Multi-target income optimization method for compressed air energy storage power station

Abstract

The invention relates to the technical field of new energy, in particular to a multi-target income optimization method for a compressed air energy storage power station, and the method comprises the steps: obtaining cold load demand data, heat load demand data and electric load demand data of a target compressed air energy storage power station planning region within a certain time, obtaining operation constraint data of the target compressed air energy storage power station; based on the cold load demand data, the thermal load demand data, the electric load demand data and the operation constraint data, the maximum pressure ratio, the compression power and the expansion power of the air storage chamber are calculated; and determining an optimal compromise solution of a pre-constructed multi-target optimization model by using the maximum pressure ratio, the compression power and the expansion power of the air storage chamber, so as to obtain a multi-target profit value meeting a certain profit condition based on the optimal compromise solution. Therefore, the problems that in the related technology, the influence of the energy storage efficiency and the annual profit rate cannot be quantitatively described through the maximum pressure ratio, the compression power and the expansion power of the air storage chamber, and the thermodynamic performance and the economic performance of the compressed air energy storage system cannot be optimal are solved.

Description

Technical Field

[0001] The present application relates to the field of new energy technology, and in particular to a multi-objective benefit optimization method for a compressed air energy storage power station. Background Art

[0002] Affected by environmental and energy issues, various clean energy sources are constantly being developed and utilized, especially wind power generation. However, as the scale of wind power continues to expand, the problem of "wind abandonment" still faces severe challenges, and the randomness and intermittency of wind power will also affect the power system. The development of large-scale energy storage technology has effectively solved these problems, especially advanced adiabatic compressed air energy storage technology, which has become the best choice for balancing the randomness of wind power generation and improving wind energy utilization with its large capacity, low cost, and high efficiency. In addition, air will generate heat during the compression stage and needs to absorb heat during the expansion stage, so it is possible to take into account both cooling and heating.

[0003] In the related technology, the required gas storage volume can be calculated by determining the basic parameters of the host and the gas storage, and then the investment and construction costs of the gas storage and the host construction costs can be obtained, and the power generation efficiency of the host can be calculated, thereby drawing the power generation efficiency and construction cost contour lines; it is also possible to take the maximum profit of the compressed air energy storage power station and the power grid profit taking into account the wind abandonment loss as the optimization target, and then construct a two-layer game model for the optimal configuration of the compressed air energy storage power station capacity, and obtain the optimal capacity of the compressed air energy storage power station by solving the model.

[0004] However, in the related technology, it is impossible to quantitatively characterize the impact of energy storage efficiency and annual profit rate through the maximum pressure ratio, compression power and expansion power of the air storage chamber, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot reach the optimal level, which urgently needs to be improved. Summary of the invention

[0005] The present application provides a multi-objective profit optimization method for a compressed air energy storage power station to solve the problems in the related art that the influence of energy storage efficiency and annual profit rate cannot be quantitatively characterized by the maximum pressure ratio, compression power and expansion power of the air storage chamber, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot reach the optimal level.

[0006] A first aspect of the present application provides a multi-objective profit optimization method for a compressed air energy storage power station, comprising the following steps: obtaining cold load demand data, heat load demand data, and electric load demand data within a preset time in a planned area of ​​a target compressed air energy storage power station, and obtaining operation constraint data of the target compressed air energy storage power station; calculating a maximum pressure ratio, compression power, and expansion power of an air storage chamber based on the cold load demand data, the heat load demand data, the electric load demand data, and the operation constraint data; determining an optimal compromise solution of a pre-constructed multi-objective optimization model using the maximum pressure ratio of the air storage chamber, the compression power, and the expansion power, so as to obtain a multi-objective profit value that meets preset profit conditions based on the optimal compromise solution.

[0007] Optionally, in one embodiment of the present application, the calculation of the maximum pressure ratio, compression power and expansion power of the air storage chamber based on the cooling load demand data, the heating load demand data, the electrical load demand data and the operation constraint data includes: obtaining a gray wolf population according to the cooling load demand data, the heating load demand data, the electrical load demand data and the Tent chaotic map, and generating an initial maximum pressure ratio, initial compression power and initial expansion power of the air storage chamber for each gray wolf in the gray wolf population based on the gray wolf population; using the initial maximum pressure ratio, initial compression power and initial expansion power of the air storage chamber The initial expansion power calculates the initial compromise solution of a pre-constructed multi-objective optimization model; based on the initial compromise solution, an alpha wolf that meets the preset fitness conditions is selected, and the initial distance between the alpha wolf and the gray wolf individuals in the gray wolf population other than the alpha wolf is calculated using the position vector adjustment weight; the initial distance is updated using a forward step length that meets a nonlinear convergence factor until the updated distance meets the preset convergence conditions, so as to obtain a gray wolf individual that meets the preset gray wolf conditions based on the updated distance, and based on the gray wolf individual, the maximum pressure ratio, compression power and expansion power of the air storage chamber of the gray wolf individual are obtained.

[0008] Optionally, in one embodiment of the present application, the expression of the Tent chaotic map may be, but is not limited to,:

[0009]

[0010] Wherein, t represents the tth iteration, β represents the chaotic parameter, x(t) represents the chaotic mapping function of the tth iteration, and x(t+1) represents the chaotic mapping function of the t+1th iteration.

[0011] Optionally, in one embodiment of the present application, the expression of the multi-objective optimization model may be, but is not limited to:

[0012]

[0013] Among them, η represents the energy storage efficiency, APM represents the annual profit rate, the first formula is the objective function of multi-objective optimization, the second formula is all the inequality constraints that the variables need to satisfy, and the third formula is all the equality constraints that the variables need to satisfy.

[0014] Optionally, in one embodiment of the present application, the expression of the position vector adjustment weight may be, but is not limited to,:

[0015]

[0016] Among them, r1 represents a random number between [0,1], λ represents the decay constant, and C k The random weight representing the impact of the individual's location on prey;

[0017] The expression of the nonlinear convergence factor may be, but is not limited to,:

[0018]

[0019] Among them, a max 、a min They represent the upper and lower limits of the nonlinear convergence factor respectively, and N represents the iteration threshold.

[0020] Optionally, in one embodiment of the present application, before determining the optimal compromise solution of the pre-constructed multi-objective optimization model using the maximum pressure ratio of the air storage chamber, the compression power and the expansion power, it also includes: constructing a thermodynamic model of the compressed air energy storage combined heat and power system in the target compressed air energy storage power station; constructing a cost-benefit model of the compressed air energy storage combined heat and power system in the target compressed air energy storage power station; and constructing a multi-objective optimization model based on the thermodynamic model and the cost-benefit model.

