents the cost of abandoned light, represents the startup standby cost, C o,w , C o,p and C u They represent wind curtailment factor, solar curtailment factor and standby factor respectively. Indicates the actual output of wind power. It indicates the predicted wind power output. Indicates the actual photovoltaic output. Indicates the predicted photovoltaic output.

[0036] Optionally, in one embodiment of the present invention, the economic evaluation of the compressed air energy storage system in combination with the optimization scheduling strategy, the mathematical model, multiple constraints and robust optimization cost includes: converting the calculation of the robust optimization cost into a robust optimization problem; decomposing the robust optimization problem into a main optimization problem and a sub-optimization problem; iteratively solving the main optimization problem and the sub-optimization problem respectively to obtain the calculation result of the robust optimization cost;

[0037] The expression of the main optimization problem is:

[0038]

[0039] Among them, x is the predicted output of each unit, ξ is the wind and solar prediction information, and z is the auxiliary variable;

[0040] The expression of the sub-optimization problem is:

[0041]

[0042] Among them, y s is the actual output adjustment value of each unit, ξ s is the actual wind and solar information, s is different actual wind and solar output scenarios, and A, B, C, D, a, and c are all coefficient matrices.

[0043] The second aspect of the present invention provides an economic evaluation device for a compressed air energy storage system participating in wind and solar power consumption, including: a first construction module, used to respectively construct mathematical models of multiple components in the compressed air energy storage system; a second construction module, used to respectively construct a wind power uncertainty model for predicting the uncertainty between wind power and actual wind power and a photovoltaic uncertainty model for predicting the uncertainty between sunlight and actual sunlight, so as to obtain the wind and solar power uncertainty of the compressed air energy storage system by using the wind power uncertainty model and the photovoltaic uncertainty model; a game module, used to regard the compressed air energy storage and the wind and solar power uncertainty in the compressed air energy storage system as game subjects, so as to obtain an optimized scheduling strategy for the compressed air energy storage system; an evaluation module, used to perform an economic evaluation on the compressed air energy storage system in combination with the optimized scheduling strategy, the mathematical model, multiple constraints and robust optimization cost, so as to obtain an economic evaluation result of the compressed air energy storage system participating in wind and solar power consumption.

[0044] Optionally, in one embodiment of the present invention, the first building module includes: a first building unit, used to construct a first mathematical model of the compressor in the compressed air energy storage system, wherein the first mathematical model is a relationship model between the compression power and air flow rate of the compressor at any time; a second building unit, used to construct a second mathematical model of the expander in the compressed air energy storage system, wherein the second mathematical model is a relationship model between the expansion power and air flow rate of the expander at any time; a third building unit, used to construct a third mathematical model of the heat exchanger in the compressed air energy storage system, wherein the third mathematical model is a relationship model between the mass, temperature and flow rate of the air fluid entering and leaving the heat exchanger at the same heat exchange efficiency.

[0045] Optionally, in one embodiment of the present invention, the expression of the first mathematical model of the compressor is:

[0046]

[0047] Where κ represents the adiabatic index, R g is the gas constant of air, n c represents the number of compressor stages, η c Indicates the efficiency of the compressor, T c,k,in and T c,k,out Respectively represent the inlet temperature and outlet temperature of the air entering the kth stage compressor, P CAESC,t represents the compression power of the compressor at time t, represents the air flow rate at time t.

[0048] The expression of the second mathematical model of the expander is:

[0049]

[0050] Among them, n g represents the number of expander stages, T g,j,in and T g,j,aut denote the inlet air temperature and outlet air temperature of the j-th stage expander, respectively, and η g Represents the efficiency of the power generation process, P CAESG,t represents the expansion power at time t, represents the air flow rate at time t;

[0051] The expression of the third mathematical model of the heat exchanger is:

[0052]

[0053] Where m represents the mass of the fluid, T represents the temperature of the fluid, and c p,air It represents the constant pressure specific heat of air. The parameters with subscripts 1 and 2 represent the corresponding hot and cold fluids respectively. The parameters with subscripts in and out represent the air flow parameters corresponding to the air flow entering and leaving the heat exchanger respectively.

[0054] Optionally, in one embodiment of the present invention, the expression of the wind power uncertainty model is:

[0055]

[0056] Among them, P wind,t and Represent the actual output and predicted output of wind power respectively, represents the wind power offset, and The upper and lower limits of wind power output, P wind represents wind power output, Γ tw Represents the uncertainty of wind power output.

[0057] The expression of the photovoltaic uncertainty model is:

[0058]

[0059] Among them, P pv,t and They represent the actual output and predicted output of photovoltaic power, represents the photovoltaic offset, and Respectively represent the upper and lower limits of photovoltaic output, Γ tp Represents the uncertainty of photovoltaic output, P pv Represents the photovoltaic output power.

[0060] Optionally, in one embodiment of the present invention, the game module includes: a fourth construction unit, configured to construct a game model based on the compressed air energy storage and the wind and solar uncertainty, so as to obtain the optimized scheduling strategy using the game model;

[0061] Among them, the expression of the game model is:

[0062]

[0063] Among them, U and W are the decision sets of the two parties in the game, x represents the constant parameter, G() represents all equality constraints and inequality constraints that the two parties in the game need to satisfy, u represents the dispatcher, w represents the uncertainty of wind and solar power, and J() represents the dispatch cost.

[0064] Optionally, in one embodiment of the present invention, the plurality of constraints include at least one of a compression power limit, an expansion power limit, an air pressure constraint, a heat storage constraint and a system power balance constraint of the power station.

[0065] Optionally, in one embodiment of the present invention, the robustness optimization cost includes operation cost and risk cost;

[0066] Among them, the expression of the robustness optimization cost is:

[0067]

[0068] Among them, F 1 represents the operating cost, F 11 represents the start-stop cost of compressed air energy storage, F 12 Represents the secondary operating cost of conventional units, N x Indicates the number of compressed air energy storage units, S n Indicates the power on. Indicates the power-on status variable, S Di represents the shutdown cost, Indicates the shutdown state variable, N g represents the number of conventional units, α i , b i , c i are constant parameters, P gi,t Indicates the output of conventional units, I i,t Indicates the main circuit current of the power plant;

[0069] The expression of risk cost is:

[0070]

[0071] in, represents the cost of wind curtailment, represents the cost of abandoned light, represents the startup standby cost, C o,w , C o,p and C u They represent wind curtailment factor, solar curtailment factor and standby factor respectively. Indicates the actual output of wind power. It indicates the predicted wind power output. Indicates the actual photovoltaic output. Indicates the predicted photovoltaic output.

