Capacity configuration optimization method of combined type compressed air energy storage system

Abstract

The invention relates to the technical field of compressed air energy storage, in particular to a capacity configuration optimization method for a combined type compressed air energy storage system, and the method comprises the steps: determining a first capacity configuration optimization problem and a second capacity configuration optimization problem of the combined type compressed air energy storage system, determining a first preset target of the first capacity configuration optimization problem and a second preset target of the second capacity configuration optimization problem, generating a solution strategy of the first capacity configuration optimization problem according to the first preset target, and generating a solution strategy of the second capacity configuration optimization problem based on the second preset target, and optimizing the capacity configuration of the combined type compressed air energy storage system according to the solving strategy of the first capacity configuration optimization problem and the solving strategy of the second capacity configuration optimization problem. Therefore, the problems that due to the fact that an energy storage mode in the related technology is greatly limited, output has uncertainty and volatility, and adverse effects are easily generated on the electric energy quality and the stability of an electric power system are solved.

Description

Technical Field

[0001] The present application relates to the technical field of compressed air energy storage, and particularly to a method for optimizing the capacity configuration of a composite compressed air energy storage system. Background Art

[0002] With the increasingly severe global environmental and climate change problems, countries and regions have been intensifying the development and efficient utilization of renewable energy. A high proportion of renewable energy power supply has become a consensus. However, due to the limitations of natural conditions, the output of renewable energy such as wind power and photovoltaic power has the characteristics of uncertainty and volatility, which is likely to have an adverse impact on the power quality and pose challenges to the power system and new energy consumption.

[0003] In a power system with a high proportion of renewable energy access, reasonably configuring an energy storage system, such as using energy storage to cut peaks and fill valleys, can effectively smooth the volatility of new energy output, improve the average utilization rate of transmission channels, provide flexible peak shaving capacity, and achieve coordinated regulation of new energy power and thermal power, which is of great significance for improving the operation efficiency of the power system, optimizing the energy structure, and reducing carbon emissions.

[0004] In related technologies, various energy storage methods such as pumped hydro energy storage, electrochemical energy storage, and traditional compressed air energy storage are usually used to realize the development and utilization of renewable energy.

[0005] However, among the above-mentioned commonly used different energy storage methods, there are the following defects: (1) Pumped hydro energy storage is greatly restricted by geographical conditions, requires specific terrain and water resources, has a long construction period, and high investment costs; (2) Electrochemical energy storage has high maintenance and investment costs, has certain safety risks, and a limited service life; (3) Traditional compressed air energy storage has low efficiency, usually requires additional fuel to heat compressed air, and has a slow response speed, making it difficult to quickly respond to the instantaneous changes in the power system, which urgently needs to be solved. Summary of the Invention

[0006] The present application provides a method for optimizing the capacity configuration of a composite compressed air energy storage system to solve the problems that the energy storage methods in related technologies are greatly restricted, resulting in uncertain and volatile output, which is likely to have an adverse impact on the power quality and the stability of the power system.

[0007] The first aspect embodiment of the present application provides a method for optimizing the capacity configuration of a composite compressed air energy storage system, including the following steps:

[0008] Determine the first capacity configuration optimization problem of the composite compressed air energy storage system and the second capacity configuration optimization problem of the composite compressed air energy storage system;

[0009] Based on the requirements of the composite compressed air energy storage system, determine the first preset objective of the first capacity configuration optimization problem and the second preset objective of the second capacity configuration optimization problem, generate a solution strategy for the first capacity configuration optimization problem according to the first preset objective, and generate a solution strategy for the second capacity configuration optimization problem based on the second preset objective;

[0010] Optimize the capacity configuration of the composite compressed air energy storage system according to the solution strategy of the first capacity configuration optimization problem and the solution strategy of the second capacity configuration optimization problem.

[0011] According to an embodiment of the present application, before generating the solution strategy for the first capacity configuration optimization problem according to the first preset objective and generating the solution strategy for the second capacity configuration optimization problem based on the second preset objective, it further includes:

[0012] Obtain the input parameters of the composite compressed air energy storage system;

[0013] Based on the input parameters, determine the initial feasible solution of the first capacity configuration optimization problem, and obtain the initial optimal solution of the second capacity configuration optimization problem according to the initial feasible solution and the second capacity configuration optimization problem;

[0014] Set the first initial upper bound objective value of the first capacity configuration optimization problem based on the initial optimal solution, determine the first initial lower bound objective value of the first capacity configuration optimization problem based on the initial feasible solution, and simultaneously determine the first initial iteration number of the first capacity configuration optimization problem.

[0015] According to an embodiment of the present application, generating the solution strategy for the first capacity configuration optimization problem according to the first preset objective includes:

[0016] Construct the first objective variable corresponding to the initial feasible solution;

[0017] Add the first objective constraint of the first objective variable to the first capacity configuration optimization problem according to the first objective variable, and obtain the first iterative optimal solution of the first capacity configuration optimization problem from the initial optimal solution and the first capacity configuration optimization problem;

[0018] Solve the first capacity configuration optimization problem according to the first iterative optimal solution.

[0019] According to an embodiment of the present application, solving the first capacity configuration optimization problem according to the first iterative optimal solution includes:

[0020] Update the first initial lower bound objective value of the first capacity configuration optimization problem by using the first iterative optimal solution to obtain the first iterative lower bound objective value of the first capacity configuration optimization problem;

[0021] Obtain the second iterative optimal solution of the second capacity configuration optimization problem according to the first iterative optimal solution and the second capacity configuration optimization problem;

[0022] Update the first initial upper bound objective value of the first capacity configuration optimization problem according to the second iterative optimal solution to obtain the first iterative upper bound objective value of the first capacity configuration optimization problem.

[0023] According to an embodiment of the present application, after obtaining the first iterative lower bound objective value of the first capacity configuration optimization problem and the first iterative upper bound objective value of the first capacity configuration optimization problem, it further includes:

[0024] Judge whether the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is less than or equal to a preset convergence value;

[0025] If the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is less than or equal to the preset convergence value, stop the iteration to obtain the target optimal solution;

[0026] If the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is greater than the preset convergence value, construct a second target variable corresponding to the first iterative optimal solution, determine a second target constraint of the second target variable, and based on the second target constraint, perform iterative calculation according to the first iterative optimal solution and the first capacity configuration optimization problem until the absolute value of the difference between the iterative upper bound objective value and the iterative lower bound objective value is less than or equal to the preset convergence value.

[0027] According to an embodiment of the present application, the above method for optimizing the capacity configuration of the composite compressed air energy storage system further includes:

[0028] Judge whether the second capacity configuration optimization problem has no solution;

[0029] If the second capacity configuration optimization problem has no solution, construct a third target variable of the first capacity configuration optimization problem, determine a third target constraint of the third target variable, and based on the third target constraint, perform iterative calculation according to the second iterative optimal solution and the second capacity configuration optimization problem until the second capacity configuration optimization problem has a solution.

[0030] According to an embodiment of the present application, the generating the solution strategy of the second capacity configuration optimization problem based on the second preset target includes:

[0031] Determine the second initial upper bound target value of the second capacity configuration optimization problem, the second initial lower bound target value of the second capacity configuration optimization problem, and the second initial iteration number of the second capacity configuration optimization problem;

[0032] Obtain the initial value of the 0-1 variable, transform the second capacity configuration optimization problem into an upper bound second capacity configuration optimization problem and a lower bound second capacity configuration optimization problem by using the preset strong duality theory, solve the upper bound second capacity configuration optimization problem to obtain the third iterative optimal solution of the upper bound second capacity configuration optimization problem, and update the second initial upper bound target value based on the third iterative optimal solution to obtain the second iterative upper bound target value of the second capacity configuration optimization problem;

[0033] According to the third iterative optimal solution and the lower bound second capacity configuration optimization problem, obtain the fourth iterative optimal solution of the lower bound second capacity configuration optimization problem, and update the second initial lower bound target value by using the fourth iterative optimal solution to obtain the second iterative lower bound target value of the second capacity configuration optimization problem;

[0034] Judge whether the absolute value of the difference between the second iterative upper bound target value and the second iterative lower bound target value is less than or equal to a preset convergence value;

[0035] If the absolute value of the difference between the second iterative upper bound target value and the second iterative lower bound target value is less than or equal to the preset convergence value, stop the iteration and update the first iterative upper bound target value of the first capacity configuration optimization problem by using the first iterative optimal solution; otherwise, construct the fourth target variable and the dual variable corresponding to the fourth iterative optimal solution, and update the second initial iteration number until the absolute value of the difference between the second iterative upper bound target value and the second iterative lower bound target value is less than or equal to the preset convergence value.