[0021] Optionally, in one embodiment of the present application, the expression of the optimal compromise solution may be, but is not limited to,:

[0022]

[0023] Among them, ED i+ represents the Euclidean distance between the ith solution and the positive ideal solution on the Pareto frontier, ED i- represents the Euclidean distance between the i-th solution and the negative ideal solution on the Pareto frontier;

[0024] The expression of the Euclidean distance may be, but is not limited to,:

[0025]

[0026]

[0027] Among them, fj，ideal and f j，nadir They represent the positive ideal solution and negative ideal solution of the j-th objective in single objective optimization.

[0028] The second aspect of the present application provides a multi-objective profit optimization device for a compressed air energy storage power station, including: an acquisition module, used to obtain cold load demand data, heat load demand data, and electric load demand data in a planned area of ​​a target compressed air energy storage power station within a preset time, and obtain the operation constraint data of the target compressed air energy storage power station; a calculation module, used to calculate the maximum pressure ratio, compression power and expansion power of the air storage chamber based on the cold load demand data, the heat load demand data, the electric load demand data and the operation constraint data; an optimization module, used to use the maximum pressure ratio of the air storage chamber, the compression power and the expansion power to determine the optimal compromise solution of a pre-constructed multi-objective optimization model, so as to obtain a multi-objective profit value that meets the preset profit conditions based on the optimal compromise solution.

[0029] Optionally, in one embodiment of the present application, the calculation module includes: a determination unit, which is used to obtain a gray wolf population based on the cooling load demand data, the heating load demand data, the electrical load demand data and the Tent chaotic map, and generate an initial air storage chamber maximum pressure ratio, an initial compression power and an initial expansion power of each gray wolf in the gray wolf population based on the gray wolf population; a first calculation unit, which is used to calculate an initial compromise solution of a pre-constructed multi-objective optimization model using the initial air storage chamber maximum pressure ratio, the initial compression power and the initial expansion power; a second calculation unit, which is used to select an alpha wolf that meets a preset fitness condition based on the initial compromise solution, and calculate the initial distance between the gray wolf individuals other than the alpha wolf in the gray wolf population and the alpha wolf using a position vector adjustment weight; a generation unit, which is used to update the initial distance using a forward step length that meets a nonlinear convergence factor until the updated distance meets a preset convergence condition, so as to obtain a gray wolf individual that meets the preset gray wolf condition based on the updated distance, and obtain the maximum air storage chamber pressure ratio, compression power and expansion power of the gray wolf individual based on the gray wolf individual.

[0030] Optionally, in one embodiment of the present application, the expression of the Tent chaotic map may be, but is not limited to,:

[0031]

[0032] Wherein, t represents the tth iteration, β represents the chaotic parameter, x(t) represents the chaotic mapping function of the tth iteration, and x(t+1) represents the chaotic mapping function of the t+1th iteration.

[0033] Optionally, in one embodiment of the present application, the expression of the multi-objective optimization model may be, but is not limited to:

[0034]

[0035] Among them, η represents the energy storage efficiency, APM represents the annual profit rate, the first formula is the objective function of multi-objective optimization, the second formula is all the inequality constraints that the variables need to satisfy, and the third formula is all the equality constraints that the variables need to satisfy.

[0036] Optionally, in one embodiment of the present application, the expression of the position vector adjustment weight may be, but is not limited to,:

[0037]

[0038] Among them, r1 represents a random number between [0, 1], λ represents the decay constant, and C k The random weight representing the impact of the individual's location on prey;

[0039] The expression of the nonlinear convergence factor may be, but is not limited to,:

[0040]

[0041] Among them, a max 、a min They represent the upper and lower limits of the nonlinear convergence factor respectively, and N represents the iteration threshold.

[0042] Optionally, in one embodiment of the present application, it also includes: a first construction module, which is used to construct a thermodynamic model of the compressed air energy storage combined heat and power system in the target compressed air energy storage power station before determining the optimal compromise solution of the pre-constructed multi-objective optimization model using the maximum pressure ratio of the air storage chamber, the compression power and the expansion power; a second construction module, which is used to construct a cost-benefit model of the compressed air energy storage combined heat and power system in the target compressed air energy storage power station; and a third construction module, which is used to construct a multi-objective optimization model based on the thermodynamic model and the cost-benefit model.

[0043] Optionally, in one embodiment of the present application, the expression of the optimal compromise solution may be, but is not limited to,:

[0044]

[0045] Among them, ED i+ represents the Euclidean distance between the ith solution and the positive ideal solution on the Pareto frontier, ED i - represents the Euclidean distance between the ith solution and the negative ideal solution on the Pareto frontier;

[0046] The expression of the Euclidean distance may be, but is not limited to,:

[0047]

[0048]

[0049] Among them, f j，ideal and f j，nadir They represent the positive ideal solution and negative ideal solution of the j-th objective in single objective optimization.

[0050] The third aspect of the present application 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 multi-objective profit optimization method of a compressed air energy storage power station as described in the above embodiment.

[0051] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the multi-objective profit optimization method of the compressed air energy storage power station as described above.

[0052] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, implements the above-mentioned multi-objective profit optimization method for a compressed air energy storage power station.

[0053] The embodiment of the present application can calculate the maximum pressure ratio, compression power and expansion power of the air storage chamber by using the acquired cold, heat and electricity load demand data and operation constraint data of the target compressed air energy storage power station, and use the maximum pressure ratio, compression power and expansion power of the air storage chamber to determine the optimal compromise solution of the pre-constructed multi-objective optimization model, and then obtain the multi-objective benefit value that meets certain benefit conditions, and provide a method for evaluating the power supply reliability of a coupled wind power compressed air energy storage system, which has the advantages of high economy and strong flexibility, and can enable users to obtain higher thermodynamic benefits and economic benefits at the same time, and has certain innovation potential and application value. Thus, it solves the problems in the related technology that the maximum pressure ratio, compression power and expansion power of the air storage chamber cannot be used to quantitatively characterize the impact of energy storage efficiency and annual profit rate, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot reach the optimal level.

