Multi-objective profit optimization method for compressed air energy storage power station

Abstract

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

Description

TECHNICAL FIELD

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

[0002] Under the influence of environmental and energy problems, various clean energies are continuously developed and utilized, and wind power generation is particularly vigorously developed. However, while the scale of wind power generation is continuously expanding, the problem of “abandoned wind” 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 effectively solves these problems, and in particular, advanced adiabatic compressed air energy storage technology has become the optimal choice for balancing the randomness of wind power generation and improving wind energy utilization rate due to its large capacity, low cost and high efficiency. In addition, air will generate heat during the compression stage, and heat needs to be absorbed during the expansion stage, so it is possible to consider both cooling and heating.

[0003] In related technologies, the required gas storage volume can be calculated by determining the host and gas storage base parameters, and then the gas storage investment construction cost and host construction cost are obtained, and the power generation efficiency of the host is calculated, thereby drawing the isograms of power generation efficiency and construction cost. The maximum benefit of compressed air energy storage power station and the grid benefit considering the loss of abandoned wind can also be used as the optimization target, and then a double-layer game model of compressed air energy storage power station capacity optimization configuration is constructed, and the optimal capacity of the compressed air energy storage power station is obtained by solving the model.

[0004] However, in related technologies, the influence of the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power on the energy storage efficiency and the annual profit rate cannot be quantitatively described, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot be optimized, which needs to be improved. SUMMARY

[0005] The present application provides a multi-objective benefit optimization method of compressed air energy storage power station to solve the problems in related technologies that the influence of the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power on the energy storage efficiency and the annual profit rate cannot be quantitatively described, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot be optimized.

[0006] The first aspect embodiment of the present application provides a multi-objective benefit optimization method of 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 of a target compressed air energy storage power station planning area within a preset time, and obtaining operation constraint data of the target compressed air energy storage power station; based on the cold load demand data, the heat load demand data, the electric load demand data and the operation constraint data, calculating the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power; using the maximum pressure ratio of the gas 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 benefit value meeting a preset benefit condition based on the optimal compromise solution.

[0007] Optionally, in an embodiment of the present application, based on the cold load demand data, the heat load demand data, the electric load demand data and the operation constraint data, the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power are calculated, comprising: obtaining a gray wolf population according to the cold load demand data, the heat load demand data, the electric load demand data and Tent chaos mapping, and generating the initial maximum pressure ratio of the gas storage chamber, the initial compression power and the initial expansion power of each gray wolf in the gray wolf population based on the gray wolf population; using the initial maximum pressure ratio of the gas storage chamber, the initial compression power and the initial expansion power to calculate the initial compromise solution of the pre-constructed multi-objective optimization model; selecting a head wolf meeting a preset fitness condition based on the initial compromise solution, and calculating the initial distance of the gray wolf individuals in the gray wolf population except the head wolf and the head wolf using the bit vector adjustment weight; updating the initial distance using the forward step length meeting the nonlinear convergence factor until the updated distance meets a preset convergence condition, so as to obtain a gray wolf individual meeting a preset gray wolf condition based on the updated distance, and obtaining the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power of the gray wolf individual based on the gray wolf individual.

[0008] Optionally, in an embodiment of the present application, the expression of the Tent chaos mapping can be but is not limited to:

[0009]

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

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

[0012]

[0013] Wherein, 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 inequality constraints that the variable needs to meet, and the third formula is all equality constraints that the variable needs to meet.

[0014] Optionally, in an embodiment of the present application, the expression of the bit vector adjustment weight can be but is not limited to:

[0015]

[0016] Wherein, r1 represents a random number between [0, 1], λ represents a decay constant, C k represents a random weight of the position of the individual on the prey;

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

[0018]

[0019] Wherein, a max , a min respectively represent the upper limit and the lower limit of the nonlinear convergence factor, and N represents an iteration threshold.

[0020] Optionally, in an embodiment of the present application, before the optimal compromise solution of the pre-constructed multi-objective optimization model is determined by using the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, it further comprises: constructing a thermodynamic model of a compressed air energy storage combined cooling heating and power system in the target compressed air energy storage power station; constructing a cost and benefit model of the compressed air energy storage combined cooling heating 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 and benefit model.

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

[0022]

[0023] Wherein, ED i+ represents the Euclidean distance between the i th solution and the positive ideal solution on the Pareto boundary, and ED i- represents the Euclidean distance between the i th solution and the negative ideal solution on the Pareto boundary.

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

[0025]

[0026]

[0027] Wherein, fj，ideal and f j，nadir and f

[0028] The second aspect embodiment of the application provides a multi-objective benefit optimization device of a compressed air energy storage power station, comprising: an acquisition module configured to acquire cold load demand data, heat load demand data, and electric load demand data of a target compressed air energy storage power station planning area within a preset time, and acquire operation constraint data of the target compressed air energy storage power station; a calculation module configured to calculate a maximum pressure ratio of a gas storage chamber, a compression power, and an expansion power based on the cold load demand data, the heat load demand data, the electric load demand data, and the operation constraint data; and an optimization module configured to determine an optimal compromise solution of a pre-constructed multi-objective optimization model by using the maximum pressure ratio of the gas storage chamber, the compression power, and the expansion power, so as to obtain a multi-objective benefit value satisfying a preset benefit condition based on the optimal compromise solution.