[0036] According to an embodiment of the present application, the solving of the upper bound second capacity configuration optimization problem includes:

[0037] If the upper bound second capacity configuration optimization problem has no solution, determine the uncertain parameters in the input parameters of the composite compressed air energy storage system.

[0038] According to the capacity configuration optimization method of the composite compressed air energy storage system in the embodiments of the present invention, the first capacity configuration optimization problem and the second capacity configuration optimization problem of the composite compressed air energy storage system are determined, the first preset objective of the first capacity configuration optimization problem and the second preset objective of the second capacity configuration optimization problem are determined, a solution strategy for the first capacity configuration optimization problem is generated according to the first preset objective, a solution strategy for the second capacity configuration optimization problem is generated based on the second preset objective, and the capacity configuration of the composite compressed air energy storage system is optimized according to the solution strategy of the first capacity configuration optimization problem and the solution strategy of the second capacity configuration optimization problem. Thus, the problems in the related art that the energy storage method is greatly limited, resulting in uncertain and fluctuating output, which is likely to have an adverse impact on the power quality and the stability of the power system are solved. An optimization configuration model of the composite compressed air energy storage system is established based on the compressed air energy storage energy hub architecture, and the configuration problem is decomposed into a main problem and sub-problems. Based on the source-load power prediction fuzzy set, an affine rule is used to establish a solution method for the capacity configuration model of the composite compressed air energy storage, so as to ensure the efficient operation of the composite compressed air energy storage system under various working conditions.

[0039] The second aspect of the present application provides a capacity configuration optimization device for a composite compressed air energy storage system, including:

[0040] A determination module, configured to determine the first capacity configuration optimization problem of the composite compressed air energy storage system and the second capacity configuration optimization problem of the composite compressed air energy storage system;

[0041] A generation module, configured to determine the first preset objective of the first capacity configuration optimization problem and the second preset objective of the second capacity configuration optimization problem based on the requirements of the composite compressed air energy storage system, generate a solution strategy for the first capacity configuration optimization problem according to the first preset objective, and generate a solution strategy for the second capacity configuration optimization problem based on the second preset objective;

[0042] An optimization module, configured to optimize the capacity configuration of the composite compressed air energy storage system according to the solution strategy of the first capacity configuration optimization problem and the solution strategy of the second capacity configuration optimization problem.

[0043] According to an embodiment of the present invention, the generation module includes:

[0044] A first acquisition unit, configured to acquire the input parameters of the composite compressed air energy storage system;

[0045] A first determination unit, configured to determine an initial feasible solution of the first capacity configuration optimization problem based on the input parameters, and obtain an initial optimal solution of the second capacity configuration optimization problem according to the initial feasible solution and the second capacity configuration optimization problem;

[0046] A second determination unit, configured to set a first initial upper bound target value of the first capacity configuration optimization problem based on the initial optimal solution, determine a first initial lower bound target value of the first capacity configuration optimization problem based on the initial feasible solution, and determine a first initial iteration number of the first capacity configuration optimization problem.

[0047] According to an embodiment of the present invention, the generation module includes:

[0048] A construction unit, configured to construct a first target variable corresponding to the initial feasible solution;

[0049] A second acquisition unit, configured to add a first target constraint of the first target variable to the first capacity configuration optimization problem according to the first target variable, and obtain a first iterative optimal solution of the first capacity configuration optimization problem from the initial optimal solution and the first capacity configuration optimization problem;

[0050] A solution unit, configured to solve the first capacity configuration optimization problem according to the first iterative optimal solution.

[0051] According to an embodiment of the present invention, the solution unit includes:

[0052] A first update subunit, configured to update the first initial lower bound target value of the first capacity configuration optimization problem by using the first iterative optimal solution to obtain a first iterative lower bound target value of the first capacity configuration optimization problem;

[0053] An acquisition subunit, configured to obtain a second iterative optimal solution of the second capacity configuration optimization problem according to the first iterative optimal solution and the second capacity configuration optimization problem;

[0054] A second update subunit, configured to update the first initial upper bound target value of the first capacity configuration optimization problem according to the second iterative optimal solution to obtain a first iterative upper bound target value of the first capacity configuration optimization problem.

[0055] According to an embodiment of the present invention, after obtaining the first iterative lower bound target value of the first capacity configuration optimization problem and the first iterative upper bound target value of the first capacity configuration optimization problem, the first update subunit and the second update subunit further include:

[0056] A judgment subunit, configured to judge whether an absolute value of a difference between the first iterative upper bound target value and the first iterative lower bound target value is less than or equal to a preset convergence value;

[0057] The first iteration sub-component is used to stop the iteration and obtain the target optimal solution if the absolute value of the difference between the first iteration upper bound target value and the first iteration lower bound target value is less than or equal to the preset convergence value.

[0058] The second iteration sub-component is used to construct the second target variable corresponding to the first iteration optimal solution, determine the second target constraint of the second target variable, and perform iterative calculation based on the second target constraint according to the first iteration optimal solution and the first capacity configuration optimization problem if the absolute value of the difference between the first iteration upper bound target value and the first iteration lower bound target value is greater than the preset convergence value, until the absolute value of the difference between the iteration upper bound target value and the iteration lower bound target value is less than or equal to the preset convergence value.

[0059] According to an embodiment of the present invention, the capacity configuration optimization device of the above-mentioned composite compressed air energy storage system further includes:

[0060] The judgment module is used to judge whether the second capacity configuration optimization problem has no solution;

[0061] The iteration module is used to construct the third target variable of the first capacity configuration optimization problem, determine the third target constraint of the third target variable, and perform iterative calculation based on the third target constraint according to the second iteration optimal solution and the second capacity configuration optimization problem if the second capacity configuration optimization problem has no solution, until the second capacity configuration optimization problem has a solution.

[0062] According to an embodiment of the present invention, the generation module includes:

[0063] The third determination unit is used to determine the second initial upper bound target value of the second capacity configuration optimization problem, the second initial lower bound target value of the second capacity configuration optimization problem, and the second initial iteration number of the second capacity configuration optimization problem;

[0064] The first update unit is used to obtain the initial value of the 0-1 variable, transform the second capacity configuration optimization problem into an upper bound second capacity configuration optimization problem and a lower bound second capacity configuration optimization problem by using the preset strong duality theory, solve the upper bound second capacity configuration optimization problem to obtain the third iteration optimal solution of the upper bound second capacity configuration optimization problem, and update the second initial upper bound target value based on the third iteration optimal solution to obtain the second iteration upper bound target value of the second capacity configuration optimization problem;

[0065] A second update unit, configured to obtain a fourth iterative optimal solution of the lower-bound second capacity configuration optimization problem according to the third iterative optimal solution and the lower-bound second capacity configuration optimization problem, and update the second initial lower-bound objective value by using the fourth iterative optimal solution to obtain a second iterative lower-bound objective value of the second capacity configuration optimization problem;

[0066] A judgment unit, configured to judge whether the absolute value of the difference between the second iterative upper-bound objective value and the second iterative lower-bound objective value is less than or equal to a preset convergence value;

[0067] A third update unit, configured to stop iteration if the absolute value of the difference between the second iterative upper-bound objective value and the second iterative lower-bound objective value is less than or equal to the preset convergence value, and update the first iterative upper-bound objective value of the first capacity configuration optimization problem by using the first iterative optimal solution; otherwise, construct a fourth objective variable and a dual variable corresponding to the fourth iterative optimal solution, and update the second initial number of iterations until the absolute value of the difference between the second iterative upper-bound objective value and the second iterative lower-bound objective value is less than or equal to the preset convergence value.