[0054] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0056] Figure 1A flowchart of a multi-objective revenue optimization method for a compressed air energy storage power station provided according to an embodiment of the present application;

[0057] Figure 2 A flowchart of an improved grey wolf algorithm provided according to an embodiment of the present application;

[0058] Figure 3 A schematic block diagram of simulation results of an optimal compromise solution example outputted according to an embodiment of the present application;

[0059] Figure 4 A flowchart of constructing a multi-objective optimization model according to an embodiment of the present application;

[0060] Figure 5 A schematic diagram of the structure of a CCHP (Combined Cooling Heating and Power) system based on AA-CAES (Advanced Adiabatic Compressed Air Energy Storage) according to an embodiment of the present application;

[0061] Figure 6 A block diagram of a multi-objective benefit optimization device for a compressed air energy storage power station provided according to an embodiment of the present application;

[0062] Figure 7 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0063] Embodiments of the present application are described in detail below, and examples of the embodiments 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 application, and should not be construed as limiting the present application.

[0064] The following describes the multi-objective benefit optimization method of the compressed air energy storage power station of the embodiment of the present application with reference to the accompanying drawings. In view of the problem mentioned in the above background technology that the maximum pressure ratio, compression power and expansion power of the air storage chamber cannot be used to quantitatively characterize the impact of energy storage efficiency and annual profit rate, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot reach the optimal level, the present application provides a multi-objective benefit optimization method for a compressed air energy storage power station, in which the cold, heat and electricity load demand data and operation constraint data of the target compressed air energy storage power station can be obtained to calculate the maximum pressure ratio, compression power and expansion power of the air storage chamber, and the maximum pressure ratio, compression power and expansion power of the air storage chamber are used to determine the optimal compromise solution of the pre-constructed multi-objective optimization model, and then obtain the multi-objective benefit value that meets certain benefit conditions, and provide a coupled wind power compressed air energy storage system power supply reliability evaluation method, which has the advantages of high economy and strong flexibility, and can enable users to obtain higher thermodynamic benefits and economic benefits at the same time, and has certain innovation potential and application value. This solves the problems in related technologies, such as the inability to quantitatively characterize the impact of energy storage efficiency and annual profit margin through the maximum pressure ratio, compression power and expansion power of the air storage chamber, and the inability to achieve optimal thermodynamic and economic performance of the compressed air energy storage system.

[0065] Specifically, Figure 1 This is a flow chart of a multi-objective profit optimization method for a compressed air energy storage power station provided according to an embodiment of the present application.

[0066] like Figure 1 As shown, the multi-objective benefit optimization method of the compressed air energy storage power station includes the following steps:

[0067] In step S101, cold load demand data, heat load demand data, and electric load demand data within a preset time period of a planned area of ​​a target compressed air energy storage power station are obtained, and operation constraint data of the target compressed air energy storage power station is obtained.

[0068] In some embodiments, the embodiments of the present application can obtain the cold, heat, and electricity load demand data of the target compressed air energy storage power station planning area within a certain period of time and the operation constraint data of the target compressed air energy storage power station, where the certain period of time can be every hour of every year or every minute of every year. The specific setting can be made by technical personnel in this field according to actual conditions, and this application does not impose any specific restrictions.

[0069] In addition, in the embodiments of the present application, the operating constraint data may include, but is not limited to, operating constraint data such as installed power supply parameters, electricity prices, and annual average system equipment investment costs, and the present application does not impose any specific restrictions.

[0070] Furthermore, in the embodiment of the present application, hyperparameters such as the initial population size, number of iterations, and adjustment coefficient of the grey wolf algorithm can be set according to the above data.

[0071] Exemplarily, the embodiment of the present application obtains the hourly cold, hot, and electric load demand data throughout the year in the planning area of ​​the target compressed air energy storage power station, as well as the operating constraint data such as the installed power supply parameters, electricity prices, and annual average system equipment investment costs of the target compressed air energy storage power station, and then sets the initial population size, number of iterations, adjustment coefficient and other hyperparameters of the grey wolf algorithm based on the above data.

[0072] In step S102, the maximum pressure ratio, compression power and expansion power of the air storage chamber are calculated based on the cooling load demand data, the heating load demand data, the electric load demand data and the operation constraint data.

[0073] As a possible implementation method, the embodiment of the present application can calculate the maximum pressure ratio, compression power and expansion power of the air storage chamber according to the cold, heat and electricity load demand data and the operation constraint data.

[0074] For example, the position of each gray wolf in the gray wolf algorithm of the present application embodiment can be represented by the three-dimensional coordinates X(β, P c , P g ), where β is the maximum pressure ratio of the air storage chamber, P c is the compression power, P g For expansion power.

[0075] It can be understood that the embodiment of the present application can input cooling load demand data, heating load demand data, electrical load demand data and operation constraint data into the Grey Wolf algorithm, and then calculate the maximum pressure ratio, compression power and expansion power of the air storage chamber.

[0076] Optionally, in one embodiment of the present application, based on the cooling load demand data, the heating load demand data, the electrical load demand data and the operation constraint data, the maximum pressure ratio, compression power and expansion power of the air storage chamber are calculated, including: obtaining a gray wolf population according to the cooling load demand data, the heating load demand data, the electrical load demand data and the Tent chaotic map, and generating an initial maximum pressure ratio, initial compression power and initial expansion power of the air storage chamber for each gray wolf in the gray wolf population based on the gray wolf population; using the initial maximum pressure ratio, initial compression power and initial expansion power of the air storage chamber to calculate an initial compromise solution of a pre-constructed multi-objective optimization model; selecting an alpha wolf that meets a preset fitness condition based on the initial compromise solution, and calculating the initial distance between the alpha wolf and the gray wolf individuals other than the alpha wolf in the gray wolf population using the position vector adjustment weight; updating the initial distance using a forward step length that meets a nonlinear convergence factor until the updated distance meets a preset convergence condition, so as to obtain an individual gray wolf that meets the preset gray wolf condition based on the updated distance, and obtaining the maximum pressure ratio, compression power and expansion power of the air storage chamber of the individual gray wolf based on the individual gray wolf. The expression of Tent chaos map can be but not limited to:

[0077]

[0078] Wherein, t represents the tth iteration, β represents the chaotic parameter, x(t) represents the chaotic mapping function of the tth iteration, and x(t+1) represents the chaotic mapping function of the t+1th iteration.

[0079] The expression of the position vector adjustment weight can be but is not limited to:

[0080]

[0081] Among them, r1 represents a random number between [0, 1], λ represents the decay constant, and C k A random weight representing the impact of an individual's location on its prey.

[0082] The expression of the nonlinear convergence factor can be, but is not limited to,:

[0083]

[0084] Among them, a max 、a min They represent the upper and lower limits of the nonlinear convergence factor respectively, and N represents the iteration threshold.