[0029] Optionally, in an embodiment of the application, the calculation module comprises: a determination unit configured to obtain a grey wolf population according to the cold load demand data, the heat load demand data, the electric load demand data, and a Tent chaotic mapping, and generate an initial maximum pressure ratio of a gas storage chamber, an initial compression power, and an initial expansion power of each grey wolf in the grey wolf population based on the grey wolf population; a first calculation unit configured to calculate an initial compromise solution of the pre-constructed multi-objective optimization model by using the initial maximum pressure ratio of the gas storage chamber, the initial compression power, and the initial expansion power; a second calculation unit configured to select a head wolf satisfying a preset fitness condition based on the initial compromise solution, and calculate an initial distance of the head wolf and grey wolf individuals other than the head wolf in the grey wolf population by using a site vector adjustment weight; and a generation unit configured to update the initial distance by using an advancing step length satisfying a nonlinear convergence factor until the updated distance satisfies a preset convergence condition, so as to obtain a grey wolf individual satisfying a preset grey wolf condition based on the updated distance, and obtain the maximum pressure ratio of the gas storage chamber, the compression power, and the expansion power of the grey wolf individual based on the grey wolf individual.

[0030] Optionally, in an embodiment of the application, an expression of the Tent chaotic mapping can be but is not limited to:

[0031]

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

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

[0034]

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

[0036] Optionally, in an embodiment of the present application, the expression of the bit vector adjustment weight can be but is not limited to:

[0037]

[0038] wherein r1 represents a random number between 0 and 1, λ represents a decay constant, and C k represents a random weight of the position of the individual on the prey;

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

[0040]

[0041] wherein a max , a min respectively represent the upper limit and the lower limit of the nonlinear convergence factor, and N represents an iteration threshold.

[0042] Optionally, in an embodiment of the present application, further comprising: a first construction module, configured to construct a thermodynamic model of a compressed air energy storage combined cooling, heating and power system in a target compressed air energy storage power station before determining an optimal trade-off solution of a pre-constructed multi-objective optimization model by using the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power; a second construction module, configured to construct a cost-benefit model of the compressed air energy storage combined cooling, heating and power system in the target compressed air energy storage power station; and a third construction module, configured to construct the multi-objective optimization model based on the thermodynamic model and the cost-benefit model.

[0043] Optionally, in an embodiment of the present application, the expression of the optimal trade-off solution can be but is not limited to:

[0044]

[0045] wherein ED i+ represents the Euclidean distance between the i th solution and the positive ideal solution on the Pareto boundary, and ED i represents the Euclidean distance between the i th solution and the negative ideal solution on the Pareto boundary.

[0046] wherein the expression of the Euclidean distance can be but is not limited to:

[0047]

[0048]

[0049] where f j，ideal and f j，nadir represent the positive ideal solution and the negative ideal solution of the jth objective in single-objective optimization, respectively.

[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 yield optimization method of the compressed air energy storage power station as described in the above embodiments.

[0051] The fourth aspect of the present application provides a computer readable storage medium, which stores a computer program executable by a processor to implement the multi-objective yield 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 comprising a computer program executable to implement the multi-objective yield optimization method of the compressed air energy storage power station as described above.

[0053] The embodiments of the present application can calculate the maximum pressure ratio of the gas holder, the compression power and the expansion power by using the obtained cold, heat and electric load demand data and operation constraint data of the target compressed air energy storage power station, and determine the optimal compromise solution of the pre-constructed multi-objective optimization model by using the maximum pressure ratio of the gas holder, the compression power and the expansion power, thereby obtaining the multi-objective yield value satisfying certain yield conditions. The embodiments of the present application provide a coupling wind power compressed air energy storage system power supply reliability evaluation method, which has the advantages of high economic efficiency and strong flexibility, can make the user obtain higher thermodynamic yield and economic yield at the same time, and has certain innovation potential and application value. Thus, the embodiments of the present application solve the problems in the related art that the influence of the energy storage efficiency and the annual profit rate cannot be quantitatively described by the maximum pressure ratio of the gas holder, the compression power and the expansion power, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot be optimized.

[0054] Additional aspects and advantages of the present application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

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

[0056] Figure 1A flow chart of a multi-objective benefit optimization method of a compressed air energy storage power station according to an embodiment of the present application is provided.

[0057] Figure 2 A flow chart of an improved grey wolf algorithm according to an embodiment of the present application is provided.

[0058] Figure 3 A block diagram of simulation results of an optimal compromise solution output according to an embodiment of the present application is provided.

[0059] Figure 4 A flow chart of constructing a multi-objective optimization model according to an embodiment of the present application is provided.

[0060] Figure 5 A structural diagram 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 is provided.

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

[0062] Figure 7 A structural diagram of an electronic device according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0063] Embodiments of the present application are described in detail below with reference to the attached drawings, which show by way of example, embodiments in which the same or similar elements have the same or similar reference numbers. The embodiments described below are examples intended to explain the present application, and are not to be understood as limiting the present application.