[0072] Optionally, in one embodiment of the present invention, the evaluation module includes:

[0073] A conversion unit, used for converting the calculation of the robustness optimization cost into a robustness optimization problem;

[0074] A decomposition unit, used for decomposing the robustness optimization problem into a main optimization problem and sub-optimization problems;

[0075] A solving unit, used for iteratively solving the main optimization problem and the sub-optimization problem respectively to obtain a calculation result of the robustness optimization cost;

[0076] The expression of the main optimization problem is:

[0077]

[0078] Among them, x is the predicted output of each unit, ξ is the wind and solar prediction information, and z is the auxiliary variable;

[0079] The expression of the sub-optimization problem is:

[0080]

[0081] Among them, y s is the actual output adjustment value of each unit, ξ s is the actual wind and solar information, s is different actual wind and solar output scenarios, and A, B, C, D, a, and c are all coefficient matrices.

[0082] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the economic evaluation method for the compressed air energy storage system participating in wind and solar power consumption as described in the above embodiment.

[0083] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the economic evaluation method for the compressed air energy storage system participating in wind and solar power consumption as described in the above embodiment.

[0084] A fifth aspect of the present invention provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for evaluating the economic feasibility of a compressed air energy storage system participating in wind and solar power consumption.

[0085] The embodiment of the present invention can respectively construct mathematical models of multiple components in the compressed air energy storage system, a wind power uncertainty model for predicting the uncertainty between wind power and actual wind power, and a photovoltaic uncertainty model for predicting the uncertainty between light and actual light, and use the wind power uncertainty model and the photovoltaic uncertainty model to obtain the wind and light uncertainty of the compressed air energy storage system, so that the compressed air energy storage and wind and light uncertainty in the compressed air energy storage system are regarded as game subjects to obtain the optimized scheduling strategy of the compressed air energy storage system, and then combine the optimized scheduling strategy, mathematical model, multiple constraints and robust optimization cost to evaluate the economic efficiency of the compressed air energy storage system, use a more sophisticated uncertainty model to fully consider the destructiveness of wind and light to the power grid, and find the optimized scheduling strategy for compressed air energy storage to participate in wind and light consumption from the perspective of game theory. The evaluation result is highly reliable and the evaluation method is widely applicable. Thus, the technical problem that the uncertainty model is relatively rough and does not fully consider the destructive effects of wind and light on the power grid, making it difficult to effectively evaluate the compressed air energy storage system from the perspective of wind and light consumption is solved.

[0086] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0088] Figure 1 A flowchart of an economic evaluation method for a compressed air energy storage system participating in wind and solar power consumption according to an embodiment of the present invention;

[0089] Figure 2 It is a schematic diagram of the principle of an economic evaluation method for a compressed air energy storage system participating in wind and solar power consumption according to an embodiment of the present invention;

[0090] Figure 3 A schematic diagram of an IEEE30 node simulation verification model according to an embodiment of the present invention;

[0091] Figure 4 A schematic diagram of the structure of an economic evaluation device for a compressed air energy storage system participating in wind and solar power consumption according to an embodiment of the present invention;

[0092] Figure 5 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0093] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0094] The following describes, with reference to the accompanying drawings, an economic evaluation method for a compressed air energy storage system participating in wind and solar power consumption according to an embodiment of the present invention. In view of the technical problem that the uncertainty model mentioned in the above background technology is relatively rough and does not fully consider the destructive effects of wind and solar on the power grid, making it difficult to effectively evaluate the compressed air energy storage system from the perspective of wind and solar consumption, the present invention provides an economic evaluation method for a compressed air energy storage system participating in wind and solar consumption. In the method, mathematical models of multiple components in the compressed air energy storage system, a wind power uncertainty model for predicting the uncertainty between wind power and actual wind power, and a photovoltaic uncertainty model for predicting the uncertainty between light and actual light can be constructed respectively, and the wind power uncertainty model and the photovoltaic uncertainty model are used to obtain the wind and solar uncertainty of the compressed air energy storage system, so that the compressed air energy storage and wind and solar uncertainty in the compressed air energy storage system are regarded as game subjects to obtain the optimal scheduling strategy of the compressed air energy storage system, and then the economic evaluation of the compressed air energy storage system is carried out in combination with the optimal scheduling strategy, mathematical model, multiple constraints and robust optimization cost, and a more sophisticated uncertainty model is used to fully consider the destructiveness of wind and solar to the power grid, and the optimal scheduling strategy for compressed air energy storage participating in wind and solar consumption is found from the perspective of game theory. The evaluation result has high reliability and the evaluation method has wide applicability. This solves the technical problem that the uncertainty model is relatively rough and does not fully consider the destructive effects of wind and solar power on the power grid, making it difficult to effectively evaluate the compressed air energy storage system from the perspective of wind and solar power consumption.

[0095] Specifically, Figure 1 A schematic flow chart of an economic evaluation method for a compressed air energy storage system participating in wind and solar power consumption provided in an embodiment of the present invention.

[0096] like Figure 1 As shown, the economic evaluation method of the compressed air energy storage system participating in wind and solar power consumption includes the following steps:

[0097] In step S101, mathematical models of multiple components in the compressed air energy storage system are constructed respectively.

[0098] In the actual implementation process, Figure 2As shown, the embodiment of the present invention can establish a mathematical model of the main components of the compressed air energy storage system, such as the compressor, expander, and heat exchanger, so as to evaluate the safety, stability, and economy of the compressed air energy storage system.

[0099] Optionally, in one embodiment of the present invention, mathematical models of multiple components in the compressed air energy storage system are constructed separately, including: constructing a first mathematical model of the compressor in the compressed air energy storage system, wherein the first mathematical model is a relationship model between the compression power and air flow rate of the compressor at any time; constructing a second mathematical model of the expander in the compressed air energy storage system, wherein the second mathematical model is a relationship model between the expansion power and air flow rate of the expander at any time; constructing a third mathematical model of the heat exchanger in the compressed air energy storage system, wherein the third mathematical model is a relationship model between the mass, temperature and flow rate of the air fluid entering and leaving the heat exchanger at the same heat exchange efficiency.

[0100] Among them, the expression of the first mathematical model of the compressor is:

[0101]

[0102] Where κ represents the adiabatic index, R g is the gas constant of air, n c represents the number of compressor stages, η c Indicates the efficiency of the compressor, T c,k,in and T c,k,out Respectively represent the inlet temperature and outlet temperature of the air entering the kth stage compressor, P CAESC,t represents the compression power of the compressor at time t, represents the air flow at time t.

[0103] The expression of the second mathematical model of the expander is

[0104]

[0105] Among them, n g represents the number of expander stages, T g,j,in and T g,j,aut denote the inlet air temperature and outlet air temperature of the j-th stage expander, respectively, and η g Represents the efficiency of the power generation process, P CAESG,t represents the expansion power at time t, represents the air flow at time t.

[0106] The expression of the third mathematical model of the heat exchanger is

[0107]

[0108] Where m represents the mass of the fluid, T represents the temperature of the fluid, and c p,air It represents the constant pressure specific heat of air. The parameters with subscripts 1 and 2 represent the corresponding hot and cold fluids respectively. The parameters with subscripts in and out represent the air flow parameters corresponding to the air flow entering and leaving the heat exchanger respectively.