[0085] It can be understood that in the embodiment of the present application, the gray wolf algorithm takes the wolf pack as the learning object. It is assumed that among the wolf pack, θ has the strongest adaptability, β has the second strongest adaptability, δ has the second strongest adaptability, and γ is the remaining adaptability. The four wolves continue to approach their prey through positioning.

[0086] In the actual implementation process, the embodiment of the present application calculates the maximum pressure ratio, compression power and expansion power of the gas storage chamber based on the improved grey wolf algorithm based on the cooling load demand data, the heating load demand data, the electric load demand data and the operation constraint data as follows: Figure 2 As shown, the main steps are:

[0087] Step S201: initial population size, number of iterations, adjustment coefficient and other hyperparameters.

[0088] Among them, in the embodiment of the present application, hyperparameters such as the initial population size, number of iterations, and adjustment coefficient of the gray wolf algorithm can be set according to the acquired load demand data and operation constraint data.

[0089] Step S202: Using Tent mapping to form an initial generation of gray wolf population.

[0090] In this embodiment of the present application, the position of each gray wolf can be represented by the three-dimensional coordinates x(β, P c , P g ), where β is the maximum pressure ratio of the air storage chamber, P c is the compression power, Pg For expansion power.

[0091] Furthermore, in the embodiment of the present application, since the load form in the power grid is relatively random, the Tent chaotic mapping is used to form the initial generation of gray wolf populations, which can cover a wider range than conventional random mapping, so that the maximum pressure ratio, compression power and expansion power of the gas storage chamber are evenly distributed in the entire space, so as to obtain the initial maximum pressure ratio, initial compression power and initial expansion power of the gas storage chamber. Among them, the expression of the Tent chaotic mapping can be but is not limited to:

[0092]

[0093] Wherein, t represents the tth iteration, β represents the chaotic parameter, x(t) represents the chaotic mapping function of the tth iteration, and x(t+1) represents the chaotic mapping function of the t+1th iteration.

[0094] Step S203: Considering the balance of cold, hot and electric loads and the operation constraints of the power station, a multi-objective optimization compromise solution is calculated and used as the individual fitness.

[0095] Among them, in the embodiment of the present application, the initial compromise solution of the pre-constructed multi-objective optimization model can be calculated based on the initial maximum pressure ratio of the gas storage chamber, the initial compression power and the initial expansion power, and used as the individual fitness, and the calculation formula thereof can be but is not limited to:

[0096]

[0097] Step S204: Re-elect the leader based on individual fitness.

[0098] Among them, the embodiment of the present application obtains the alpha wolf that meets certain fitness conditions according to the size of individual fitness. For example, the top three wolves with the largest fitness can be selected as the alpha wolf, which are respectively denoted as θ, β, and δ. The certain fitness conditions can be set by technical personnel in this field according to actual conditions, and this application does not make specific restrictions.

[0099] Step S205: Adjust the position vector weight and establish the search target.

[0100] It is understandable that the present embodiment calculates the distance between the three alpha wolves and the individual gray wolves in the gray wolf population other than the alpha wolf, and the calculation formula can be, but is not limited to, expressed as:

[0101]

[0102] Among them, the position vector adjustment weight C k The expression can be, but is not limited to:

[0103]

[0104] Among them, Xα (n), X β (n), X δ (n) represents the current position of the three alpha wolves, X(n) represents the current position of the remaining gray wolves, r1 represents a random number between [0, 1], C k represents a coefficient vector, which represents the random weight of the influence of the individual's location on the prey. It should be noted that in this embodiment of the application, C k The weight of is adjusted to decrease exponentially over time, which helps to improve the global optimization ability of the algorithm.

[0105] Step S206: updating the population position.

[0106] In this embodiment of the present application, after determining the distance between the alpha wolf and the individual gray wolves other than the alpha wolf, the wolf pack will gradually approach and surround the prey, and the forward progress of the individual gray wolves other than the alpha wolf toward the three alpha wolves can be expressed as, but not limited to:

[0107]

[0108] The final positions of the individual gray wolves except the leader wolf can be expressed as, but not limited to:

[0109]

[0110] Among them, n is the number of iterations of the algorithm, n max is the maximum number of iterations of the algorithm, a is the convergence factor, A k is the coefficient vector, and r2 is a random number between [0, 1].

[0111] It can be seen that as the number of iterations increases, the convergence factor decreases linearly to 0, resulting in A k The value of also changes within the corresponding interval. That is, when |A k When |>1, the gray wolf will separate from the local optimum and expand the scope to find a better solution; when |A k |<1, the wolf pack attacks the prey and searches for the optimal solution in the local area. k It guarantees global optimization and local optimization, but due to the complexity of the optimization process, the linear decrease of the convergence factor does not conform to the actual optimization process for A k The value requirement, therefore, the embodiment of the present application proposes a nonlinear convergence factor, and the expression of the nonlinear convergence factor can be but is not limited to:

[0112]

[0113] Among them, a max 、a minThey represent the upper and lower limits of the nonlinear convergence factor respectively, and N represents the iteration threshold. When the number of iterations is less than the iteration threshold, the nonlinear convergence factor converges slowly. When it is greater than the iteration threshold, the nonlinear convergence factor is a constant, which helps to enhance the global search capability of the algorithm while enabling detailed local search, and can better balance the global search and local search performance of the algorithm.

[0114] Step S207: Determine whether the termination condition is met.

[0115] In the embodiment of the present application, the updated distance can be judged to meet a certain convergence condition to obtain a gray wolf individual that meets a certain gray wolf condition. The certain convergence condition and the certain gray wolf condition can be set by a person skilled in the art according to actual conditions, and the present application does not impose any specific restrictions.

[0116] For example, the embodiment of the present application can determine whether the maximum number of iterations or the expected convergence accuracy is reached. If not, steps S203 to S206 are repeated; if reached, the optimal gray wolf individual is output.

[0117] Step S208: Output the optimal capacity ratio and the optimal compromise solution.

[0118] Among them, the embodiment itself can obtain the optimal capacity ratio and the optimal compromise solution according to the optimal gray wolf individual, and then the final maximum pressure ratio, compression power and expansion power of the gas storage chamber. For example, the optimal compromise solution obtained in the embodiment of the present application is as follows Figure 3 shown.

[0119] Optionally, in one embodiment of the present application, before determining the optimal compromise solution of a pre-constructed multi-objective optimization model using the maximum pressure ratio of the air storage chamber, the compression power, and the expansion power, it also includes: constructing a thermodynamic model of a compressed air energy storage combined heat and power system in a target compressed air energy storage power station; constructing a cost-benefit model of a compressed air energy storage combined heat and power system in a target compressed air energy storage power station; and constructing a multi-objective optimization model based on the thermodynamic model and the cost-benefit model.