[0064] A multi-objective yield optimization method of compressed air energy storage power stations according to an embodiment of the present application is described below with reference to the accompanying drawings. In view of the fact that the influence of the energy storage efficiency and the annual profit rate cannot be quantitatively characterized by the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot be optimized in the background art, the present application provides a multi-objective yield optimization method of compressed air energy storage power stations. In this method, the cold, heat and electricity load demand data and the operation constraint data of the target compressed air energy storage power station are calculated to obtain the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, and the optimal compromise solution of the pre-constructed multi-objective optimization model is determined by using the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, thereby obtaining the multi-objective yield value that meets certain yield conditions. A power supply reliability evaluation method for a compressed air energy storage system coupled with wind power is provided, which has the advantages of high economic efficiency and strong flexibility, can enable users to obtain high thermodynamic yield and economic yield at the same time, and has certain innovative potential and application value. Thus, the problems in the related art, such as the fact that the influence of the energy storage efficiency and the annual profit rate cannot be quantitatively characterized by the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot be optimized, are solved.

[0065] Specifically, Figure 1 A flowchart of a multi-objective yield optimization method of compressed air energy storage power stations according to an embodiment of the present application is shown.

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

[0067] In step S101, the cold load demand data, the heat load demand data and the electricity load demand data of the target compressed air energy storage power station in a predetermined time in the planning area are obtained, and the operation constraint data of the target compressed air energy storage power station is obtained.

[0068] In some embodiments, the cold, heat and electricity load demand data in a certain time in the planning area of the target compressed air energy storage power station and the operation constraint data of the target compressed air energy storage power station can be obtained, wherein the certain time can be every hour or every minute of each year, which can be set by a person skilled in the art according to the actual situation, and the present application does not make specific limitations.

[0069] In addition, in the present application, the operation constraint data can include, but is not limited to, the installed power parameters, the electricity price and the annual average system equipment investment cost, and the present application does not make specific limitations.

[0070] Further, the initial population size, the number of iterations, the adjustment coefficient and other hyperparameters 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, heat and electricity load demand data in the whole year in the planning area of the target compressed air energy storage power station, and the installed power parameters, electricity price and annual average system equipment investment cost of the target compressed air energy storage power station, and then sets the initial population size, iteration number, adjustment coefficient and other hyperparameters of the grey wolf algorithm according to the above data.

[0072] In step S102, the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power are calculated based on the cold load demand data, the heat load demand data, the electricity load demand data and the operation constraint data.

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

[0074] Exemplarily, the position of each grey wolf in the grey wolf algorithm of the embodiment of the present application can be represented by three-dimensional coordinates X(β, P c , P g ), wherein β is the maximum pressure ratio of the gas storage chamber, P c is the compression power, and P g is the expansion power.

[0075] It can be understood that the embodiment of the present application can input the cold load demand data, the heat load demand data, the electricity load demand data and the operation constraint data into the grey wolf algorithm, and then calculate the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power.

[0076] Optionally, in an embodiment of the present application, the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power are calculated based on the cold load demand data, the heat load demand data, the electricity load demand data and the operation constraint data, including: obtaining the grey wolf population according to the cold load demand data, the heat load demand data, the electricity load demand data and the Tent chaotic mapping, and generating the initial maximum pressure ratio of the gas storage chamber, the initial compression power and the initial expansion power of each grey wolf in the grey wolf population based on the grey wolf population; calculating the initial compromise solution of the pre-constructed multi-objective optimization model by using the initial maximum pressure ratio of the gas storage chamber, the initial compression power and the initial expansion power; selecting the head wolf satisfying the preset fitness condition based on the initial compromise solution, and calculating the initial distance of the head wolf and the grey wolf individuals other than the head wolf in the grey wolf population by using the position vector adjustment weight; updating the initial distance by using the forward step length satisfying the nonlinear convergence factor until the updated distance satisfies the preset convergence condition, to obtain the grey wolf individual satisfying the preset grey wolf condition based on the updated distance, and obtaining the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power of the grey wolf individual based on the grey wolf individual. The expression of the Tent chaotic mapping can be but is not limited to:

[0077]

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

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

[0080]

[0081] wherein r1 represents a random number between 0 and 1, λ represents a decay constant, and C k represents a random weight of the position of the individual on the prey.

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

[0083]

[0084] wherein a max and a min respectively represent the upper limit and the lower limit of the nonlinear convergence factor, and N represents an iteration threshold.

[0085] It can be understood that in the embodiments of the present application, the grey wolf algorithm takes a wolf pack as the learning object, and in the wolf pack, θ has the strongest adaptability, β has the second strongest adaptability, δ has the third strongest adaptability, and γ has the weakest adaptability. The four kinds of wolves continuously approach the prey through positioning.

[0086] In the actual execution process, the process for calculating the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power based on the improved grey wolf algorithm based on the cold load demand data, the thermal load demand data, the electrical load demand data and the operation constraint data is as shown in Figure 2 The main steps are as follows:

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

[0088] In the embodiments of the present application, the initial population size, iteration number, adjustment coefficient and other hyperparameters of the grey wolf algorithm can be set according to the obtained load demand data and operation constraint data.

[0089] Step S202: Tent mapping is used to form the initial grey wolf population.