[0109] In some embodiments, the first mathematical model is constructed (for the compression stage): in the energy storage stage, the abandoned wind power is used to drive the compressor to compress the air and store the high-pressure air.

[0110] When the compressed air energy storage system adopts multi-stage compression and inter-stage cooling mode, the air flow will undergo the compression-heat exchange process in sequence until the compressor outlet air pressure reaches the target pressure value of the air storage chamber. CAESC,t and the air flow rate at time t Relationship:

[0111]

[0112] Where κ is the adiabatic index; R g is the gas constant of air; n c is the number of compressor stages; η c is the efficiency of the compressor; T c,k,in and T c,k,out are the inlet temperature and outlet temperature of the air entering the kth stage compressor respectively.

[0113] Construction of the second mathematical model (for the expansion stage): In the energy discharge stage, high-pressure air is released, and after being heated by utilizing the compression heat stored in the heat storage device, it enters the expander to realize coupled power generation.

[0114] When the compressed air energy storage system adopts a multi-stage expansion and inter-stage reheating mode, the high-pressure air leaving the air storage chamber undergoes a heat absorption-expansion process in sequence until the air pressure at the outlet of the final expander drops to the ambient pressure. cAESG,t and air flow The relationship between can be described as:

[0115]

[0116] Among them, n g is the number of expander stages; T g,j,in and T g,j,aut is the inlet and outlet air temperatures of the j-th stage expander; η g The efficiency of the power generation process.

[0117] Construction of the third mathematical model (for the heat exchange stage): The heat exchanger and heat storage are thermal energy storage devices of the AA-CAES power station system. In the energy storage stage, the compressed air has a very high temperature and air pressure. If it is not cooled down, it will cause damage to the equipment. Therefore, it must be cooled by heat exchange in the heat exchanger before entering the next stage. The heat exchanger recovers the compression heat and stores it in the heat storage; in the energy release and discharge stage, the high-pressure air needs to pass through the heat exchanger and use the compression heat stored in the heat storage to heat up and increase the working capacity before entering the expander for coupled power generation. At this stage, assuming that the heat exchangers on the compressor side and the expander side have the same heat exchange efficiency, and the heat capacity of the hot heat carrier and the cold heat carrier are the same, it can be obtained:

[0118]

[0119] Where m represents the mass of the fluid; T represents the temperature of the fluid; c p,air It represents the constant pressure specific heat of air. The parameters with subscripts 1 and 2 represent the corresponding hot and cold fluids. The parameters with subscripts in and out represent the air flow parameters corresponding to the air flow entering and leaving the heat exchanger.

[0120] For example, T in1 Indicates the temperature of the heat exchanger in the thermal fluid machine, T out1 Indicates the temperature of the hot fluid leaving the heat exchanger, T on2 Indicates the temperature of the heat exchanger in the cold fluid machine, T out2 Indicates the temperature of the cold fluid leaving the heat exchanger.

[0121] In step S102, a wind power uncertainty model for predicting the uncertainty between wind power and actual wind power and a photovoltaic uncertainty model for predicting the uncertainty between sunlight and actual sunlight are constructed respectively, so as to obtain the wind-solar uncertainty of the compressed air energy storage system using the wind power uncertainty model and the photovoltaic uncertainty model.

[0122] As a possible way to achieve this, Figure 2 As shown, for wind power, in order to reduce the penalty for wind abandonment and the cost increase caused by starting the standby unit as much as possible, the embodiment of the present invention can characterize the uncertainty between the predicted wind power and the actual wind power through a robust optimization method to obtain a wind power uncertainty model;

[0123] For photovoltaics, the embodiments of the present invention can establish a gamma distribution model of average irradiance changing over time by mining historical data of typical load days in different seasons, weather, etc., and use a robust optimization method to characterize the uncertainty between predicted illumination and actual illumination to obtain a photovoltaic uncertainty model.

[0124] Optionally, in one embodiment of the present invention, a wind power uncertainty model for predicting the uncertainty between wind power and actual wind power and a photovoltaic uncertainty model for predicting the uncertainty between sunlight and actual sunlight are respectively constructed, including:

[0125] The expression of wind power uncertainty model is:

[0126]

[0127] Among them, P wind,t and Represent the actual output and predicted output of wind power respectively, represents the wind power offset, and The upper and lower limits of wind power output, P wind represents wind power output, Γ tw Represents the uncertainty of wind power output.

[0128] The expression of the photovoltaic uncertainty model is:

[0129]

[0130] Among them, P pv,t and They represent the actual output and predicted output of photovoltaic power, represents the photovoltaic offset, and Respectively represent the upper and lower limits of photovoltaic output, Γ tp Represents the uncertainty of photovoltaic output, P pv Represents the photovoltaic output power.

[0131] Specifically, for wind power, since the wind turbine generator set in a wind farm is mainly composed of a wind rotor and a generator, and the wind drives the wind rotor to rotate and drive the generator to generate electricity, it can be considered that the output power of wind power generation is directly linearly related to the wind speed, as shown in the following formula:

[0132]

[0133] Among them, v t is the predicted wind speed; P WR is the rated capacity of the wind farm; v R is the rated wind speed; v in is the lower limit of wind speed; v out The upper limit of wind speed.

[0134] Robust optimization can be used to optimize and solve uncertainties without knowing the specific probability distribution of uncertainties in advance. Compared with other uncertainty optimization methods, its advantage is that it does not require the precise probability distribution function and can be optimized with only a small amount of information. For large-scale power systems, robust optimization has the advantages of low computational complexity, safety and economy.

[0135]

[0136] Among them, P wind,t and Represent the actual output and predicted output of wind power respectively, represents the wind power offset, and They represent the upper and lower limits of wind power output respectively.

[0137] When Γ tw = 0, that is, the uncertainty of wind parameters is not considered, it is the optimal dispatch under the determination of wind power, and the system robustness is the worst; as Γ t As Γ increases, the system robustness improves, but the system economy continues to decline; tw When =1, it is the most conservative form expressed by the above formula.

[0138] For photovoltaics, since the power generation mainly comes from the photoelectric effect, and the size of the photocurrent is only related to the irradiance and has nothing to do with the frequency of light, it can be considered that the output power of photovoltaic power generation is directly linearly related to the average solar irradiance. In addition, the solar irradiance is considered to obey the Beta distribution within a certain period of time, and its probability density function satisfies:

[0139]

[0140] Among them, Γ(·), α, β represent the shape parameters of the gamma function and Beta distribution respectively; r, r max They represent solar irradiance and maximum solar irradiance respectively.

[0141] From this, the probability model of photovoltaic output power (the expression of photovoltaic uncertainty model) can be deduced as follows:

[0142]

[0143] Among them, P pv,t and They represent the actual output and predicted output of photovoltaic power, represents the photovoltaic offset, and The upper and lower limits of photovoltaic output, Γ tp is the uncertainty of photovoltaic output.

[0144] In step S103, the compressed air energy storage and wind-solar uncertainty in the compressed air energy storage system are regarded as game entities to obtain an optimized scheduling strategy for the compressed air energy storage system.