[0068] According to an embodiment of the present invention, the first update unit includes:

[0069] A fourth determination unit, configured to determine the uncertain parameters in the input parameters of the composite compressed air energy storage system if the upper-bound second capacity configuration optimization problem has no solution.

[0070] The capacity configuration optimization device of the composite compressed air energy storage system according to the embodiment of the present invention determines the first capacity configuration optimization problem and the second capacity configuration optimization problem of the composite compressed air energy storage system, determines the first preset objective of the first capacity configuration optimization problem and the second preset objective of the second capacity configuration optimization problem, generates a solution strategy for the first capacity configuration optimization problem according to the first preset objective, generates a solution strategy for the second capacity configuration optimization problem based on the second preset objective, and optimizes the capacity configuration of the composite compressed air energy storage system according to the solution strategy of the first capacity configuration optimization problem and the solution strategy of the second capacity configuration optimization problem. Thus, the problems in the related art that the energy storage method has large limitations, resulting in uncertain and fluctuating output, and is likely to have an adverse impact on the power quality and the stability of the power system are solved. An optimization configuration model of the composite compressed air energy storage system is established based on the compressed air energy storage energy hub architecture, and the configuration problem is decomposed into a main problem and a sub-problem. Based on the source-load power prediction fuzzy set, an affine rule is used to establish a solution method for the capacity configuration model of the composite compressed air energy storage, so as to ensure that the composite compressed air energy storage system can operate efficiently under various working conditions.

[0071] A third aspect embodiment of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the capacity configuration optimization method of the composite compressed air energy storage system as described in the above embodiments.

[0072] A fourth aspect embodiment of the present application provides a computer-readable storage medium storing computer instructions for causing the computer to execute the capacity configuration optimization method of the composite compressed air energy storage system as described in the above embodiments.

[0073] A fifth aspect embodiment of the present invention provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the steps of the method as described in the above embodiments are implemented.

[0074] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings

[0075] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the drawings, where:

[0076] Figure 1 is a flowchart of a capacity configuration optimization method for a composite compressed air energy storage system according to an embodiment of the present application;

[0077] Figure 2 is a schematic diagram of the composite compressed air energy storage energy hub architecture according to an embodiment of the present application;

[0078] Figure 3 is the solution process of the capacity configuration model of the composite compressed air energy storage system according to an embodiment of the present application;

[0079] Figure 4 is an example diagram of a capacity configuration optimization device for a composite compressed air energy storage system according to an embodiment of the present application;

[0080] Figure 5 is a schematic diagram of the structure of an electronic device according to an embodiment of the present application.

[0081] Description of the reference numerals: 10 - capacity configuration optimization device for the composite compressed air energy storage system, 100 - determination module, 200 - generation module, 300 - optimization module. Detailed Embodiments

[0082] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where like or similar reference numerals denote like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application.

[0083] The method for optimizing the capacity configuration of a hybrid compressed air energy storage system according to an embodiment of the present application will be described below with reference to the accompanying drawings. In view of the problem that the energy storage methods in the related art mentioned in the above background art are greatly limited, resulting in uncertain and fluctuating output, which is likely to have an adverse impact on the power quality and the stability of the power system, the present application provides a method for optimizing the capacity configuration of a hybrid compressed air energy storage system. In this method, a first capacity configuration optimization problem and a second capacity configuration optimization problem of the hybrid compressed air energy storage system are determined, a first preset target of the first capacity configuration optimization problem and a second preset target of the second capacity configuration optimization problem are determined, a solution strategy for the first capacity configuration optimization problem is generated according to the first preset target, a solution strategy for the second capacity configuration optimization problem is generated based on the second preset target, and the capacity configuration of the hybrid compressed air energy storage system is optimized according to the solution strategy of the first capacity configuration optimization problem and the solution strategy of the second capacity configuration optimization problem. Thus, the problems that the energy storage methods in the related art are greatly limited, resulting in uncertain and fluctuating output, which is likely to have an adverse impact on the power quality and the stability of the power system, etc. are solved. An optimization configuration model of the hybrid compressed air energy storage system is established based on the compressed air energy storage energy hub architecture, and the configuration problem is decomposed into a main problem and sub-problems. Based on the source-load power prediction fuzzy set, an affine rule is used to establish a solution method for the capacity configuration model of the hybrid compressed air energy storage, so as to ensure the efficient operation of the hybrid compressed air energy storage system under various working conditions.

[0084] Specifically, Figure 1 is a schematic flow chart of a method for optimizing the capacity configuration of a hybrid compressed air energy storage system provided by an embodiment of the present application.

[0085] As Figure 1 shown, the method for optimizing the capacity configuration of the hybrid compressed air energy storage system includes the following steps:

[0086] In step S101, a first capacity configuration optimization problem and a second capacity configuration optimization problem of the hybrid compressed air energy storage system are determined.

[0087] Specifically, based on the deficiencies in aspects such as geographical conditions, cost, flexibility, environmental impact, and multi - energy complementary capabilities in the related art, the embodiments of the present application propose a composite compressed air energy storage energy hub architecture, and optimize the capacity configuration based on this composite compressed air energy storage energy hub architecture, thereby solving a series of challenges faced by the high - proportion access of renewable energy to the power system, not only improving the flexibility and stability of the power system, but also realizing multi - energy complementarity and efficient utilization, thus achieving the optimization of system configuration, reducing costs, enhancing environmental friendliness, and improving the robustness of the system.

[0088] Further, as Figure 2 shown, the composite compressed air energy storage energy hub architecture mainly includes a wind turbine 1, a photovoltaic unit 2, a power grid 3, a composite compressed air energy storage system 4, a solar thermal collector system 5, an electric chiller 6, a ground - source heat pump 7, an electrical load 8, a thermal load 9, a cooling load 10, an electric energy flow 11, a thermal energy flow 12, and a cooling energy flow 13.

[0089] Among them, the electric energy flow 11, including the wind turbine 1, the photovoltaic unit 2, the composite compressed air energy storage system 4, and the power grid 3, jointly meets the electricity demand of the electric chiller 6, the ground - source heat pump 7, and the electrical load 8; the thermal energy flow 12, including the solar thermal collector system 5, the composite compressed air energy storage system 4, and the ground - source heat pump 7, jointly meets the heat demand of the thermal load 9; the cooling energy flow 13, including the composite compressed air energy storage system 4 and the electric chiller 6, jointly meets the cooling demand of the cooling load 10.

[0090] Specifically, the composite compressed air energy storage energy hub architecture adopted in the embodiments of the present application, that is, on the basis of the classic AA - CAES (Advanced Adiabatic Compressed Air Energy Storage) structure, the ground - source heat pump 7, the electric chiller 6, and the solar thermal collector system 5 are introduced to provide additional heat supply for the energy flow hub, thereby realizing flexible combined cooling, heat, and power supply and combined cooling, heat, and power storage. The composite system can supply power and heat (cooling) externally, where the cooling is provided by the turbine exhaust and the electric chiller, and the external heat supply heat source can be taken from the heat pump, the solar thermal collector system, and the compressed heat collection system. Generally speaking, the thermoelectric energy hub needs to have the buffering capabilities of thermal energy and electric energy. The electric energy and thermal energy buffering functions of the energy hub are both realized by the built - in AA - CAES system, that is, the heat storage tank realizes the thermal energy buffering, and the gas storage tank and the heat storage tank jointly provide the electric energy buffering effect.