[0090] In the embodiments of the present application, the position of each grey wolf can be represented by three-dimensional coordinates x(β, P c , P g ), wherein β is the maximum pressure ratio of the gas storage chamber, P c is the compression power, and Pg for expansion power.

[0091] Further, due to the random form of the load in the power grid, the initial gray wolf population is formed by using Tent chaotic mapping in the embodiment of the application, which can cover more ranges than the conventional random mapping, so that the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power are uniformly distributed in the whole space, so as to obtain the initial maximum pressure ratio of the gas storage chamber, the initial compression power and the initial expansion power. The expression of the Tent chaotic mapping can be, but is not limited to, as follows:

[0092]

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

[0094] Step S203: considering the cold, heat and electric load balance and the power station operation constraint, a multi-objective optimization compromise solution is calculated and used as the individual fitness.

[0095] In the embodiment of the 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. The calculation formula can be, but is not limited to, as follows:

[0096]

[0097] Step S204: re-electing the alpha wolf according to the individual fitness.

[0098] In the embodiment of the application, the alpha wolf satisfying a certain fitness condition is obtained according to the individual fitness, for example, the first three alpha wolves with the largest fitness can be selected as the alpha wolves, which are denoted as θ, β and δ respectively. The certain fitness condition can be set by those skilled in the art according to the actual situation, and the application does not make specific limitation.

[0099] Step S205: adjusting the site vector weight to determine the search target.

[0100] It can be understood that the distance between the three alpha wolves and the gray wolf individuals other than the alpha wolves in the gray wolf population is calculated in the embodiment of the application, and the calculation formula can be, but is not limited to, expressed as:

[0101]

[0102] wherein the expression of the site vector adjustment weight C k can be, but is not limited to, as follows:

[0103]

[0104] wherein Xα (n), X β (n), X δ (n) represents the current position of the three head wolves, X(n) represents the current position of the remaining gray wolf individuals, r1 represents a random number between [0, 1], C k represents a coefficient vector, and represents a random weight of the position where the individual is located on the prey, and it should be noted that the weight of C k is adjusted to exponentially decrease over time in the embodiment of the application, which helps to improve the global optimization ability of the algorithm.

[0105] Step S206: population position updating.

[0106] After determining the distance between the head wolf and the gray wolf individuals other than the head wolf, the wolf pack will start to gradually approach and surround the prey, and the advancing step of the gray wolf individuals other than the head wolf towards the three head wolves can be but is not limited to represented as:

[0107]

[0108] The final position of the gray wolf individuals other than the head wolf can be but is not limited to represented as:

[0109]

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

[0111] It can be seen that the convergence factor linearly decreases to 0 as the iteration number increases, resulting in the value of A k also changing in the corresponding interval. That is, when |A k |>1, the gray wolf will separate from the local optimum and expand the range to find a better solution; when |A k |<1, the wolf pack will attack the prey and search for the optimal solution in the local area. Although the adaptive a and A k ensure global optimization and local optimization, due to the complexity of the optimization process, the linear decrease of the convergence factor does not meet the requirements of the value of A k in the actual optimization process, therefore, the embodiment of the application proposes a nonlinear convergence factor, and the expression of the nonlinear convergence factor can be but is not limited to:

[0112]

[0113] wherein a max , a minrespectively represent the upper limit and the lower limit of the nonlinear convergence factor, N represents an iteration threshold, the nonlinear convergence factor is slow when the number of iterations is less than the iteration threshold; and when greater than the iteration threshold, the nonlinear convergence factor is a constant, which helps to enhance the global search ability of the algorithm and can also perform detailed local search, and can better balance the global search and local search performance of the algorithm.

[0114] Step S207: judging whether a termination condition is reached.

[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 meeting 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 the actual situation, and the present application does not make specific limitations.

[0116] For example, the embodiment of the present application can judge whether the maximum number of iterations or the expected convergence precision is reached, if not, repeat steps S203-S206; if so, output the optimal gray wolf individual.

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

[0118] In the embodiment of the present application, the optimal capacity ratio and the optimal compromise solution can be obtained from the optimal gray wolf individual, and then the final maximum pressure ratio of the gas storage chamber, the compression power and the expansion power. For example, the optimal compromise solution obtained by the embodiment of the present application is as shown in the following formula (3). Figure 3

[0119] Optionally, in an embodiment of the present application, before the optimal compromise solution of the pre-constructed multi-objective optimization model is determined by using the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, it further comprises: constructing a thermodynamic model of the compressed air energy storage combined cooling heating and power system in the target compressed air energy storage power station; constructing a cost-benefit model of the compressed air energy storage combined cooling heating and power system in the target compressed air energy storage power station; constructing a multi-objective optimization model based on the thermodynamic model and the cost-benefit model.

[0120] In the embodiment of the present application, the expression of the multi-objective optimization model can be but is not limited to the following formula (2).

[0121]

[0122] In the embodiment of the present application, η represents the energy storage efficiency, APM represents the annual profit rate, the first formula is the objective function of the multi-objective optimization, the second formula is all inequality constraints required to be met by the variable, and the third formula is all equality constraints required to be met by the variable.

[0123] The expression of the optimal compromise solution can be but is not limited to the following formula (3).

[0124] The expression of the optimal compromise solution can be but is not limited to the following formula (3).