[0145] How to formulate an optimal dispatching problem that enables the wind-solar power system to operate safely and stably in an uncertain environment can be regarded as a typical robust optimization problem. At this time, for the system, it is necessary to know whether the formulated dispatching strategy can still meet the reliability and safety in an uncertain environment, that is, the system needs to know what the worst impact of uncertain factors on the reliability and safety of the system is, and adjust the strategy to avoid it as much as possible. In this way, it can be seen that the artificial decision makers of the system and the uncertainty factors of wind power - that is, the natural decision makers, form a game relationship: the decision makers of the power grid system hope to formulate dispatching strategies to ensure that the system can still optimize the planning and operation indicators when it is in an uncertain environment; on the other hand, nature tries to make the various indicators of the system worse.

[0146] Based on the above problems, Figure 2 As shown, the embodiment of the present invention can regard compressed air energy storage and wind and solar uncertainty as the main players in the game, where the compressed air energy storage party wants to smooth the fluctuations of the power grid as much as possible, while the wind and solar uncertainty party tries to damage the stability of the power grid as much as possible. The compressed air energy storage system can deal with the impact of wind and solar uncertainty on the power grid through the only strategy of adjusting the output.

[0147] Optionally, in one embodiment of the present invention, the compressed air energy storage and wind-solar uncertainty in the compressed air energy storage system are regarded as game subjects, and an optimized scheduling strategy for the compressed air energy storage system is obtained, including: constructing a game model based on the compressed air energy storage and wind-solar uncertainty to obtain an optimized scheduling strategy using the game model;

[0148] Among them, the expression of the game model is

[0149]

[0150] Among them, U and W are the decision sets of the two parties in the game, x represents the constant parameter, G() represents all equality constraints and inequality constraints that the two parties in the game need to satisfy, u represents the dispatcher, w represents the uncertainty of wind and solar power, and J() represents the dispatch cost.

[0151] Furthermore, in the embodiment of the present invention, nature (i.e., uncertain environment) and the artificial scheduler can be regarded as the two parties of the game, and the decisions made by the two parties are U and W respectively, and the game model of the robust optimization problem is constructed as follows:

[0152]

[0153] (1) Optimal decision results

[0154] From the above formula, we can see that the best choice for engineering games is to avoid the worst situation. In other words, after the optimal solution under the constraint is obtained, no matter how the two parties change their strategies, the final result will not be better than this result, that is, the system can operate stably and economically in the worst situation.

[0155] (2) Decision-making order of the two parties in the game

[0156] In power system optimization, the decision order of the engineering game determined by the above formula must be of the min-max type. In the actual power system, when the grid dispatchers make dispatch decisions, they are in a very unfavorable situation for nature. That is to say, the manual dispatchers belong to the "defense" party, and nature belongs to the "attack" party. When making dispatch decisions, it is also necessary to consider what decisions nature will make. That is to say, the best choice for manual decision-making at this time is to observe its worst interference first, and then establish a response strategy. And due to the uncertainty of wind power output, grid dispatchers are often in a passive position, that is, they can only make decisions passively. Therefore, when the decision of nature is unclear, the manual dispatcher can only consider the worst case first, so as to optimize the dispatch strategy to deal with the worst situation.

[0157] (3) Decision-making requirements for both parties

[0158] Both parties in the game hope to maximize their own benefits through the game, which is the connotation of the game, but if they do so blindly, the conditions for forming a game pattern will be missing. Therefore, engineering games require both parties to be rational. In the model shown in the above formula, both the grid dispatch decision maker and nature are rational, that is, the grid decision maker always hopes to make dispatch decisions that can maximize the economic efficiency while maintaining the safe and stable operation of the system, while nature always wants to maximize the destruction of the stability and security of the grid and bring negative impacts to the system.

[0159] In step S104, the economic evaluation of the compressed air energy storage system is performed in combination with the optimization scheduling strategy, mathematical model, multiple constraints and robust optimization cost to obtain the economic evaluation result of the compressed air energy storage system participating in wind and solar power consumption. Among them, the multiple constraints include at least one of the compression power limit, expansion power limit, air pressure constraint, heat storage constraint and system power balance constraint of the power station.

[0160] In the actual implementation process, the embodiment of the present invention can comprehensively optimize the scheduling strategy, mathematical model, multiple constraints and robust optimization cost to evaluate the economic feasibility of the compressed air energy storage system, wherein the constraints considered may include load balance constraints, line loss constraints, wind power and photovoltaic constraints, maximum / minimum output constraints, start-stop constraints, compressed air energy storage power station constraints and state of charge (SOC) constraints; the final operating cost (payment) may include start-stop costs, operating costs, wind abandonment penalties, solar abandonment penalties and startup costs.

[0161] Optionally, in one embodiment of the present invention, the robustness optimization cost includes operation cost and risk cost;

[0162] Among them, the expression of robust optimization cost is

[0163]

[0164] Among them, F 1 represents the operating cost, F 11 represents the start-stop cost of compressed air energy storage, F 12 Represents the secondary operating cost of conventional units, N x Indicates the number of compressed air energy storage units, S n Indicates the power on. Indicates the power-on status variable, S Di represents the shutdown cost, Indicates the shutdown state variable, N g represents the number of conventional units, α i , b i , c i are constant parameters, P gi,t Indicates the output of conventional units, I i,t Indicates the main circuit current of the power plant.

[0165] The expression of risk cost is

[0166]

[0167] in, represents the cost of wind curtailment, represents the cost of abandoned light, represents the startup standby cost, C o,w , C o,p and C u They represent wind curtailment factor, solar curtailment factor and standby factor respectively. Indicates the actual output of wind power. It indicates the predicted wind power output. Indicates the actual photovoltaic output. Indicates the predicted photovoltaic output.

[0168] As a possible way to achieve this, Figure 2 As shown, when conducting economic evaluation, the embodiment of the present invention can be implemented through analysis of two aspects.

[0169] 1. Constraint Analysis

[0170] 1. Constraints of compressed air energy storage power stations

[0171] P CAESC,min v C,t ≤P CAESC,t ≤P CAESC,min v C,t

[0172] P CAESG,min v G,t ≤P CAESG,t ≤P CAESG,max v G,t

[0173] p st,min ≤p st,t ≤p st,max

[0174] 0≤Q H,t ≤Q H,max

[0175] The above equations represent the compression / expansion power limit, gas pressure constraint and heat storage constraint of the power station, v C,t and v G,t is a binary variable representing the working state of the compressed air energy storage system.

[0176] System power balance constraints

[0177]

[0178] The above items respectively represent the output of conventional units, wind power output, photovoltaic output, compressed air energy storage discharge, load consumption and compressed air energy storage charging.

[0179] 2. Robust Optimization Cost Analysis

[0180] For the robust optimization dispatch model of the power system including wind and solar power, its cost mainly consists of two aspects, namely operating cost and risk cost (penalty cost).