[0120] The expression of the multi-objective optimization model can be, but is not limited to,:

[0121]

[0122] Among them, η represents the energy storage efficiency, APM represents the annual profit rate, the first formula is the objective function of multi-objective optimization, the second formula is all the inequality constraints that the variables need to satisfy, and the third formula is all the equality constraints that the variables need to satisfy.

[0123] The expression of the optimal compromise solution can be, but is not limited to,:

[0124]

[0125] Among them, ED i+ represents the Euclidean distance between the ith solution and the positive ideal solution on the Pareto frontier, ED i - represents the Euclidean distance between the ith solution and the negative ideal solution on the Pareto frontier;

[0126] The expression of Euclidean distance can be, but is not limited to,:

[0127]

[0128]

[0129] Among them, f j，idedl and f j，nadir They represent the positive ideal solution and negative ideal solution of the j-th objective in single objective optimization.

[0130] As a possible implementation method, the flowchart of constructing a multi-objective optimization model in the embodiment of the present application is as follows: Figure 4 As shown, the main steps are:

[0131] Step S401: construct a thermodynamic model.

[0132] In the embodiment of the present application, the schematic diagram of the CCHP system structure based on AA-CAES is as follows: Figure 5 As shown, C1 is the first compressor, C2 is the second compressor, E1 is the first expander, E2 is the second expander, CHE1 is the first heat exchanger, CHE2 is the second heat exchanger, EHE1 is the third heat exchanger, EHE2 is the fourth heat exchanger, CT is the low-temperature heat storage tank, HT is the high-temperature heat storage tank, PRV is the pressure regulating valve, P1 is the first heat storage medium pump, P2 is the second heat storage medium pump, GSC is the gas storage chamber, and M / G is an electric and generator integrated machine.

[0133] It can be understood that the system of the embodiment of the present application is composed of a two-stage compressor (a first compressor C1 and a second compressor C2), an expander (a first expander E1 and a second expander E2), four heat exchangers (a first heat exchanger CHE1, a second heat exchanger CHE2, a third heat exchanger EHE1 and a fourth heat exchanger EHE2), a low-temperature and high-temperature heat storage tanks (a low-temperature heat storage tank CT and a high-temperature heat storage tank HT), a pressure regulating valve PRV, a heat storage medium pump (a first heat storage medium pump P1 and a second heat storage medium pump P2), a gas storage chamber GSC, an electric and generator integrated machine M / G and various pipelines.

[0134] Furthermore, in the embodiment of the present application, the operation of the system is mainly divided into an energy storage process, an energy storage and release interval phase, and an energy release process.

[0135] Among them, in the embodiment of the present application, during the energy storage process (low electricity consumption period), the compressor (the first compressor C1 and the second compressor C2) is driven by the electric motor M, and the remaining low-valley electricity is used to pressurize the working fluid to the design pressure. At the same time, the working fluid temperature increases. The compressed high-temperature and high-pressure working fluid enters the energy storage process heat exchanger (the first heat exchanger CHE1 and the second heat exchanger CHE2) in sequence. At the same time, the low-temperature heat storage medium flows out of the low-temperature heat storage tank CT under the action of the first heat storage medium pump P1, and enters each stage of the energy storage process heat exchanger to exchange heat with the high-temperature and high-pressure working fluid. The outlet working fluid pressure of the second-stage heat exchanger is controlled by the pressure regulating valve PRV (if used). The cooled high-pressure air is then stored in the air storage chamber GSC. At the same time, the compression heat generated by the compressors at each stage is transferred to the heat storage medium, and the low-temperature heat storage medium enters the high-temperature heat storage tank HT and is stored for subsequent use.

[0136] During the energy storage and release interval, there is no material exchange between the gas storage chamber and the external environment. If there is a temperature difference between the working fluid and the wall of the gas storage chamber, heat exchange will occur between them.

[0137] During the energy release process (peak electricity consumption period), a part of the high-temperature heat storage medium is sent from the hot tank to the heat exchanger (the third heat exchanger EHE1 and the fourth heat exchanger EHE2) by the second heat storage medium pump P2, and transfers the heat to the high-pressure working fluid flowing out of the gas storage chamber, and then enters the cold tank for later use. The high-temperature and high-pressure working fluid expands in the expander (the first expander E1 and the second expander E2) and drives the generator G to generate electricity. The working fluid discharged from the final expansion machine is much lower than the atmospheric temperature, so it can be transported to cold users for refrigeration. Another part of the heat storage medium is transported to hot users for heating, and then enters the cold tank for the next cycle. Similarly, the heat exchange and expansion process will be repeated until the pressure of the working fluid stored in the gas storage chamber returns to the minimum design value.

[0138] In addition, it should be noted that in the compression stage of the AA-CAES system of the embodiment of the present application, the heat generated by each stage of the compressor during compression is sent to the inter-stage heat exchanger, and the compression process can be regarded as a reversible adiabatic process:

[0139] The compression work consumed by the working fluid in the i-th stage compressor can be expressed as, but not limited to:

[0140]

[0141] The total compression work consumed can be expressed as, but not limited to:

[0142]

[0143] Where p0 represents the ambient pressure, in Pa; T0 represents the ambient temperature, in K; β max , β minRespectively represent the maximum and minimum pressure ratios of the air storage chamber; V represents the volume of the air storage chamber, in m 3 ;c p Indicates the constant pressure specific heat capacity of the working fluid, in J·kg -1 ·K -1 ; R g Represents the gas constant of the working fluid; η c,i represents the isentropic efficiency of the i-th stage compressor; β c，i represents the compression ratio of the i-th stage compressor; κ represents the adiabatic index of the working fluid; Represents the working medium inlet temperature of the i-th stage compressor.

[0144] The amount of heat delivered to the user can be expressed as, but not limited to:

[0145]

[0146] Where x represents the heat distribution ratio, which is the ratio of the heat in the hot tank used to heat the compressed air before the expander to the total heat; T hot , T cold Indicates the temperature of cold and hot media, unit K; Indicates the mass of the heat storage medium in the i-th compression stage, in kg; c hsm Indicates the specific heat capacity of the heat storage medium, in J·kg -1 ·K -1 .