[0125] where 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] where the expression of the Euclidean distance can be but is not limited to:

[0127]

[0128]

[0129] where f j，idedl and f j，nadir respectively represent the positive ideal solution and the negative ideal solution of the jth objective in single-objective optimization.

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

[0131] Step S401: Constructing a thermodynamic model.

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

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

[0134] Further, 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 stage, and an energy release process.

[0135] In the energy storage process (off-peak period), the compressors (first compressor C1 and second compressor C2) are driven by the motor M, and the remaining off-peak electricity is used to pressurize the working medium to the design pressure, and at the same time, the temperature of the working medium is raised. The compressed high-temperature and high-pressure working medium enters the energy storage process heat exchanger (first heat exchanger CHE1 and second heat exchanger CHE2) in turn, and 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 is heated with the high-temperature and high-pressure working medium in each stage of the energy storage process heat exchanger. The outlet working medium pressure of the second stage heat exchanger is controlled by the pressure regulating valve PRV (if used). Then the cooled high-pressure air is stored in the gas storage chamber GSC, and at the same time, the compression heat generated by each stage of compressor 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] In the energy storage and release interval stage, the gas storage chamber has no material exchange with the external environment, and if there is a temperature difference between the working medium and the wall surface of the gas storage chamber, heat exchange will occur between them.

[0137] In the energy release process (peak period), a part of the high-temperature heat storage medium is sent to the heat exchanger (third heat exchanger EHE1 and fourth heat exchanger EHE2) by the second heat storage medium pump P2 from the hot tank, and the heat is transferred to the high-pressure working medium flowing out of the gas storage chamber, and then enters the cold tank for subsequent use. The high-temperature and high-pressure working medium is expanded in the expander (first expander E1 and second expander E2) and drives the generator G to generate electricity, and the working medium discharged from the last stage of the expander is far below the atmospheric temperature, so it can be transported to the cold user for refrigeration. Another part of the heat storage medium is transported to the hot user for heating, and then enters the cold tank for use in the next cycle. Similarly, the heat exchange and expansion processes are repeated until the pressure of the working medium 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, the heat generated by each stage of 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 medium in the i-th stage of compressor can be but not limited to expressed as:

[0140]

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

[0142]

[0143] Wherein, p0 represents the environmental pressure, unit Pa; T0 represents the environmental temperature, unit K; β max , β minrespectively represent the maximum and minimum pressure ratio of the gas storage chamber; V represents the volume of the gas storage chamber, with the unit of m 3 ; c p represents the specific heat capacity of the working medium at constant pressure, with the unit of J·kg -1 ·K -1 ; R g represents the gas constant of the working medium; η c,i represents the isentropic efficiency of the i-th compressor; β c，i represents the compression ratio of the i-th compressor; κ represents the adiabatic index of the working medium; represents the working medium inlet temperature of the i-th compressor.

[0144] The heat delivered to the user can be but is not limited to represented as:

[0145]

[0146] wherein x represents a heat distribution ratio, the ratio of the heat in the hot tank for heating the compressed air before the expander to the total heat; T hot , T cold represent the cold and hot medium temperatures, with the unit of K; represents the mass of the i-th compression stage heat storage medium, with the unit of kg; c hsm represents the specific heat capacity of the heat storage medium, with the unit of J·kg -1 ·K -1 .

[0147] In addition, it needs to be explained that in the expansion stage of the AA-CAES system of the embodiments of the present application, each stage of the expander is heated through a heat exchanger, and the expansion process can be regarded as a reversible adiabatic process.

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

[0149]

[0150] The total expansion work can be but is not limited to represented as:

[0151]

[0152] When the exhaust temperature of the last-stage expander is lower than the ambient temperature, the cold energy provided to the cold user can be but is not limited to represented as:

[0153]

[0154] wherein, represents the working medium outlet temperature of the i-th expander.

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

[0156]

[0157] wherein, 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 containing subscripts 1 and 2 represent the hot and cold fluids, respectively; the parameters containing subscripts in and out represent the fluid entering and leaving the heat exchanger. In addition, the embodiment of the application assumes that the specific heat capacity of the hot fluid is equal to that of the cold fluid, and the outlet temperature of the air after passing through the i th heat exchanger can be obtained from the definition formula of the heat exchanger efficiency parameter, and the temperature is also the inlet temperature of the i+1 th compressor.

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

[0159] It can be understood that, in the embodiment of the application, the cost-benefit model is constructed based on cost analysis and benefit analysis, and specifically:

[0160] The main content of the cost analysis in the embodiment of the application is that: the annual total cost ATC of the AA-CAES-based CCHP system can include, but is not limited to, the annual equipment investment cost AC inv , the annual operation and maintenance cost AC om , and the annual power consumption cost AC ele , at this time, the calculation formula of the annual total cost can be, but is not limited to, expressed as:

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

[0162] wherein, the annual equipment investment cost AC inv can include, but is not limited to, the annual cost of the main equipment such as the compressor, the expander, the heat exchanger, and the gas storage chamber, and the calculation formula thereof can be, but is not limited to, expressed as:

[0163]

[0164] wherein, j represents the interest rate; n represents the system cycle life; C k represents the investment cost of each device of the system, in units of yuan.