[0181] Considering the joint access of AA-CAES power stations and wind and photovoltaic fields to the grid, the scheduling model is established using the robust optimization method. Its operating cost mainly includes the operating cost of conventional units that provide load demand, as well as the operating cost of the AA-CAES power station.

[0182] The risk cost is that due to the uncertainty of wind and solar power output, the actual wind and solar power output is different from the dispatched output. After the dispatcher makes a decision, there may be a large error, which will make the system state worse and cause losses. For the dispatch strategy that has been formulated by the power system, when the actual wind and solar power output in the future is greater than the predicted wind and solar power output, according to the goal of economic optimization, the load demand will be maintained by abandoning the part of the wind and solar power that exceeds the forecast, that is, the phenomenon of abandoning wind and solar power will occur, resulting in the cost of abandoning wind and solar power; when the actual wind and solar power output is less than the predicted wind and solar power output, the total power generation is insufficient and the reserve capacity needs to be invested to maintain the system power balance, which will incur certain reserve capacity costs.

[0183] The operating cost includes the start-up and shutdown costs and the secondary operating costs of conventional units:

[0184]

[0185] Risk costs include wind abandonment costs, solar abandonment costs and startup backup costs:

[0186]

[0187] The items on the right side of the above formula represent the cost of wind abandonment, solar abandonment and the cost of starting backup due to insufficient wind and solar output; C o,w , C o,p and C u They represent wind power abandonment coefficient, solar power abandonment coefficient and standby coefficient respectively.

[0188] Optionally, in one embodiment of the present invention, an economic evaluation of a compressed air energy storage system is performed in combination with an optimization scheduling strategy, a mathematical model, a plurality of constraints and a robust optimization cost, including: converting the calculation of the robust optimization cost into a robust optimization problem; decomposing the robust optimization problem into a main optimization problem and a sub-optimization problem; iteratively solving the main optimization problem and the sub-optimization problem respectively to obtain a calculation result of the robust optimization cost;

[0189] The expression of the main optimization problem is:

[0190]

[0191] Among them, x is the predicted output of each unit, ξ is the wind and solar prediction information, and z is the auxiliary variable;

[0192] The expression of the sub-optimization problem is:

[0193]

[0194] Among them, y s is the actual output adjustment value of each unit, ξ sis the actual wind and solar information, s is different actual wind and solar output scenarios, and A, B, C, D, a, and c are all coefficient matrices.

[0195] In the actual implementation process, the C&CG algorithm can be used to solve the robust optimization problem, that is, to decompose the robust optimization problem into two separate optimization problems: the main problem and the sub-problem, and then iteratively solve them separately. This method has fast convergence speed and high accuracy, and decomposes the problem into two stages: day-ahead scheduling and actual adjustment. It is suitable for compressed air energy storage systems to absorb wind and solar power. The specific method is as follows:

[0196] The main problem can be described as:

[0197]

[0198] Among them: x is the predicted output of each unit, ξ is the wind and solar prediction information, and z is the auxiliary variable;

[0199] The unit output status in the real-time stage is determined by the day-ahead prediction stage, and the unit output value is adjusted according to the changes in the wind and solar real-time output scenario, thereby achieving optimal scheduling in the real-time scenario. Its sub-problem can be expressed as:

[0200]

[0201] Where: y s is the actual output adjustment value of each unit, ξ s is the actual wind and solar information, s is different actual wind and solar output scenarios, A, B, C, D, a, c are all coefficient matrices;

[0202] In each iteration, the embodiment of the present invention can first solve the main problem of the day-ahead stage, determine the unit dispatch output x* and return the lower bound L of the objective function B , and then solve the real-time stage sub-problem to determine the unit adjustment output y s And return the upper bound U of the objective function B , iterate until L B with U B The difference is smaller than the iteration accuracy requirement.

[0203] Considering all constraints, we seek the dominant equilibrium of the two-player game. The corresponding objective function (payment) is the minimum operating cost, which can be expressed as Figure 3 The IEEE30-node system shown in the figure is simulated and verified. If the cost is low, it proves that the compressed air energy storage system is economically sound in participating in wind and solar power consumption and is suitable for commercial operation; if the cost is high, it proves that the compressed air energy storage system is economically bad in participating in wind and solar power consumption and can be improved from the aspects of equipment level and machine-grid coupling.

[0204] According to the economic evaluation method of the compressed air energy storage system participating in wind and solar power consumption proposed in the embodiment of the present invention, mathematical models of multiple components in the compressed air energy storage system, wind power uncertainty models for predicting the uncertainty between wind power and actual wind power, and photovoltaic uncertainty models for predicting the uncertainty between light and actual light can be constructed respectively, and the wind power uncertainty model and photovoltaic uncertainty model are used to obtain the wind and solar uncertainty of the compressed air energy storage system, so that the compressed air energy storage and wind and solar uncertainty in the compressed air energy storage system are regarded as game subjects to obtain the optimal scheduling strategy of the compressed air energy storage system, and then the economic evaluation of the compressed air energy storage system is carried out in combination with the optimal scheduling strategy, mathematical model, multiple constraints and robust optimization cost, and a more sophisticated uncertainty model is used to comprehensively consider the destructiveness of wind and solar power to the power grid, and the optimal scheduling strategy of compressed air energy storage participating in wind and solar power consumption is found from the perspective of game theory, and the reliability of the evaluation results is high, and the applicability of the evaluation method is wide. Therefore, the technical problem that the uncertainty model is relatively rough and does not fully consider the destructive effect of wind and solar power on the power grid, making it difficult to effectively evaluate the compressed air energy storage system from the perspective of wind and solar power consumption is solved.

[0205] Next, the economic evaluation device for the compressed air energy storage system participating in wind and solar power consumption according to an embodiment of the present invention is described with reference to the accompanying drawings.

[0206] Figure 4 It is a block diagram of an economic evaluation device for a compressed air energy storage system participating in wind and solar power consumption according to an embodiment of the present invention.

[0207] like Figure 4 As shown, the economic evaluation device 10 for the compressed air energy storage system participating in wind and solar power consumption includes: a first building module 100, a second building module 200, a game module 300 and an evaluation module 400.

[0208] Specifically, the first construction module 100 is used to construct mathematical models of multiple components in the compressed air energy storage system respectively.

[0209] The second construction module 200 is used to respectively construct a wind power uncertainty model for predicting the uncertainty between wind power and actual wind power and a photovoltaic uncertainty model for predicting the uncertainty between sunlight and actual sunlight, so as to obtain the wind-solar uncertainty of the compressed air energy storage system using the wind power uncertainty model and the photovoltaic uncertainty model.

[0210] The game module 300 is used to regard the compressed air energy storage and wind and solar uncertainty in the compressed air energy storage system as game subjects to obtain an optimized scheduling strategy for the compressed air energy storage system.

[0211] The evaluation module 400 is used to perform an economic evaluation on the compressed air energy storage system by combining the optimization scheduling strategy, mathematical model, multiple constraints and robust optimization cost, so as to obtain the economic evaluation result of the compressed air energy storage system participating in wind and solar power consumption.