[0147] In addition, it should be noted that in the expansion stage of the AA-CAES system of the embodiment of the present application, each stage of the expander is heated by a heat exchanger, and the expansion process can be regarded as a reversible adiabatic process:

[0148] The expansion work done by the i-th stage expander can be expressed as, but not limited to:

[0149]

[0150] The total expansion work can be expressed as, but not limited to:

[0151]

[0152] When the exhaust temperature of the final expander is lower than the ambient temperature, the cooling capacity provided to the cold user can be expressed as, but not limited to:

[0153]

[0154] in, Represents the outlet temperature of the working medium of the i-th stage expander.

[0155] In addition, it should be noted that in the heat exchange stage of the AA-CAES system of the present application embodiment, the air compressed by the compressor will generate compression heat, and the high-temperature and high-pressure air enters the heat exchanger to complete the heat exchange with the heat carrier medium. The calculation formula of the heat exchanger efficiency parameter can be, but is not limited to, expressed as:

[0156]

[0157] Among them, ε represents the heat exchanger efficiency parameter, which is a key parameter of the heat exchange process; m represents the mass of the fluid; T represents the temperature of the fluid, and the parameters with subscripts 1 and 2 represent the corresponding hot and cold fluids; the parameters with subscripts in and out represent the fluid entering and leaving the heat exchanger. In addition, the embodiment of the present application assumes that the specific heat capacity of the hot fluid is equal to that of the cold fluid. The definition of the heat exchanger efficiency parameter can be used to obtain the outlet temperature of the air after passing through the i-th stage heat exchanger, and this temperature is also the inlet temperature of the i+1-th stage compressor.

[0158] Step S402: construct a cost-benefit model.

[0159] It can be understood that the embodiment of the present application constructs a cost-benefit model based on cost analysis and benefit analysis. Specifically:

[0160] The main contents of the cost analysis of the embodiment of the present application are: the annual total cost ATC of the CCHP system based on AA-CAES may include but is not limited to the annual equipment investment cost AC inv , annual operation and maintenance costs AC om And the annual electricity cost AC ele , at this time, the calculation formula of the annual total cost can be expressed as but not limited to:

[0161] ATC=AC inv +AC om +AC ele ,

[0162] Among them, the annual equipment investment cost AC inv The annual cost of the main equipment including, but not limited to, compressor, expander, heat exchanger and gas storage chamber can be calculated as, but not limited to, the following formula:

[0163]

[0164] Where j represents the interest rate; n represents the system cycle life; C k It represents the investment cost of each equipment in the system, in Yuan.

[0165] Annual operation and maintenance costs AC om The calculation formula can be expressed as but not limited to:

[0166]

[0167] in, Indicates the ratio of system operation and maintenance costs, generally set to 0.06.

[0168] Annual electricity cost AC ele The calculation formula can be expressed as but not limited to:

[0169] AC ele =W c ×c off-peak ×365,

[0170] Among them, C off-peak It indicates the off-peak electricity price, which is generally set at 0.421 yuan / kWh.

[0171] Furthermore, the main content of the revenue analysis of the embodiment of the present application is: the annual total revenue may include, but is not limited to, the electricity revenue during the peak period of electricity consumption, heating and cooling revenue. At this time, the calculation formula of the annual total revenue may be, but is not limited to, expressed as:

[0172] ATR=365×(W e ×c on-peak +Q he ×c hot water +Q co ×c cooling ),

[0173] Among them, c on-peak Indicates the peak electricity price, which is generally set at 0.938 yuan / kWh; c hotwater Indicates the hot water price, generally set to 0.672 yuan / kWh; c cooling Indicates the cooling price, which is generally set to 0.773 yuan / kWh.

[0174] The calculation formula for the total annual profit can be expressed as, but not limited to:

[0175] ATP=ATR-ATC,

[0176] Step S403: construct a multi-objective optimization model.

[0177] It can be understood that the energy storage efficiency of the system in the embodiment of the present application is defined as the ratio of the total work generated by the expander in the energy release stage to the total work consumed by the compressor in the energy storage stage, reflecting the electrical-electrical conversion performance of the system, and its calculation formula can be, but is not limited to, expressed as:

[0178]

[0179] The annual profit rate of the system is defined as the ratio of the annual total profit to the annual total cost, which reflects the application potential of the system. The calculation formula can be expressed as but not limited to:

[0180]

[0181] At this time, the expression of the constructed multi-objective optimization model can be but is not limited to:

[0182]

[0183] Among them, η represents the energy storage efficiency, APM represents the annual profit rate, the first formula is the objective function of multi-objective optimization, the second formula is all the inequality constraints that the variables need to satisfy, and the third formula is all the equality constraints that the variables need to satisfy.

[0184] It can be seen from this that it is obvious that in the embodiment of the present application, η(X) and APM(X) cannot be optimal at the same time, so the optimal compromise solution can only be found on the Pareto front.

[0185] To solve this multi-objective optimization problem, the approximate ideal ranking method can be used. When selecting the final point, this method usually first determines the positive ideal solution and the negative ideal solution, and then determines the ideal solution closest to the positive ideal solution and farthest from the negative ideal solution as the optimal compromise solution based on the objective weight of the decision maker. The Euclidean distance between the i-th solution and the positive ideal solution and the negative ideal solution on the Pareto frontier can be expressed as, but not limited to:

[0186]

[0187]

[0188] Among them, f j，ideal and f j，nadir They represent the positive ideal solution and negative ideal solution of the j-th objective in single objective optimization.

[0189] Furthermore, the embodiment of the present application may take the solution with the smallest Yi as the desired optimal compromise solution, which may be expressed as but not limited to:

[0190]

[0191] In addition, the embodiment of the present application takes into account that the conventional convex optimization method has high computational complexity and slow convergence speed in solving the above-mentioned multi-objective optimization problem, and therefore proposes a gray wolf algorithm to solve the above-mentioned multi-objective optimization model, and makes corresponding improvements based on the characteristics of the compressed air energy storage system itself.

[0192] In step S103, the optimal compromise solution of the pre-constructed multi-objective optimization model is determined using the maximum pressure ratio of the air storage chamber, the compression power, and the expansion power, so as to obtain a multi-objective benefit value that meets the preset benefit conditions based on the optimal compromise solution.

[0193] Through the above analysis, it can be seen that the embodiment of the present application can use the maximum pressure ratio of the air storage chamber, the compression power and the expansion power to determine the optimal compromise solution of the pre-constructed multi-objective optimization model, and then obtain the multi-objective profit value that meets certain profit conditions. Among them, the certain profit conditions can be set by technicians in this field according to actual conditions, and this application does not make specific restrictions.