[0091] Furthermore, based on the above-mentioned composite compressed air energy storage energy hub architecture, the embodiment of the present application proposes a method for solving the capacity configuration model of a composite compressed air energy storage system. This method decomposes the configuration optimization problem into two problems, namely the first capacity configuration optimization problem of the composite compressed air energy storage system, that is, the main problem (min problem), and the second capacity configuration optimization problem of the composite compressed air energy storage system, that is, the sub-problem (max-min problem). Thus, based on the source-load power prediction fuzzy set, an affine rule is used to establish a method for solving the capacity configuration model of the composite compressed air energy storage, and the main problem and the sub-problem are iteratively solved, providing an effective solution for the high-proportion renewable energy access to the power system.

[0092] In step S102, based on the requirements of the composite compressed air energy storage system, determine the first preset objective of the first capacity configuration optimization problem and the second preset objective of the second capacity configuration optimization problem, and generate a solution strategy for the first capacity configuration optimization problem according to the first preset objective, and generate a solution strategy for the second capacity configuration optimization problem based on the second preset objective.

[0093] According to an embodiment of the present application, before generating a solution strategy for the first capacity configuration optimization problem according to the first preset objective and generating a solution strategy for the second capacity configuration optimization problem based on the second preset objective, it further includes: obtaining the input parameters of the composite compressed air energy storage system; based on the input parameters, determining the initial feasible solution of the first capacity configuration optimization problem, and obtaining the initial optimal solution of the second capacity configuration optimization problem according to the initial feasible solution and the second capacity configuration optimization problem; setting the first initial upper bound objective value of the first capacity configuration optimization problem based on the initial optimal solution, and determining the first initial lower bound objective value of the first capacity configuration optimization problem based on the initial feasible solution, and at the same time determining the first initial iteration number of the first capacity configuration optimization problem.

[0094] According to an embodiment of the present application, generating a solution strategy for the first capacity configuration optimization problem according to the first preset objective includes: constructing the first objective variable corresponding to the initial feasible solution; adding the first objective constraint of the first objective variable to the first capacity configuration optimization problem according to the first objective variable, and obtaining the first iterative optimal solution of the first capacity configuration optimization problem from the initial optimal solution and the first capacity configuration optimization problem; solving the first capacity configuration optimization problem according to the first iterative optimal solution.

[0095] According to an embodiment of the present application, solving the first capacity configuration optimization problem according to the first iterative optimal solution includes: updating the first initial lower bound objective value of the first capacity configuration optimization problem by using the first iterative optimal solution to obtain the first iterative lower bound objective value of the first capacity configuration optimization problem; obtaining the second iterative optimal solution of the second capacity configuration optimization problem according to the first iterative optimal solution and the second capacity configuration optimization problem; updating the first initial upper bound objective value of the first capacity configuration optimization problem according to the second iterative optimal solution to obtain the first iterative upper bound objective value of the first capacity configuration optimization problem.

[0096] Wherein, both the first preset objective and the second preset objective can be set by those skilled in the art according to actual configuration requirements, and no specific limitation is made here.

[0097] Specifically, in the embodiment of the present application, first, according to the requirements of the composite compressed air energy storage system, the first preset objective of the first capacity configuration optimization problem and the second preset objective of the second capacity configuration optimization problem can be determined. Among them, since the first capacity configuration optimization problem is usually a large-scale linear programming problem, the column and constraint generation algorithm can be used for solution. The first preset objective of the first capacity configuration optimization problem is to find a set of capacity configurations to minimize the total cost of the system. Therefore, the objective of the first capacity configuration optimization problem is to minimize the configuration cost of the system according to the prediction information; the second capacity configuration optimization problem is usually a mixed integer linear programming problem, and the strong duality theory can be used to transform it into a maximum value problem (Max problem) for solution. Therefore, the objective of the second capacity configuration optimization problem is to maximize the worst performance index of the system under the given capacity configuration, that is, when dealing with the uncertainty of the source-load power, the affine strategy is used to adjust the model to make the system meet the worst case within the fuzzy set.

[0098] Furthermore, as Figure 3 shown, the embodiment of the present application generates a solution strategy for the first capacity configuration optimization problem according to the first preset objective, that is, the solution strategy of the master problem, and its expression can be:

[0099]

[0100] Wherein, η is a newly introduced variable; l and l′ are respectively the current iteration number and the total iteration number of the master problem. In the master problem and the sub-problem, the variable with an asterisk in the upper right corner represents that the value of the current variable is the optimal value solved in another problem. Since the matrix F has the variable n, the fifth constraint form has a non-linear form of multiplying two variables. For the convenience of solution, F(n) of the master problem is split into F 2 (n) and F 1 , where F 1 is multiplied by j, instead of solving Substitute y, which is a known parameter, directly into F 2 (n) is multiplied so that the main problem becomes a linear optimization problem, which is convenient for solving. In the sub-problem, the variable n is a parameter, so there is no problem of multiplying two variables in this constraint form.

[0101] Therefore, based on the solution strategy of the main problem, the column and constraint generation algorithm can be used for iterative solution. First, obtain the input parameters of the composite compressed air energy storage system, and based on the input parameters, determine the initial feasible solution of the main problem Substitute the initial feasible solution into the sub-problem to obtain the initial optimal solution of the sub-problem Set the first initial upper bound target value UB of the main problem based on the initial optimal solution of the sub-problem out = +∞, and determine the first initial lower bound target value LB of the main problem based on the initial feasible solution out = -∞, and at the same time determine the first initial iteration number of the main problem. For example, initialize the iteration number as l = 1.

[0102] Secondly, construct the first target variables (y l , z l ) corresponding to the initial feasible solution of the main problem, and add the first target constraint η≥c T yl to the main problem according to the first target variables, and substitute the initial optimal solution into the main problem to obtain the first iterative optimal solution of the main problem And solve the first capacity configuration optimization problem according to the first iterative optimal solution. Among them, (n l , x l ) is the system configuration capacity plan at the current stage, and the sub-problem is solved as a known parameter.

[0103] Thirdly, use the first iterative optimal solution to update the first initial lower bound target value of the main problem to obtain the first iterative lower bound target value LB out = a T nl + b T xl + ηl, substitute the first iterative optimal solution into the sub-problem to obtain the second iterative optimal solution of the sub-problem Update the first initial upper bound target value UB of the main problem according to the second iterative optimal solution out to obtain the first iterative upper bound target value UB of the main problem out = a T n l + b T x / + UB in .

[0104] According to an embodiment of the present application, after obtaining the first iterative lower bound objective value of the first capacity configuration optimization problem and the first iterative upper bound objective value of the first capacity configuration optimization problem, it further includes: determining whether the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is less than or equal to a preset convergence value; if the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is less than or equal to the preset convergence value, stop the iteration to obtain the target optimal solution; if the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is greater than the preset convergence value, construct a second objective variable corresponding to the first iterative optimal solution, and determine the second objective constraint of the second objective variable, and based on the second objective constraint, perform iterative calculations according to the first iterative optimal solution and the first capacity configuration optimization problem until the absolute value of the difference between the iterative upper bound objective value and the iterative lower bound objective value is less than or equal to the preset convergence value.

[0105] Wherein, the preset convergence value can be set by those skilled in the art according to actual configuration requirements, and no specific limitation is made here.

[0106] Specifically, after obtaining the first iterative upper bound objective value and the first iterative lower bound objective value of the master problem in the embodiment of the present application, it is necessary to determine the convergence of the first iterative upper bound objective value and the first iterative lower bound objective value, that is, to determine whether the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is less than or equal to the preset convergence value ε. If the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is less than or equal to the preset convergence value, that is, |UB out -LB out |≤ε, then stop the iteration to obtain the target optimal solution of the second capacity configuration optimization problem. Wherein, is the target optimal solution of the sub-problem.