[0165] The calculation formula of the annual operation and maintenance cost AC om can be, but is not limited to, expressed as:

[0166]

[0167] wherein, represents the system operation and maintenance cost ratio, which is generally set to 0.06.

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

[0169] AC ele = W c × c off-peak × 365,

[0170] wherein, C off-peak represents the electricity off-peak price, which is generally set to 0.421 yuan / kWh.

[0171] Further, the main content of the benefit analysis of the embodiments of the present application is that the annual total benefit can include but is not limited to electricity generation income in the electricity peak period, heating and refrigeration income, at this time, the calculation formula of the annual total benefit can be but is not limited to represented as:

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

[0173] wherein, c on-peak represents the electricity peak price, which is generally set to 0.938 yuan / kWh; c hotwater represents the hot water price, which is generally set to 0.672 yuan / kWh; and c cooling represents the refrigeration price, which is generally set to 0.773 yuan / kWh.

[0174] The calculation formula of the annual total profit can be but is not limited to represented as:

[0175] ATP = ATR - ATC,

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

[0177] It can be understood that the energy storage efficiency of the system of the embodiments of the present application is defined as the ratio of the total power generated by the expander in the energy release stage to the total power consumed by the compressor in the energy storage stage, which reflects the electric-electric conversion performance of the system, and the calculation formula can be but is not limited to represented 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, and the calculation formula can be but is not limited to represented as:

[0180]

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

[0182]

[0183] wherein η represents the energy storage efficiency, APM represents the annual profit rate, the first formula is a target function of multi-objective optimization, the second formula is all inequality constraints required to be satisfied by the variables, and the third formula is all equality constraints required to be satisfied by the variables.

[0184] It can be seen that the embodiments of the present application cannot simultaneously achieve the optimal η(X) and APM(X), and therefore can only obtain an optimal compromise solution on the Pareto frontier.

[0185] To solve the multi-objective optimization problem, an approximation ideal ranking method can be selected. In the method, the positive ideal solution and the negative ideal solution are usually determined first, and then the ideal solution closest to the positive ideal solution and farthest from the negative ideal solution is determined as the optimal compromise solution according to the objective weight of the decision maker. The Euclidean distance of the ith solution to the positive ideal solution and the negative ideal solution on the Pareto boundary can be but is not limited to represented as:

[0186]

[0187]

[0188] wherein f j，ideal and f j，nadir represent the positive ideal solution and the negative ideal solution of the jth target in the single-objective optimization, respectively.

[0189] Further, the embodiments of the present application can take the solution with the minimum Yi as the expected optimal compromise solution, which can be but is not limited to represented as:

[0190]

[0191] In addition, the embodiments of the present application consider that the conventional convex optimization method has high computational complexity and slow convergence speed in solving the above multi-objective optimization problem, and therefore proposes a grey wolf algorithm to solve the above multi-objective optimization model, and makes corresponding improvements in combination with the characteristics of the compressed air energy storage system.

[0192] In step S103, the optimal compromise solution of the pre-constructed multi-objective optimization model is determined by using the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, to obtain a multi-objective revenue value satisfying the preset revenue condition based on the optimal compromise solution.

[0193] It can be known through the above analysis that the optimal compromise solution of the pre-constructed multi-objective optimization model can be determined by using the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, and then the multi-objective yield value meeting certain yield conditions can be obtained. The certain yield conditions can be set by a person skilled in the art according to actual conditions, and the application does not make specific limitations.

[0194] The embodiment of the application performs multi-objective optimization of energy storage efficiency and annual profit rate based on the AA-CAES CCHP system, and performs multi-objective optimization solution based on the improved grey wolf algorithm; in order to match the characteristics of the compressed air energy storage system, Tent chaotic mapping is performed in the initialization stage; in order to obtain higher convergence speed and global optimization ability to provide better thermodynamic benefits and economic benefits for users, time-varying displacement vector adjustment weight and nonlinear convergence factor are used, and the output optimal compromise solution (Pareto optimal) can make users obtain higher thermodynamic benefits and economic benefits at the same time, and has certain innovation potential and application value.

[0195] The multi-objective yield optimization method of the compressed air energy storage power station provided by the embodiment of the application can calculate the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power by using the obtained target compressed air energy storage power station cold, heat, electricity load demand data and operation constraint data, and determine the optimal compromise solution of the pre-constructed multi-objective optimization model by using the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, and then obtain the multi-objective yield value meeting certain yield conditions, provide a coupling wind power compressed air energy storage system power supply reliability evaluation method, which has the advantages of high economy and strong flexibility, can make users obtain higher thermodynamic benefits and economic benefits at the same time, and has certain innovation potential and application value. Therefore, the problems in the related art that the influence of energy storage efficiency and annual profit rate cannot be quantitatively described by using the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot be optimized are solved.

[0196] Secondly, the multi-objective yield optimization device of the compressed air energy storage power station provided by the embodiment of the application is described with reference to the accompanying drawings.

[0197] Figure 6 The block schematic diagram of the multi-objective yield optimization device of the compressed air energy storage power station provided by the embodiment of the application is shown.