[0212] Optionally, in one embodiment of the present invention, the first building block 100 includes: a first building unit, a second building unit and a third building unit.

[0213] Among them, the first construction unit is used to construct a first mathematical model of the compressor in the compressed air energy storage system, wherein the first mathematical model is a relationship model between the compression power and air flow of the compressor at any time.

[0214] The second construction unit is used to construct a second mathematical model of the expander in the compressed air energy storage system, wherein the second mathematical model is a relationship model between the expansion power and air flow of the expander at any moment.

[0215] The third construction unit is used to construct a third mathematical model of the heat exchanger in the compressed air energy storage system, wherein the third mathematical model is a relationship model between the mass, temperature and flow rate of the air fluid entering and leaving the heat exchanger at the same heat exchange efficiency.

[0216] Optionally, in one embodiment of the present invention, the expression of the first mathematical model of the compressor is:

[0217]

[0218] Where κ represents the adiabatic index, R g is the gas constant of air, n c represents the number of compressor stages, η c Indicates the efficiency of the compressor, T c,k,in and T c,k,out Respectively represent the inlet temperature and outlet temperature of the air entering the kth stage compressor, P CAESC,t represents the compression power of the compressor at time t, represents the air flow at time t.

[0219] The expression of the second mathematical model of the expander is

[0220]

[0221] Among them, n g represents the number of expander stages, T g,j,in and T g,j,aut denote the inlet air temperature and outlet air temperature of the j-th stage expander, respectively, and η g Represents the efficiency of the power generation process, P CAESG,t represents the expansion power at time t, represents the air flow at time t.

[0222] The expression of the third mathematical model of the heat exchanger is

[0223]

[0224] Where m represents the mass of the fluid, T represents the temperature of the fluid, and c p,air It represents the constant pressure specific heat of air. The parameters with subscripts 1 and 2 represent the corresponding hot and cold fluids respectively. The parameters with subscripts in and out represent the air flow parameters corresponding to the air flow entering and leaving the heat exchanger respectively.

[0225] Optionally, in one embodiment of the present invention, the expression of the wind power uncertainty model is:

[0226]

[0227] Among them, P wind,t and Represent the actual output and predicted output of wind power respectively, represents the wind power offset, and The upper and lower limits of wind power output, P wind represents wind power output, Γ tw Represents the uncertainty of wind power output.

[0228] The expression of the photovoltaic uncertainty model is:

[0229]

[0230] Among them, P pv,t and They represent the actual output and predicted output of photovoltaic power, represents the photovoltaic offset, and Respectively represent the upper and lower limits of photovoltaic output, Γ tp Represents the uncertainty of photovoltaic output, P pv Represents the photovoltaic output power.

[0231] Optionally, in one embodiment of the present invention, the game module 300 includes: a fourth building unit.

[0232] The fourth construction unit is used to construct a game model based on compressed air energy storage and wind and solar uncertainty, so as to obtain an optimized scheduling strategy using the game model;

[0233] Among them, the expression of the game model is

[0234]

[0235] Among them, U and W are the decision sets of the two parties in the game, x represents the constant parameter, G() represents all equality constraints and inequality constraints that the two parties in the game need to satisfy, u represents the dispatcher, w represents the uncertainty of wind and solar power, and J() represents the dispatch cost.

[0236] Optionally, in one embodiment of the present invention, the plurality of constraints include at least one of a compression power limit, an expansion power limit, an air pressure constraint, a heat storage constraint and a system power balance constraint of the power station.

[0237] Optionally, in one embodiment of the present invention, the robustness optimization cost includes operation cost and risk cost;

[0238] Among them, the expression of robust optimization cost is

[0239]

[0240] Among them, F 1 represents the operating cost, F 11 represents the start-stop cost of compressed air energy storage, F 12 Represents the secondary operating cost of conventional units, N x Indicates the number of compressed air energy storage units, S n Indicates the power on. Indicates the power-on status variable, S Di represents the shutdown cost, Indicates the shutdown state variable, N g represents the number of conventional units, α i , b i , c i are constant parameters, P gi,t Indicates the output of conventional units, I i,t Indicates the main circuit current of the power plant.

[0241] The expression of risk cost is

[0242]

[0243] in, represents the cost of wind curtailment, represents the cost of abandoned light, represents the startup standby cost, C o,w , C o,p and C u They represent wind curtailment factor, solar curtailment factor and standby factor respectively. Indicates the actual output of wind power. It indicates the predicted wind power output. Indicates the actual photovoltaic output. Indicates the predicted photovoltaic output.

[0244] Optionally, in one embodiment of the present invention, the evaluation module includes: a conversion unit, a decomposition unit and a solution unit.

[0245] Wherein, the conversion unit is used to convert the calculation of the robustness optimization cost into a robustness optimization problem;

[0246] A decomposition unit, used to decompose the robust optimization problem into a main optimization problem and sub-optimization problems;

[0247] A solving unit, used for iteratively solving the main optimization problem and the sub-optimization problem respectively, so as to obtain the calculation result of the robust optimization cost;

[0248] The expression of the main optimization problem is:

[0249]

[0250] Among them, x is the predicted output of each unit, ξ is the wind and solar prediction information, and z is the auxiliary variable;

[0251] The expression of the sub-optimization problem is:

[0252]

[0253] Among them, y s is the actual output adjustment value of each unit, v s is the actual wind and solar information, s is different actual wind and solar output scenarios, and A, B, C, D, a, and c are all coefficient matrices.

[0254] It should be noted that the aforementioned explanation of the embodiment of the method for economic evaluation of the compressed air energy storage system participating in wind and solar power consumption is also applicable to the device for economic evaluation of the compressed air energy storage system participating in wind and solar power consumption of this embodiment, and will not be repeated here.

[0255] According to the economic evaluation device for the participation of a compressed air energy storage system in wind and solar power consumption proposed in an embodiment of the present invention, mathematical models of multiple components in the compressed air energy storage system, a wind power uncertainty model for predicting the uncertainty between wind power and actual wind power, and a photovoltaic uncertainty model for predicting the uncertainty between light and actual light can be constructed respectively, and the wind power uncertainty model and the photovoltaic uncertainty model are used to obtain the wind and solar uncertainty of the compressed air energy storage system, so that the compressed air energy storage and wind and solar uncertainty in the compressed air energy storage system are regarded as game subjects to obtain the optimal scheduling strategy of the compressed air energy storage system, and then the economic evaluation of the compressed air energy storage system is carried out in combination with the optimal scheduling strategy, mathematical model, multiple constraints and robust optimization cost, and a more sophisticated uncertainty model is used to comprehensively consider the destructiveness of wind and solar power to the power grid, and the optimal scheduling strategy for the participation of compressed air energy storage in wind and solar power consumption is found from the perspective of game theory, and the reliability of the evaluation result is high, and the applicability of the evaluation method is wide. Thus, the technical problem that the uncertainty model is relatively rough and does not comprehensively consider the destructive effect of wind and solar power on the power grid, making it difficult to effectively evaluate the compressed air energy storage system from the perspective of wind and solar power consumption is solved.