[0107] Furthermore, if the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is greater than the preset convergence value, let and construct a second objective variable (y l+1 , z l+1 ) corresponding to the first iterative optimal solution, and determine the second objective constraint η≥c T y l+1 of the second objective variable. Based on the second objective constraint, perform iterative calculations according to the first iterative optimal solution and the first capacity configuration optimization problem, update the current iteration number, that is, the iteration number is l = l + 1, and then continue to execute the above-mentioned related steps based on the first iterative optimal solution of the master problem to solve the first capacity configuration optimization problem according to the first iterative optimal solution until the absolute value of the difference between the iterative upper bound objective value and the iterative lower bound objective value is less than or equal to the preset convergence value.

[0108] According to an embodiment of the present application, the method for optimizing the capacity configuration of the above-mentioned composite compressed air energy storage system further includes: determining whether the second capacity configuration optimization problem has no solution; if the second capacity configuration optimization problem has no solution, constructing a third objective variable of the first capacity configuration optimization problem, determining the third objective constraint of the third objective variable, and based on the third objective constraint, performing iterative calculation according to the second iterative optimal solution and the second capacity configuration optimization problem until the second capacity configuration optimization problem has a solution.

[0109] Specifically, if it is calculated based on the above solution that the sub-problem has no solution, then construct the third objective variable (y l+1 , z l+1 ) of the main problem, determine the third objective constraint of the third objective variable, and based on the third objective constraint, perform iterative calculation by substituting the second iterative optimal solution into the main problem, and update the current iteration number, that is, the iteration number is l = l + 1, and then continue to execute the above-mentioned first iterative optimal solution based on the main problem until the sub-problem has a solution.

[0110] According to an embodiment of the present application, the solution strategy for generating the second capacity configuration optimization problem based on the second preset objective includes: determining the second initial upper bound objective value of the second capacity configuration optimization problem, the second initial lower bound objective value of the second capacity configuration optimization problem, and the second initial iteration number of the second capacity configuration optimization problem; obtaining the initial value of the 0-1 variable, using the preset strong duality theory to transform the second capacity configuration optimization problem into an upper bound second capacity configuration optimization problem and a lower bound second capacity configuration optimization problem, solving the upper bound second capacity configuration optimization problem to obtain the third iterative optimal solution of the upper bound second capacity configuration optimization problem, and updating the second initial upper bound objective value based on the third iterative optimal solution to obtain the second iterative upper bound objective value of the second capacity configuration optimization problem; according to the third iterative optimal solution and the lower bound second capacity configuration optimization problem, obtaining the fourth iterative optimal solution of the lower bound second capacity configuration optimization problem, and using the fourth iterative optimal solution to update the second initial lower bound objective value to obtain the second iterative lower bound objective value of the second capacity configuration optimization problem; determining whether the absolute value of the difference between the second iterative upper bound objective value and the second iterative lower bound objective value is less than or equal to a preset convergence value; if the absolute value of the difference between the second iterative upper bound objective value and the second iterative lower bound objective value is less than or equal to the preset convergence value, stop the iteration, and update the first iterative upper bound objective value of the first capacity configuration optimization problem using the first iterative optimal solution, otherwise, construct the fourth objective variable and the dual variable corresponding to the fourth iterative optimal solution until the absolute value of the difference between the second iterative upper bound objective value and the second iterative lower bound objective value is less than or equal to the preset convergence value.

[0111] Among them, the preset strong duality theory can be selected by those skilled in the art according to actual configuration requirements, and no specific limitation is made here.

[0112] Specifically, the embodiment of the present application includes a 0-1 variable z. Therefore, this sub-problem is a mixed-integer linear problem. Thus, the embodiment of the present application uses the strong duality theory to transform the Max-Min problem into a Max problem, and then uses a linear programming or mixed-integer linear programming solver to solve the sub-problem.

[0113] Specifically, as Figure 3 shown, determine the second initial upper bound objective value UB in = +∞, the second initial lower bound objective value LB in = -∞ of the sub-problem, and the second initial iteration number of the sub-problem. For example, o = 1, and obtain the initial value of the 0-1 variable z from the master problem Let Since the KKT (Karush-Kuhn-Tucker Conditions) conditions will introduce more large M constraints than the strong duality theory, therefore, the embodiment of the present application uses the preset strong duality theory to transform the sub-problem into a linear problem, that is, into an upper bound second capacity configuration optimization problem (upper bound sub-problem) and a lower bound second capacity configuration optimization problem (lower bound sub-problem). Its expression can be:

[0114]

[0115] where τ is the newly introduced variable, o and o′ are the current iteration number and the total iteration number of the upper bound sub-problem respectively, and γ, λ, v, ξ, and π are the dual variables of y corresponding to five constraint forms respectively.

[0116] Furthermore, since the bilinear term u T π c is introduced, the solution becomes complicated. Therefore, the bilinear constraint needs to be changed to a linear constraint. When the dual problem obtains the optimal solution, the value of the uncertain parameter u should be the boundary value of the uncertainty set U. In the integrated energy system proposed in the embodiment of the application, when the uncertain output u re,s,h of a single renewable energy device obtains the minimum value, and the uncertain value u l,s,h of the load obtains the maximum value, PESR, CO2ERR, and ACTSR will all obtain relatively small values, which can reflect the "worst" scenario. Therefore, the uncertainty set is rewritten as:

[0117]

[0118] where β re,s,h and β l,s,h are the 0-1 variables of the output of a single renewable energy device and the load status respectively. When their values are 1, the value of the uncertain parameter at the current moment will take the boundary value; Γ reWith Γ l are the uncertainty adjustment parameters for the output of a single renewable energy device and the load status respectively, and their values vary between 1 and 24.

[0119] Further, the embodiment of the present application solves the upper bound sub-problem to obtain the third iterative optimal solution u * of the upper bound sub-problem, and updates the second initial upper bound objective value UB in = +∞ to obtain the second iterative upper bound objective value UB in = τ of the sub-problem. Substitute the third iterative optimal solution u * into the lower bound sub-problem to obtain the fourth iterative optimal solution of the lower bound sub-problem as the system energy scheduling scheme for the current stage, and use the fourth iterative optimal solution to update the second initial lower bound objective value to obtain the second iterative lower bound objective value LB in = max{LB in , c T y}, and the expression can be:

[0120]

[0121] Further, after obtaining the second iterative upper bound objective value and the second iterative lower bound objective value, the embodiment of the present application needs to further determine the convergence of the absolute value of the difference between the second iterative upper bound objective value and the second iterative lower bound objective value, that is, whether the absolute value of the difference between the second iterative upper bound objective value and the second iterative lower bound objective value is less than or equal to a preset convergence value. If the absolute value of the difference between the second iterative upper bound objective value and the second iterative lower bound objective value is less than or equal to the preset convergence value, that is, |UB in - LB in | ≤ ε, then stop the iteration, return u * to the main problem, and use the first iterative optimal solution to update the first iterative upper bound objective value UB out = a T n l + b T x / + UB in , otherwise, return to the upper bound sub-problem, construct the fourth objective variable y o+1 corresponding to the fourth iterative optimal solution and the dual variable, update o = o + 1, and continue to execute the above-mentioned solution process of the upper bound sub-problem until the absolute value of the difference between the second iterative upper bound objective value and the second iterative lower bound objective value is less than or equal to the preset convergence value.

[0122] According to an embodiment of the present application, solving the upper bound second capacity configuration optimization problem includes: if the upper bound second capacity configuration optimization problem has no solution, then determine the uncertain parameters in the input parameters of the composite compressed air energy storage system.

[0123] Specifically, if there is no solution to the upper bound sub-problem, the uncertain parameters in the input parameters of the hybrid compressed air energy storage system are determined, that is, a u is directly returned to the main problem, and its expression can be:

[0124]

[0125] In step S103, the capacity configuration of the hybrid compressed air energy storage system is optimized according to the solution strategy of the first capacity configuration optimization problem and the solution strategy of the second capacity configuration optimization problem.