[0198] As shown in Figure 6 The multi-objective yield 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] The acquisition module 100 is used to obtain cooling load demand data, heating load demand data, and electric load demand data of the target compressed air energy storage power station planning area within a preset time, and to obtain 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 gas storage chamber based on the cooling load demand data, the heating load demand data, the electrical load demand data and the operation constraint data.

[0201] The optimization module 300 is used to determine the optimal compromise solution of the pre-built 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 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-built multi-objective optimization model by using the initial maximum pressure ratio of the gas storage chamber, the initial compression power, and the initial expansion power.

[0205] The second calculation unit is used to select the 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 meets the 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] Where t represents the t-th iteration, β represents the chaotic parameter, x(t) represents the chaotic mapping function of the t-th iteration, and x(t+1) represents the chaotic mapping function of the t+1-th iteration.

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

[0211]

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

[0213] Optionally, in an embodiment of the present application, the expression of the bit vector adjustment weight can be but is not limited to:

[0214]

[0215] wherein r1 represents a random number between 0 and 1, λ represents a decay constant, and C k represents a random weight of the position of the individual on the prey;

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

[0217]

[0218] wherein a max , a min respectively represent the upper limit and the lower limit of the nonlinear convergence factor, and N represents an iteration threshold.

[0219] Optionally, in an embodiment of the present application, it further comprises a first construction module, a second construction module and a third construction module.

[0220] wherein the first construction module is configured to construct a thermodynamic model of the compressed air energy storage combined cooling, heating 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 by using the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power.

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

[0222] The third construction module is configured to construct the multi-objective optimization model based on the thermodynamic model and the cost-benefit model.

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

[0224]

[0225] wherein ED i+EDi represents the Euclidean distance between the ith solution and the positive ideal solution on the Pareto boundary. i EDi represents the Euclidean distance between the ith solution and the negative ideal solution on the Pareto boundary.

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

[0227]

[0228]

[0229] wherein f j，idedl and f j，nadir respectively represent the positive ideal solution and the negative ideal solution of the jth objective in single-objective optimization.

[0230] It should be noted that the foregoing 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 the embodiment, which will not be described here again.

[0231] The multi-objective benefit optimization device of the compressed air energy storage power station provided by the embodiment of the present application can calculate the maximum pressure ratio of the gas holder, the compression power and the expansion power by using the obtained cold, heat and electric load demand data and operation constraint data of the target compressed air energy storage power station, determine the optimal compromise solution of the pre-constructed multi-objective optimization model by using the maximum pressure ratio of the gas holder, the compression power and the expansion power, and further obtain the multi-objective benefit value that meets a certain benefit condition, thereby providing a coupling wind power compressed air energy storage system power supply reliability evaluation method, which has the advantages of high economy and strong flexibility, can enable the user to obtain higher thermodynamic benefit and economic benefit at the same time, and has certain innovation potential and application value. Thus, the problems in the related art that the influence of the energy storage efficiency and the annual profit rate cannot be quantitatively described by the maximum pressure ratio of the gas holder, the compression power and the expansion power, and the thermodynamic performance and economic performance of the compressed air energy storage system cannot be optimized are solved.

[0232] Figure 7 A structural schematic diagram of an electronic device according to the embodiment of the present application is provided. The electronic device can include:

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

[0234] The processor 702 implements the multi-objective benefit optimization method of the compressed air energy storage power station provided in the above embodiments when executing the program.

[0235] Further, the electronic device further includes:

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

[0237] a memory 701 for storing a computer program which can be run on the processor 702.

[0238] The memory 701 can include a high-speed RAM memory, and can also include a non-volatile memory, for example 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 complete communication between 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, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 In the figure, only one thick line is used to represent, but it does not mean that there is only one bus or 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 complete communication between each other through an internal interface.

[0241] The processor 702 can be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement one or more embodiments of the present application.

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

[0243] The embodiments of the present application also provide a computer program product, comprising a computer program which, when executed, implements the compressed air energy storage power station multi-objective benefit optimization method as above.

[0244] In the description of the application, reference to "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" means that a particular feature, structure, material, or characteristic being described is included in at least one embodiment or example of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment or example. Furthermore, the described specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. In addition, the usage of "N" means at least two, for example, two, three or the like, unless explicitly stated otherwise.

[0245] Furthermore, the terms "first", "second", or the like, are used merely as a designation of certain elements or features, and do not imply or connote relative importance or a specific order of categorization thereof. Accordingly, features described as "first" or "second" can be explicitly or implicitly included in at least one of the features. In the description of the application, the meaning of "N" is at least two, for example, two, three, etc., unless explicitly specified otherwise.

[0246] Any process or method descriptions or blocks in flow charts or otherwise described herein represent embodiments which can be managed as one or more modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions or steps, and alternate implementations are possible. In some embodiments, the processes and methods described can be executed by one or more apparatuses or devices, either directly or after conversion to another language. Alternate implementations are possible.

[0247] The logic and / or steps represented in the flowcharts and / or described herein, for example, can be considered as a sequence of executable instructions, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor- containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this specification, a "computer-readable medium" can be any apparatus that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be a product of the manufacturing and / or processing. The computer-readable medium can include, but is not limited to, the following: an electronic connection (an electronic device with one or N wires), a portable computer diskette (a magnetic device), a RAM (random access memory), a ROM (read-only memory), an EPROM (erasable programmable ROM) or a Flash memory, an optical fiber, and a portable CD ROM. In addition, the computer-readable medium can even be paper or other suitable medium upon which the program can be printed, because the program can be electronically captured, via the optically scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in an electronic manner into a computer storage medium, and then stored in the computer storage medium.