[0194] The embodiment of the present application performs multi-objective optimization of energy storage efficiency and annual profit rate based on the CCHP system of AA-CAES, and performs multi-objective optimization solution based on the improved Grey Wolf Algorithm; to meet the characteristics of the compressed air energy storage system, Tent chaos mapping is performed in the initialization stage; to obtain a higher convergence speed and global optimization capability to provide users with better thermodynamic and economic benefits, time-varying position vector adjustment weights and nonlinear convergence factors are used, and the output optimal compromise solution (Pareto optimality) can enable users to obtain higher thermodynamic and economic benefits at the same time, and has certain innovation potential and application value.

[0195] According to the multi-objective benefit optimization method of the compressed air energy storage power station proposed in the embodiment of the present application, the acquired cold, heat, and electricity load demand data of the target compressed air energy storage power station and the operation constraint data can be used to calculate the maximum pressure ratio, compression power, and expansion power of the air storage chamber, and the maximum pressure ratio, compression power, and expansion power of the air storage chamber are used to determine the optimal compromise solution of the pre-constructed multi-objective optimization model, thereby obtaining a multi-objective benefit value that meets certain benefit conditions, and providing a method for evaluating the power supply reliability of a coupled wind power compressed air energy storage system, which has the advantages of high economy and strong flexibility, and can enable users to obtain higher thermodynamic benefits and economic benefits at the same time, and has certain innovation potential and application value. Thus, the problems in the related art that the maximum pressure ratio, compression power, and expansion power of the air storage chamber cannot be used to quantitatively characterize the impact of energy storage efficiency and annual profit margin, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot reach the optimal level are solved.

[0196] Next, a multi-objective benefit optimization device for a compressed air energy storage power station proposed according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0197] Figure 6 A block diagram of a multi-objective revenue optimization device for a compressed air energy storage power station provided according to an embodiment of the present application.

[0198] like Figure 6 As shown, the multi-objective benefit optimization device 10 of the compressed air energy storage power station includes: an acquisition module 100, a calculation module 200 and an optimization module 300.

[0199] Among them, the acquisition module 100 is used to obtain the cold load demand data, heat load demand data, and electric load demand data of the target compressed air energy storage power station planning area within a preset time, and obtain the operation constraint data of the target compressed air energy storage power station.

[0200] The calculation module 200 is used to calculate the maximum pressure ratio, compression power and expansion power of the air storage chamber based on the cooling load demand data, the heating load demand data, the electric load demand data and the operation constraint data.

[0201] The optimization module 300 is used to determine the optimal compromise solution of the pre-constructed multi-objective optimization model using the maximum pressure ratio of the air storage chamber, the compression power and the expansion power, so as to obtain the multi-objective benefit value that meets the preset benefit conditions based on the optimal compromise solution.

[0202] Optionally, in one embodiment of the present application, the calculation module 200 includes: a determination unit, a first calculation unit, a second calculation unit and a generation unit.

[0203] Among them, the determination unit is used to obtain the gray wolf population according to the cooling load demand data, the heating load demand data, the electric load demand data and the Tent chaotic map, and generate the initial air storage chamber maximum pressure ratio, the initial compression power and the initial expansion power of each gray wolf in the gray wolf population based on the gray wolf population.

[0204] The first calculation unit is used to calculate an initial compromise solution of a pre-constructed multi-objective optimization model by using an initial maximum pressure ratio of the air storage chamber, an initial compression power and an initial expansion power.

[0205] The second calculation unit is used to select an alpha wolf that meets the preset fitness conditions based on the initial compromise solution, and use the position vector to adjust the weight to calculate the initial distance between the alpha wolf and the gray wolf individuals other than the alpha wolf in the gray wolf population.

[0206] A generation unit is used to update the initial distance using a forward step length that satisfies a nonlinear convergence factor until the updated distance satisfies a preset convergence condition, so as to obtain a gray wolf individual that satisfies a preset gray wolf condition based on the updated distance, and to obtain the maximum pressure ratio, compression power and expansion power of the gray wolf individual's air storage chamber based on the gray wolf individual.

[0207] Optionally, in one embodiment of the present application, the expression of the Tent chaotic map may be, but is not limited to,:

[0208]

[0209] Wherein, t represents the tth iteration, β represents the chaotic parameter, x(t) represents the chaotic mapping function of the tth iteration, and x(t+1) represents the chaotic mapping function of the t+1th iteration.

[0210] Optionally, in one embodiment of the present application, the expression of the multi-objective optimization model may be, but is not limited to:

[0211]

[0212] Among them, η represents the energy storage efficiency, APM represents the annual profit rate, the first formula is the objective function of multi-objective optimization, the second formula is all the inequality constraints that the variables need to satisfy, and the third formula is all the equality constraints that the variables need to satisfy.

[0213] Optionally, in one embodiment of the present application, the expression of the position vector adjustment weight may be, but is not limited to:

[0214]

[0215] Among them, r1 represents a random number between [0, 1], λ represents the decay constant, and C k The random weight representing the impact of the individual's location on prey;

[0216] The expression of the nonlinear convergence factor can be, but is not limited to,:

[0217]

[0218] Among them, a max 、a min They represent the upper and lower limits of the nonlinear convergence factor respectively, and N represents the iteration threshold.

[0219] Optionally, in one embodiment of the present application, it further includes: a first building module, a second building module and a third building module.

[0220] Among them, the first construction module is used to construct a thermodynamic model of the compressed air energy storage combined heating and power system in the target compressed air energy storage power station before using the maximum pressure ratio, compression power and expansion power of the air storage chamber to determine the optimal compromise solution of the pre-constructed multi-objective optimization model.

[0221] The second construction module is used to construct a cost-benefit model of the compressed air energy storage combined heating and power system in the target compressed air energy storage power station.

[0222] The third building module is used to build a multi-objective optimization model based on the thermodynamic model and the cost-benefit model.

[0223] Optionally, in one embodiment of the present application, the expression of the optimal compromise solution may be, but is not limited to,:

[0224]

[0225] Among them, ED i+represents the Euclidean distance between the ith solution and the positive ideal solution on the Pareto frontier, ED i - represents the Euclidean distance between the ith solution and the negative ideal solution on the Pareto frontier;

[0226] The expression of Euclidean distance can be, but is not limited to,:

[0227]

[0228]

[0229] Among them, f j，idedl and f j，nadir They represent the positive ideal solution and negative ideal solution of the j-th objective in single objective optimization.

[0230] It should be noted that the aforementioned explanation of the embodiment of the multi-objective benefit optimization method of the compressed air energy storage power station is also applicable to the multi-objective benefit optimization device of the compressed air energy storage power station of this embodiment, and will not be repeated here.