[0126] Specifically, in the embodiments of the present application, based on the above solution strategies for the first capacity configuration optimization problem and the second capacity configuration optimization problem, iterative calculations are continuously performed, and the upper and lower bounds are gradually updated in each iteration, so that a solution close to the optimal solution can be found within a limited number of iterations, and the capacity configuration of the hybrid compressed air energy storage system is optimized to ensure the economy and robustness of the system.

[0127] In summary, based on the detailed description of the above specific embodiments, the present application can realize the advantages of accurate configuration, cost reduction, enhanced reliability, improved solution efficiency, enhanced flexibility and adaptability, easy implementation, and data-driven optimization through the capacity configuration optimization method of the hybrid compressed air energy storage system, significantly improving the overall performance and economic benefits of the system.

[0128] According to the capacity configuration optimization method of the hybrid compressed air energy storage system according to the embodiments of the present invention, the first capacity configuration optimization problem and the second capacity configuration optimization problem of the hybrid compressed air energy storage system are determined, the first preset objective of the first capacity configuration optimization problem and the second preset objective of the second capacity configuration optimization problem are determined, and a solution strategy for the first capacity configuration optimization problem is generated according to the first preset objective, a solution strategy for the second capacity configuration optimization problem is generated based on the second preset objective, and the capacity configuration of the hybrid compressed air energy storage system is optimized according to the solution strategy of the first capacity configuration optimization problem and the solution strategy of the second capacity configuration optimization problem. Thereby, the problems that the energy storage methods in the related art are greatly limited, resulting in uncertain and volatile output, and being prone to adverse effects on power quality and the stability of the power system are solved. An optimization configuration model of the hybrid compressed air energy storage system is established based on the compressed air energy storage energy hub architecture, and the configuration problem is decomposed into a main problem and sub-problems. Based on the source-load power prediction fuzzy set, an affine rule is used to establish a solution method for the capacity configuration model of the hybrid compressed air energy storage, so as to ensure the efficient operation of the hybrid compressed air energy storage system under various working conditions.

[0129] Next, a capacity configuration optimization device for a hybrid compressed air energy storage system according to an embodiment of the present application is described with reference to the accompanying drawings.

[0130] Figure 4 It is a block diagram of an apparatus for optimizing the capacity configuration of a composite compressed air energy storage system according to an embodiment of the present application.

[0131] As Figure 4 shown, the apparatus 10 for optimizing the capacity configuration of the composite compressed air energy storage system includes: a determination module 100, a generation module 200, and an optimization module 300.

[0132] Among them, the determination module 100 is configured to determine a first capacity configuration optimization problem of the composite compressed air energy storage system and a second capacity configuration optimization problem of the composite compressed air energy storage system;

[0133] The generation module 200 is configured to determine a first preset target of the first capacity configuration optimization problem and a second preset target of the second capacity configuration optimization problem based on the requirements of the composite compressed air energy storage system, generate a solution strategy for the first capacity configuration optimization problem according to the first preset target, and generate a solution strategy for the second capacity configuration optimization problem based on the second preset target;

[0134] The optimization module 300 is configured to optimize the capacity configuration of the composite compressed air energy storage system according to the solution strategy of the first capacity configuration optimization problem and the solution strategy of the second capacity configuration optimization problem.

[0135] According to an embodiment of the present invention, the generation module 200 includes:

[0136] A first acquisition unit configured to acquire input parameters of the composite compressed air energy storage system;

[0137] A first determination unit configured to determine an initial feasible solution of the first capacity configuration optimization problem based on the input parameters, and obtain an initial optimal solution of the second capacity configuration optimization problem according to the initial feasible solution and the second capacity configuration optimization problem;

[0138] A second determination unit configured to set a first initial upper bound target value of the first capacity configuration optimization problem based on the initial optimal solution, determine a first initial lower bound target value of the first capacity configuration optimization problem based on the initial feasible solution, and determine a first initial iteration number of the first capacity configuration optimization problem.

[0139] According to an embodiment of the present invention, the generation module 200 includes:

[0140] A construction unit configured to construct a first target variable corresponding to the initial feasible solution;

[0141] A second acquisition unit, configured to add a first objective constraint of a first objective variable to a first capacity configuration optimization problem according to the first objective variable, and obtain a first iterative optimal solution of the first capacity configuration optimization problem from the initial optimal solution and the first capacity configuration optimization problem;

[0142] A solving unit, configured to solve the first capacity configuration optimization problem according to the first iterative optimal solution.

[0143] According to an embodiment of the present invention, the solving unit includes:

[0144] A first update subunit, configured to update a first initial lower bound objective value of the first capacity configuration optimization problem by using the first iterative optimal solution, and obtain a first iterative lower bound objective value of the first capacity configuration optimization problem;

[0145] An acquisition subunit, configured to obtain a second iterative optimal solution of the second capacity configuration optimization problem according to the first iterative optimal solution and the second capacity configuration optimization problem;

[0146] A second update subunit, configured to update a first initial upper bound objective value of the first capacity configuration optimization problem according to the second iterative optimal solution, and obtain a first iterative upper bound objective value of the first capacity configuration optimization problem.

[0147] According to an embodiment of the present invention, after obtaining the first iterative lower bound objective value of the first capacity configuration optimization problem and the first iterative upper bound objective value of the first capacity configuration optimization problem, the first update subunit and the second update subunit further include:

[0148] A judgment subunit, configured to judge whether the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is less than or equal to a preset convergence value;

[0149] A first iteration subunit, configured to stop iteration and obtain a target optimal solution if the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is less than or equal to the preset convergence value;

[0150] A second iteration subunit, configured to, if the absolute value of the difference between the first iterative upper bound objective value and the first iterative lower bound objective value is greater than the preset convergence value, construct a second objective variable corresponding to the first iterative optimal solution, determine a second objective constraint of the second objective variable, and perform iterative calculation based on the second objective constraint according to the first iterative optimal solution and the first capacity configuration optimization problem until the absolute value of the difference between the iterative upper bound objective value and the iterative lower bound objective value is less than or equal to the preset convergence value.

[0151] According to an embodiment of the present invention, the above-mentioned capacity configuration optimization device 10 of the composite compressed air energy storage system further includes:

[0152] A judgment module, configured to judge whether the second capacity configuration optimization problem has no solution;

[0153] An iterative module, configured to, if the second capacity configuration optimization problem has no solution, construct a third objective variable of the first capacity configuration optimization problem, determine a third objective constraint of the third objective variable, and based on the third objective constraint, perform iterative calculations on the second iterative optimal solution and the second capacity configuration optimization problem until the second capacity configuration optimization problem has a solution.

[0154] According to an embodiment of the present invention, the generation module 200 includes;

[0155] A third determination unit, configured to determine a second initial upper bound target value of the second capacity configuration optimization problem, a second initial lower bound target value of the second capacity configuration optimization problem, and a second initial iteration number of the second capacity configuration optimization problem;

[0156] A first update unit, configured to obtain an initial value of a 0-1 variable, convert the second capacity configuration optimization problem into an upper bound second capacity configuration optimization problem and a lower bound second capacity configuration optimization problem by using a preset strong duality theory, solve the upper bound second capacity configuration optimization problem to obtain a third iterative optimal solution of the upper bound second capacity configuration optimization problem, and update the second initial upper bound target value based on the third iterative optimal solution to obtain a second iterative upper bound target value of the second capacity configuration optimization problem;

[0157] A second update unit, configured to obtain a fourth iterative optimal solution of the lower bound second capacity configuration optimization problem according to the third iterative optimal solution and the lower bound second capacity configuration optimization problem, and update the second initial lower bound target value by using the fourth iterative optimal solution to obtain a second iterative lower bound target value of the second capacity configuration optimization problem;

[0158] A judgment unit, configured to judge whether the absolute value of the difference between the second iterative upper bound target value and the second iterative lower bound target value is less than or equal to a preset convergence value;

[0159] A third update unit, configured to, if the absolute value of the difference between the second iterative upper bound target value and the second iterative lower bound target value is less than or equal to the preset convergence value, stop the iteration and update the first iterative upper bound target value of the first capacity configuration optimization problem by using the first iterative optimal solution; otherwise, construct a fourth objective variable and a dual variable corresponding to the fourth iterative optimal solution, and update the second initial iteration number until the absolute value of the difference between the second iterative upper bound target value and the second iterative lower bound target value is less than or equal to the preset convergence value.