[0256] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0257] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0258] When the processor 502 executes the program, the economic evaluation method for the compressed air energy storage system participating in wind and solar power consumption provided in the above embodiment is implemented.

[0259] Furthermore, the electronic device further comprises:

[0260] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0261] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0262] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0263] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0264] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0265] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0266] This embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for evaluating the economic feasibility of a compressed air energy storage system participating in wind and solar power consumption.

[0267] An embodiment of the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the economic evaluation method for the compressed air energy storage system participating in wind and solar power consumption provided by an embodiment of the present invention.

[0268] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0269] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0270] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0271] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0272] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0273] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0274] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0275] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating the economic performance of a compressed air energy storage system in wind and solar power consumption, characterized in that: The following steps are involved: Construct mathematical models of multiple components in the compressed air energy storage system respectively; A wind power uncertainty model for predicting the uncertainty between wind power and actual wind power and a photovoltaic uncertainty model for predicting the uncertainty between sunlight and actual sunlight are respectively constructed, so as to obtain the wind-solar uncertainty of the compressed air energy storage system by using the wind power uncertainty model and the photovoltaic uncertainty model; The compressed air energy storage in the compressed air energy storage system and the wind and solar uncertainty are regarded as game subjects to obtain an optimized scheduling strategy for the compressed air energy storage system; The economic performance of the compressed air energy storage system is evaluated in combination with the optimization scheduling strategy, the mathematical model, multiple constraints and robust optimization cost to obtain an economic performance evaluation result of the compressed air energy storage system participating in wind and solar power consumption.

2. The economic evaluation method for compressed air energy storage system participating in wind and solar power consumption according to claim 1 is characterized in that: The mathematical models of the multiple components in the compressed air energy storage system are constructed separately, including: Constructing a first mathematical model of the compressor in the compressed air energy storage system, wherein the first mathematical model is a relationship model between the compression power and air flow of the compressor at any time; Constructing a second mathematical model of the expander in the compressed air energy storage system, wherein the second mathematical model is a relationship model between the expansion power and air flow of the expander at any time; A third mathematical model of the heat exchanger in the compressed air energy storage system is constructed, wherein the third mathematical model is a relationship model between the mass, temperature and flow rate of the air fluid entering and leaving the heat exchanger at the same heat exchange efficiency.

3. The economic evaluation method for compressed air energy storage system participating in wind and solar power consumption according to claim 2 is characterized in that: The mathematical models of the multiple components in the compressed air energy storage system are constructed separately, including: The expression of the first mathematical model of the compressor is: Where κ represents the adiabatic index, R g is the gas constant of air, n c represents the number of compressor stages, η c Indicates the efficiency of the compressor, T c,k,in and T c,k,out Respectively represent the inlet temperature and outlet temperature of the air entering the kth stage compressor, P CAESC,t represents the compression power of the compressor at time t, represents the air flow rate at time t; The expression of the second mathematical model of the expander is: Among them, n g represents the number of expander stages, T g,j,in and T g,j,aut denote the inlet air temperature and outlet air temperature of the j-th stage expander, respectively, and η g Represents the efficiency of the power generation process, P CAESG,t represents the expansion power at time t, represents the air flow rate at time t; The expression of the third mathematical model of the heat exchanger is: Where m represents the mass of the fluid, T represents the temperature of the fluid, and c p,air It represents the constant pressure specific heat of air. The parameters with subscripts 1 and 2 represent the corresponding hot and cold fluids respectively. The parameters with subscripts in and out represent the air flow parameters corresponding to the air flow entering and leaving the heat exchanger respectively.

4. The economic evaluation method for compressed air energy storage system participating in wind and solar power consumption according to claim 1 is characterized in that: The wind power uncertainty model for predicting the uncertainty between wind power and actual wind power and the photovoltaic uncertainty model for predicting the uncertainty between sunlight and actual sunlight are respectively constructed, including: The expression of the wind power uncertainty model is: Among them, P wind,t and Represent the actual output and predicted output of wind power respectively, represents the wind power offset, and The upper and lower limits of wind power output, P wind represents wind power output, Γ tw Indicates the uncertainty of wind power output; The expression of the photovoltaic uncertainty model is: Among them, P pv,t and They represent the actual output and predicted output of photovoltaic power, represents the photovoltaic offset, and Respectively represent the upper and lower limits of photovoltaic output, Γ tp Represents the uncertainty of photovoltaic output, P pv Represents the photovoltaic output power.

5. The economic evaluation method for compressed air energy storage system participating in wind and solar power consumption according to claim 1 is characterized in that: The compressed air energy storage in the compressed air energy storage system and the wind and solar uncertainty are regarded as game subjects to obtain an optimized scheduling strategy for the compressed air energy storage system, including: Building a game model based on the compressed air energy storage and the wind and solar uncertainty, so as to obtain the optimized scheduling strategy by using the game model; Wherein, the expression of the game model is: Among them, U and W are the decision sets of the two parties in the game, x represents the constant parameter, G() represents all equality constraints and inequality constraints that the two parties in the game need to satisfy, u represents the dispatcher, w represents the uncertainty of wind and solar power, and J() represents the dispatch cost.

6. The economic evaluation method for compressed air energy storage system participating in wind and solar power consumption according to claim 1 is characterized in that: The plurality of constraints include at least one of a compression power limit, an expansion power limit, a gas pressure constraint, a heat storage constraint, and a system power balance constraint of the power station.

7. The economic evaluation method for compressed air energy storage system participating in wind and solar power consumption according to claim 1 is characterized in that: The robustness optimization cost includes operation cost and risk cost; The expression of the robustness optimization cost is: Among them, F1 represents the operating cost, F 11 represents the start-stop cost of compressed air energy storage, F 12 Represents the secondary operating cost of conventional units, N x Indicates the number of compressed air energy storage units, S n Indicates the power on. Indicates the power-on status variable, S Di represents the shutdown cost, Indicates the shutdown state variable, N g represents the number of conventional units, α i , b i , c i are constant parameters, P gi,t Indicates the output of conventional units, I i,t Indicates the main circuit current of the power plant; The risk cost expression is: in, represents the cost of wind curtailment, represents the cost of abandoned light, represents the startup standby cost, C o,w , C o,p and C u They represent wind curtailment factor, solar curtailment factor and standby factor respectively. Indicates the actual output of wind power. It indicates the predicted wind power output. Indicates the actual photovoltaic output. Indicates the predicted photovoltaic output.