[0231] According to the multi-objective benefit optimization device of the compressed air energy storage power station proposed in the embodiment of the present application, the acquired cold, heat, and electricity load demand data and operation constraint data of the target compressed air energy storage power station can be used to calculate the maximum pressure ratio, compression power, and expansion power of the air storage chamber, and the maximum pressure ratio, compression power, and expansion power of the air storage chamber are used to determine the optimal compromise solution of the pre-constructed multi-objective optimization model, thereby obtaining a multi-objective benefit value that meets certain benefit conditions, and providing a method for evaluating the power supply reliability of a coupled wind power compressed air energy storage system, which has the advantages of high economy and strong flexibility, and can enable users to obtain higher thermodynamic benefits and economic benefits at the same time, and has certain innovation potential and application value. Thus, the problems in the related art that the maximum pressure ratio, compression power, and expansion power of the air storage chamber cannot be used to quantitatively characterize the impact of energy storage efficiency and annual profit rate, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot be optimized are solved.

[0232] Figure 7 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. The electronic device may include:

[0233] A memory 701 , a processor 702 , and a computer program stored in the memory 701 and executable on the processor 702 .

[0234] When the processor 702 executes the program, the multi-objective benefit optimization method of the compressed air energy storage power station provided in the above embodiment is implemented.

[0235] Furthermore, the electronic device further comprises:

[0236] The communication interface 703 is used for communication between the memory 701 and the processor 702 .

[0237] The memory 701 is used to store computer programs that can be executed on the processor 702 .

[0238] The memory 701 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.

[0239] If the memory 701, the processor 702 and the communication interface 703 are implemented independently, the communication interface 703, the memory 701 and the processor 702 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 Interconnect (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 7 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.

[0240] Optionally, in a specific implementation, if the memory 701, the processor 702 and the communication interface 703 are integrated on a chip, the memory 701, the processor 702 and the communication interface 703 can communicate with each other through an internal interface.

[0241] The processor 702 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 application.

[0242] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-objective profit optimization method of a compressed air energy storage power station as described above.

[0243] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, implements the above-mentioned multi-objective profit optimization method for a compressed air energy storage power station.

[0244] 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 application. 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.

[0245] 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 technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0246] 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 application 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 application belong.

[0247] 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.

[0248] It should be understood that the various parts of the present application 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. If implemented by hardware, as in another embodiment, it can be implemented by any one or a combination of multiple of the following technologies known in the art: 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.

[0249] 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.

[0250] In addition, each functional unit in each embodiment of the present application 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.

[0251] 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 application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A multi-objective benefit optimization method for a compressed air energy storage power station, characterized in that: The following steps are involved: Obtaining cooling load demand data, heating load demand data, and electric load demand data within a preset time in the planning area of ​​the target compressed air energy storage power station, and obtaining operation constraint data of the target compressed air energy storage power station; Calculating the maximum pressure ratio, compression power and expansion power of the air storage chamber based on the cooling load demand data, the heating load demand data, the electrical load demand data and the operation constraint data; The optimal compromise solution of the pre-constructed multi-objective optimization model is determined by utilizing the maximum pressure ratio of the air storage chamber, the compression power and the expansion power, so as to obtain a multi-objective benefit value that meets a preset benefit condition based on the optimal compromise solution.

2. The method according to claim 1, characterized in that The calculating the maximum pressure ratio, compression power and expansion power of the air storage chamber based on the cooling load demand data, the heating load demand data, the electric load demand data and the operation constraint data comprises: A gray wolf population is obtained according to the cooling load demand data, the heating load demand data, the electric load demand data and the Tent chaotic map, and an initial air storage chamber maximum pressure ratio, an initial compression power and an initial expansion power of each gray wolf in the gray wolf population are generated based on the gray wolf population; Calculating an initial compromise solution of a pre-constructed multi-objective optimization model using the initial maximum air storage chamber pressure ratio, the initial compression power, and the initial expansion power; Selecting an alpha wolf that meets a preset fitness condition based on the initial compromise solution, and calculating an initial distance between the alpha wolf and the individual gray wolves in the gray wolf population other than the alpha wolf using a position vector to adjust the weight; The initial distance is updated using a forward step length that satisfies a nonlinear convergence factor until the updated distance satisfies a preset convergence condition, so as to obtain a gray wolf individual that satisfies a preset gray wolf condition based on the updated distance, and obtain the maximum pressure ratio, compression power and expansion power of the air storage chamber of the gray wolf individual based on the gray wolf individual.

3. The method according to claim 2, characterized in that The expression of the Tent chaotic map is: Wherein, t represents the tth iteration, β represents the chaotic parameter, x(t) represents the chaotic mapping function of the tth iteration, and x(t+1) represents the chaotic mapping function of the t+1th iteration.

4. The method according to claim 1, characterized in that The expression of the multi-objective optimization model is: Among them, η represents the energy storage efficiency and aPM represents the annual profit margin.

5. The method according to claim 2, characterized in that: in, The expression of the position vector adjustment weight is: Among them, r1 represents a random number between [0,1], λ represents the decay constant, and C k The random weight representing the impact of the individual's location on prey; The expression of the nonlinear convergence factor is: Among them, a max 、a min They represent the upper and lower limits of the nonlinear convergence factor respectively, and N represents the iteration threshold.

6. The method according to claim 1, characterized in that Before determining the optimal compromise solution of the pre-constructed multi-objective optimization model by using the maximum pressure ratio of the air storage chamber, the compression power and the expansion power, the method further includes: Construct a thermodynamic model of the compressed air energy storage combined heating and power system in the target compressed air energy storage power station; Construct a cost-benefit model for the compressed air energy storage combined heating and power system in the target compressed air energy storage power station; A multi-objective optimization model is constructed based on the thermodynamic model and the cost-benefit model.

7. The method according to claim 1, characterized in that The expression of the optimal compromise solution is: Among them, ED i+ represents the Euclidean distance between the ith solution and the positive ideal solution on the Pareto frontier, ED i- represents the Euclidean distance between the i-th solution and the negative ideal solution on the Pareto frontier; Wherein, the expression of the Euclidean distance is: Among them, f j,ideal and f j,nadir They represent the positive ideal solution and negative ideal solution of the j-th objective in single objective optimization.

8. 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 multi-objective profit optimization method for a compressed air energy storage power station as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the multi-objective profit optimization method of a compressed air energy storage power station as described in any one of claims 1 to 7.

10. A computer program product, characterized in that It comprises a computer program which, when executed, is used to implement the multi-objective profit optimization method of a compressed air energy storage power station as described in any one of claims 1 to 7.

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