[0160] According to an embodiment of the present invention, the first update unit includes:

[0161] A fourth determination unit, configured to determine an uncertain parameter in the input parameters of the composite compressed air energy storage system if the upper bound second capacity configuration optimization problem has no solution.

[0162] An optimization device for capacity configuration of a composite compressed air energy storage system according to an embodiment of the present invention determines a first capacity configuration optimization problem and a second capacity configuration optimization problem of the composite compressed air energy storage system, determines a first preset target of the first capacity configuration optimization problem and a second preset target of the second capacity configuration optimization problem, generates a solution strategy for the first capacity configuration optimization problem according to the first preset target, generates a solution strategy for the second capacity configuration optimization problem based on the second preset target, and optimizes the capacity configuration of the composite compressed air energy storage system according to the solution strategy of the first capacity configuration optimization problem and the solution strategy of the second capacity configuration optimization problem. Thus, the problems in the related art that the energy storage method has large limitations, resulting in uncertain and fluctuating output, and is likely to have an adverse impact on power quality and the stability of the power system are solved. An optimization configuration model of the composite compressed air energy storage system is established based on the compressed air energy storage energy hub architecture, and the configuration problem is decomposed into a main problem and sub-problems. Based on the source-load power prediction fuzzy set, an affine rule is used to establish a solution method for the capacity configuration model of the composite compressed air energy storage, so as to ensure the efficient operation of the composite compressed air energy storage system under various working conditions.

[0163] Figure 5 The following is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device may include:

[0164] A memory 501, a processor 502, and a computer program stored on the memory 501 and executable on the processor 502.

[0165] When the processor 502 executes the program, it implements the capacity configuration optimization method of the composite compressed air energy storage system provided in the above embodiment.

[0166] Furthermore, the electronic device further includes:

[0167] A communication interface 503 for communication between the memory 501 and the processor 502.

[0168] The memory 501 is used to store a computer program executable on the processor 502.

[0169] The memory 501 may include a high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0170] If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 only a thick line is used to represent it in Figure 5 , but it does not mean that there is only one bus or one type of bus.

[0171] Optionally, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a single chip, the memory 501, the processor 502, and the communication interface 503 can communicate with each other through an internal interface.

[0172] The processor 502 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0173] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method for optimizing the capacity configuration of the composite compressed air energy storage system as described above is implemented.

[0174] The embodiments of the present invention also provide a computer program product, including computer programs / instructions. When the computer programs / instructions are executed by a processor, the steps of the method in the above embodiments are implemented.

[0175] In the description of this specification, the descriptions with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this 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 can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0176] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0177] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application belong.

[0178] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite ordered list of executable instructions for implementing logical functions, and can be embodied specifically 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, a system including a processor, or other systems that can fetch instructions from and execute the instructions of the instruction execution system, apparatus, or device. As used in this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion (electronic device) having one or N wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other suitable processing as necessary, and then storing it in a computer memory.

[0179] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well-known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0180] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0181] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, or each unit may exist physically alone, 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. When 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.

[0182] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, 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 should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A capacity configuration optimization method for a composite compressed air energy storage system, characterized in that: The following steps are involved: Determining a first capacity configuration optimization problem of the composite compressed air energy storage system and a second capacity configuration optimization problem of the composite compressed air energy storage system; Based on the demand of the composite compressed air energy storage system, determine a first preset target of the first capacity configuration optimization problem and a second preset target of the second capacity configuration optimization problem, and generate a solution strategy for the first capacity configuration optimization problem according to the first preset target, and generate a solution strategy for the second capacity configuration optimization problem based on the second preset target; The capacity configuration of the composite compressed air energy storage system is optimized according to the solution strategy of the first capacity configuration optimization problem and the solution strategy of the second capacity configuration optimization problem.

2. The method according to claim 1, characterized in that Before generating a solution strategy for the first capacity configuration optimization problem according to the first preset target and generating a solution strategy for the second capacity configuration optimization problem based on the second preset target, the method further includes: Obtaining input parameters of the composite compressed air energy storage system; Based on the input parameters, determining an initial feasible solution to the first capacity configuration optimization problem, and obtaining an initial optimal solution to the second capacity configuration optimization problem according to the initial feasible solution and the second capacity configuration optimization problem; A first initial upper bound target value of the first capacity configuration optimization problem is set based on the initial optimal solution, and a first initial lower bound target value of the first capacity configuration optimization problem is determined based on the initial feasible solution, and a first initial number of iterations of the first capacity configuration optimization problem is determined.

3. The method according to claim 1, characterized in that The step of generating a solution strategy for the first capacity configuration optimization problem according to the first preset target includes: Construct the first objective variable corresponding to the initial feasible solution; Adding a first objective constraint of the first objective variable to the first capacity configuration optimization problem according to the first objective variable, and obtaining a first iterative optimal solution of the first capacity configuration optimization problem by combining the initial optimal solution and the first capacity configuration optimization problem; The first capacity configuration optimization problem is solved according to the first iterative optimal solution.

4. The method according to claim 3, characterized in that: Solving the first capacity configuration optimization problem according to the first iterative optimal solution includes: Using the first iterative optimal solution, updating a first initial lower bound target value of the first capacity configuration optimization problem to obtain a first iterative lower bound target value of the first capacity configuration optimization problem; Obtaining a second iterative optimal solution to the second capacity configuration optimization problem according to the first iterative optimal solution and the second capacity configuration optimization problem; The first initial upper bound target value of the first capacity configuration optimization problem is updated according to the second iterative optimal solution to obtain a first iterative upper bound target value of the first capacity configuration optimization problem.

5. The method according to claim 4, characterized in that After obtaining the first iteration lower bound target value of the first capacity configuration optimization problem and the first iteration upper bound target value of the first capacity configuration optimization problem, the method further includes: Determine whether the absolute value of the difference between the first iteration upper bound target value and the first iteration lower bound target value is less than or equal to a preset convergence value; If the absolute value of the difference between the first iteration upper bound target value and the first iteration lower bound target value is less than or equal to the preset convergence value, the iteration is stopped to obtain the target optimal solution; If the absolute value of the difference between the first iterative upper bound target value and the first iterative lower bound target value is greater than the preset convergence value, a second objective variable corresponding to the first iterative optimal solution is constructed, and a second objective constraint of the second objective variable is determined. Based on the second objective constraint, iterative calculation is performed according to the first iterative optimal solution and the first capacity configuration optimization problem until the absolute value of the difference between the iterative upper bound target value and the iterative lower bound target value is less than or equal to the preset convergence value.

6. The method according to claim 2 or 4, characterized in that: The capacity configuration optimization method of the composite compressed air energy storage system further includes: Determining whether the second capacity configuration optimization problem has no solution; If the second capacity configuration optimization problem has no solution, construct the third objective variable of the first capacity configuration optimization problem, determine the third objective constraint of the third objective variable, and based on the third objective constraint, iteratively calculate the second iterative optimal solution and the second capacity configuration optimization problem until the second capacity configuration optimization problem has a solution.