8. The economic evaluation method for compressed air energy storage system participating in wind and solar power consumption according to claim 7 is characterized in that: The economic evaluation of the compressed air energy storage system is performed by combining the optimization scheduling strategy, the mathematical model, multiple constraints and robust optimization cost, including: Converting the calculation of the robust optimization cost into a robust optimization problem; Decomposing the robust optimization problem into a main optimization problem and sub-optimization problems; Iteratively solving the main optimization problem and the sub-optimization problem respectively to obtain the calculation result of the robustness optimization cost; The expression of the main optimization problem is: Among them, x is the predicted output of each unit, ξ is the wind and solar prediction information, and z is the auxiliary variable; The expression of the sub-optimization problem is: Among them, y s is the actual output adjustment value of each unit, ξ s is the actual wind and solar information, s is different actual wind and solar output scenarios, and A, B, C, D, a, and c are all coefficient matrices.

9. An economic evaluation device for a compressed air energy storage system participating in wind and solar power consumption, characterized in that: include: A first building module is used to respectively build mathematical models of multiple components in the compressed air energy storage system; The second construction module is used to respectively construct a wind power uncertainty model for predicting the uncertainty between wind power and actual wind power and a photovoltaic uncertainty model for predicting the uncertainty between sunlight and actual sunlight, so as to obtain the wind-solar uncertainty of the compressed air energy storage system by using the wind power uncertainty model and the photovoltaic uncertainty model; A game module, used to regard the compressed air energy storage in the compressed air energy storage system and the wind and solar uncertainty as game subjects, so as to obtain an optimized scheduling strategy for the compressed air energy storage system; An evaluation module is used to perform an economic evaluation on the compressed air energy storage system in combination with the optimization scheduling strategy, the mathematical model, multiple constraints and robust optimization cost, so as to obtain an economic evaluation result of the compressed air energy storage system participating in wind and solar power consumption.

10. The economic evaluation device for compressed air energy storage system participating in wind and solar power consumption according to claim 9 is characterized in that: The first building block comprises: A first construction unit is used to construct a first mathematical model of the compressor in the compressed air energy storage system, wherein the first mathematical model is a relationship model between the compression power and air flow of the compressor at any time; A second construction unit is used to construct a second mathematical model of the expander in the compressed air energy storage system, wherein the second mathematical model is a relationship model between the expansion power and air flow of the expander at any time; The third construction unit is used to construct a third mathematical model of the heat exchanger in the compressed air energy storage system, wherein the third mathematical model is a relationship model between the mass, temperature and flow rate of the air fluid entering and leaving the heat exchanger at the same heat exchange efficiency.

11. The economic evaluation device for compressed air energy storage system participating in wind and solar power consumption according to claim 10 is characterized in that: The expression of the first mathematical model of the compressor is: Where κ represents the adiabatic index, R g is the gas constant of air, n c represents the number of compressor stages, η c Indicates the efficiency of the compressor, T c,k,in and T c,k,out Respectively represent the inlet temperature and outlet temperature of the air entering the kth stage compressor, P CAESC,t represents the compression power of the compressor at time t, represents the air flow rate at time t; The expression of the second mathematical model of the expander is: Among them, n g represents the number of expander stages, T g,j,im and T g,j,aut denote the inlet air temperature and outlet air temperature of the j-th stage expander, respectively, and η g Represents the efficiency of the power generation process, P CAESG,t represents the expansion power at time t, represents the air flow rate at time t; The expression of the third mathematical model of the heat exchanger is: Where m represents the mass of the fluid, T represents the temperature of the fluid, and c p,air It represents the constant pressure specific heat of air. The parameters with subscripts 1 and 2 represent the corresponding hot and cold fluids respectively. The parameters with subscripts in and out represent the air flow parameters corresponding to the air flow entering and leaving the heat exchanger respectively.

12. The economic evaluation device for compressed air energy storage system participating in wind and solar power consumption according to claim 9 is characterized in that: The expression of the wind power uncertainty model is: Among them, P wind,t and Represent the actual output and predicted output of wind power respectively, represents the wind power offset, and The upper and lower limits of wind power output, P wind represents wind power output, Γ tw Indicates the uncertainty of wind power output; The expression of the photovoltaic uncertainty model is: Among them, P pv,t and They represent the actual output and predicted output of photovoltaic power, represents the photovoltaic offset, and Respectively represent the upper and lower limits of photovoltaic output, Γ tp Represents the uncertainty of photovoltaic output, P pv Represents the photovoltaic output power.

13. The economic evaluation device for compressed air energy storage system participating in wind and solar power consumption according to claim 9 is characterized in that: The game module includes: A fourth construction unit is used to construct a game model based on the compressed air energy storage and the wind and solar uncertainty, so as to obtain the optimized scheduling strategy by using the game model; Wherein, the expression of the game model is: Among them, U and W are the decision sets of the two parties in the game, x represents the constant parameter, G() represents all equality constraints and inequality constraints that the two parties in the game need to satisfy, u represents the dispatcher, w represents the uncertainty of wind and solar power, and J() represents the dispatch cost.

14. The economic evaluation device for compressed air energy storage system participating in wind and solar power consumption according to claim 9 is characterized in that: The plurality of constraints include at least one of a compression power limit, an expansion power limit, a gas pressure constraint, a heat storage constraint, and a system power balance constraint of the power station.

15. The economic evaluation device for compressed air energy storage system participating in wind and solar power consumption according to claim 9 is characterized in that: The robustness optimization cost includes operation cost and risk cost; The expression of the robustness optimization cost is: Among them, F1 represents the operating cost, F 11 represents the start-stop cost of compressed air energy storage, F 12 Represents the secondary operating cost of conventional units, N x Indicates the number of compressed air energy storage units, S n Indicates the power on. Indicates the power-on status variable, S Di represents the shutdown cost, Indicates the shutdown state variable, N g represents the number of conventional units, α i , b i , c i are constant parameters, P gi,t Indicates the output of conventional units, I i,t Indicates the main circuit current of the power plant; The risk cost expression is: in, represents the cost of wind curtailment, represents the cost of abandoned light, represents the startup standby cost, C o,w , C o,p and C u They represent wind curtailment factor, solar curtailment factor and standby factor respectively. Indicates the actual output of wind power. It indicates the predicted wind power output. Indicates the actual photovoltaic output. Indicates the predicted photovoltaic output.

16. The economic evaluation device for compressed air energy storage system participating in wind and solar power consumption according to claim 15, characterized in that: The evaluation module includes: A conversion unit, used for converting the calculation of the robustness optimization cost into a robustness optimization problem; A decomposition unit, used for decomposing the robustness optimization problem into a main optimization problem and sub-optimization problems; A solving unit, used for iteratively solving the main optimization problem and the sub-optimization problem respectively to obtain a calculation result of the robustness optimization cost; The expression of the main optimization problem is: Among them, x is the predicted output of each unit, ξ is the wind and solar prediction information, and z is the auxiliary variable; The expression of the sub-optimization problem is: Among them, y s is the actual output adjustment value of each unit, ξ s is the actual wind and solar information, s is different actual wind and solar output scenarios, and A, B, C, D, a, and c are all coefficient matrices.

17. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the economic evaluation method for the compressed air energy storage system participating in wind and solar power consumption as described in any one of claims 1 to 8.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement an economic evaluation method for a compressed air energy storage system participating in wind and solar power consumption as described in any one of claims 1 to 8.

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