[0248] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the N steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware and in another embodiment, the hardware can be implemented using any or a combination of the following technologies, which are all well known in the art: a discrete logic circuit having logic gates for implementing logic functions on data signals, an application specific integrated circuit having appropriate combinational logic gates, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0249] Those of skill in the art would understand that the steps carried out in the above-mentioned embodiments can be implemented by programs instructing relevant hardware to complete all or part of the steps, and the programs can be stored in a computer-readable storage medium. When the programs are executed, they include one of the steps of the method embodiments or a combination thereof.

[0250] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing module, or each of the units can be physically present separately, or two or more units can be integrated in one module. The integrated module can be realized in the form of hardware or in the form of a software functional module. When the integrated module is realized in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer readable storage medium.

[0251] The storage medium mentioned above can 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 should be understood that the above embodiments are exemplary and should not be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. A method for multi-objective benefit optimization of a compressed air energy storage power plant, characterized in that, The method comprises the following 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 area within a preset time, and obtaining operation constraint data of the target compressed air energy storage power station; based on the cold load demand data, the heat load demand data, the electric load demand data and the operation constraint data, calculating the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power; determining an optimal compromise solution of a pre-constructed multi-objective optimization model by using the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, so as to obtain a multi-objective profit value satisfying a preset profit condition based on the optimal compromise solution; wherein, based on the cold load demand data, the heat load demand data, the electric load demand data and the operation constraint data, calculating the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power comprises: obtaining a grey wolf population according to the cold load demand data, the heat load demand data, the electric load demand data and Tent chaotic mapping, and generating initial maximum pressure ratio of the gas storage chamber, initial compression power and initial expansion power of each grey wolf in the grey wolf population based on the grey wolf population; calculating an initial compromise solution of the pre-constructed multi-objective optimization model by using the initial maximum pressure ratio of the gas storage chamber, the initial compression power and the initial expansion power; selecting a head wolf satisfying a preset fitness condition based on the initial compromise solution, and calculating an initial distance of the head wolf and grey wolf individuals other than the head wolf in the grey wolf population by using a position vector adjustment weight; updating the initial distance by using an advancing step length satisfying a nonlinear convergence factor until the updated distance satisfies a preset convergence condition, so as to obtain a grey wolf individual satisfying a preset grey wolf condition based on the updated distance, and obtaining the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power of the grey wolf individual based on the grey wolf individual.

2. The method of claim 1, wherein, The expression of the Tent chaotic mapping is: wherein, t represents the tth iteration, β represents a chaotic parameter, x(t) represents a chaotic mapping function of the tth iteration, and x(t+1) represents a chaotic mapping function of the (t+1)th iteration.

3. The method of claim 1, wherein, The expression of the multi-objective optimization model is: wherein, η represents energy storage efficiency, APM represents annual profit rate, g(X)≤0 is all inequality constraints required to be satisfied by variables, and h(X)=0 is all equality constraints required to be satisfied by variables.

4. The method of claim 1, wherein, wherein, The expression of the position vector adjustment weight is: wherein r1represents a random number between [0, 1], λ represents an attenuation constant, C k represents a random weight of the position where the individual is located on the prey; The expression of the nonlinear convergence factor is: where a max , a min represent the upper and lower limits of the nonlinear convergence factor, respectively, and N represents an iteration threshold.

5. The method of claim 1, wherein, Before the step of determining the optimal compromise solution of the pre-constructed multi-objective optimization model by using the maximum pressure ratio of the gas storage chamber, the compression power and the expansion power, the method further comprises the following steps: constructing a thermodynamic model of a compressed air energy storage combined cooling heating and power system in the target compressed air energy storage power station; constructing a cost and benefit model of the compressed air energy storage combined cooling heating and power system in the target compressed air energy storage power station; constructing a multi-objective optimization model based on the thermodynamic model and the cost and benefit model.

6. The method of claim 1, wherein, The expression of the optimal compromise solution is: where ED i+ represents the Euclidean distance of the ith solution from the positive ideal solution on the Pareto frontier, ED i- represents the Euclidean distance of the ith solution from the negative ideal solution on the Pareto frontier; wherein, the expression of the Euclidean distance is: where f j,ideal and f j,nadir denote the positive and negative ideal solution of the jth objective in single-objective optimization, respectively.

7. An electronic device, comprising: comprising: - a memory, a processor and a computer program stored on the memory and runable on the processor, the processor executing the program to implement the method for multi-objective yield optimization of a compressed air energy storage power plant according to any one of claims 1 to 6.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, - the program being executed by a processor for implementing the method for multi-objective yield optimization of a compressed air energy storage power plant according to any one of claims 1 to 6.

9. A computer program product, characterised in that, - a computer program comprising program elements which, when executed by a processor, implement the method for multi-objective yield optimization of a compressed air energy storage power plant according to any one of claims 1 to 6.

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