7. The method according to claim 1, characterized in that The generating a solution strategy for the second capacity configuration optimization problem based on the second preset target includes: Determining a second initial upper bound target value for the second capacity configuration optimization problem, a second initial lower bound target value for the second capacity configuration optimization problem, and a second initial number of iterations for the second capacity configuration optimization problem; Obtaining an initial value of a 0-1 variable, transforming the second capacity configuration optimization problem into an upper bound second capacity configuration optimization problem and a lower bound second capacity configuration optimization problem by using a preset strong duality theory, solving the upper bound second capacity configuration optimization problem, obtaining a third iterative optimal solution of the upper bound second capacity configuration optimization problem, and updating the second initial upper bound target value based on the third iterative optimal solution to obtain a second iterative upper bound target value of the second capacity configuration optimization problem; According to the third iterative optimal solution and the lower bound second capacity configuration optimization problem, a fourth iterative optimal solution of the lower bound second capacity configuration optimization problem is obtained, and the second initial lower bound target value is updated by using the fourth iterative optimal solution to obtain a second iterative lower bound target value of the second capacity configuration optimization problem; Determine whether the absolute value of the difference between the second iteration upper bound target value and the second iteration lower bound target value is less than or equal to a preset convergence value; If the absolute value of the difference between the second iteration upper bound target value and the second iteration lower bound target value is less than or equal to the preset convergence value, the iteration is stopped, and the first iteration upper bound target value of the first capacity configuration optimization problem is updated using the first iteration optimal solution; otherwise, the fourth objective variable and the dual variable corresponding to the fourth iteration optimal solution are constructed, and the second initial number of iterations is updated until the absolute value of the difference between the second iteration upper bound target value and the second iteration lower bound target value is less than or equal to the preset convergence value.

8. The method according to claim 7, characterized in that The solving of the upper bound second capacity configuration optimization problem includes: If the upper bound second capacity configuration optimization problem has no solution, then the uncertain parameters in the input parameters of the composite compressed air energy storage system are determined.

9. A capacity configuration optimization device for a composite compressed air energy storage system, characterized in that: include: A determination module, used to determine a first capacity configuration optimization problem of the composite compressed air energy storage system and a second capacity configuration optimization problem of the composite compressed air energy storage system; A generation module, used to determine a first preset target of the first capacity configuration optimization problem and a second preset target of the second capacity configuration optimization problem based on the demand of the composite compressed air energy storage system, and generate a solution strategy for the first capacity configuration optimization problem according to the first preset target, and generate a solution strategy for the second capacity configuration optimization problem based on the second preset target; An optimization module is used to optimize the capacity configuration of the composite compressed air energy storage system according to a solution strategy for the first capacity configuration optimization problem and a solution strategy for the second capacity configuration optimization problem.

10. The device according to claim 9, characterized in that The generating module comprises: A first acquisition unit, used to acquire input parameters of the composite compressed air energy storage system; A first determining unit, configured to determine an initial feasible solution to the first capacity configuration optimization problem based on the input parameters, and obtain an initial optimal solution to the second capacity configuration optimization problem according to the initial feasible solution and the second capacity configuration optimization problem; A second determination unit is used to set a first initial upper bound target value of the first capacity configuration optimization problem based on the initial optimal solution, and to determine a first initial lower bound target value of the first capacity configuration optimization problem based on the initial feasible solution, and to determine a first initial number of iterations of the first capacity configuration optimization problem.

11. The device according to claim 9, characterized in that The generating module comprises: A construction unit, used to construct a first target variable corresponding to an initial feasible solution; a second acquisition unit, configured to add a first objective constraint of the first objective variable to the first capacity configuration optimization problem according to the first objective variable, and obtain a first iterative optimal solution of the first capacity configuration optimization problem by combining the initial optimal solution and the first capacity configuration optimization problem; A solving unit is used to solve the first capacity configuration optimization problem according to the first iterative optimal solution.

12. The device according to claim 11, characterized in that The solving unit comprises: A first updating subunit is configured to update a first initial lower bound target value of the first capacity configuration optimization problem using the first iterative optimal solution to obtain a first iterative lower bound target value of the first capacity configuration optimization problem; an acquisition subunit, configured to obtain a second iterative optimal solution to the second capacity configuration optimization problem according to the first iterative optimal solution and the second capacity configuration optimization problem; The second updating subunit is used to update the first initial upper bound target value of the first capacity configuration optimization problem according to the second iterative optimal solution to obtain a first iterative upper bound target value of the first capacity configuration optimization problem.

13. The device according to claim 12, characterized in that After obtaining the first iteration lower bound target value of the first capacity configuration optimization problem and the first iteration upper bound target value of the first capacity configuration optimization problem, the first updating subunit and the second updating subunit further include: A judgment sub-component, used to judge whether the absolute value of the difference between the first iteration upper bound target value and the first iteration lower bound target value is less than or equal to a preset convergence value; A first iteration sub-component, configured to stop iteration and obtain a target optimal solution if the absolute value of the difference between the first iteration upper bound target value and the first iteration lower bound target value is less than or equal to the preset convergence value; The second iterative sub-component is used to construct a second objective variable corresponding to the first iterative optimal solution if the absolute value of the difference between the first iterative upper bound target value and the first iterative lower bound target value is greater than the preset convergence value, and determine the second objective constraint of the second objective variable, and based on the second objective constraint, perform iterative calculation according to the first iterative optimal solution and the first capacity configuration optimization problem until the absolute value of the difference between the iterative upper bound target value and the iterative lower bound target value is less than or equal to the preset convergence value.

14. The device according to claim 10 or 12, characterized in that The capacity configuration optimization device of the composite compressed air energy storage system further includes: A judgment module, used to judge whether the second capacity configuration optimization problem has no solution; an iterative module, for constructing a third objective variable of the first capacity configuration optimization problem if the second capacity configuration optimization problem has no solution, and determining a third objective constraint of the third objective variable, and iteratively calculating the second iterative optimal solution and the second capacity configuration optimization problem based on the third objective constraint until the second capacity configuration optimization problem has a solution.

15. The device according to claim 9, characterized in that The generating module comprises: A third determining unit, configured to determine a second initial upper bound target value of the second capacity configuration optimization problem, a second initial lower bound target value of the second capacity configuration optimization problem, and a second initial number of iterations of the second capacity configuration optimization problem; A first updating unit is used to obtain an initial value of a 0-1 variable, transform the second capacity configuration optimization problem into an upper bound second capacity configuration optimization problem and a lower bound second capacity configuration optimization problem by using a preset strong duality theory, solve the upper bound second capacity configuration optimization problem, obtain a third iterative optimal solution of the upper bound second capacity configuration optimization problem, and update the second initial upper bound target value based on the third iterative optimal solution to obtain a second iterative upper bound target value of the second capacity configuration optimization problem; A second updating unit is configured to obtain a fourth iterative optimal solution of the lower-bound second capacity configuration optimization problem according to the third iterative optimal solution and the lower-bound second capacity configuration optimization problem, and to update the second initial lower-bound target value by using the fourth iterative optimal solution to obtain a second iterative lower-bound target value of the second capacity configuration optimization problem; A judging unit, configured to judge whether an absolute value of a difference between the second iteration upper bound target value and the second iteration lower bound target value is less than or equal to a preset convergence value; The third updating unit is used to stop the iteration if the absolute value of the difference between the second iteration upper bound target value and the second iteration lower bound target value is less than or equal to the preset convergence value, and use the first iteration optimal solution to update the first iteration upper bound target value of the first capacity configuration optimization problem; otherwise, construct a fourth objective variable and a dual variable corresponding to the fourth iteration optimal solution, and update the second initial number of iterations until the absolute value of the difference between the second iteration upper bound target value and the second iteration lower bound target value is less than or equal to the preset convergence value.

16. The device according to claim 15, characterized in that The first updating unit comprises: The fourth determination unit is used to determine the uncertain parameters in the input parameters of the composite compressed air energy storage system if there is no solution to the upper bound second capacity configuration optimization problem.

17. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the capacity configuration optimization method of the composite compressed air energy storage system as described in any one of claims 1 to 8.

18. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a capacity configuration optimization method for a composite compressed air energy storage system as described in any one of claims 1 to 8.

19. A computer program product comprising a computer program, characterized in that The computer program is executed to implement the capacity configuration optimization method of the composite compressed air energy storage system as described in any one of claims 1-8.

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